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The effects of computer simulation and learning styles on emergency vehicle drivers' competency in training course

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Title:
The effects of computer simulation and learning styles on emergency vehicle drivers' competency in training course
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English
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Lindsey, Jeffrey T
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University of South Florida
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Tampa, Fla.
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Subjects / Keywords:
driver training
evoc
emergency vehicle driving
Dissertations, Academic -- Secondary Education -- Doctoral -- USF   ( lcsh )
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government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Summary:
ABSTRACT: The number of accidents over the past decade involving emergency vehicles is a major concern for emergency service providers. This study assessed the effectiveness of adding a driving simulator to a traditional training program. Potential relationships with students' learning styles using Gregorc Mind Style Delineator were also examined. The general research design consisted of a quantitative portion (quasi-experimental) and a qualitative portion (phenomenological). The sample population consisted of Emergency Medical Technician students attending the National EMS Academy in Lafayette, LA. The didactic session was conducted first with 102 participants in attendance. The driving portion was conducted over five days. The group self-scheduled which day they would attend the driving portion of the class. This resulted in 52 participants in the control group and 50 participants in the treatment group. The treatment group used a driving simulator prior to driving on the competency course. The results indicated that the treatment group took significantly less time to drive through the competency course on the first run (t=3.74, p=0.0003), acquired significantly fewer penalty points on the first run (t=2.41, p=0.0178), and required significantly fewer runs to complete the course (t=3.53, p=0.0006). Participants with Abstract Random learning styles performed significantly better on a written, knowledge test than those with Abstract Random/Concrete Random learning styles and Abstract Sequential learning styles. When examining the participants' performance on the competency course in relationship to their learning styles, those with a sequential learning style took less total time to drive the competency course on the first run than those with random learning styles. A t-test was significant, t=2.13, p=0.0357. A simulator improves the individual's ability to drive an ambulance on the required competency course. The use of a driving simulator has potential savings for the emergency service industry and increases the safety of training drivers. In addition, the qualitative portion of the study found all participants had a favorable attitude toward using a simulator to learn to drive an emergency vehicle as part of the training program.
Thesis:
Thesis (Ph.D.)--University of South Florida, 2004.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Jeffrey T. Lindsey.
General Note:
Includes vita.
General Note:
Title from PDF of title page.
General Note:
Document formatted into pages; contains 212 pages.

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aleph - 001478764
oclc - 56389496
notis - AJS2454
usfldc doi - E14-SFE0000406
usfldc handle - e14.406
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The effects of computer simulation and learning styles on emergency vehicle drivers' competency in training course
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ABSTRACT: The number of accidents over the past decade involving emergency vehicles is a major concern for emergency service providers. This study assessed the effectiveness of adding a driving simulator to a traditional training program. Potential relationships with students' learning styles using Gregorc Mind Style Delineator were also examined. The general research design consisted of a quantitative portion (quasi-experimental) and a qualitative portion (phenomenological). The sample population consisted of Emergency Medical Technician students attending the National EMS Academy in Lafayette, LA. The didactic session was conducted first with 102 participants in attendance. The driving portion was conducted over five days. The group self-scheduled which day they would attend the driving portion of the class. This resulted in 52 participants in the control group and 50 participants in the treatment group. The treatment group used a driving simulator prior to driving on the competency course. The results indicated that the treatment group took significantly less time to drive through the competency course on the first run (t=3.74, p=0.0003), acquired significantly fewer penalty points on the first run (t=2.41, p=0.0178), and required significantly fewer runs to complete the course (t=3.53, p=0.0006). Participants with Abstract Random learning styles performed significantly better on a written, knowledge test than those with Abstract Random/Concrete Random learning styles and Abstract Sequential learning styles. When examining the participants' performance on the competency course in relationship to their learning styles, those with a sequential learning style took less total time to drive the competency course on the first run than those with random learning styles. A t-test was significant, t=2.13, p=0.0357. A simulator improves the individual's ability to drive an ambulance on the required competency course. The use of a driving simulator has potential savings for the emergency service industry and increases the safety of training drivers. In addition, the qualitative portion of the study found all participants had a favorable attitude toward using a simulator to learn to drive an emergency vehicle as part of the training program.
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The Effects of Computer Simu lation and Learning Styles on Emergency Vehicle Drivers Co mpetency in Training Course by Jeffrey T. Lindsey A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Secondary Education College of Education University of South Florida Major Professor: Ann Barron, Ed. D. James White, Ph.D. Cynthia Parshall, Ph.D. William Young, Ed. D. Date of Approval: May 27, 2004 Keywords: EVOC, Emergency Vehi cle Driving, Driver Training Copyright 2004, Jeffrey T. Lindsey

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Dedication This study is dedicated to my loving fa mily; my wife Kandace, daughters Natasha and Melissa, and my son Matthew for enduring the time I devoted to research and writing this dissertation. I would also li ke to dedicate this research study to my parents, Thomas and Janet, for their continued support and encouragement throughout my academic endeavors. I also would like to dedicate this study to the emergenc y services industry. This study emerged as a result of the number of injuries caused by emergency vehicles involved in severe accidents. It is my sin cere desire that this study will benefit the industry and reduce the number of injuries and deaths that result from emergency vehicles being involved in accidents. Finally, I would like to praise God for the many blessings He has bestowed upon me and the opportunities He has given me through this study.

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Acknowledgements I need to thank my committee: Majo r Professor Dr. Ann Barron, and Committee Members: Dr. James White, Dr Cynthia Parshall, and Dr. William Young. These individuals provided me with excellent guidance and were wonderful to work with. David LaCombe, Director of the Nationa l EMS Academy for working with me on the details and allowing me to conduct my study at the Academy in Lafayette, LA. Mitch Trahan and Nick Monk for serving as the liai sons and coordinators for the study at the Academy. Richard Pellerin and Gene Salassi for their dedication and for enduring the elements on the driving course. In addition to these, instructors James Doyle, David Marcus, Liz Mrak, Chad Matt, and the other i ndividuals who helped make my time at the academy successful. I would like to acknowledge Road Safety and their commitment to the study, along with President Larry Selditz and his fa ith in my ability to conduct this study. Fred Craft for working closely with me and seeing the value in such a study, and Carey Kriger, the engineer for taking his time to be at the study site for any technical glitches. In addition, I would like to acknowledge a few friends who helped me in this process. Richard Patrick of VFIS who not only read and reviewed my dissertation, but was there as a friend when I needed one. Lenny Enz with RDG who took his time to come and witness the study and help me in any way I needed. Keri Losavio from JEMS, for reviewing and editing the final version. Finally, I would like to acknow ledge Fire Chief Dennis Merrifield of Estero Fire Rescue and the entire fire district, thank you for your support and allowing me to complete this academic milestone in my career.

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iv Table of Contents Table of Figures .................................................................................................................ix List of Tables ...................................................................................................................xiv Abstract ..........................................................................................................................xviii Chapter 1 Introduction .......................................................................................................20 Statement of the Problem ......................................................................................20 Causes of Accidents ..............................................................................................23 Study Rationale .....................................................................................................25 Current Programs ..................................................................................................25 Theoretical Framework .........................................................................................28 Purpose Statement .................................................................................................33 Research Questions ...............................................................................................33 Hypotheses ............................................................................................................34 The Significance of the Study ...............................................................................34 Threats to Internal Validity ...................................................................................35 Threats to External Validity ..................................................................................37 Delimitations .........................................................................................................38 Variables ...............................................................................................................38 Definitions .............................................................................................................39

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v Summary ...............................................................................................................41 Chapter 2 Literature Review ..............................................................................................42 Introduction ...........................................................................................................42 Virtual Reality .......................................................................................................43 Defining the Aspects of Simulations ....................................................................45 Applications of Simulations ..................................................................................48 Vehicle Simulators ................................................................................................56 History of Learning Styles ....................................................................................73 Learning Styles .....................................................................................................75 Learning Styles and Computers ............................................................................77 Learning Style Inventories ....................................................................................82 Summary ...............................................................................................................88 Chapter 3 Method ..............................................................................................................89 Research Design ....................................................................................................89 Participants ............................................................................................................90 Quantitative Instruments .......................................................................................92 VFIS Emergency Vehicle Driver Training Program ...................................92 Emergency Vehicle Operators Course Preand Post-Test ..........................93 Emergency Vehicle Operators Course Competency Test ............................94 The Simulator ...............................................................................................97 Learning Style Inventory .............................................................................99 Qualitative Instrument ........................................................................................100 Type of Pragmatist Study ....................................................................................101

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vi Limitations ..........................................................................................................102 Pilot Study ...........................................................................................................102 Ethical Consideration of Study ...........................................................................103 Quantitative Procedures ......................................................................................103 Qualitative Procedures ........................................................................................104 Statistical Analysis ..............................................................................................105 Quantitative Analysis .................................................................................105 Qualitative Analysis ...................................................................................108 Summary .............................................................................................................108 Chapter 4 Results .............................................................................................................110 Introduction .........................................................................................................110 Demographics .....................................................................................................111 Research Question One .......................................................................................113 First drive through competency cour se with time only being analyzed ....115 Penalty points for first dr ive through competency course .........................119 Total runs to successfully complete the competency course .....................123 Summary ....................................................................................................126 Research Question Two ......................................................................................126 Written test data .........................................................................................126 Research data on the le arning style inventory ...........................................130 Analysis for Research Question Two ........................................................131 Research Question Three ....................................................................................136 Random Sequential Grouping ....................................................................144

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vii Qualitative Results ..............................................................................................154 Research Question Four ......................................................................................154 Describe how the simulator did or did not help you prepare you for the driving course. .............................................................................155 What is your opinion of simulators teaching emergency vehicle operators to drive an emergency vehicle? ...............................................156 Treatment Group ........................................................................................157 Control Group ............................................................................................158 Do you feel the simulator was bene ficial as part of your training? ...........158 Treatment Group ........................................................................................159 Control Group ............................................................................................160 Should the simulator be incorporated into the driver training program? ...162 Treatment Group ........................................................................................162 Control Group ............................................................................................163 Should the simulator be used instead of the competency course in the driver training program? ...............................................................165 Treatment Group ........................................................................................165 Control Group ............................................................................................167 Qualitative Summary ..........................................................................................168 Chapter 5 Discussion .......................................................................................................170 Summary of Findings ..........................................................................................170 Statement of the Problem ....................................................................................170 Purpose Statement ...............................................................................................170

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viii Research Questions .............................................................................................171 Conclusion of Findings .......................................................................................172 Time of First Run .......................................................................................172 Penalty Points on First Run .......................................................................173 Number of Runs through Competency Course ..........................................174 Learning Styles and the Written Test .........................................................175 Learning Styles and the Co mpetency Course Score ..................................176 Conclusion of Findings ..............................................................................178 Recommendations for Future Research ..............................................................180 Use of Simulators .......................................................................................180 Design of Simulators ..................................................................................183 Recommendations for Future Practice ................................................................184 References ........................................................................................................................186 Appendix A Competency Course Instrument ..................................................................202 Appendix B Simulation Evaluation Survey .....................................................................205 Appendix C Informed Consent ........................................................................................209 About the Author .............................................................................................................212

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ix Table of Figures Figure 1. The Training Triangle maps FAPV steps to training methods. .........................32 Figure 2. HMD and Game Controls. ................................................................................65 Figure 3. Torque Feel and Monitor. .................................................................................65 Figure 4. Free Standing Console. .....................................................................................66 Figure 5. Game Console. ..................................................................................................66 Figure 6. Cab with Projection. ..........................................................................................66 Figure 7. Gregorc scoring chart. (Re-printed with permission from Dr. Gregorc) ...........84 Figure 8. Flowchart of instruments for study. ...................................................................90 Figure 9. Competency course diagram. ............................................................................96 Figure 10. Simulator. ........................................................................................................97 Figure 11. Flow chart of partic ipant progression through program. ...............................104 Figure 12. Gender of sample. ..........................................................................................112 Figure 13. Ethnicity of sample. .......................................................................................113 Figure 14. Stem-leaf graph of the first driv e through the competency course with time being analyzed for the control group. .....................................................................116 Figure 15. Stem-leaf graph of the first driv e through the competency course with time being analyzed for the treatment group. .................................................................117

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x Figure 16. Competency course mean time scores on first run through competency course. .................................................................................................118 Figure 17. Stem-leaf graph of the first drive through the competency course with penalty points being analyzed for the control group. ......................................120 Figure 18. Stem-leaf graph of the first drive through the competency course with penalty points being anal yzed for the treatment group. ..................................120 Figure 19. Competency course mean scor es of points on first drive through competency course. .................................................................................................122 Figure 20. Stem-leaf graph of the first drive through the competency course with the number of runs being analyzed for the control group. ..............................124 Figure 21. Stem-leaf graph of the first drive through the competency course with the number of runs being analyzed for the treatment group. ..........................124 Figure 22. Stem and leaf chart of the total number of runs to complete the competency course for the control group. ...............................................................125 Figure 23. Stem and leaf chart of the total number of runs to complete the competency course for the treatment group. ...........................................................125 Figure 24. Learning style demographics. ........................................................................131 Figure 25. Stem-leaf graph shows the written test scores for Abstract Random learners. ....................................................................................................132 Figure 26. Stem-leaf graph shows the written test scores for Abstract Sequential learners. .................................................................................................132 Figure 27. Stem-leaf graph shows the written test scores for Concrete Random learners. ....................................................................................................133

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xi Figure 28. Stem-leaf graph shows the written test scores for Concrete Sequential learners. .................................................................................................133 Figure 29. Stem-leaf graph shows the written test scores for Abstract Random/Concrete Random learners. ......................................................................133 Figure 30. Learning styles mean scores of written test. ..................................................135 Figure 31. Stem-leaf graph of the combined score of the competency course on the first drive through of the Abstract Random learners. ...................................137 Figure 32. Stem-leaf graph of the combined score of the competency course on the first drive through of th e Abstract Sequential learners. ...............................138 Figure 33. Stem-leaf graph of the combined score of the competency course on the first drive through of the Concrete Random learners. ..................................138 Figure 34. Stem-leaf graph of the combined score of the competency course on the first drive through of the Concrete Sequential learners. ..............................139 Figure 35. Stem-leaf graph of the combined score of the competency course on the first drive through of the Abst ract Random/Concrete Random learners. ....139 Figure 36. Learning styles descriptive statis tics competency course mean scores of ANOVA analysis. ...............................................................................................142 Figure 37. Competency course mean scores of the control and treatment analysis. ...................................................................................................144 Figure 38. Stem-leaf graph for the combined driving scores of the Random group. ........................................................................................................146 Figure 39. Stem-leaf graph for the combined driving scores of the Sequential group. ....................................................................................................146

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xii Figure 40. Mean scores for Random ve rsus Sequential le arning style on competency course scores. ......................................................................................148 Figure 41. Stem-leaf graph of the competen cy course scores for the Random control group. ..........................................................................................................150 Figure 42. Stem-leaf graph of the competen cy course scores for the Random treatment group. ......................................................................................................150 Figure 43. Stem-leaf graph of the competen cy course scores for the Sequential control group. ..........................................................................................................151 Figure 44. Stem-leaf graph of the competen cy course scores for the Sequential treatment group. ......................................................................................................151 Figure 45. Learning styles with or without simulation mean of the competency course scores of ANOVA analysis. ........................................................................154 Figure 46. Treatment group responses to wh ether simulator was beneficial as part of their training. ...............................................................................................159 Figure 47. Control group responses to whet her simulator would be beneficial as part of their training. ...........................................................................................161 Figure 48. Treatment group responses to whether the simulator should be incorporated into the driver training program. ........................................................162 Figure 49. Control group responses to whether the simulator should be incorporated into the driver training program. ........................................................164 Figure 50. Treatment group responses to whether the simulator should be used instead of the competency course. ..........................................................................166

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xiii Figure 51. Control group responses to wh ether the simulator should be used instead of the competency course. ..........................................................................167

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xiv List of Tables Table 1 Fire Department Vehicle Accidents .....................................................................23 Table 2. Average Evaluation Scores -Students and Firehouse Users. ............................50 Table 3. Interviewer's Evaluation of the RVHT Training Software. ................................51 Table 4. Interviewer's Perceptions of E ffectiveness of RVHT Training Software ...........52 Table 5. Recommendation for Future Use of RVHT Training Tool. ...............................52 Table 6. Means and standard deviations from NASA-TLX ratings for five different routes. .........................................................................................................60 Table 7. Means of coefficients of variation of EDA, driving speed, acceleration, and brake activity for the five difficult routes. .........................................................61 Table 8. Mean speed (kph) at each data point (curves). ...................................................62 Table 9. Mean speed (kph) at each data point (straight). ..................................................63 Table 10. Definitions of similar te rms relating to learning styles. ....................................76 Table 11. Means and standard deviations for learning styles groups. ..............................78 Table 12. Results of ANOVA on the post-test scores. .....................................................80 Table 13. Results of ANOVA on post-test sc ores for subject groups in each courseware version. ...................................................................................................81 Table 14. The Least Square Means of posttest of each subject group in four courseware versions. .................................................................................................81 Table 15. Score guide for competency course. ................................................................95

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xv Table 16. Penalty point schedule. .....................................................................................95 Table 17. Sample t-test charts. ........................................................................................106 Table 18. Sample ANOVA for post-test scores and learning styles. ..............................106 Table 19. Sample ANOVA for competency course scores and learning styles. .............107 Table 20.Gender of sample. ............................................................................................112 Table 21. Ethnicity of sample. ........................................................................................112 Table 22. Competency course time scores on first run through competency course. .................................................................................................118 Table 23. t-test results of time on the first drive through competency course. ...............119 Table 24. Competency course point s scores on first drive through competency course. .................................................................................................121 Table 25. t-test results of penalty points on the first drive through competency course. .................................................................................................123 Table 26. Competency course total number of runs to successfully complete. ..............125 Table 27. T-test results of number of runs to successfully complete the competency course. .................................................................................................126 Table 28. Discrimination levels for written test. .............................................................128 Table 29. Item analysis of written test. ...........................................................................129 Table 30. Learning style demographics. .........................................................................130 Table 31. ANOVA statistics for written test scores and learning styles. ........................134 Table 32. Learning styles mean and sta ndard deviation for the written test. ..................134 Table 33. Duncan results of written test scores ANOVA. ..............................................136 Table 34. Results of ANOVA for competency course scores by learning styles. ..........141

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xvi Table 35. Learning styles desc riptive statistics competency course scores of ANOVA analysis. ...................................................................................................142 Table 36. Competency course mean and standard deviati on statistics of ANOVA analysis. ...................................................................................................143 Table 37. Mean, median, mode, standard de viation, Kurtois, and skewness for Random versus Sequential learning styl e of competency course scores. ...............147 Table 38. T-test results of total points the first run through the competency course for random versus sequential learners. ........................................................149 Table 39. Results of ANOVA for competency course scores by learning styles with or without simulation. .....................................................................................152 Table 40. Learning styles with or wit hout simulation mean and standard deviation of the competency course scores of ANOVA analysis. ..........................153 Table 41. Thematic responses for simulator preparing treatment group. .......................156 Table 42. Treatment groups thematic respons es using simulator to teach driving. .......157 Table 43. Control groups thematic responses using simulator to teach driving. ...........158 Table 44. Treatment groups thematic re sponse to benefits of driver training program. .....................................................................................................160 Table 45. Control groups thematic response to benefits of simulator in driver training program. .....................................................................................................161 Table 46. Treatment groups thematic respons e to incorporating simulator into driver training program. ..........................................................................................163 Table 47. Control group's thematic respons e to incorporating simulator into driver training program. ..........................................................................................164

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xvii Table 48. Treatment group's thematic res ponse to whether the simulator should be used instead of the competency course. .............................................................166 Table 49. Control group's thematic res ponse to whether the simulator should be used instead of the competency course. .............................................................168

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xviii The Effects of Computer Simu lation and Learning Styles on Training Emergency Vehicle Drivers Competency Jeffrey T. Lindsey ABSTRACT The number of accidents over the past decade involving emergency vehicles is a major concern for emergency service providers This study assessed th e effectiveness of adding a driving simulator to a traditional training program. Potential relationships with students learning styl es using Gregorc Mind Style Delin eator were also examined. The general research design consiste d of a quantitative portion (quasiexperimental) and a qualita tive portion (phenomenological). The sample population consisted of Emergency Medical Technici an students attending the National EMS Academy in Lafayette, LA. The didactic session was conducted first with 102 participants in attendance. The driving portion was conducted over five days. The group self-scheduled which day they would attend the driving portion of the class. This resulted in 52 participants in the c ontrol group and 50 participants in the treatment group. The treatment group used a driving simulator prio r to driving on the competency course. The results indicated that the treatment group took significantly less time to drive through the competency course on the firs t run (t=3.74, p=0.0003), acquired significantly

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xix fewer penalty points on the first run (t=2.41, p=0.0178), and required significantly fewer runs to complete the course (t=3.53, p=0.0006). Participants with Abstract Random learni ng styles performed significantly better on a written, knowledge test than those with Abstract Random/Concrete Random learning styles and Abstract Sequen tial learning styles. When examining the participants performance on the competency course in rela tionship to their learning styles, those with a sequential learning style took less total time to drive the competency course on the first run than those with random learning styles A t-test was signif icant, t=2.13, p=0.0357. A simulator improves the individuals ab ility to drive an ambulance on the required competency course. The use of a dr iving simulator has potential savings for the emergency service industry and increases the sa fety of training driv ers. In addition, the qualitative portion of the study found all part icipants had a favorable attitude toward using a simulator to learn to drive an emerge ncy vehicle as part of the training program.

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Chapter 1 Introduction Each year, many emergency vehicle drivers are killed or injured when responding to or returning from an incident. Due to the number of injuries and deaths, the training program for these professionals is critical. This Chapter provides statistics to illustrate the depth of the problem, discusses common causes of accidents, and notes the training programs available to teach emergency vehicle drivers. This research study focused on the population of emergency vehicle drivers who did not have any experience driving emergency vehicles. The population was a group of students that were completing their basic emergency medical services (EMS) training. After completing this training, the student was able to drive an ambulance or fire truck and respond with lights and sirens, driving at speeds and taking risks that are not associated with everyday driving of other vehicles. Statement of the Problem Emergency vehicles are operated by drivers who may or may not be trained to operate them in a safe manner. The number of accidents has continued to be an issue over the past decade. The literature suggests that human error continues to be the primary reason for the number of emergency vehicle accidents. The statistics gathered illustrate 20

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the number of emergency responders who are injured or killed as a result of an emergency vehicle accident. Four sources currently track incident rates and deaths of emergency service personnel. The most comprehensive source for data of emergency vehicle incidents is the National Highway Traffic Safety Administration (NHTSA); however, they do not have complete data or a mandatory reporting system (Green, 2002). The second source is the United States Fire Administration (USFA). The USFA collects data on the number of firefighters that are killed annually (Firefighter Fatality Retrospective Study, 2002). Part of this data includes fatalities as a result of an emergency vehicle responding to or from an incident. The third source discussed in this study is the Centers for Disease Control and Prevention (CDC). The final source is insurance companies. This information was difficult to obtain since it is proprietary. According to the CDC (2003) during the time period of 1991 2000 there were 300 fatal crashes that involved ambulances, resulting in the deaths of 82 ambulance occupants and 275 occupants of other vehicles and pedestrians. There were 816 ambulance occupants involved in the 300 ambulance crashes ("Ambulance crash-related injuries among emergency medical services workers United States, 1991-2002," 2003). An 11-year study conducted from 1987 to 1997 revealed similar statistical data on ambulance crashes (Kahn, Pirrallo, & Kuhn, 2001). During this period, there were 339 ambulance crashes with 405 fatalities and 838 injuries (Kahn et al., 2001). Overall, ambulances are said to have the highest danger level of vehicles driven on the job than any other vehicle (Zagaroli, 2003a). Emergency medical workers have an occupational fatality rate of 9.6 per 100,000 workers per year due to transportation21

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related incidents. In contrast, police officers have an occupational fatality rate of 6.3, firefighters have an occupational fatality rate of 4.5, and the average population has 2 deaths per 100,000 that result from crashes (Zagaroli, 2003a). According to the CDC, it is essential to note that these statistics may be low due to two limitations. The reporting system records only those accidents that occur in a public setting and does not take into account any injuries or fatalities that occur in a private setting (Zagaroli, 2003a). Second, the statistics do not differentiate if the person injured or killed was an EMS worker, a patient in the ambulance, or a member of the public Emergency Medical Service (EMS) workers are not necessarily definitive by who was fatally injured in the accident ("Ambulance crash-related injuries among emergency medical services workers United States, 1991-2002," 2003). There is other statistical information to consider when looking at ambulance crashes and the impact they have on the industry. The average cost of an ambulance crash is about $1 million if an injury is involved (Zagaroli, 2003b). It is estimated that 60% of the accidents that the general public are involved in do not involve another motor vehicle. In contrast, ambulance accidents that result in a fatality have a 20% occurrence involving only the ambulance and no other vehicle (Kahn et al., 2001). Furthermore, an ambulance service is 10 times more likely to be sued as a result of operating a vehicle than for committing a medical malpractice error (Zagaroli, 2003b). Additionally, 74% of the time the ambulance was the striking vehicle in fatal crashes, and greater than 50% of the deadly incidents were at intersections (Kahn et al., 2001). Each year the USFA publishes statistics of firefighters killed in the line of duty. An average of 100 firefighters are killed annually in the United States -18% of these are 22

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killed while responding to or returning from an incident (Firefighter Fatality Retrospective Study, 2002). Table 1 provides data regarding fire department vehicle accidents and resulting firefighter injuries while responding to or returning from incidents during the period of 1994-2002. Table 1 Fire Department Vehicle Accidents Involving Fire Department Emergency Vehicles Involving Firefighters Personal Vehicles Year Accidents Firefighter Injuries Accidents Firefighter Injuries 1994 13,755 1,035 1,610 285 1995 14,670 950 1,690 190 1996 14,200 910 1,400 240 1997 14,950 1,350 1,300 180 1998 14,650 1,050 1,350 315 1999 15,450 875 1,080 90 2000 15,300 990 1,160 170 2001 14,900 960 1,325 140 2002 15,550 1,040 1,030 210 (NFPA's survey of fire departments for U.S. fire experience 1994-2002, 2003) Causes of Accidents The cause of accidents is an important element to review when discussing the number of incidents involving emergency vehicles. As noted previously, the relevant data were difficult to obtain since data keeping is not centralized in the United States. The leading insurer of emergency vehicles illustrates their loss ratio and the causes they have experienced. The researcher believes this would be a fair representation of the industry overall. A three-year study by VFIS revealed the statistics of emergency vehicle accidents. Over a three-year period, intersections were identified as having the greatest frequency of accidents; one out of every four emergency vehicle accidents occurred at intersections 23

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(Klein, Lane, & Steffens, 1997). The authors of the VFIS program do not delineate the three-year period; instead this three-year period could be any three-year period. The numbers have been found to be consistent during the statistical gathering of this information. Based upon the severity or the cost of the accident, intersections accounted for 45% of insurance costs (Klein et al., 1997). The driver was the contributing factor in both the number of incidents that occurred and the monetary insurance cost of the incident. The first nine months of 2002, there were seven serious vehicle accidents involving wildland fire apparatus. There were nine fatalities and 26 injuries (What you don't know at the wheel can hurt, 2003). It is important to note that the contributing factor in these accidents was human error and not mechanical failure. Fatigue was identified as the primary factor in the cause of these accidents; a contributing factor was operator proficiency and experience (What you don't know at the wheel can hurt, 2003). The operator in many instances had multiple years of experience driving a sedan or light-duty truck, but he or she often was relatively inexperienced at operating an engine, utility truck, or a 15-passenger van (What you don't know at the wheel can hurt, 2003). There are accidents even during training classes. On November 20, 2003, Collier County EMS crews in Florida were training on their new $100,000 ambulance when it rolled over on its side after the driver lost control ("Medics injured in ambulance crash released from hospital," 2003). The causes of accidents center around human error in the majority of studies noted from the training session and beyond. A change, beginning at the training of emergency vehicle drivers, may be warranted. 24

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Study Rationale This study focused on the population of emergency vehicle drivers who did not have any experience driving emergency vehicles. The population was a group of students completing their basic EMS training. After completing this training, the student was able to get behind the wheel of an ambulance or fire truck and respond with lights and sirens, driving at speeds and taking risks that are not associated with everyday driving of other vehicles. Current Programs Because human behavior is a primary cause of accidents, driver training programs are considered to be one of the solutions to resolve the issues surrounding emergency vehicle accidents. There are a number of driver programs that are designed to train emergency vehicle drivers and prepare them to operate an emergency vehicle. NFPA 1451 3-3.8 states, Fire departments shall train operators for inclement weather driving conditions and the proper handling of apparatus, particularly where auxiliary braking devices are to be used ("NFPA 1451 standard for a fire service vehicle operations training program," 1997, pg. 9). This standard, as set forth by a national consensus organization, requires drivers to perform skills they may not have the opportunity to perform except in a real emergency situation. In most instances, the only training an individual has prior to driving an emergency vehicle is a 16-hour driver training course the same course that was used in this study ("NFPA 1002 standard for fire apparatus driver/operator professional qualifications," 1998). This study investigated the effectiveness of adding a simulation 25

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component to the course and determine if there was a significant difference in competency course scores. There are very few recognized emergency vehicle driver-training programs (EVDTP) available. Three of the most popular programs are the VFIS driver-training program, the National Safety Councils Certified Emergency Vehicle Operator (CEVO) program, and NHTSA, which has a standard curriculum through the Department of Transportation for ambulance driver training. Each of the driver training programs is relatively similar in design and delivery. This study used the VFIS program. VFISs program cites four critical components to a comprehensive EVDTP. The components consists of: eight hours of classroom instruction, eight hours of the competency course completion, eight hours of street and highway driving, and a combination of knowledge and skill testing (Klein et al., 1997). This study centered on the didactic and competency course driving components. Although it is recommended that the competency course consist of an eight-hour session, this amount of time is not per individual, but rather for a typical class of 25 to 30 students to be able to drive a minimum of two times each through the course (Klein et al., 1997). In most cases the actual time the student spends driving on the competency course is less than 30 minutes (Zagaroli, 2003b). The traditional EVDTP competency course consists of the student maneuvering their emergency vehicles around traffic cones on a parking lot. The eight specific maneuvers required to meet the National Fire Protection Association (NFPA) standard on emergency vehicle driver qualifications ("NFPA 1002 standard for fire apparatus driver/operator professional qualifications," 1998) are: straight-line forward and 26

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backward, confined space turn, alley dock backing, serpentine, parallel parking, lane change, diminishing clearance, and stop sign (Appendix A). The rationale behind the inclusion of the event in the competency course is described in NFPA 1002. The student must maneuver through each event that is established with traffic cones without knocking the cone over, crossing a line, or brushing against the cone. The student is scored on the basis of time and accuracy of maneuvering through the cones. After the students successfully complete the classroom and competency course, they complete the next phase, which is the highway and street driving (Klein et al., 1997). The highway and street driving component has many limitations. The first limitation is the mere fact that the amount of driving time and the requirements of this component vary from zero hours upward, depending on the agencys requirement, even though the recommended time is eight hours (Zagaroli, 2003b). Another limitation is safety. Other than driving on the competency course, this may be the first time the student has driven a vehicle the size of an ambulance or a fire truck on a main roadway. Driving these vehicles is much different than driving a typical passenger car. The last limitation that is identified pertinent to this study is the conditions under which the person drives the emergency vehicle. Depending on the geographic location of the student, they may not encounter adverse weather conditions during training. During training, it is impossible to provide the various conditions the student will encounter when they drive in real emergency situations. Emergency vehicle driver trainees are not immune to accidents when driving on a training course. The Trends and Hazards in Firefighter Training (May 2003) report from the Federal Emergency Management Agency (FEMA) illustrated a number of issues 27

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surrounding the current risks of training using emergency vehicles on a competency training course. The data that are noted in this report are not available in statistical format; however, the problem associated with driving on a competency course is suggested by the anecdotal remarks of interviewed driver trainees. Almost every trainee who was interviewed could relate an accident or close call during driver training (Thiel, Stern, Kimball, & Hankin, 2003). The strict adherence to safety practices when training new drivers of emergency apparatus is underscored by these anecdotes. A specific example of an incident was illustrated in the report. A firefighter was injured in 1998 when the engine in which he was riding rolled over during driver training. The incident occurred on the fire departments driver training course. The driver panicked while descending a hill, and stepped on the accelerator instead of the brake. He received minor injuries; the engine, a newly delivered unit that had not yet been placed in service, was severely damaged (Thiel et al., 2003, pg. 10). The safety issues surrounding driving emergency vehicles during training sessions on the competency course are exemplified by the fact that apparatus/equipment drills are the second leading cause of fatalities in training deaths. This is followed by live-fire training as third, and preceded by physical fitness training, which is first (Thiel et al., 2003). Theoretical Framework Research on learning driving skills has indicated a positive effect on students who use computer simulation (Gredler, 2001). Reductions in accident rates, insurance, and vehicle maintenance costs have been realized by various mass transit companies (Wetzel, 28

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2000). If computer simulation has such a significant effect on mass transit drivers, can it achieve the same effect if introduced into the training of fire and EMS vehicle drivers, who also have a high vehicle accident rate (Zagaroli, 2003b)? Computer simulation allows the learner to operate a vehicle in conditions that cannot be safely replicated in a real life situation. Flight simulators are a prime example. They have been in existence for a number of years and have demonstrated positive effects (Gredler, 2001). Computer simulation allows an individual to go one step further by creating a realistic environment, similar to what they may experience in the real world, but in a controlled setting. According to McLellan (2001), cab simulators are being used to practice high-speed and dangerous driving conditions for police officers. There are a number of different types of computer simulations. This study focused on the Cab Simulator Environment. Cab simulator environment is defined as: Usually an entertainment or experience simulation form of virtual reality, which can be used by a small group or by a single individual. The illusion of presence in the virtual environment is created by the use of visual elements greater than the field of view, three-dimensional sound inputs, computer-controlled motion bases, and more than a bit of theatre (Hamit, 1993, pg. 428). This study utilized interpretivist goals as defined by Reeves (Reeves, 2000). It describes and interprets the phenomena related to the effect of an emergency vehicle driver completing a simulated virtual driving course. According to Gredler (2001), there are two concepts that are important in the analysis of the nature of games and simulations -surface structure and deep structure. Surface structure refers to the paraphernalia and 29

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observable mechanics of an exercise. An essential in simulations is a scenario or set of data to be addressed by the participant. Deep structure may be defined as the psychological mechanisms operating in the exercise. Further, deep structure refers to the nature of the interactions (a) between the learner and the major task in the exercise; and, (b) among the students involved in the exercise (Gredler, 2001). Emergency vehicle drivers are placed into situations that are not typical of the driving patterns for most drivers. A combination of radios blaring, sirens wailing, and reaching a destination that is frequently unknown, creates an unusual environment. Simulators have been effective in testing individuals in situations with similar distractions. Johansson and Nordin (2002) note in their studies that drivers were tested in environments with such distractions as deer running in front of the drivers vehicle. They also tested drivers on their performance when impaired by drugs and alcohol. It is important to note that their study describes these various scenarios as instances in which the danger and ethical consequences of subjecting these individuals in a real environment are far too great (Johansson & Nordin, 2002). A combination of live, virtual and constructive training should be considered. Frank, Helms, and Voor (2000) discuss in their study how the military has always conducted live training, but now other training methods are often incorporated in addition to the live training. The lethality, expense, and complexity of modern weapon systems have increased and training budgets have tightened. Live training is no longer sufficient as the sole training method (Frank, Helms, & Voor, 2000). A training method was developed for the analysis of learning by doing. The method encompassed four steps in the learning process for each task to be performed. 30

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These steps are familiarize, acquire skill, practice skill and validate skill -otherwise known as the FAPV (Familiarize, Acquire skills, Practice skills, Validate skills) method (Frank et al., 2000). Familiarize is the passive process the student learns. The student acquires knowledge by absorbing information through a presentation or taking a guided tour. The next step is acquiring skill. This is when the student learns the technique and procedure by being tutored. If the student makes a mistake the tutor gives immediate feedback. The third step is practicing skill. The student performs the skill without prompting from the tutor. There is usually a delay between the action and the feedback from the tutor. The exception to this may be when the student performs a dangerous procedure; the tutor would then provide immediate feedback. The last step is validating the skill. At this level, the student is on their own, demonstrating their proficiency by testing what they have learned. The training triangle developed by Frank, Helms, and Voor (2000) is shown in the following figure. Familiarize, acquire, and practice skills were examined in this research study. 31

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Figure 1. The Training Triangle maps FAPV steps to training methods. Validate Skills { Live There has been much discussion on the validity of learning styles. Stahl notes that the learning style of an individual may change from month to month, or even from week to week (Stahl, 1999). The discussion on learning styles has been an intriguing topic for the researcher. As part of this study, the researcher examined whether the dominant learning style that is denoted on a learning style inventory established a relationship to the written test score of the participant. In addition the study examined Constructive Practice Skills { Acquire Skills { Virtual Familiarize { Traditional Classroom Lecture 32

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whether the participants learning style showed a relationship with the scores on the competency course. Purpose Statement Although computer simulator-based training for emergency vehicle drivers has intuitive appeal, little is known about its effectiveness. Thus, this study examined the effectiveness of the simulator-based learning environment in comparison with similar training conducted in a non-simulated learning environment. Research Questions This study investigated some of the obvious, yet essential questions related to the effectiveness of computer simulation-based training for emergency vehicle drivers. Quantitative questions: The following research was addressed using quantitative techniques. 1. Is there a significant difference in competency course scores of emergency vehicle operators who were trained to drive an emergency vehicle via a simulator prior to driving on a standardized competency course and those of emergency vehicle operators who were not trained using a simulator? 2. Is there a relationship between a students learning style and his or her performance on the written post-test? 3. Is there a relationship between a students learning style and his or her performance on the standardized competency course (with or without the simulation segment)? 33

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Qualitative questions: 4. What are emergency vehicle operators perceptions of using a driving simulator as part of an emergency vehicle training course? Hypotheses The following hypotheses were assumed. 1. There is no significant difference in the competency course scores of emergency vehicle drivers who utilize a driving simulator before driving through a competency course and those who do not use a simulator. 2. There is no significant relationship between students learning style and their written post-test scores. 3. There is no significant relationship between students learning style and their competency course scores. Additionally, the qualitative component of this study will investigate the emergency vehicle operators perceptions of using a driving simulator compared with not using a simulator as part of an emergency vehicle training course. The Significance of the Study To date, there are few studies that measure the effectiveness of utilizing a driving simulator to train emergency vehicle drivers prior to driving an emergency vehicle. Furthermore, the studies in computer simulation showing effectiveness in driver improvement do not simulate the environment of the emergency vehicle operator. Emergency vehicles are typically driven by a multitude of individuals. They operate in adverse conditions and are subjected to wear and tear from driving over curbs and obstacles that typical vehicles do not encounter. Emergency vehicles also have lights and 34

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sirens, used to clear the path en route to an emergency, that create distractions few other drivers encounter. It is hoped that the findings of the present investigation will provide vital information regarding the efficacy of computer simulator-based training for emergency vehicle drivers, thereby contributing to the knowledge base. The purpose of this research was to study the effect of a computer simulator for training emergency vehicle drivers versus traditional methods. Traditionally, emergency vehicle drivers complete an eight-hour didactic class followed by driving a vehicle on a competency course (Klein et al., 1997); however, driver training programs range from one hour of training to several days of on the road and classroom instruction (Zagaroli, 2003b). Further, the type of vehicle participants use during the driving portion of instruction ranges from personal vehicles to the large ambulance or fire truck they may be driving in an emergency scenario (Zagaroli, 2003b). This study used only Type III ambulances for the participant to drive on the competency course. The accident rates of emergency vehicles continue to rise (Zagaroli, 2003b). Is the traditional driver training education for emergency vehicle drivers effective in reducing accidents? Computer simulation may be the needed component to train emergency vehicle drivers and reduce the accident rate. This study focused on the training of emergency vehicle drivers and the effect a driving simulator has on the emergency vehicle drivers ability to drive. Threats to Internal Validity Testing was the first concern of a threat to internal validity to this study. Pre-testing and pre-testing sensitization occur when the participant takes a preand post-test (Onwuegbuzie, 2003). This study had a pre-test administered at the beginning of the 35

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didactic portion of the class and post-test administered at the end of the didactic session, corresponding to the end of an eight-hour day. Instrumentation is the second form of a threat to internal validity for this study. The preand post-test may not generate reliable and valid scores in the study. In addition, the simulator used in this study is a new simulator and may have some issues associated with its use in an emergency vehicle driver training course that may not be currently known. Onwuegbuzie (2003) cites four areas of concern with instrumentation. They are: (a) the post-intervention measure is not parallel (e.g., different level of difficulty) to the pre-intervention measure (i.e., the test has low equivalent-forms reliability); (b) the pre-intervention instrument leads to unstable scores regardless of whether or not an intervention takes place (i.e., has low test-retest reliability); (c) at least one of the measures utilized does not generate reliable scores (i.e., low internal-consistency reliability); and, (d) the data are collected through observation, and the observing or scoring is not consistent from one situation to the next within an observer (i.e., low intra-rater reliability) or is not consistent among two or more data collectors/analysts (i.e., low inter-rater reliability). (Onwuegbuzie, 2003, pg. 76) Behavior bias was another internal threat to validity for this study. This is when a participant may have bias toward an intervention, either positively or negatively (Onwuegbuzie, 2003). In this investigation this could have been a threat to the internal validity of the study. 36

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Researcher bias had as little of an effect on the internal validity as possible; however, it was an internal threat to the study. Every attempt to remain neutral was exercised. Evaluation anxiety is when the participant is subjected to a time event, or placed into a situation that causes them anxiety (Onwuegbuzie, 2003). This was an internal validity threat to this study. The participants were subjected to driving emergency vehicles, most if not all for the first time. Further, they were required to complete the competency course within a certain time limit and with a limited number of penalty points. This adds anxiety to the participant during the study. Threats to External Validity Population validity was the first external validity that may have affected this investigation. Population validity is the extent to which findings are generalizable from the sample of individuals on which a study was conducted to the larger target population of individuals, as well as across different subpopulations within the larger target population (Onwuegbuzie, 2003). This study was conducted in Louisiana and there was a threat to external validity due to the limited and narrow sampling of the population of emergency vehicle drivers. Ecological validity results when the findings from the study can be generalized across settings, conditions, variables, and contexts (Onwuegbuzie, 2003). The ethnicity, socioeconomic status and the academic achievement of the participants in this study were unknown. However, the participants in this study were from a central location in Louisiana and represent a different group of population than if the study were conducted in another location. 37

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Another external validity threat was temporal validity. Temporal validity is the extent to which research findings can be generalized across time (Onwuegbuzie, 2003). This study was conducted within a six-day period and thus created a potential external threat to the validity of this study. The specificity of variables is considered an external threat in almost every study (Onwuegbuzie, 2003). Onwuegbuzie (2003) lists a number of specificity of variables that any given inquiry may include: (a) a specific type of individual; (b) a specific time; (c) at a specific location; (d) under a specific set of circumstances; (e) based on a specific operational definition of the independent variables; and, (f) using specific instruments to measure all the variables. (Onwuegbuzie, 2003, pg. 81) This study was not an exception to this external threat. Delimitations A delimitation of the study is that only one simulator and one driver training program were used for this investigation. Another delimiter was that only students were used, and there were no individuals with experience driving emergency vehicles. Additionally, the sole use of only EMS students from Louisiana was a delimiter. Variables For research question one, the dependent variable was the competency course scores and the independent variable was the training with or without the simulator. The dependent variable for research question number two was the written post-test and the independent variables were the learning style category and the training with or without the simulator. The dependent variable for research question number three was the 38

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competency course score and the independent variables were the learning style category and the training with or without the simulator. Definitions Cab simulator Usually an entertainment or experience simulation form of virtual reality, which can be used by a small group or by a single individual. The illusion of presence in the virtual environment is created by the use of visual elements greater than the field of view, three-dimensional sound inputs, computer-controlled motion bases, and more than a bit of theatre (Hamit, 1993, pg. 428). CDC Centers for Disease Control and Prevention Cognitive strategy Adopting a plan of action in the process of organizing and processing information (McLoughlin, 1999). Cognitive style A systematic and habitual mode of organizing and processing information (McLoughlin, 1999). EMS Emergency Medical Services EVDTP Emergency Vehicle Driver Training Program Far transfer Being able to use learned knowledge or skills in very different environments (Alessi & Trollip, 2001, pg. 230). FEMA Federal Emergency Management Agency Learning preference Favoring one method of teaching over another (McLoughlin, 1999). Learning strategy Adopting a plan of action in the acquisition of knowledge, skills or attitudes (McLoughlin, 1999). 39

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Learning style Behaviors, characteristics and mannerisms, which are symptoms of mental qualities used for gathering data from a schooling environment (Gregorc, 2003). Microworlds A collection of objects that can be assembled, manipulated, turned on and off, measured, and so on (Alessi & Trollip, 2001, pg. 236). Near transfer Applying the learned information or skill in a new environment that is very like the original one (Alessi & Trollip, 2001, pg. 230). NFPA National Fire Protection Agency NHTSA National Highway Traffic Safety Administration Simulation A model of some phenomenon or activity that users learn about through interaction with the simulation (Alessi & Trollip, 2001, pg. 213). USFA United States Fire Administration Virtual reality A class of computer-controlled multi-sensory communication technologies that allow more intuitive interactions with data and involve human senses in new ways (Mc Lellan & Mc Lellan, 2001, pg. 457). Wildland Wildland fires are the uncontrolled destruction of forests, brush, field crops and grasslands caused by nature or humans (Washington state hazard identification and vulnerability assessment, 2003). 40

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Summary This section identified the high incidence of accident rates for emergency vehicle operators. It discussed the lack of information available to assess thoroughly the extent of the problem and the solution to remedy the high accident rates of emergency vehicles. The current training programs to train emergency vehicle operators were also reviewed. The chapter also stated the research questions and hypotheses for this study and laid the theoretical framework. External and internal threats were discussed for the study. The chapter concluded with definitions pertaining to this study. 41

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Chapter 2 Literature Review Introduction Many studies were conducted in the 1980s and 1990s on driver assessment and training. Sivak, Flannagan, and Schoettle (2001) queried the Citation Index Expanded and the Social Sciences Citation Index databases and found 16.9 million citations. They did not conduct any research involving these topics, it was only a query. The search included the following query: (driver OR drivers OR driving OR car OR cars) AND (evaluation OR assessment OR performance OR ability OR abilities OR training OR vision OR visual OR perception OR perceptual OR cognition OR cognitive OR attention OR attentional OR information processing OR sensory OR psychomotor) (Sivak, Flannagan, & Schoettle, 2001, pg. 1) The query showed the top studies using these terms, and noted only driver simulator performance in 1985-1989 (Sivak et al., 2001). The literature review that follows illustrates the number of other studies involving driving simulators. This study focused on driving simulators for emergency vehicle drivers. 42

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The following literature review illustrates the lack of literature available on driving simulation studies in the fire and EMS industry. The literature review discusses the effectiveness of simulation in general and focuses on driving simulators. Additionally, the literature review looks at the studies and information regarding learning styles. Virtual Reality Virtual reality (VR) is a common instructional strategy used in simulations (Alessi & Trollip, 2001). Rose (1995) discusses in his paper seven steps to problem solving using VR. They are: (a) VR may prove to be a powerful visualization tool for representing abstract problem situations; (b) virtual worlds allow for a high degree of trial and error, which may encourage students to explore a greater range of possible solutions; (c) the student is free to interact directly with virtual objects which allows for firsthand hypothesis testing; (d) the virtual world can be programmed to offer feedback which focus the students attention on specific mistakes, thereby enhancing students ability to monitor their own progress; (e) the VR system can collect and display complex data in real time, which may help students obtain their desired goals; (f) the immersive nature of VR might enhance students capability to retain and recall information, which could facilitate the evaluation of solutions; and, (g) the virtual world is a fluid environment well suited for the iterative process of refinement (Rose, 1995, pg. 21) 43

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Hullfish (1996) defines Virtual Reality Monitoring as the decision process in which people distinguish between real, virtual, and imagined events, as represented in memory (Hullfish, 1996, pg. 1). He goes on to note that the goal of virtual reality is to convince one that they are in reality, not to recreate reality (Hullfish, 1996). The reality and improvement in technology will continue to enhance the simulated environments. Moores Law states that computing power doubles every 18 months (Harris, 2003). Four issues relating to VR research include (Mc Lellan & Mc Lellan, 2001): (a) How is learning in virtual reality different from that of a traditional education environment? (b) What do we know about multi-sensory learning that will be of value in determining the effectiveness of this technology? (c) How are learning styles enhanced or changed by VR? (d) What kinds of research will be needed to assist instructional designers in developing effective VR learning environments? (2001). Billinghurst, Kato, and Poupyrev (2001) differentiate tangible interfaces, which lie to the left on the reality-virtuality line and immersive virtual environments on the right extremity. Al-Shihabi and Mourant (2001) note that almost all studies of VR driving simulators have used the hierarchical control structure model for simulating driving behavior (Al-Shihabi & Mourant, 2001). The hierarchical control structure divides the driving task into three levels of control: (a) a strategic level that primarily addresses route planning in addition to other general considerations, (b) a maneuvering level that addresses maneuver control; and, (c) an operational level that addresses the direct low-level control of the vehicle (Mourant & Schultheis, 2001). 44

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Defining the Aspects of Simulations Simulation is an environment created to place the user in a position of thinking they are in a real environment. Hullfish (1996) describes this environment as distal attribution or externalization. This is a phenomenon in which our sensory organs are stimulated to a level that is outside their limits. Basically, what occurs is the perception in this phenomenon has our thoughts originating externally versus internally (Hullfish, 1996). Simulators are divided into different subsystems. They include such items as visuals, sound, force feedback, vehicle model, and scenario. When all the systems work together they create the illusion of driving or operating a vehicle. The visual system relies on several factors to create an optimum simulation. They are transport delay, frame rate, display size, resolution, and acuity (Johansson & Nordin, 2002). According to Johansson and Nordin (2002), the total delay in the simulator should be around 40-60 ms. If the frame rate is 60Hz, which corresponds to 17 ms, the transport delay must be shorter than 30 ms. The most important element with visuals is the frame rate versus the graphical acuity (Johansson & Nordin, 2002). Johansson and Nordin (2002) do not put as much effort in the sound system. The sound does not give the driver as much direct information about what is happening. The information they have noted is that the sound in a car is the range of 20 500 Hz. Force feedback is the reaction or the feel the driver senses when operating a vehicle. Renault did testing on the force feedback a driver typically receives when operating a vehicle (Johansson & Nordin, 2002). Force feedback includes braking and accelerating, cornering, suspension and road elevation, suspension and cornering, and 45

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steering (Johansson & Nordin, 2002). Most simulators have some type of force feedback associated with them. The complexity depends upon the simulator. The remaining two items of simulation, vehicle model and scenario control, are really dependent upon the vehicle you are training the person to operate and the environment in which the driver will be driving. These two elements are critical to provide the most realism. Ehret, Gray, and Kirschenbaum (2000) identify three dimensions to compare simulated task environments: tractability, realism, and engagement. Tractability is how effectively the researcher can use the simulated environment. Matching the experience to the real and simulated worlds is realism. The ability to suspend the disbelief of the experimental participants is engagement(Ehret, Gray, & Kirschenbaum, 2000). There have been instances in which the participant is involved in an adverse situation, such as a truck driver being involved in accident, and they have left the simulation very upset. At this point it might be reasonable to ask if the participant was too engaged in the simulated environment. Simulators can be traced back to the early 1950s and have a long and rich body of scientific and technical literature about their use for training (Brock, Jacobs, & Buchter, 2001). The study by Brock, Jacobs and Buchter discussed how the literature can be categorized into four main categories. They are: (a) descriptions of simulators, or simulator components, their characteristics, and how they are being used; (b) advice on what characteristics are required in a simulator; (c) results of research on the effects of simulator characteristics on performance; and, (d) results of 46

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research on the effects of simulator characteristics on training (2001, pg. 2). There are training environments that are complex and too difficult to create a prototype or, as in this study, too dangerous to test in the real setting (Sukthankar, Hancock, Pomerleau, & Thorpe, 1996). These situations are ideal for simulation (Sukthankar et al., 1996). It was noted that empirical research explored the instructional potential of immersive reality as an interface for simulation-based training (Mc Lellan & Mc Lellan, 2001). According to these researchers, virtual reality may hold promise for simulation-based training because the interface preserves: (a) visual-spatial characteristics of the simulated world; and, (b) the linkage between motor actions of the student and resulting effects in the simulated world (Mc Lellan & Mc Lellan, 2001). Simulations are divided into two groups, based upon whether they only impart knowledge or also teach physical actions (Alessi & Trollip, 2001). They are further divided into two subcategories. Knowledge simulations are divided into Physical and Iterative and action simulations are divided into Procedural and Situational (Alessi & Trollip, 2001). Some researchers describe simulations as based on a model of a real system (de Jong & van Joolingen, 1996). A real system is divided into physical systems, which are present in the natural world, artificial systems, which are created by human beings, and hypothetical systems, which have no direct counterpart in the real world. Ross (2002) listed a number of reasons why simulations differ from traditional classroom environments. They are: (a) it can be set up immediately; (b) a wider variety of situations can be replicated than with any other method of training; (c) records and results are automatically and objectively gathered and logged; (d) its easily repeatable, adding a 47

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dimension of consistency for benchmarking; (e) its more accessible to the student and the training department; (f) its more resource friendly; (g) its inherently safer than a live exercise; and, (h) it delivers cost effectiveness. The Turing-Test is a highly disputed cognitive test; however it is used in many instances to put simulation to the test (Saygin, Cicekli, & Akman, 2001). The Turing Test was developed in 1950 to determine if the observer could detect the difference between a human and a machine (Kantowitz, 2001). If the observer could not conclude any difference, the machine was thought to be as intelligent as a human (Kantowitz, 2001). Hullfish (1996) proposes that if the virtual reality is so close to being real that it generates a memory, then the simulation is sufficient to meet the expectations of the user of the simulation. His study found that there was no evidence of artifacts in memory which distinguished the virtual reality from the real environment (Hullfish, 1996). Applications of Simulations The medical community has seen a rise in simulation training. The training allows physicians to perform procedures that have a high risk of liability in an essential risk free, but realistic environment (Billinghurst, Savage-Carmona, Oppenheimer, & Edmond, 1995). A Penn State University study estimates that the average operation at its teaching hospital lasts three to four hours; physicians spend one hour of that time teaching. According to Kiser (2002), operating room time costs about $1,000 per hour. Also it is a far greater strain on the patients health to have students learning during a surgical procedure. Thus it is clear that the cost is extremely high for medical training. The liability associated with medical procedures is similar to emergency vehicle operations. 48

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The cost, on average, is greater than $34,000 for an accident involving an ambulance -eight times greater than a typical traffic accident (Shaw, 1997). The director of Penn States anesthesia training has been studying the comfort level of residents when anesthetizing patients. He asks them how they feel about performing 40 tasks 22 of which they practice on the simulator in the first three days of their training. On day four they move into the operating room and the confidence level of students intubating a patient rises from 55% to 75%. For the 18 tasks they do not first practice on the simulator, confidence rises from 55% to 58% (Kiser, 2000). Weaver, Kizakevich, Stoy, Magee, Ott, and Wilson (2002) conducted a usability analysis of VR simulation software in the EMS industry. A qualitative significant finding was related to user immersion during the tutorial of the software program (Weaver et al., 2002). The users wanted to perform emergency procedures instead of learning the software and became frustrated over this portion of the program. These individuals took longer to complete the tutorial than the remainder of the participants. The VirtualEMS was rated by the users on a scale of 1 to 5, 5 being the best or highest. The overall usefulness of the program was rated high by the firehouse users; the EMS students rated it as moderately useful (Weaver et al., 2002). 49

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Table 2. Average Evaluation Scores -Students and Firehouse Users. Test User Overall usefulness Meaningfulness for training/practice Likelihood of using outside classroom/work environment Likelihood of using if approved for continuing ed. EMS Students 3.4 4.9 3.7 ** Firehouse Users 4.0 4.8 4.8 4.3 **Participants were not asked this question. In addition, the Subject Matter Experts (SMEs) for the VirtualEMS study identified a number of issues related to the realism of the software and the accuracy; however, they deem it acceptable for EMS training (Weaver et al., 2002). Examples of the issues the participants cited were visual representations of wounds that did not appear to be realistic, and vital signs that were deemed to be way too good for the severity of the injury that was being depicted in most scenarios. Virtual reality is part of training in the telemarketing or telephone interviewing industry. Responsive Virtual Human Technology is a technology that creates a simulated dialogue environment using an emotive behavioral engine to create natural, interactive dialogues with intelligent, emotive VR agents (Link, Armsby, Hubal, & Guinn, 2002). A study was conducted using this technology, and users were asked if they bought into the virtual environment as part of the training. A diverse group of 48 respondents filled out the questionnaires. The researcher collected empirical data by observing the interaction of the user with the technology as well as recording their perceptions of the interaction. The response was somewhat mixed. The sessions were found to be helpful, but the slowness of the responses and the limited different questions/objections offered by the virtual respondent was a negative (Link et al., 2002). The use of simulators in this environment 50

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was an effort to reduce the amount of live training the interviewer received. The results of the study are illustrated in the following tables. Table 3. Interviewer's Evaluation of the RVHT Training Software. Extremely Very Somewhat Not Too Not At All In general, how easy was the application to use? 52.1% (25) 31.3% (15) 12.5% (6) 4.2% (2) 0% (0) In general, how realistic did you find the overall conversation with the virtual respondent? 2.1% (1) 14.6% (7) 43.8% (21) 16.7% (8) 22.9% (11) In general, how realistic did you find the objections, concerns, questions posed by the virtual respondent? 12.5% (6) 35.4% (17) 39.6% (19) 8.3% (4) 4.2% (2) How easily could you determine the virtual respondents emotional state or attitude based on the tone of his/her voice ? 22.9% (11) 43.8% (21) 29.2% (14) 4.2% (2) 0% (0) How easily could you determine the virtual respondents emotional state or attitude based on the words used or objectives raised by him/her? 8.3% (4) 54.2% (26) 27.1% (13) 10.4% (5) 0% (0) 51

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Table 4. Interviewer's Perceptions of Effectiveness of RVHT Training Software A Lot Somewhat A Little Not at All Respond to questions / concerns raised by sample members 25.5% (12) 47.9% (23) 16.7% (8) 10.4% (5) Better gain respondent cooperation during the first seconds of a call 25.5% (12) 31.3% (15) 29.2% (14) 14.6% (7) Enhance your ability to adapt to differences in respondents tone/mood 25.5% (12) 29.2% (14) 29.2% (14) 16.7% (8) Think on your feet 20.8% (10) 39.6% (19) 27.1% (13) 12.5% (6) Enhance your ability to adapt to differences in respondents pace of speaking 18.8% (9) 33.3% (16) 27.1% (13) 20.8% (10) Avoid refusals at the outset of an interview 16.7% (8) 35.4% (17) 31.3% (15) 16.7% (8) Table 5. Recommendation for Future Use of RVHT Training Tool. Assessment Questions Yes No Would you recommend the RVHT program as a training tool for other interviewers? 83% (40) 17% (8) Would you like to use the RVHT program again as a training tool? 73% (35) 27% (13) Was using RVHT fun and enjoyable? 65% (31) 35% (17) 52

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Simulators are being used to determine how humans interact in an urban environment by creating a virtual city and monitoring how human participants cope with various urban dilemmas and environmental concerns (Farenc et al., 1998). This was created through the use of modeling smart objects. The process is to design a complete framework in which the designer can model not only the objects geometry but also extra information so that the user can interact with specific parts. This has been created in context, but has not been studied to identify its effectiveness. The armed forces are taking advantage of computer simulated training in their efforts to fight the Iraqi war; technology that was not available ten years earlier is now the preferred mode of training (Harris, 2003). The generational culture contributes to this preferred method -the current generation of soldiers grew up in the Nintendo age, and most are very accustomed to a simulated environment (Harris, 2003). Full Spectrum Warrior was developed in participation with the entertainment software maker Pandemic Studios. The simulation was developed to be operated on the Microsoft Xbox (Harris, 2003). This could revolutionize the training of vehicle driving for the entire population. A large number of children play video games, and if they begin learning the techniques and skills of driving at an early age, it could create a much safer and more educated population when they begin to drive. Flight simulators came into existence in 1910 when an attempt was made by the Sanders Teacher (Johansson & Nordin, 2002). The simulator taught the basics of controlling an aircraft. In the late 1920s, Edwin Link developed a more realistic flight simulator by adding rudder, aileron, and elevator inputs. By the 1930s, flight simulators 53

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were used to train pilots on instrumentation. It was not until the 1960s that flight simulators began to rely on digital computers and more advanced visuals (Johansson & Nordin, 2002). The effectiveness of flight simulators is referred to in many instances as Transfer Effectiveness Ratio (TER) (Why use simulation? Return on investment, 2003). The literature suggests that military flight simulators have greater than 0.33 TERs, which is 59% of the tasks they use in training. This means that for every three hours spent in the simulator, one hour of actual flight time could be eliminated for 54% of the tasks (Why use simulation? Return on investment, 2003). Additionally, the cost to operate a flight simulator is 5-20% of the cost of the aircraft. The Air Force Mobility Command is planning to replace up to 50% of the hours they conduct flight training with flight simulators. Flight Deck Automation Issues conducted 18 experiments, 25 surveys, and 15 observation studies, plus four additional studies and compiled a document relaying this information. Some of the studies were conducted in simulators or in laboratories. Others were observation studies in which a researcher observed pilots in simulators or in flight operations. One such study that was noted to have a significant finding was one that tested a hypothesis that flight crews respond faster to air traffic control clearances when flying the airplane manually than when using the flight management system. This experiment used a part-task simulator model to train line-pilot. The subjects flew several scenarios, half of them manually and half with the flight management system. The results of the study showed that the mean time to begin complying with the air traffic control clearance takes, on the average, 4.5 seconds manually and 8.1 seconds with a flight 54

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management system. The difference was significant at a p=0.0963 level. Additionally the information in this report showed that 14 out of 18 experiments reviewed showed evidence for flight deck automation. There were 10 out of 15 observation studies reviewed that evidence was found for flight deck automation. Out of 23 surveys reviewed, 22 were found to have evidence of flight deck automation. Essentially this illustrates that simulators have a positive effect on training pilots (Accident analysis, 2003). A Level C flight simulator, which represents a 30-passenger, three-crew, turboprop airplane with wing-mounted twin engines and counter-rotating propellers, was used by 42 crews of regional airplane pilots. The simulator was a high-quality visual system with wide-angle collimated cross-cockpit viewing with a 150 degree horizontal and 40 degree vertical field view available to each pilot. There were two studies. The first study was named First Look and evaluated the aviating skills of the pilots existing skills. The second experiment, Training and Transfer, examined the use of simulators as training tools for aviating skills that would need to be transferred to the airplane. There were no statistically significant differences for either performance or workload measures between groups of the First Look study. Integrated Yaw Activity and motion/no-motion resulted in a p=0.033; RMS Heading Deviation and motion/no-motion resulted in a p=0.126; and. Mean Abs Lateral Deviation resulted in a p=0.906. The Training Transfer group had significant findings. The motion group controlled airspeed better (p=0.006) at the expense of increased STD Pitch Angle (p=0.025). This group also displayed higher Integrated Yaw Activity compared to the No-Motion group (p=0.024) (Tiauw, Burki-Cohen, & Soja, 2000). 55

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Driving simulators have their roots in flight simulators. Driving simulators date back to the 1970s when General Motors and Virginia Polytechnic Institute and State University did pioneering work on human-in-loop driving simulation (Johansson & Nordin, 2002). Driving simulators have continued to evolve and progress to their current state. Johansson and Nordin (2002) found a difference between flight simulators and driving simulators, hence the need for additional studies on driving. An acceptable time delay for a flight simulator is higher: 150 ms compared to 50 ms for a driving simulator (Johansson & Nordin, 2002). Demands of the visual systems are also higher in a flight simulator because all objects are closer to cars than to airplanes (Johansson & Nordin, 2002). The pitch roll motions in a car are mechanically limited to +/6 degrees, but in an airplane there is no such limitation (Johansson & Nordin, 2002). Vehicle Simulators Simulators are being or have been deployed in many areas to instruct individuals learning to drive. High school students are using simulators to learn to drive vehicles (Allen, Park, Cook, & Rosenthal, 2003). Commercial truck drivers are being monitored in studies using a simulator called Sim Val (simulation validation) to re-assess their driving abilities (Pierowicz, Robin, & Gawron, 2001). Vehicle driving simulators are being used to monitor and study driver fatigue and stress, with initial results demonstrating significant findings (Rimini-Doering et al., 2001). Olsen (1997) cited three benefits for using a simulator to perform driver assessment; (a) a more timeand cost-efficient method for evaluations (e.g., weather concerns would be eliminated); (b) the ability to evaluate drivers under 56

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complex conditions where failures are likely to occur; and, (c) the safety of both the evaluator and driver would be improved. (Olsen, 1996, pg. 1). According to Dols, Prado, Falkmer, Uneken, and Verwey (2001) simulators are developed and used for the following reasons: (a) drivers training tool for complex (and safety critical) traffic scenarios in driving schools; and, (b) drivers assessment tool (for all drivers or particular driver subgroups, such as the elderly and cognitive disabled, standard drivers with knowledge acquisition problems or after serious accidents for re-training), installed at central assessment points (Dols, Pardo, Falkmer, Uneken, & Verwey, 2001, pg. 5) Additionally, the main objectives for the development of simulations are: (a) to develop appropriate scenarios to support driver training and assessment by the use of simulators; (b) to develop a low-cost driving simulator to support driver training in tactical and control tasks, according to the Michon model; and, (c) to develop a mean cost driving simulator with high reliability for support and assessment of particular drivers cohorts. (Dols et al., 2001) The emergency vehicle operator drives a vehicle in an environment unlike any that has been previously simulated and tested as an effective training method. Lack of transfer effectiveness and cost-effectiveness are both concerns of validation information in simulation studies (Meyer, Slick, Westra, Noblot, & Kuntz, 2001). Novice drivers (especially males) have a higher incidence of accidents compared with experienced drivers (Allen, Cook, & Rosenthal, 2001). 57

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Brock, Jacobs, and Buchter (2001) conducted a survey on the satisfaction of bus drivers using simulators to learn how to drive buses. There was a high level of satisfaction (92%) reported from all locations of respondents for training purposes. Further, 58% of the respondents reported that the simulator was more effective than traditional training methods (Brock et al., 2001). The satisfaction of using a simulator has also created other benefits. The drop-out rate of students decreased. A 35% reduction in attrition was noted in courses that used a simulator, compared with the more conventional courses (Brock et al., 2001). The success rate of the courses also increased with the simulator. One of the training courses realized a 95% pass rate (Brock et al., 2001). Increased safety and reduction of accidents are essential components of training drivers. A combination of an inexperienced driver with an unfamiliar vehicle that is not the participants own vehicle creates a potentially hazardous situation for on-the-road driver training (Olsen, 1996). The accident rate was monitored by a group using a mid-range simulator. This group realized an 18% reduction in accidents during the 90 days after the simulation training (Brock et al., 2001). The accident rate of the drivers conventionally trained was almost 32%. Another group had 17 accidents reported by those who participated in the simulator training and 154 for those not participating in simulator training (Brock et al., 2001). Allen, Cook, and Rosenthal (2001) investigated the feasibility of training novice drivers to deal with cognitively complex traffic hazards using low-cost simulator technology. The simulator was a desktop configured simulator. The subjects for this study were 16 novice drivers and 10 drivers with greater than 10 years of driving experience. Each of the groups had two experimental sessions. Each of the sessions lasted 58

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about 20 minutes. Using the low-cost technology, the study revealed that the total number of accidents decreased with experience in the simulator (p<0.05). Initially the novice drivers had twice as many accidents in their first session as did the experienced drivers; however, by the second time through the simulation, the accident rate dropped to nearly the experienced operators rate. This study demonstrates that a simulator can be used to effectively train novice drivers in complex and critical road/traffic situations and reduce the number of accidents (Allen et al., 2001). In Europe there is a high incident of young people dying each year in road accidents (Dols et al., 2001). It was noted that the majority of these drivers are killed in accidents at intersections (Dols et al., 2001). For emergency vehicle drivers, the greatest number of and the most severe accidents occur at intersections (Klein et al., 1997). Ceci, Hogman, and Patten (2001) conducted a study measuring the drivers behavior and cognitive workload in a driving simulator and in a real traffic environment. The study was designed to plan the construction of a road or tunnel. The study has measured the results of the simulation portion and will conclude when the tunnel is constructed. Twenty-one subjects drove five different predefined routes of the Stockholm road tunnel system that was being designed for construction. The driving simulator for this study comprised an advanced construction with a motion system, a wide-angle (120 degree) visual system, a vibratory generating system, a sound system, and a temperature regulating system. Each participant was put through five different tunnel routes. Prior to subjecting them to the five tunnel routes, they had an opportunity to complete a trial route and received instructions regarding the subjective ratings and driving procedure. The subjects were interviewed regarding their experiences using the simulator. 59

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The results demonstrated that route 5 was the most difficult route followed by routes 4, 1, 3, and 2. The results of the simulation study showed little effect on driving performance of the navigation mode; however, the peripheral detection task device that was used as a standard method for measuring cognitive load, was influenced by the navigation mode (Ceci, Hogman, & Patten, 2001). In other words, when the drivers were distracted it affected their scores negatively. The following table illustrates the results of the study. A higher variability indicates an increase in cognitive workload. Table 6. Means and standard deviations from NASA-TLX ratings for five different routes. NAS-TLX Route n-r 1 2 3 4 5 Driver demands 27.5+/-18 30.4+/-19 26.2+/-14 31.0+/-21 32.9+/-21 Time pressure 23.4+/-22 19.9+/-10 29.1+/-23 39.1+/-23 30.8+/-23 Feeling of uncertainty 30.5+/-25 22.2+/-15 18.9+/-12 25.9+/-14 44.0+/-21 Performance 36.3+/-19 26.6+/-19 29.5+/-21 22.2+/-15 29.6+/-17 Overall difficulty* 27.1 24.3 24.7 32.0 36.0 *Overall difficulty is an index based on the mean from the ratings driver demand, tie pressure and feeling of uncertainty (Sd cannot be calculated for this index). The following table depicts the mean co-efficient from measures of electro dermal activity (EDA), driving speed, acceleration and braking activity as group means of coefficients of variations (CV) from the five routes. There was an interesting finding in regard to routes 4 and 5. This finding was confirmed by the high correlations between the psycho-physiological reaction and subjective ratings of overall difficulty (rxy = 0.90, p=0.05) and feeling of uncertainty (rxy=0.85). The results also showed that 50% of drivers missed important road signs. Additionally, 30% to 50% of the subjects made lane 60

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choice errors resulting in loss of orientation and missed their target exits (Ceci et al., 2001). Table 7. Means of coefficients of variation of EDA, driving speed, acceleration, and brake activity for the five difficult routes. Variability of driver responses Route n:r 1 2 3 4 5 EDA 0.171 0.161 0.155 0.181 0.179 Driving speed 0.091 0.073 0.079 0.075 0.084 Acceleration 0.313 0.307 0.323 0.298 0.285 Braking activity 7.166 20.27 18.22 14.25 12.51 There are a variety of driving simulators. They take a variety of styles, and the complexities of driving simulators vary. The simplest and lowest cost simulators are those that are tabletop setups with a monitor or head-mounted display (HMD). The various simulators have both positive and negative features associated with them. HMD simulators may contribute to simulator sickness resulting from vestibular-visual conflicts or dizziness from conflict between what you see and hear while operating the simulator, accommodate difficulty presumed to be associated with instrument myopia or difficulty in seeing the instruments of the simulator through the use of the HMD, binocular function difficulties due to a mismatch between the device and the individual users visual system they wear, and binocular difficulties associated with the de-coupling of the natural relationship between accommodation and convergence in stereo binocular HMDs employing image disparity (Mourant & Schultheis, 2001). There are two types of validity to take into consideration when using driving simulators: absolute and relative (Kantowitz, 2001). If the simulator produces results and effect sizes that are identical to the real world, it is called absolute validity. 61

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Comparatively, relative validity is more commonly found and is when the simulator produces the same rank order as in reality (Kantowitz, 2001). Jamson (2001) discovered during a study that drivers tend to drive slower on curves and faster on straights during simulator testing than their real-world counterparts. The initial study had 100 subjects, divided evenly between females and males. An additional 96 participants, 50 male and 46 females were used for this study. The data was gathered at four points associated with each curve on the simulated roadway: the approach, the entrance, the apex, and the exit. A two-way ANOVA was conducted to research the main effects of display resolution and horizontal field of view, followed by pairwise comparisons to highlight the simple effects. The dependent variables were spot speed and lateral position at the ten data points that were established. The results are illustrated in the following table. Table 8. Mean speed (kph) at each data point (curves). Data Point Real World High resolution Low resolution 50 degree 120 degree 230 degree 50 degree 120 degree 230 degree 1 approach 64.4 53.2 51.5 57.2 59.4 47.0 54.9 2 entrance 50.3 46.4 45.6 52.0 49.2 43.3 47.4 3 apex 43.7 43.8 42.9 49.4 44.1 41.7 43.8 4 exit 45.7 45.3 45.3 51.5 46.9 43.6 51.5 5 entrance 50.3 43.9 45.1 50.2 47.4 42.3 46.5 6 apex 57.0 49.2 47.9 55.1 51.8 49.1 50.6 7 exit 56.3 56.0 55.3 61.6 58.3 55.7 57.3 A main effect of field view was discovered at points 1-6 (p=0.01). There is no main effect of image resolution. Pairwise comparisons revealed that on the approach to the curves, there was a significant difference between real-life and simulated driving 62

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speeds in all simulated conditions. At the 230 degree condition, this error was reduced. In all simulated conditions, drivers drove significantly faster than the real-life drivers (p<0.01). The following table depicts the speeds on a straight way (Jamson, 2001). Table 9. Mean speed (kph) at each data point (straight). Data Point Real World High resolution Low resolution 50 degree 120 degree 230 degree 50 degree 120 degree 230 degree 8 90.7 93.9 92.5 95.0 89.9 91.1 90.2 9 80.7 93.6 91.7 94.6 91.5 91.7 92.7 10 83.4 94.2 91.1 94.7 94.1 92.7 93.2 In contrast, there were no differences in speed when comparing simulation configurations. Jamson (2001) concluded that there does not appear to be any negative effects in the image resolution with simulators; however, it is best to remain cautious until other testing can be conducted to further investigate if the coarser image resolution may contribute to other driving performance issues (Jamson, 2001). Another feature on simulators is the controlling mechanism. Most simulators emulate a vehicle with the incorporation of a steering wheel to control the simulated vehicle. However, Haas and Kunze (2001) found in their study that there was no significant difference between using a steering wheel and using a joystick to operate the simulated vehicle at relatively low speeds of 15 mph. When drivers increased their speeds to exceed 45 mph, the difference between the participant using a joystick or a steering wheel remained small enough not to have a practical significant difference (Haas & Kunze, 2001). The study included eight U.S. Army Department of Defense male, right-handed civilian volunteers. The subjects were screened for vision normalcy, and the study 63

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was conducted at HRED, Building 459, Aberdeen Proving Ground, Maryland. Each subject was given a 30-minute training session in which he was introduced to the driving task and the controller used in the first experimental session. The practice test was driven at 45 mph for a duration of 30 minutes. There was a 30-minute break between sessions. An ANOVA was performed for each dependent variable to determine statistical significance. The ANOVA for mean driving speed indicated significant main effects for controller (F=7.24, p=0.031), for assigned driving speed (F=2130.84, p=0.000, and for the control x speed interaction (F=8.412, p=0.023). At assigned speeds of 15 mph, subjects obtained a mean driving speed of 14.7 mph using a steering wheel, and a mean driving speed of 14.5 mph using a joystick. This difference was not statistically significant (p<0.05). At assigned speeds of 45 mph, subjects using a steering wheel obtained a mean driving speed of 39.9 mph, and 38.4 mph using a joystick. The results were found to be statistically significant; however, the investigators concluded it may have little practical significance because the difference was less than 5 mph (Haas & Kunze, 2001). According to Allen, et al. (1998), the real cab enclosure simulator with the rear projection, which displays the image at a distance consistent with far field eye focus, provides the highest surround fidelity. This type of simulator is the closest to actual driving (Allen et al., 1998). The cost associated with simulators ranges from $1,000 to $80,000 for single-screen, non-motion based systems on up to full, motion-based systems that are in the multi-million dollar range (Olsen, 1995). Further, Sukthankar, Hancock, Pomerleau, and Thorpe (1996) identify three simulators that address the tactical-level modeling of intelligent vehicles. They are: Pharos, SmartPath, and SmartAHS 64

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(Sukthankar et al., 1996). The following photos depict the various types of simulators as described by Allen, et al. (1998). Figure 2. HMD and Game Controls. Figure 3. Torque Feel and Monitor. 65

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Figure 4. Free Standing Console. Figure 5. Game Console. Figure 6. Cab with Projection. 66

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Allen, Park, Cook, and Rosenthal (2003) conducted a study using a PC-based simulator for novice drivers. The investigators noted in their study that young drivers are inexperienced and gain their experience during the first years of driving at a cost of having a high incidence of accidents (Allen et al., 2003). The initial training involved 111 novice drivers at the high school age with anticipation of increasing this number to 500 participants and then comparing the accident and violation rates of these drivers. The initial study was found to be successful with a significant interaction in configuration and gender. Multivariate tests showed statistical significance for Configuration (p=0.001), Gender (p=0.012), and Trials (p=0.001). Significant interactions included Trials x Configuration (p=.0.03) and Trials x Configuration x Gender (p=0.015). Additionally, Speed Limit Exceedance with the three simulator configurations for the first six training trials shows significance also. The configuration and trial effects and the interaction are statistically significant (p=0.01, p=0.005 and p=0.05, respectively). The parameters included accidents, speeding, road edge incursions and time-to-collision. The performance was compared from the first interaction through the sixth interaction, which in most cases showed a marked improvement (Allen et al., 2003). Kantowitz (2001) noted a study that was conducted with 120 simulator drivers and 192 test track drivers. The study found remarkable agreement and no statistical differences between simulator and test track total brake reaction time and time to initial steering (Kantowitz, 2001). This is important to note in that there is no statistical difference between track testing and simulator. The safety factor is a critical element in the total context of competency course training. 67

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Olsen (1996) conducted a study that did not have a significant positive correlation between road and simulator driving. The investigator concluded that it was related to the high drop-out rate as a result of the simulator discomfort (Olsen, 1996). A 26.3% drop-out rate was realized from this study of which 5% could not finish the experiment, which seems to be relative to the simulator sickness experienced by the participants (Olsen, 1996). Simulator sickness is a side effect from using simulators. Olsen (1996) recommends the following to help prevent simulator sickness. Keep rooms very cool at 66 to 68 degrees Fahrenheit. Have quiet, fan(s) on at all times. Consider a low breeze directly on the participant. Have operable fans inside the simulator for further ventilation. Orient the participant to the simulator with the screens blanked before an image is presented. Always stop the simulated vehicle before exiting the simulator. Ideally, the screen would be blanked every time one exits or enters the simulator. (Olsen, 1996, pg. 2) Carnegie Mellon Driver Training and Safety Institute has implemented a driving simulator as part of their driver training program (Meyer et al., 2001). A study was conducted with a small group of individuals, the group was too small for meaningful statistical data, but did establish a clear trend. In this study, the researchers found the drivers who took the actual driving portion of the course first did better on the simulator portion of the course than their counterparts who did the opposite (Meyer et al., 2001). However, the results for the range test trials demonstrated that simulator training resulted in transfer to the range (Meyer et al., 2001). Additionally, testing was conducted on a skid pad on the simulator and in a real environment. It was discovered that the stopping 68

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distances on the virtual skid pad was shorter than on the real skid pad at the same speed (Meyer et al., 2001). In order to compensate for the difference, the virtual skid pad had a 9% decline (Meyer et al., 2001). A normal skid pad has a 1% decline. By adjusting the decline of the skid pad, it created a virtual environment to test the driver in a similar fashion to that in which they are tested in the real environment. A study was conducted to examine the relationship between school bus drivers and their collision history (K. C. Mills, Hubal, & Ward, 2002). One of the findings of this study showed that the drivers who became disoriented and overwhelmed in a high-demand computerized assessment were more likely to have had collisions in the real environment (K. C. Mills et al., 2002). The results showed a significant difference on the overall score of those who had collisions (n=27) compared with those drivers who had not had collisions (n=82) (t=2.74, p=0.015) (K. C. Mills et al., 2002). Additionally, the non-collision drivers also demonstrated a significantly smoother steering score in the test (t=2.39, p=0.019). The drivers who had a high incidence of collisions showed a significantly higher unnecessary response on both the brake pedal and the hand responses to visual targets (t-brake=3.55, p=0.0006; t-targets=4.317, p=0.0001) (K. C. Mills et al., 2002). Finally, the correlation between collision cost and overall score (n=23) was significant (r=-0.51, p=0.02), which illustrated that drivers with lower overall scores were more likely to have higher collision costs (K. C. Mills et al., 2002). Profiler is one of a limited number of computer simulation programs designed for emergency vehicle driver training (Profiler Driving safety through PC based driver testing and training, 2002). It is designed to improve performance, reduce tunnel vision, and provide feedback (Profiler Driving safety through PC based driver testing and 69

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training, 2002). The program was developed as a result of a 10-year process using computer-based testing to assess visual and decision-making skills (Profiler Driving safety through PC based driver testing and training, 2002). Mills and Hubal (2001) conducted a number of studies with the Profiler simulator and law enforcement personnel as the participants in the studies. An informal assessment of the simulator revealed that the low-cost simulator was useful for gaining some basic driving skills, or assessing some abilities, on an initial and periodic basis, but their limits, including realism and capabilities, became a negative for those who used the trainer often over short periods of time (K. C. Mills & Hubal, 2001). Additionally, a group of police cadets were tested on the Profiler system first, then drove on a competency course with traffic cones. The results showed that the cadets who had higher test scores on the Profiler had fewer driving errors on the track (K. C. Mills & Hubal, 2001). It was concluded that pre-testing of driving skills in a controlled environment may have some usefulness in assessing and predicting driving skills in the real world (K. C. Mills & Hubal, 2001). The FDNY (Fire Department of New York) received two full-scale, motion-based vehicle simulators to train fire and EMS drivers (NASCAR donates driving simulators to FDNY, 2002). The units were donated courtesy of NASCAR early in August 2002 (NASCAR donates driving simulators to FDNY, 2002). FDNY was the first municipal fire department cited as an agency using simulators for its driver-training program (NASCAR donates driving simulators to FDNY, 2002). It was felt that it would represent the most effective means of training more than 100 drivers, as the result of the loss of drivers from 70

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the World Trade Center tragedy in September, 2001 (NASCAR donates driving simulators to FDNY, 2002). The New Jersey Transit experienced a high incidence of vehicle accidents in 1994 (Wetzel, 2000). There were 42.6 accidents for every million miles driven (Wetzel, 2000). The Transit instituted a simulator driver-training program. The Transit appreciated a reduction in insurance, maintenance, and gas costs, with a realized savings of $375,000 per year (Wetzel, 2000). In 1997, the New Jersey Transit saw its accident rate reduced by 75% (Wetzel, 2000). They also saw their training and testing time decrease from 19 days to 18 days (Wetzel, 2000). A number of other transit agencies across the country are using simulators for their transit drivers. These agencies are in Cleveland, OH, Philadelphia, PA, Wilmington, DE, Norfolk, VA, Hartford, CT, Orange County, CA, Raleigh, NC, and Pompano Beach, FL (Wetzel, 2000). There are some advantages to adopting the approach of using a simulator in training (F. Ross, 2002). They are: (a) according to a 1990 national survey of the United Kingdom, companies found training time was reduced by 30%; (b) automatic logging of individuals performances eliminates manual marking. Retraining then can be accurately targeted, because participation in training can be easily tracked and monitored, according to a 1995 study; and, (c) technology-based training can achieve similar results at lower cost than conventional methods (F. Ross, 2002). Ross (2002) notes that technology-based training appears to be the most cost-effective in situations when: (a) the course content is relatively stable; (b) the content is largely knowledge based; (c) there is a long-term training need; (d) trainees are scattered geographically; (e) large numbers of people have to be trained in a relatively short period 71

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of time on a regular basis; and, (f) equipment emulation and special, dangerous or unusual situations have to be created or facilitated through the use of simulation (F. Ross, 2002). Jaeger (1998) conducted a study investigating the potential of virtual environments-based computer training for near-field navigation accuracy. There were two goals: (a) determine whether training in a rendered 3-D environment significantly enhances performances in the actual near-field setting; (b) identify which level of visual detail results in the best performance accuracy in both the virtual and real-world settings. There were 60 subjects that ranged in age from 19 40 years of age. There were 39 male and 21 female subjects. The participants were randomly divided into two groups. The design of the study had half of the participants complete the computer-generated virtual environment first and then perform in the actual field setting. The second group performed in the reverse order (Jaeger, 1998). The results of the study were highly significant. Sheffes post-hoc analyses identified significant superior performance accuracy in the field setting for subjects that were first exposed to the virtual environment than those who received no prior training (p<.01) (Jaeger, 1998). An Analysis of Variable (ANOVA) test illustrated an interaction of the two groups with order of exposure as highly significant F(2, 98) = 5.304 p=0.007) (Jaeger, 1998). In her conclusion, Jaeger (1998) noted that potential beneficiaries and target populations for using simulated environments prior to actual field settings could include: military personnel, law enforcement officers, firefighters, nuclear emergency teams, medical professionals and anyone who may need to acquire knowledge about a setting accurately and rapidly. Brock, Jacobs, and Buchter (2001) believe that if you use a 72

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simulator correctly, student performance improves, cost savings are realized, and safety in the domain being simulated is improved. History of Learning Styles There are varied learning style inventories. Learning styles are not new. Historically, Hippocrates, the Father of Medicine suggested that the difference that humans exhibit could be divided into four distinct groups called temperaments (Hedges, 1997). His proposition was that the four temperaments were formed by the secretions coming from the blood of the heart (Sanguine), the yellow bile of the liver (Choleric), the phlegm of the lungs (Phlegmatic), and the black bile from the kidneys (Melancholic) (Ouellette, 2000). Through the years, the thought process of Hippocrates was amplified by other medical doctors including Galen (A.D. 129-200) a Greek physician and philosopher who thought the temperaments were a positive rather than negative (Hedges, 1997). Paracelsus (1493-1541), a Swiss-born Renaissance healer, traveled Europe expanding his knowledge in healing and earning his living as a physician and writer building on Hippocrates temperaments. Paracelsus work was entitled Nymphs, Gnomes, Sylphs, and Salamanders (Hedges, 1997). Kretschmers theories in 1925 were also similar to Hippocrates personality distinctions (Hedges, 1997). Carl Gustav Jung was a collaborator of Sigmund Freud (Lowry-Mosley, 2003). Jungs observations led him to believe individuals could be classified into certain psychological categories (Lowry-Mosley, 2003). It is important to note that the learning style inventories currently in use do not support the psychological theory types Jung originally proposed (Lowry-Mosley, 2003). The Myers-Briggs Type Indicator was a result of Jungs work by classifying individuals into Jungs typology (Lowry-Mosley, 73

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2003). Myers and Briggs (1980) identified three major differences between their work and Jungs original theory: (a) everyday types vs. pure types, (b) an auxiliary balancing preference in addition to the dominant process; and (c) a different interpretation of Jungs rational/irrational vs. Briggs judging/perception types (Briggs & Myers, 1980). Kolbs experiential learning, which focused on a cycle of learning based primarily on an experimental approach to making information meaningful, evolved in 1974 (Lowry-Mosley, 2003). Then, in 1982, Gregorc developed a model to delineate the individuals nature of how they perceive and order information that makes up their world; this model did not rely strictly on a personality indicator (Lowry-Mosley, 2003). Felder (1993) defines a students learning style in part by answering these five questions: (a) What type of information does the student preferentially perceive: sensory (e.g., sights, sounds, and physical sensations) or intuitive (e.g., memories, ideas, and insights)? (b) Through which modality is sensory information most effectively perceived: visual (e.g., through pictures, diagrams, graphs, and demonstrations), or verbal (e.g., through sounds, written and spoken words, and formulas)? (c) With which organization of information is the student most comfortable: inductive (e.g., facts and observations are given; underlying principles are inferred) or deductive (e.g., principles are given; consequences and applications are deduced)? (d) How does the student prefer to process information: actively (e.g., through engagement in physical activity or discussion) or reflectively (through introspection)? (e) How does the student progress toward 74

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understanding: sequentially (e.g., in a logical progression of small incremental steps) or globally (e.g., in large jumps, holistically)? (Felder, 1993, pp. 1-2). Learning Styles This section covers the literature on learning styles in general. There has been much discussion on the validity of learning styles. Stahl (1999) notes that if one is to use a learning style inventory, it must yield reliable scores. He further states the Myers-Brigg Inventory has been found to yield score reliability coefficients in the neighborhood of .60 and the .70s (Stahl, 1999). Stahl also notes that the learning style of an individual may change from month to month or even from week to week (Stahl, 1999). McLoughlin (1999) cites that the reason there has been a lack of confidence in the research of learning styles is because the inventories and definitions of learning styles vary. It can be further considered that research is conducted by researchers in their own unique manner and, therefore, may cause some of the disparity in the results of the research regarding learning styles (McLoughlin, 1999). Gregorc (2003) refers to the dominant points in his delineator similarly to the uniqueness of DNA and fingerprints of individuals; these traits remain consistent throughout ones life. However, it is noted that the negative characteristics are also included as part of the descriptors for the Gregorc model. A person can change a negative to a positive dominant point (Gregorc, 2003). More particularly, the discussion has centered on the value of learning styles and how to stir students into the learning environment they supposedly learn best. When we look around we quickly realize that people are not alike; they are different. Each of us sees the world through a different perspective. D.W. Mills (2002) defines this idea as an individuals perception. He further notes that our perception determines our natural 75

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learning strengths or learning styles (D. W. Mills, 2002). Heineman (1995) relates how an individuals behavior resulting from interaction with the environment corresponds to the theories of personality, learning, and learning styles. Phenomenology is a term used in the world of learning styles that needs to be defined. Gregorc (2003) breaks the word into the following components. Pheno means outward appearance or what is typically referred to as style (Gregorc, 2003). Noumena means the invisible driving forces that give rise to the style. Logos is the word, nature of, root of or the cause of things (Gregorc, 2003). The term 'phenomenology' is often used in a general sense to refer to subjective experiences of various types. In a more specialized sense it refers to a disciplined study of consciousness from a 1st-person perspective (Dictionary of philosophy of mind, 2003). McLoughlin (1999) illustrated the definitions of similar terms relating to learning styles in the following table. Table 10. Definitions of similar terms relating to learning styles. Term Explanation Learning preference Favoring one method of teaching over another Learning strategy Adopting a plan of action in the acquisition of knowledge, skills or attitudes Learning style Adopting a habitual and distinct mode of acquiring knowledge Cognitive strategy Adopting a plan of action in the process of organizing and processing information Cognitive style A systematic and habitual mode of organizing and processing information Santo (2003) identifies three approaches to learning styles and instruction (Santo, 2003c). The first approach is taken when the participants learning style is identified through a learning style inventory and then the instruction is adapted toward the participants learning preference. The second approach is the opposite, in that the 76

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learning style is identified as in the first approach; however, the instruction is geared toward the opposite preference. This is thought to strengthen the learners weaknesses. The final or third approach does not identify the learners style, but incorporates a variety of instructional methods and media in the overall course design (Santo, 2003c). OConnor (1997) discusses how learning styles can be used as a means to find groups of individuals who use similar patterns to perceive and interpret situations (O'Connor, 1997). Hence, the educational environment should become more efficient by adapting to create the environment conducive to the learning style. In contrast, for those who dont perform well, the cause may be that the environment does not meet their preferred style of learning (O'Connor, 1997). Learning Styles and Computers The literature on learning styles connected with computer simulations is minimal (Hsiao, 1997). There were few citations found in a literature search involving both computer simulations and learning styles. The lack of studies on the influence of learning styles when using computer simulations is a motivator for the researcher of this study to include it in this study. This section covers the literature on learning styles and its relationship to computer learning in general. Rourke and Lysynchuk (2000) presented a study of the influence of learning styles on achievement in hypertext. The study involved 21 female and 20 male participants who were enrolled in an Introduction to Psychology class. The participants completed the Kolbs Learning Style Inventory. Subjects were presented with a hypertext module from a web-based course and a printed version of the same module. Their achievement was assessed with four, 20-question multiple-choice quizzes, each 77

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composed of 10 factual and 10 conceptual questions. The quizzes were administered in two parts: One quiz was administered immediately, and the alternate test was administered seven days later. The researchers hypothesized, using a two-tailed hypothesis, that the achievement scores of Assimilators would be highest and achievement scores of Accomodators would be the lowest. The results of this study were limited and provided minimal support for the hypothesis of the Accomodators achievement being lower. Results of the study are shown in the following table (Rourke & Lysynchuk, 2000). Table 11. Means and standard deviations for learning styles groups. Nonhypertext Hypertext M S M S Accomodators 6.18(a) 2.06 5.88(a) 1.62 Assimilators 7.00 1.80 6.41 1.70 Convergers 7.10(b) 1.39 6.85 1.68 Divergers 6.43 7.15 7.15(b) 2.02 Note: (a) vs. (b) significant at p<0.05 Chuang (1999) conducted a study on teaching in a multimedia computer environment. The study showed the effects of learning style, gender and math achievement. A goal of the study was to find out if there existed a significant difference in learning styles between Field Independence/Field Dependence (FI/FD) subjects, between males and females, or among subjects with different math aptitude, in a multi-media learning environment. The research involved 175 seventh grade students who came from eight classes of a rural junior high school in Taipei County, Taiwan. The field dependent individuals were defined as those who rely more on external references and focus on individual parts of an object. This group solves problems through common sense and intuition and uses 78

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trial-and-error approaches. In contrast, the field independent learner relies on internal references, perceives objects as a whole, and tends to reduce problem situations to a set of underlying casual relationships. The researcher administered an embedded figure test to 330 students from eight classes to determine their FI/FD learning style. The top 25 % of each class were identified as FI subjects, and the lowest 25% of each class was identified as the FD subjects for a total of 175 subjects. There were 89 subjects in the FI group with an average score of 11.73 on the embedded figure test. The FD group consisted of 86 subjects with an average score of -1.68 on the embedded figure test. A significant difference was found between the two groups from an ANOVA analysis (MS=7864.47, F=705.63, p=0.001). The groups were further divided into three groups based upon their previous semester math grades. There were 50 subjects in the top group, 57 in the lowest grade group, and 68 subjects in the average grade group. The results of the grades showed a significant difference in math scores (F=346.29, p=0.001). Additionally, the group was divided by gender, 90 were males and 85 were females (Chuang, 1999). There were four courseware versions for this study: (a) animation+text; (b) animation+voice; (c) animation+text+voice: and (d) a free choice version. In the free choice version, the subjects were able to choose their favorite interface design from the three versions. The results are shown in the tables below (Chuang, 1999). An effective factor indicated on the post-test for FI/FD learning style results is shown in Table 12. The FI subjects scored significantly higher than the FD subjects on the post-test, F=7.27, p=0.01. In contrast Table 13 shows the FI and FD subjects, post-test scores differed significantly only in the animation+text+voice version (F=4.13, 79

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p=0.05), or free choice version (F=9.74, p=0.001). There was no difference found in the animation+text version or in the animation+voice version. The study results shown in Table 14 revealed that there were significant differences on the post-test among four courseware versions, F=3.11, p=0.05, for the FI subjects. The FI subjects in the animation+text+voice group or in the free choice group scored significantly higher than those in the animation+text group or in the animation+voice group. There was no significant presentation effect found for the FD subjects. Table 12 shows the significant differences on the post-test scores of males and females, F=7.36, p=0.01. The male subjects performed better than the female subjects. Only the animation+test+voice interface post-test scores were different. Males had a significant difference on the post-test among the four courseware versions, F=3.00, p=0.05. Table 12. Results of ANOVA on the post-test scores. Independent Variables DF SS MS F Prob>F Courseware Versions 3 3521.76 1173.92 4.20 .0069* FI/FD 1 2032.47 2032.47 7.27 .0078* Gender 1 2055.85 2055.85 7.36 .0074* Math Achievement 2 8426.21 4213.11 15.07 .0001* *reach a significant level 80

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Table 13. Results of ANOVA on post-test scores for subject groups in each courseware version. Courseware Version Group DF SS MS F Prob>F Animation+text FI/FD 1 116.06 116.06 0.46 .5027 Gender 1 1.17 1.17 0.00 .9463 Math 2 3282.91 1641.45 6.47 .0037 Animation+voice FI/FD 1 1.72 1.72 0.00 .9463 Gender 1 495.00 495.00 1.32 .2575 Math 2 6586.62 3293.31 8.79 .0007 Animation+text+voice FI/FD 1 1007.19 1007.19 4.13 .0490 Gender 1 1965.54 1965.54 8.06 .0072 Math 2 1022.73 511.36 2.10 .1366 Free Choice FI/FD 1 2417.86 2417.86 9.74 .0034 Gender 1 766.05 766.05 3.09 .0869 Math 2 798.64 399.32 1.16 .2133 Dependent variable: Post-test score *reach a significant level Table 14. The Least Square Means of post-test of each subject group in four courseware versions. Group Animation + Text Animation+ Voice Animation+ Text+ Voice Free Choice F Prob>F FI subjects 70.00 70.91 80.45 81.36 3.11 .0305* FD subjects 44.76 58.57 60.91 58.18 2.55 .0616 Males 62.11 64.17 78.19 74.40 3.00 .0351* Females 54.80 65.79 63.19 63.68 1.23 .3037* High math 76.67 81.67 83.85 86.92 1.23 .3079 Low math 40.00 44.00 60.63 60.77 5.79 .0017* Average math 65.00 65.24 70.00 63.89 0.30 .8282 *The post-test scores of the group subjects were significantly different among four courseware versions. The three various math achievement groups showed a significant difference on the post-test, F=15.07, p=0.001. Only subjects with low math achievement had 81

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significantly different post-test scores among the four courseware versions as shown in Table 14, F=5.79, p=0.01. Learning Style Inventories There are several learning style inventories available on the market. Three of the leading inventories (Kolb, Myers-Briggs, and Gregorc) are discussed in this section. The first learning style inventory model is the Kolb Learning Style. Kolb developed the Learning Style Inventory, commonly called the LSI, as a means to evaluate the way people learn and work with ideas in day-to-day life (Cooper, 2001). The Kolb LSI was developed in 1981 according to Cooper (2001). The Kolb Learning Style Inventory defines two preferred ways students learn information -abstractness or concreteness and reflection or activity (Santo, 2003b). It further defines the learning modes into learning styles by a representation of two of the four learning modes (2003b). The learning styles are classified as Type 1 (concrete, reflective), Type 2 (abstract, reflective), Type 3 (abstract, active), and Type 4 (concrete, active) (Felder, 1996). In relation to the preferred style for simulation learning, Kolb conceptualized that the converger is the learning style that tends to enjoy simulations more than the other leaning styles (Santo, 2003b). The Myers-Briggs Type Indicator (MBTI) is probably the oldest learning style inventory in use today. It was originally published in 1923 and evolved out of Carl Jungs work on psychological types (Cooper, 2001). The MBTI requires special training and certification to administer, according to Cooper (2001). The MBTI classifies students into four groups -extroverts or introverts, sensors or intuitors, thinkers or feelers, judgers or perceivers (Felder, 1996). 82

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The Gregorc Learning Style Inventory is similar to the Kolb Learning Style inventory in that both break the learning process into two types of preference, perception and ordering (D. W. Mills, 2002). This learning style delineator further breaks the two types of abilities into two qualities for each preference. The two qualities of perception are abstractness and concreteness. Mills (2002) defines the two perceptual qualities by the following statements: Concrete It is what it is. Abstract It is not always what it seems. The two qualities of ordering are sequential and random. The preferences are combined into four types of learners. The four learning styles are concrete-sequential, concrete-random, abstract-sequential, and abstract-random (Cooper, 2001; Ouellette, 2000). The concrete-random learner is typically the dominant learner with computer games and simulations as identified on the Gregorc Mind Styles Learner Characteristics Chart (Gregorc, 1982a). The literature reviewed suggested that one needs to be cautious about the score reliability for such inventory assessments. The reliability of the Gregorc Style delineator has been tested. Correlation between first and second test on the same population yielded a correlation of around 0.87, which is significant at a p value of <0.001 (Gregorc, 1982b). In 1982 Gregorc developed a model to delineate the individual nature of each person to perceive and order the information that the world comprises (Lowry-Mosley, 2003) He did not focus only on a model that describes personality or learning style (Lowry-Mosley, 2003). Santo (2003) has a website devoted to learning styles. She notes on her page discussing Gregorc Learning Styles that this inventory falls on a continuum rather than being polar (Santo, 2003a). 83

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Figure 7. Gregorc scoring chart. (Re-printed with permission from Dr. Gregorc) The scoring chart for the Gregorc Mind Style Delineator is illustrated in Figure 7. The participant completes the word matrix consisting of 10 categories. The participant selects the word that best represents them in each of the categories. There are a total of four one-word responses in each category. They rate the four words in the order that is most like them, with four being the most like them and one being the least like them. The participant adds the scores according to the matrix and plots their score on the Style Profile as seen in Figure 7. The reliability of the Gregorc Style delineator has been tested. A standard alpha coefficient measuring the Delineator's reliability ranges from 0.89 to 0.93 (Gregorc, 1982b). A score over 27 in any one mediation channel reflects strength in that area. For analysis purposes, the researcher used the subjects' highest scores as an indication of their dominant learning style. In addition, the subjects' lowest score is used 84

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as an indication of their least preferred learning style. Gregorc (1982a) explains that the lowest score attained in The Gregorc Style Delineator is a valuable measure. Although not as potent as the highest score, the lowest value can illustrate the individual's least preferred method of learning. The following are several characteristics of individuals of each learning style: Concrete-Sequential: World of Reality concrete world of the physical senses; Ordering Ability sequential, step-by-step linear progression; Thinking Processes instinctive, methodical, deliberate; Validation Process personal proof via the senses, accredited experts; Focus of Attention material reality, physical objects; Creativity product, prototype, refinements, duplication; Environmental Preferences ordered, practical, quiet, stable. Abstract-Sequential: World of Reality abstract world of the intellect based on the concrete world; Ordering Ability sequential and two-dimensional, tree-like; Thinking Processes intellectual, logical, analytical, correlative; Validation Process personal intellectual formulae, conventionally accredited experts; Focus of Attention knowledge, facts, documentation, concepts, ideas; Creativity synthesis, theories, models and matrices; 85

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Environmental Preferences mentally stimulating, ordered, quiet, non-authoritative. Abstract-Random: World of Reality abstract world of feeling and emotion; Ordering Ability random, web-like, multidimensional; Thinking Processes emotional, psychic, perceptive, critical; Validation Process inner guidance system; Focus of Attention emotional attachments, relationships, memories; Creativity imagination, the arts, refinements, relationships; Environmental Preferences emotional and physical freedom, rich, active, colorful. Concrete-Random: World of Reality concrete world of reality and abstract world of intuition; Ordering Ability random three-dimensional patterns; Thinking Processes intuitive, instinctive, impulsive, independent; Validation Process practical demonstration, personal proof, rarely accepting of outside authority; Focus of Attention applications, methods, processes, ideals; Creativity intuition, originality, inventive, futuristic; Environmental Preferences informative, lively, colorful, "words do not convey true meaning." (Ackerman & Willson, 1997) 86

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This learning style assessment was selected by the investigator because: (a) it is easy to administer; (b) it is easy to interpret; (c) it is a self-scoring battery; (d) it is relatively quick to administer and complete; it takes less than 10 minutes to complete; (e) it is inexpensive; (f) it is discrete and has easily reportable scales; (g) there are valid and reliable measures that have been partially supported by research (e.g., Gregorc, 1982a); and (h) it uses the context of one word instead of using phrases, which according to Gregorc (1982) can be misinterpreted even more than the participant reading one word. Zywno (2003) found that although the longer questionnaires like the Myers-Brigg and Kolbs Learning Style Inventory typically yield a higher Cronbachs alpha measure for collected data, the usefulness of the inventory in the classroom setting may be limited. The learning style instrument needs to be no longer than 10 minutes in length (Zywno, 2003). The Gregorc Mind Style Delineator takes less than five minutes to complete (Gregorc, 2003). There are a number of other learning style inventories that are not discussed in this study. They include such inventories as the Hemispheric Dominance Model; the Perceptual Modalities Model; the Cognitive Styles Analysis; and the Developmental Cognitive Styles Metamodel: The Onion Model, Sternbergs Mental Self-Governmental Model, and Psycho-Geometric Personality Styles. The Hermann Brain Dominance Instrument and the Felder-Silverman Learning Style Model are primarily used to assess learning styles in engineer students (Felder, 1996). Its evident that there are many learning style inventory instruments available to assess learning styles. The most common instruments were discussed in this chapter. 87

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There is concern when selecting a learning style inventory that you select one that is the real thing and not one that is a replicate of another model (Gregorc, 2003). Summary This chapter defined the realms of virtual reality, simulation and driving simulators. The literature, as discussed in this chapter, illustrates the positive outcomes simulators have had on the effect of training many professionals. The literature shows a positive effect in using simulators to instruct driving techniques. The history of learning styles and various learning style inventories were also discussed. The discussion illustrated that, as a society, there has been an interest for thousands of years in how we learn in relation to what type of class environment in which individuals like to learn. The limited literature discussed in this chapter has showed a positive relation to the studies of significance in learning styles and the success of students. 88

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Chapter 3 Method Research Design The general research design for the quantitative portion of the study was quasi-experimental. Quasi-experiment primarily involves two interrelated topics: the theory of the validity of casual inferences and a taxonomy of the research designs that enable us to examine causal hypotheses (Trochim, 1986). What Trochim is referring to with these two topics is that the validity of causal inferences can be attributed to the establishment of a causal relationship and on the other hand to its generalizability. A taxonomy of the research design refers to the multiple analysis that is used to analyze the evidence that is collected. The research design of the qualitative portion of the study was phenomenological. Phenomenological methods are when a stimulus is presented to the participants and they are asked to describe what they perceive (Moghaddam, Walker, & Harre, 2003). Differentiating experimental and case study is defined by where the study occurs. Experiments occur in a controlled environment and case studies occur in natural settings (Tashakkori & Teddlie, 1998). Furthermore, this study was a mixed method QUAN qual study, which is discussed further in this section. 89

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The process of the study is depicted in the following flow chart. Day One Participants take learning style inventory and the EVDTP written pre-test at the beginning of the class Participants take a written post-test at the end of the didactic portion of the class Day Two Treatment group takes simulator portion of course and obtains a score Control group and treatment group both drive on competency course and obtain a score Figure 8. Flowchart of instruments for study. Participants The type of sampling for this study was cluster sampling. Kemper, Stringfield, and Teddlie (2003) define cluster sampling as the most appropriate sampling strategy 90

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when the sampling unit is not an individual, but rather a group that is naturally occurring in the population, such as in a classroom (Kemper, Stringfield, & Teddlie, 2003). This study used a class of EMS students. There are approximately 30,300 fire departments and more than a million firefighters in the United States (The U.S. fire service, 2003). However, there are no sources that record the actual number of ambulance services in the United States. It is estimated that there were 13,070 emergency ambulances operating in the United States in 2000 (Kahn et al., 2001). The sample for this study was an EMS school based in Lafayette, LA, where students complete EMT and paramedic training. The participants were a group of EMS students who have not previously driven an emergency vehicle prior to this course. The students were mixed in gender and ethnicity, in the age range of 18 65 years old. These students began their education process to become paramedics in January 2004. An orientation conducted at the commencement of the program by the course instructor included an overview of this study and their participation in the study. The true demographics of the group are defined in Chapter 4. The researcher was the direct contact with the students for this study. The classroom instruction, administration of the learning style assessment, post-test, and simulation observation were conducted by the investigator of this study. The competency course scoring was overseen by the researcher. There was a maximum of six individuals who assisted in the scoring of the participants. Theses individuals were given the parameters for scoring the participants on the Friday after the didactic portion and before any driving component. During the Saturday and Sunday driving sessions, the assistant scorekeepers were observed by the researcher to ensure the consistency of scoring 91

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participants when they drove on the competency course. These individuals scored the participants. This helped to reduce the inter-rater reliability threat of this study. A total sample of 120 participants was selected for this study to give a large enough power of .80 to detect a one-tailed difference at the 5% level of significance. At least 51 participants per group should be used when conducting a one-tailed hypothesis for a .80 probability (Onwuegbuzie, Jiao, & Bostick, 2004). The sample population was randomly divided into a group of 60 participants for the treatment group and 60 for the control group. This is further explained in the procedure section. Quantitative Instruments VFIS Emergency Vehicle Driver Training Program The driver program used for this study was VFISs 1997 Edition of the Emergency Vehicle Driver Training Program (EVDTP). The program cites four critical components to a comprehensive EVDTP. They are: eight hours of classroom instruction, eight hours of the competency course completion, eight hours of street and highway driving, and a combination of knowledge and skill testing (Klein et al., 1997). This study centered on the didactic and competency course driving components. Although it is recommended that the competency course consist of an eight-hour session, this amount of time is not per individual, but rather for a typical class of 28 to 32 students to be able to each drive twice through the driving competency course (Klein et al., 1997). The EVDTP competency course consists of the student maneuvering the emergency vehicle around traffic cones on a parking lot in a limited amount of time. The eight specific maneuvers required to meet the National Fire Protection Association (NFPA) standard on emergency vehicle driver qualifications ("NFPA 1002 standard for 92

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fire apparatus driver/operator professional qualifications," 1998) are: straight-line forward and backward, confined space turn, alley dock backing, serpentine, parallel parking, lane change, diminishing clearance, and stop sign (Appendix A). The student must maneuver through each event that is established with traffic cones without knocking the cone over, crossing over any line, or brushing against the cone. The student is scored on the basis of the time to complete the course and accuracy of maneuvering through the cones. The scores for the driving course typically range from three minutes to 15 minutes, with low scores reflecting better driving skills. The scores are measured using time as a basis. Penalty points are added on as additional seconds to the score. The actual scoring mechanics are discussed later in this chapter. After the students successfully complete the classroom and competency course, they complete the next phase, highway and street driving (Klein et al., 1997). The highway and street driving were not part of this study. Emergency Vehicle Operators Course Preand Post-Test EVDTP consists of a pre-test, a post-test and an alternative post-test, which is administered if a participant fails the post-test on the first attempt. The participant must score a 72% on the post-test to pass the course. The pre-test consists of 10 multiple-choice and true/false questions. The post-test and the alternate post-test consist of 25 multiple-choice and true/false questions. The tests were developed by VFIS as part of the EVDTP program. VFIS does not require the instructor to report the scores to them or any other agency; therefore, they did not have any reliability or validity studies for the scores of any of the three written tests. The pre-test was administered at the beginning of the 93

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first day of the didactic portion of the program. The post-test was administered at the end of the didactic portion of the program. Emergency Vehicle Operators Course Competency Test The VFIS EVDTP has an evaluation form that the driving competency course observer completes when testing the emergency vehicle driver on the competency course (Appendix A). The participant is allowed, and typically takes, multiple times to drive through the course and receive a passing score. Each driver must successfully drive at least twice through the course in order to complete the course and receive a certificate. A successful drive is a score of less than 480 points. The points are calculated by the following method. There is one point for every second it takes the driver to drive through the course, with the addition of penalty points as described on the score sheet added to the time. This is the total score for the driver. The drivers first score on this form was used when computing the t-test for this study. The competency course is designed to test the ability of the driver in his/her proficiency in handling an emergency vehicle. The eight components of the competency course are designed to emulate the situations an emergency vehicle driver may encounter. These components test the emergency vehicle drivers in their driving skill, judgment, and knowledge of the limitations of the emergency vehicle. The course is based upon the NFPA 1002 standard. The scoring of the driver on the competency test is based upon time and penalty points for various infractions occurred by the driver. The maximum time a driver is allowed to take is based on the wheel base of the vehicle. The longer the wheel base, the more time allowed for the participant to complete the course, as illustrated in the following table. A driver who exceeds the maximum time allowed is required to 94

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repeat the driving course until they drive through the course under the time allowed in two runs. The maximum driving scores on the competency course were validated by VFIS (Klein et al., 1997). Table 15. Score guide for competency course. Wheel Base Maximum time to complete competency course Vehicles <170" 8 minutes Vehicles 170" 220" 9 minutes Vehicles >220" 10 minutes Time is applied only as acceptable or unacceptable (too slow). The purpose for recording the time of drivers is for the instructor to measure the drivers individual improvement. Penalty points are awarded and depicted in the following table. The score is then computed by adding the total time to drive through the course with the total of the penalty points. This becomes the students competency course score. The scores are reported in time by using seconds as the reporting score. Table 16. Penalty point schedule. Station Error Penalty No. 1-8 All Each cone brushed, moved, or overturned Cross any line, each time crossed 10 points 3 points No. 3 and 8 Alley dock and stop exercise Stop more than 6" but less than 12" from the measured point Stop 12" or more but less than 18" from the measured point Stop 18" or more from or go past the measured point 3 points 6 points 10 points No. 6 Parallel park Park 12" or more from the curb 3 points 95

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Figure 9. Competency course diagram. 96

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The Simulator The computer simulation is a program designed and developed by Road Safety International. The program is a simulation designed to allow the participant to maneuver an emergency vehicle through situations they are likely to encounter during a typical response. The treatment group received this portion of the program after they completed their first day of training and prior to driving on the competency course. Figure 10. Simulator. The specifications of the simulator are as follows: The simulator has plasma displays for exceptionally bright, crisp images. It is a three-channel visual system with full 180-degree field of view for instinctive checking of intersection traffic. There are 97

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side-view mirrors. The participant sits in a virtual 3-D driving cockpit with inset gauges. There are six speakers that comprise a 5.1 surround sound system with subwoofers. The simulator offers a seat vibration transducer to give it an authentic road feel. In addition, the simulator is equipped with a tilt steering wheel with feedback, properly weighted accelerator and brake pedals, automatic column shifter, turn signals, ignition key, horn, lights, wipers, and cruise control. The seat is adjustable with an integrated seatbelt. Accurate, physics-based driving simulation provides for realistic vehicle performance and handling. The simulator supports new custom driving scenarios and multimedia training curricula content. A scoring system provides both real time and post driving scores. It includes Road Safetys Black Box technology. The simulator operates on a standard PC-based architecture and Windows XP operating system, which provides easy upgrades and maintenance. It can be relocated because it is on integrated casters and a hinged base. The dimensions of the unit are 7'11" wide by 4'3" deep by 4'8" high. The hood adds approximately 2'. Total weight of the unit is approximately 1,000 pounds. It operates off of 120 volts, 60 Hz, and 10 amps of power. The design of the simulator emulated the driving competency course. This resulted in a near transfer learning environment for the participant. A near transfer is when the participant is placed into an environment very similar to the environment in which the participant will be functioning in the real world (Alessi & Trollip, 2001). The time the participant took on the simulator was measured by the same scoring process as when they drove on the competency course. They were required to drive through the competency course simulation at least twice. The first time an instructor provided individualized instruction, coaching the student through the process and illustrating the 98

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position and maneuvering of the vehicle through the aerial view that can be used with the simulator. The simulator has a feature that you can toggle to an aerial view of the unit position so the driver can see the units position on the course. The anticipated time for each participant should not exceed 20 minutes in duration. Because this simulator is new, no studies have been conducted with emergency vehicle operators using it. Learning Style Inventory The Gregorc Style Delineator was used as the instrument for this study to measure the learning styles of the participants. This was conducted through a traditional pencil-paper method. The Gregorc Style Delineator is not computerized. According to Gregorc (2003), the pencil-paper self-assessment instrument mode yields the caliber of results desired to assess learning styles for this model. Further, using the computer introduces additional variables, which affect the results (Gregorc, 2003). The Delineator was administered at the beginning of the didactic portion of the program on the first day. In 1982 Gregorc developed a model to delineate the individual nature of each person to perceive and order the information that comprises the world. Santo (2003) has a website devoted to learning styles. She notes on her page discussing Gregorc Learning Styles that this inventory falls on a continuum rather than being polar (Santo, 2003a). The Gregorc Mind Style Delineator represents two types of mediation abilities. They are perception and ordering (Ouellette, 2000). The mind style delineator is based on the concept that individuals learn through concrete experience and abstraction either in a sequential or a random way (Ouellette, 2000). The two abilities are further delineated into a four-quadrant model. They are paired as Concrete Sequential, Abstract Sequential, Abstract Random, and Concrete Random (Cooper, 2001). The literature reviewed 99

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suggested that one needs to be cautious about the score reliability for such inventory assessments. Correlation between a first and second test on the same population yield a correlation of around 0.87, which is significant at a p value of <0.001 (Gregorc, 1982b). Qualitative Instrument The investigator developed an instrument to survey the participants of this study (Appendix B). There were separate surveys for the control group and the treatment group. A panel of experts reviewed the survey for content related validity. These experts consisted of individuals who have been trained to drive emergency vehicles by the investigator in a prior program. They were given the non-simulator survey. The investigator interviewed each participant to make sure they understood the questions. Additionally, a group of individuals who previously completed an EVDTP, and used a PC-based driving simulator, completed the survey as a means to establish the surveys validity. This group of individuals was seasoned emergency vehicle drivers. They used a PC-simulated driving program called Profiler. After the person completed the simulation, which consisted of four runs on a driving course, they completed the survey. The course was not similar to the course used in this study, but emulated a fast high-precision driving similar to that of law enforcement. All participants for each survey were asked to give the researcher feedback on the surveys. Additionally, both surveys were sent to VFIS and Road Safety. Richard Patrick from VFIS reviewed the survey. Mr. Patrick is considered an expert in the emergency vehicle driving field. Fred Craft from Road Safety reviewed the survey. Mr. Craft is considered an expert in simulation. 100

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Type of Pragmatist Study This study was a mixed method study. Tashakkori and Teddlie (1998) define mixed method studies as those that combine qualitative and quantitative approaches into the research methodology of a single study or multi-phased study. Further, the study was a sequential study. Essentially, a sequential study is one in which the quantitative or qualitative component is conducted first, and then the other component is conducted next. In this type of study, the quantitative study is conducted first followed by the qualitative study (Tashakkori & Teddlie, 1998). The survey, which was the qualitative component of the study, was administered to the sample population after they had completed the quantitative component. The quantitative instruments include the learning style delineator, the testing component, and the scores on the driving course. In an effort to collect data to investigate the effectiveness of a simulator as part of the driver training process, a mixed method approach was determined to be the best approach. Hence, complementary results are the design for this mixed methods study. Complementary results are constructed by the methods that are applied, and different methods highlight the different aspects of the study or they may even constitute different phenomena (Erzberger & Kelle, 2003). The effects of the simulator may not be totally illustrated by using quantitative measures alone. The opinions of the participants may demonstrate that the simulator is beneficial to use as part of the training for emergency vehicle drivers. These data give a complementary result to the study that you would not have by doing a quantitative analysis only. 101

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Limitations The results did not yield data related to memory retention of material; rather, the analysis considered behavioral change as a result of the methodology of training an individual to react in a situation. It was anticipated that as a result of the simulation-based training, the participant would recognize they had been in a similar situation during the training and recall how to appropriately react. However, it was impossible to subject the participant to every potential scenario that they may encounter in a real situation. Another limitation of this study was that the results were evaluated only at the moderate level. Kirkpatricks four levels of evaluation are Level I Reaction; Level II Learning; Level III Transfer; and Level IV Business Results. Level I Reaction is what the student thought or felt about the class. The survey instrument and course evaluation encompassed this level. Level II Learning is an assessment of what the student learned (Carliner, 2001). The post-test, the simulator, and the competency course were the instruments to measure this level. This study was limited in the time allotted. Hence, this investigation included only Levels I and II of Kirkpatricks Four Levels of Evaluation. Additional studies need to be conducted to evaluate simulator use at Levels III and IV of Kirkpatricks Four Levels of Evaluation. Pilot Study A pilot study was not conducted due to the limited availability of the simulator. This simulator is the first unit to be developed by this company and is limited in its availability. 102

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Ethical Consideration of Study To ensure ethical compliance and sensitivity in the study, each of the participants was made aware of the study and informed of the studys goals and objectives (Appendix C). An application was submitted to the IRB of the University of South Florida to comply with the ethical considerations of the participants for this study. Approval was given for this study by the IRB of the University of South Florida. Quantitative Procedures The simulator training was supervised and monitored by the investigator. This study was scheduled over a six-day period. The entire sample population took the classroom portion on Thursday. The class was taught at the Lafayette site; however, there were also students at other remote locations who joined the class by interactive satellite television. During this portion participants were administered the pre-test and the Gregorc Learning Style Delineator at the start of the course. The learning style inventory was given to each participant to establish what his or her preferred learning style was at the time of the instruction. This survey used the inventory assessment to determine the participants learning styles for the day they participated in the training session. The eight hours of instruction were followed by a post-test at the end of the first day of instruction. The next eight-hour component consisted of the treatment group receiving the simulator training, and both the control and treatment group receiving the competency course component. The class was divided into two groups. The remote group was to consist of approximately 60 students and take the driving portion only on Saturday and Sunday. The remaining 60 students were to receive the treatment of the simulator and then drive the competency course. The treatment group was divided into groups of 20, 103

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and took the simulator and competency course on Monday, Tuesday, or Wednesday. The following flowchart illustrates the process used for the study. Flow Chart of Process Participants attended the 8 hour classroom portion Figure 11. Flow chart of participant progression through program. Qualitative Procedures At the end of the two-day session, the entire sample was surveyed by the investigator, using the survey instrument developed for this study (Appendix B). The treatment group was asked on the survey their opinions regarding their use of the simulator as part of the driver training program. Both groups were asked to offer their Saturday Class Monday 30 participants for driving course. This group did not receive treatment. 20 participants received treatment and then drove on com p etenc y course Tuesday 20 participants received treatment and then drove on com p etenc y course Sunday Class 30 participants for driving course. This group did not receive Wednesday 20 participants received treatment and then drove on competenc y course 104

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opinion on what they think of the value of a computer simulator to instruct emergency driving training. The written survey asked if the simulator did, or if it could, help in the preparation of instructing an emergency vehicle driver to drive an emergency vehicle. This information was categorized, examined for trends, and presented in a qualitative format. Statistical Analysis Quantitative Analysis Once the study was complete, a t-test was conducted to compare the scores of the driving competency course for the treatment group, which received the simulator training and the non-simulator group, which did not receive the simulator training. The assumptions of normality, homogeneity of variance, and independence of the observations was checked for the t-test analysis (Stevens, 1999). In order to reduce the Type I error, an alpha equal to .05 was used for this study. The results demonstrated whether there is a difference in effectiveness between the traditional and the simulation training, as measured by the time of the first run of the competency course, the penalty points of the first run, the total points of the first run and the number of runs to successfully complete the competency course. 105

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Table 17. Sample t-test charts. Competency Course Driving Time Mean Scores Standard Deviation Control Group (non-simulator) Treatment Group (simulator) Competency Course Points Mean Scores Standard Deviation Control Group (non-simulator) Treatment Group (simulator) Competency Course Total Runs Mean Scores Standard Deviation Control Group (non-simulator) Treatment Group (simulator) The results from the Gregorc Mind Style Delineator were used to divide each of the groups into four categories on the basis of the highest score on the participants inventory worksheet. The scores of the post-test were used to show any relationship the treatment group has to the control based on the learning style of each group. This was conducted by using an Analysis of Variables (ANOVA) to show any interactions between the four groups. Table 18. Sample ANOVA for post-test scores and learning styles. Treatment Group Non-Treatment Group Learning Style Post-test score Post-test score CR AR CS AS 106

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The scores of the competency course were used to show any relationship the treatment group has to the control based on the learning style of each group. This was conducted by using an Analysis of Variables (ANOVA) to show any interactions between the four groups. Table 19. Sample ANOVA for competency course scores and learning styles. Treatment Group Non-Treatment Group Learning Style Competency course scores Competency course scores CR AR CS AS ANOVA is based on the following three assumptions: (a) the observations are normally distributed on the dependent variable in each group; (b) the population variances for the groups are equal; and, (c) the observations in each group are independent (Stevens, 1999). The normality, independence, and equal variance assumptions were assessed. Effect sizes were reported for any statistically significant findings. A between group variation was used to show the group means by comparing the written post-test scores of the four different learning styles of the simulation group to the four different learning styles of the non-simulation groups written post-test scores. A between group variation was used to show the group means by comparing the competency course scores to the four different learning styles of the non-simulation groups competency course scores. The data are reported through figures and tables. Descriptive statistics are also reported. SAS (Statistical Analysis Software) was the software program used to compute the statistical analyses for this study (SAS software, 2003). 107

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Qualitative Analysis The qualitative analysis of the data was used to find any trends in common responses from the surveys. The results were tabulated by the investigator without the use of a software program. The results of the survey were analyzed using a thematic approach. The first approach was to determine if those who used the simulator had positive opinions about the use of the simulator and if they were positive in their response to actually using the simulator as part of the driving program. In contrast, the perception of the control group who did not use the simulator were reviewed to see if they felt the simulator would have been beneficial to them in the training program. The investigator developed the themes based on the responses by the control and treatment groups. Using a written survey for the qualitative portion helped to avoid any bias on the part of the investigator. Categories were determined a posteriori using an exploratory, variable-oriented analysis. Emergent themes were analyzed and quantified (Tashakkori & Teddlie, 2003) for frequency analysis. The investigator used the numbered nature of phenomena (Sandelowski, 2001). Through computing the frequency with which each theme occurred in the data and expressing these frequencies as percentages, frequency or manifest effect sizes were obtained (Onwuegbuzie & Teddlie, 2003). Summary This study was a QUAN qual research. The results from the quantitative portion were addressed if there was a significant difference in competency course scores between emergency vehicle operators who were trained to drive an emergency vehicle via a simulator prior to driving on a standardized competency course and emergency vehicle 108

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operators who were not trained using a simulator. It also identified if there is a relationship between a students learning style and his or her performance on the written post-test (with or without the simulation segment). Additionally, it addressed if there was a relationship between a students learning style and his or her performance on the standardized competency course (with or without the simulation segment). The qualitative part of this study determined what the emergency vehicle operators perceptions were of using a driving simulator as part of an emergency vehicle training course. This study investigated some of the obvious, yet essential questions related to the effectiveness of computer simulation-based training for emergency vehicle drivers. 109

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Chapter 4 Results Introduction This chapter discusses the results of the research for the study. The chapter reports the results of the data by answering each of the research questions. The responses are also illustrated in tables and charts. This study investigated some of the obvious, yet essential, questions related to the effectiveness of computer simulation-based training for emergency vehicle drivers. Quantitative questions: The following research was addressed using quantitative techniques. Is there a significant difference in competency course scores of emergency vehicle operators who were trained to drive an emergency vehicle via a simulator prior to driving on a standardized competency course and those of emergency vehicle operators who were not trained using a simulator? Is there a relationship between a students learning style and his or her performance on the written post-test (with or without the simulation segment)? 110

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Is there a relationship between a students learning style and his or her performance on the standardized competency course (with or without the simulation segment)? Qualitative question: What are emergency vehicle operators perceptions of using a driving simulator as part of an emergency vehicle training course? Demographics There were 122 participants registered to attend the emergency vehicle driver training course at the National EMS Academy in Lafayette, LA. The didactic session was conducted on Thursday, March 11, 2004, with 105 of the 122 participants in attendance. This reduced the total population by 17 participants. The driving portion was conducted the following Saturday through Wednesday March 13 17, 2004, with 102 of the 105 who had attended the didactic portion driving the competency course. The group had self-scheduled which day they would attend the driving portion of the class. The participants were not aware which days the simulator was scheduled to be part of the training. This self-scheduling process resulted in 52 participants in the control group and 50 participants in the treatment group. Each of the participants signed an informed consent form (Appendix C) to participate in the study. The participants noted their gender and ethnicity on the survey that was administered (Appendix B). The gender is in Table 20, and the ethnicity is in Table 21. There were 17 male (34%) and 25 female (50%) participants in the treatment group with eight (16%) participants not specifying gender. The control group had 19 male (37%) and 30 female (58%) participants with three (5%) people who did not specify their gender. Ethnicity of the treatment group included 38 Caucasians (76%), four African-Americans 111

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(8%), and eight unknown (16%). The control group ethnicity was divided as follows: 36 Caucasians (69%), 10 African-Americans (19%), three Hispanics (6%), one Asian (1%), and three unknown (5%). Table 20.Gender of sample. Male Female Unknown Control Group 19 30 3 Treatment Group 17 25 8 Total 36 55 11 60 50 40 Control Group 30 Treatment Group Total 20 10 0 Male Female Unknown Figure 12. Gender of sample. Table 21. Ethnicity of sample. Caucasian African American Hispanic Asian Unknown Control Group 36 10 3 1 3 Treatment Group 38 4 0 0 8 Total 74 14 3 1 11 112

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80 70 60 50 Control Group 40 Treatment Group Total 30 20 10 0 Caucasians Unknown African Hispanic Asian American Figure 13. E thnicity of sample. Research Question One The first research question asked if there was a significant difference in competency course scores of emergency vehicle operators who were trained to drive an emergency vehicle via a simulator prior to driving on a standardized competency course and those of emergency vehicle operators who were not trained using a simulator. The driver training course consists of eight hours of didactic training and an eight-hour session of driving time. The participants take a written test at the end of the eight-hour didactic portion. The written test is covered in more detail later in this chapter with the discussion of research question two. The driving portion of the class consists of the participant driving an ambulance through a cone competency course. There are eight obstacles the driver must maneuver the ambulance through within a certain time frame. There are also penalty points assessed for crossing over lines, brushing or knocking over cones, and for being too far away from a designated reference point. The score sheet and diagram are found in Appendix A. 113

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The score is recorded in seconds. This is accomplished by adding the time and the penalty points to reach a final score. A penalty point is equivalent to one second. A participant must have 480 seconds (i.e., eight minutes) or less in two runs in order to successfully complete the driving course. The time element signifies the comfort level of the driver in negotiating through the course. The speed tends to be slower in the beginning runs and then becomes faster when the driver is more comfortable with how the course is laid out. The time is used to show how well the drivers know the routes they are traveling and how comfortable they are with the dynamics of the vehicle, such as the depth and the ability to maneuver the vehicle. Penalty points are assessed when a driver crosses over lines, brushes or knocks over cones, or drives too far away from a designated reference point. The time and the penalty points combined make up the total score. The data collected for research question number one was divided into three areas. Each area was compared for the control group and treatment group: The data analyzed were the time for the first run, the penalty points of the first run, and the total number of runs on the actual driving course in order to successfully complete it. Additionally, the participants final score was analyzed; however, because all participants had to have a score of less than 480 seconds, the total point spread for the final scores was not enough to result in any significant differences. Therefore, the results are not included in this document. The participants viewed a videotape of the competency course and a demonstration of how to maneuver through the course at the end of the didactic training. Additionally, each of the participants walked through the competency course as an 114

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instructor drove through the course demonstrating how to proceed from obstacle to obstacle and what to do at each obstacle. The participants who received the simulation treatment were first shown how to use the simulator by the researcher demonstrating. Each of the participants in the treatment group drove through the simulation twice and then went to the competency course and drove the ambulance through the course after the walk-through demonstration. When the participant completed the simulation, a screen appeared at the completion of the course with the participants total time and the number of cones they hit. This information was used for instruction only and was not recorded or used in any other way for this study. The t-tests conducted for research question one was based on three assumptions: normality, homogeneity of variance, and independence of the observations. Normality was tested by using a stem-leaf graph for each of the groups in each t-test. The homogeneity of variance is based upon the following. According to Glass and Hopkins (1996) it has been shown that the t-test is robust with respect to violation of the homogeneity of variance assumption when n1=n2. For practical purposes, one need not even test the assumptions of homogeneity of variance when the ns are equal. In this study the ns are virtually equal for the treatment group (n=50) and the control group (n=52). Independence of the observations is when the two groups were not paired, dependent, correlated, or associated in any way, which is the case in this analysis. First drive through competency course with time only being analyzed The data collected for the control group (n=52) yielded the following descriptive statistics for the first time driving through the competency course. The stem-leaf graph 115

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illustrates the distribution of the time in seconds of the participant driving through the competency course the first time. The stem-leaf for the control group showed a positively skewed normal distribution of time driving through the competency course using seconds as the unit of measure. The treatment group also was a positive skewed distribution with two outliers. The outliers were 200 seconds greater from the highest score within the distribution. Control Group Stem Leaf # Boxplot 8 3 1 | 7 6 1 | 7 00 2 | 6 5589 4 | 6 0123 4 | 5 6779 4 +-----+ 5 1123 4 | | 4 56667899 8 *--+--* 4 12222344 8 | | 3 67788 5 +-----+ 3 0111334 7 | 2 58 2 | 2 44 2 | ----+----+----+----+ Figure 14. Stem-leaf graph of the first drive through the competency course with time being analyzed for the control group. 116

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Treatment Stem Leaf # Boxplot 7 14 2 0 6 6 5 5 04 2 | 4 566678 6 | 4 0000111122344 13 +-----+ 3 55667788 8 *--+--* 3 0222233344 10 +-----+ 2 6778889 7 | 2 23 2 | ----+----+----+----+ Figure 15. Stem-leaf graph of the first drive through the competency course with time being analyzed for the treatment group. The results are provided in Table 22. The mean was 476 seconds; the standard deviation was 142; the median was 460 seconds; the mode was 384 seconds; a kurtosis of -0.478 and a skewness of 0.382 were obtained. The data collected for the treatment group (n=50) yielded the following descriptive statistics for the first driving through the competency course. The results are provided in Table 22. A total of 50 participants were in the treatment group. The mean was 385 seconds; the standard deviation was 101; the median was 374 seconds; the mode was 409 seconds; a kurtosis of 3.655 and a skewness of 1.427 were obtained. Figure 16 illustrates the mean of the time on the competency course for the control and treatment group by using a box plot graph. The + sign indicates the mean on each chart. 117

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Table 22. Competency course time scores on first run through competency course. n Mean Median Mode Standard Deviation Kurtosis Skewness Control Group (non-simulator) 52 476 seconds 460 seconds 384 seconds 142 -0.478 0.382 Treatment Group (simulator) 50 385 seconds 374 seconds 409 seconds 101 3.655 1.427 900 + | | | | 800 + | | | | | 0 | | 0 700 + | | | | | | | 600 + | | +-----+ | | | | | | | | 500 + | | | | | + | | | *-----* | | | | +-----+ 400 + | | | | | +-----+ *--+--* | | | | | | +-----+ 300 + | | | | | | | | | | 200 + ------------+-----------+----------program Control Treatment Figure 16. Competency course mean time scores on first run through competency course. 118

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The data collected for each participant were the number of seconds it took them to drive through the competency course on the first attempt. To test the effect of the simulator versus non-simulator, scores were analyzed using an independent t-test. Table 23 provides a summary of these results. The t-test was significant, t=3.74, p=0.0003.On average, the simulator group took significantly less time to drive through the competency course on their first attempt. Table 23. t-test results of time on the first drive through competency course. Mean Difference Std Dev Difference df t value p Time 91.69 seconds 123.66 100 3.74 0.0003 Penalty points for first drive through competency course Penalty points occur when a driver crosses a line, brushes a cone, or other infractions as noted on the score sheet (Appendix A). One penalty point converts to one second and is added to the time for the total drive time through the competency course. The statistics are reported in seconds. The penalty points were analyzed to see if there were significantly fewer penalty points in one group than in the other. The stem-leaf graph illustrates the distribution of the points in seconds of the participant driving through the competency course the first time. The control group had a normal distribution that had a positive skew. The treatment group had a normal distribution. 119

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Control Group Stem Leaf # Boxplot 26 31 2 | 24 | 22 | 20 | 18 6836 4 | 16 36689 5 | 14 369399 6 +-----+ 12 62 2 | | 10 333592336 9 *--+--* 8 256659 6 | | 6 6669 4 | | 4 6999999 7 +-----+ 2 09336 5 | 0 09 2 | ----+----+----+----+ Figure 17. Stem-leaf graph of the first drive through the competency course with penalty points being analyzed for the control group. Treatment Group Stem Leaf # Boxplot 16 8 1 | 15 68 2 | 14 35 2 | 13 399 3 | 12 69 2 | 11 239 3 +-----+ 10 23669 5 | | 9 06 2 | | 8 02223699 8 *--+--* 7 666 3 | | 6 66699 5 | | 5 33 2 +-----+ 4 36 2 | 3 333369 6 | 2 69 2 | 1 39 2 | ----+----+----+----+ Figure 18. Stem-leaf graph of the first drive through the competency course with penalty points being analyzed for the treatment group. 120

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The data collected for the control group (n=52) yielded the following descriptive statistics for the penalty points during the first drive through the competency course. The results were converted to seconds and are provided in Table 24. The mean was 109 seconds; the standard deviation was 61; the median was 103 seconds; the mode was 49 seconds; a kurtosis of 0.003 and a skewness of 0.555 were obtained. The data collected for the treatment group (n=50) yielded the following descriptive statistics for the penalty points on the first run through the competency course. The results are provided in Table 24. The mean was 84 seconds; the standard deviation was 40; the median was 82 seconds; the mode was 33 seconds; a kurtosis of -0.794 and a skewness of 0.175 were obtained. Figure 19 illustrates the mean of the points on the competency course for the control and treatment group by using a box plot graph. The + sign indicates the mean on each chart. Table 24. Competency course points scores on first drive through competency course. n Competency Course Points Mean Scores Median Mode Standard Deviation Kurtosis Skewness Control Group (non-simulator) 52 109 seconds 103 seconds 49 seconds 61 0.003 0.555 Treatment Group (simulator) 50 84 seconds 82 seconds 33 seconds 40 0.794 0.175 121

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| 275 + | | | | | | 250 + | | | | | | | 225 + | | | | | | | 200 + | | | | | | | 175 + | | | | | | | | +-----+ | 150 + | | | | | | | | | | | | | | | 125 + | | | | | | | | | | +-----+ | | + | | | 100 + *-----* | | | | | | | | | | | | | | | *--+--* 75 + | | | | | | | | | | | | | | | +-----+ | | 50 + | +-----+ | | | | | | | | | 25 + | | | | | | | | | 0 + ------------+-----------+----------Control Treatment Figure 19. Competency course mean scores of points on first drive through competency course. 122

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To test the effect of the simulator versus non-simulator, penalty point scores were analyzed using an independent t-test. Table 25 provides a summary of these results. The t-test was significant, t=2.41, p=0.0178. The simulator group accumulated fewer penalty points when driving through the competency course on the first attempt. Table 25. t-test results of penalty points on the first drive through competency course. Mean Difference Std Dev Difference Df t value p Time 24.74 51.81 100 2.41 0.0178 Total runs to successfully complete the competency course The driver trainee has to drive successfully through the competency course in less than 480 seconds. This includes the time and the penalty points added together. They must do this in two runs in order to successfully complete the course. The total number of runs it took to complete successfully two runs with less than 480 seconds was analyzed between the two groups. The participants need two runs, which do not have to be consecutive to pass the course. The stem-leaf graph illustrates the distribution of the number of runs it took the participant to drive through the competency course the first time. The control group had a positively skewed normal distribution. The treatment group was a normal distribution with a large positively skewed distribution. 123

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Control Group Stem Leaf # Boxplot 6 0 1 0 5 00 2 | 4 0000000000 10 | 3 00000000000000000000000000 26 *--+--* 2 0000000000000 13 | ----+----+----+----+----+Figure 20. Stem-leaf graph of the first drive through the competency course with the number of runs being analyzed for the control group. Treatment Group Stem Leaf # Boxplot 5 0 1 0 4 00000 5 | 3 000000000000 12 +-----+ 2 00000000000000000000000000000000 32 *-----* ----+----+----+----+----+----+-Figure 21. Stem-leaf graph of the first drive through the competency course with the number of runs being analyzed for the treatment group. The data collected for the control group (n=52) yielded the following descriptive statistics for the total runs through the competency course. The results are provided in Table 26. The mean was 3.08 runs; the standard deviation was 0.882; the median was 3 runs; the mode was 3 runs; a kurtosis of 1.371 and a skewness of 0.915 were obtained. The data collected for the treatment group (n=50) yielded the following descriptive statistics for the total runs through the competency course. The results are provided in Table 26. The mean was 2.5 runs; the standard deviation was 0.763; the median was 2 runs; the mode was 2 runs; a kurtosis of 1.384 and a skewness of 1.438 were obtained. Figure 22 illustrates the mode of the total number of runs on the 124

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competency course for the control and Figure 23 for the treatment group by using stem leaf graph. In this analysis the mode appeared to be a better indicator to look at than the mean. Table 26. Competency course total number of runs to successfully complete. n Mean Median Mode Standard Deviation Kurtosis Skewness Control Group (non-simulator) 52 3.08 runs 3 runs 3 runs 0.882 1.371 0.915 Treatment Group (simulator) 50 2.5 runs 2 runs 2 runs 0.763 1.384 1.438 Stem Leaf # 60 0 1 50 00 2 40 0000000000 10 30 00000000000000000000000000 26 20 0000000000000 13 ----+----+----+----+----+Figure 22. Stem and leaf chart of the total number of runs to complete the competency course for the control group. Stem Leaf # 50 0 1 40 00000 5 30 000000000000 12 20 00000000000000000000000000000000 32 ----+----+----+----+----+----+-Figure 23. Stem and leaf chart of the total number of runs to complete the competency course for the treatment group. 125

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To test the effect of the simulator versus non-simulator, the total number of runs was analyzed using an independent t-test. Table 27 provides a summary of these results. The t-test was significant, t=3.88, p=0.0002. The simulator group took fewer runs to successfully complete the competency course. Table 27. T-test results of number of runs to successfully complete the competency course. Mean Difference Std Dev Difference df t value p Time 0.577 0.826 100 3.53 0.0006 Summary The results were significant when conducting a one-tailed t-test on the time on the first run through the driving course. A statistical significance (t=3.74, p=0.0003) was found between the two variables. A one-tailed t-test was conducted on the penalty points on the first run through the driving course. A statistical significant finding (t=2.41, p=0.0178) was found between the two variables. A one-tailed t-test was conducted on the total runs through the competency course. A statistical significance (t=3.53, p=0.0006) was found between the two variables. The results of the treatment group versus the control group in each of these areas showed that the treatment group performed better on the first run through the competency course than the control group. Research Question Two The second research question asked if there was a relationship between a students learning style and his or her performance on the written post-test. Written test data The written test was used to answer research questions two and three. The following is information pertaining to the written test. It was comprised of 25 questions 126

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developed as part of the training program by VFIS the curriculum developer. Of the 25 questions, there are 14 multiple-choice questions with four possible responses and 11 true/false questions. The test is designed to determine whether each participant has achieved the concepts of the didactic portion of the driving program. The test is administered immediately after the eight hours of didactic instruction. In this situation, the test was a criterion-referenced test rather than a norm-referenced test. The reliability of the scores for the written test had not been assessed prior to this study. The written test is part of the training program and was required to be administered. Utilizing SAS, a Cronbach Coefficient Alpha was conducted to test for reliability of the scores. The Raw Cronbach Coefficient Alpha was 0.357 with no standardized score. There was no standardized score on either set of data due to everyone answering one question correctly. Because there was no variance on that item, the standardized Cronbach Alpha, which is based on covariance matrix, cannot be computed at all, hence no standardized score (Yu, 2004). Yu (2004) also notes that a Cronbach Alpha of 0.7 or greater typically indicates a reliable test score. In this situation the test scores do not appear to be very reliable. The low Alpha may be attributed to the test being a criterion-referenced test versus a norm-referenced test. Therefore, the researcher conducted an item analysis to assess the p-value and discrimination of each question. The total population was multiplied by 0.27. The top 27% and the lowest 27% scores were used to calculate the p-values and discrimination factors. The discrimination factors were evaluated using the following scale. 127

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Table 28. Discrimination levels for written test. Reliability Interpretation .90 and above Excellent reliability; at the level of the best standardized tests .80 .90 Very good for a classroom test .70 .80 Good for a classroom test; in the range of most. There are probably a few items which could be improved. .60 .70 Somewhat low. This test needs to be supplemented by other measures (e.g., more tests) to determine grades. There are probably some items which could be improved. .50 .60 Suggests need for revision of test, unless it is quite short (ten or fewer items). The test definitely needs to be supplemented by other measures (e.g., more tests) for grading. .50 or below Questionable reliability. This test should not contribute heavily to the course grade, and it needs revision. (Nunnally, 1967) 128

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The results of the written test used in this study are provided in table 29. Table 29. Item analysis of written test. Type of question p-value discrimination 1 MC 0.963 0.074 2 MC 0.981 0.037 3 T/F 0.963 0.074 4 T/F 0.981 0.037 5 MC 0.796 0.333 6 T/F 0.574 0.185 7 MC 0.926 0.148 8 T/F 0.556 0.222 9 MC 0.481 0.444 10 MC 0.907 0.185 11 T/F 0.944 0.111 12 MC 0.815 0.148 13 T/F 0.870 0.259 14 MC 0.981 0.037 15 T/F 1.000 0 16 MC 0.740 0.296 17 T/F 1.000 0 18 MC 0.926 0.148 19 MC 0.778 0.370 20 T/F 0.778 0.370 21 MC 0.944 0.037 22 T/F 0.907 0.111 23 MC 0.704 0.444 24 MC 0.889 0.222 25 T/F 0.722 0.481 Using the results in Table 29 and the guide of discrimination values in Table 28, there were no discriminations scores above .60 which indicates that these test questions need to be re-written. The written test is part of the course material provided by VFIS and requires a passing score of 72%. The results indicate that the test needs revision for future use. 129

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Research data on the learning style inventory The following research questions were based upon the participants learning styles. The Gregorc Mind Style delineator was administered at the beginning of the didactic portion of the course on Thursday. There were 102 total participants of which 88 had one dominating learning style. There were 14 who had two or more dominating learning styles. Eight of these had Abstract Random/Concrete Random (AR/CR) as their two dominating learning styles; they were included in the analysis as an additional category. There were six who were in a category alone and were not included in the results. The total number of usable scores for this test was 96. The data is listed in Table 30. Table 30. Learning style demographics. Learning Style n Abstract Random (AR) 21 Abstract Sequential (AS) 12 Concrete Random (CR) 14 Concrete Sequential (CS) 41 Abstract Random/Concrete Random (AR/CR) 8 Other (multiple) 6 Total 102 n 96 130

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45 40 35 25 30 20 15 10 5 0 AR AR/CR Other AS CR CS Figure 24. Learning style demographics. Analysis for Research Question Two The data collected for each participant were his/her learning style and written test score on the post-test for the emergency vehicle driver training program. To test the effect learning styles may have had on the score of the written post-test, these scores were analyzed with a one-way ANOVA. The ANOVA conducted for research question two was based on three assumptions: normality, homogeneity of variance, and independence of the observations. Normality was tested by using a stem-leaf graph for each of the groups. The AR and the AR/CR group had a normal distribution. The AS group was a flat distribution. The CR and CS groups had a positively skewed distribution. The homogeneity of variance is based upon the following. The groups were not equal among the five groups; therefore, the variance for each group was analyzed. The AR group (n=21) had a variance of 92.19, the AS group (n=12) had a variance of 62.42, the CR group (n=14) had a variance of 131

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44.74, the CS group (n=41) had a variance of 46.47, and the AR/CR group (n=8) had a variance of 41.14. Independence of the observations is satisfied when the groups are not paired, dependent, correlated, or associated in any way, which is the case in this analysis. The stem-leaf graph shows the distribution of the written test scores by the percentage scores of the test. Abstract Random (AR) Stem Leaf # Boxplot 9 2 1 0 8 4 1 | 7 56 2 | 6 011 3 +-----+ 5 0002488 7 *--+--* 4 1246 4 +-----+ 3 59 2 | 2 6 1 | ----+----+----+----+ Figure 25. Stem-leaf graph shows the written test scores for Abstract Random learners. Abstract Sequential (AS) Stem Leaf # Boxplot 7 0 1 | 6 57 2 | 6 +-----+ 5 55 2 | | 5 | | 4 5 1 | + | 4 1 1 *-----* 3 89 2 +-----+ 3 123 3 | ----+----+----+----+ Figure 26. Stem-leaf graph shows the written test scores for Abstract Sequential learners. 132

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Concrete Random (CR) Stem Leaf # Boxplot 8 7 1 | 7 | 6 4799 4 +-----+ 5 2478 4 *--+--* 4 01126 5 +-----+ 3 ----+----+----+----+ Figure 27. Stem-leaf graph shows the written test scores for Concrete Random learners. Concrete Sequential (CS) Stem Leaf # Boxplot 8 7 1 | 7 | 6 4799 4 +-----+ 5 2478 4 *--+--* 4 01126 5 +-----+ 3 ----+----+----+----+ Figure 28. Stem-leaf graph shows the written test scores for Concrete Sequential learners. Abstract Random/Concrete Random (AR/CR) Stem Leaf # Boxplot 8 1 1 0 7 7 6 6 5 677 3 +-----+ 5 0 1 *--+--* 4 88 2 +-----+ 4 0 1 | ----+----+----+----+ Figure 29. Stem-leaf graph shows the written test scores for Abstract Random/Concrete Random learners. 133

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The results were significant, F (4) = 2.56, p=0.044. The mean square was 147.80 and the R-square was 0.101. The mean score was 85.38 with a Root MSE of 7.60. The results are provided in Table 31. Table 31. ANOVA statistics for written test scores and learning styles. Source DF Sum of Squares Mean Square F Value Pr > F Model 4 591.19 147.80 2.58 0.044 Error 91 5259.31 57.79 Corrected Total 95 5850.50 R-Square Coeff Var Root MSE SCORE Mean 0.101 8.90 7.60 95.38 The mean for the AR (n=21) on the written test was 88.76 with a standard deviation of 9.60. The mean for the AS (n=12) group was 81.67 with a standard deviation of 7.90. The mean for the CR (n=14) was 83.14 with a standard deviation of 6.69. The CS (n=41) group had a mean of 86.15 with a standard deviation of 6.82. The mean for the AR/CR (n=8) was 82.00, and the standard deviation was 6.41. The results are provided in Table 32. Figure 30 illustrates the mean of each of the learning styles percentages on the written test is shown on the box plot graph. The + sign indicates the mean on each chart. Table 32. Learning styles mean and standard deviation for the written test. n Mean Standard Deviation AR 21 88.76 9.60 AS 12 81.67 7.90 CR 14 83.14 6.69 CS 41 86.15 6.82 AR/CR 8 82.00 6.41 134

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| 100 + | | | | | | | | | +-----+ | | 95 + | | | | | | | | | | *-----* | | +-----+ | | | | | | | | | 90 + | | | | | | | | | + | | | | | | | | | +-----+ +-----+ *-----* | | | | | | | | | + | +-----+ 85 + | | | | | | | | | | | +-----+ | | *--+--* +-----+ | | | *-----* | | | *--+--* | | + | | | | | | 80 + | | +-----+ | | | | | | | | | | | | | | | +-----+ | +-----+ | | | 75 + | | | | | | | | | | | | | | | | | 70 + | | | | | | | | 0 | 65 + | 0 | | 60 + ------------+-----------+-----------+-----------+-----------+----------LS AR AS CR CS AR/CR Figure 30. Learning styles mean scores of written test. A Duncan multiple range test (p<0.05) post hoc test was conducted to determine if there was a significant difference between the groups. A Duncan multiple range test is conducted to investigate differences between levels of the independent variables. The results are provided in Table 33. The groups are ordered from the highest mean to the 135

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lowest mean. Groups AR, CS, and CR are not significantly different between groups. These groups have the letter A in the Grouping column. Essentially the groups with an A do not differ and those with a B do not differ. The AR group is significantly superior to the AR/CR and the AS groups. The AR group does not have a B in the grouping column and the AR/CR and AS groups do not have an A in their grouping column indicated that there is a statistically significant difference between them and the AR learning style. These results show that the AR learning style do significantly better on written tests than do those with AR/CR learning styles and AS learning styles. Table 33. Duncan results of written test scores ANOVA. Grouping Mean n LS A 88.76 21 AR A B A 86.15 41 CS B A B A 83.14 14 CR B B 82.00 8 AR/CR B B 81.67 12 AS Research Question Three The third research question asked if there was a relationship between a students learning style and his or her performance on the standardized competency course (with or without the simulation segment). The data collected for each participant were his/her learning style and combined driving score on the competency course. To test the effect of learning styles on the first run, total points scored on the competency course, a one-way ANOVA was used. 136

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The ANOVA conducted for research question three was based on three assumptions: normality, homogeneity of variance, and independence of the observations. Normality was tested by using a stem-leaf graph for each of the groups. The AR group had a normal distribution. The AS, CR, and AR/CR groups had a flat distribution, and the CS group had a positively skewed distribution. The homogeneity of variance is based upon the following. The groups were not equal among the five groups; therefore, the variance for each group was analyzed. The AR group (n=21) had a variance of 26,932, the AS group (n=12) had a variance of 19,763, the CR group (n=14) had a variance of 19,898, the CS group (n=41) had a variance of 24,363, and the AR/CR group (n=8) had a variance of 14,838. Independence of the observations is when the five groups are not paired, dependent, correlated, or associated in any way, which is the case in this analysis. The only assumption that was met was the independence on this test. The stem-leaf graph shows the distribution of the competency course scores on the first run. Abstract Random Stem Leaf # Boxplot 9 2 1 0 8 4 1 | 7 56 2 | 6 011 3 +-----+ 5 0002488 7 *--+--* 4 1246 4 +-----+ 3 59 2 | 2 6 1 | ----+----+----+----+ Multiply Stem Leaf by 10**+2 Figure 31. Stem-leaf graph of the combined score of the competency course on the first drive through of the Abstract Random learners. 137

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Abstract Sequential Stem Leaf # Boxplot 7 0 1 | 6 57 2 | 6 +-----+ 5 55 2 | | 5 | | 4 5 1 | + | 4 1 1 *-----* 3 89 2 +-----+ 3 123 3 | ----+----+----+----+ Multiply Stem Leaf by 10**+2 Figure 32. Stem-leaf graph of the combined score of the competency course on the first drive through of the Abstract Sequential learners. Concrete Random Stem Leaf # Boxplot 8 7 1 | 7 | 6 4799 4 +-----+ 5 2478 4 *--+--* 4 01126 5 +-----+ 3 ----+----+----+----+ Multiply Stem Leaf by 10**+2 Figure 33. Stem-leaf graph of the combined score of the competency course on the first drive through of the Concrete Random learners. 138

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Concrete Sequential Stem Leaf # Boxplot 8 6 1 | 8 4 1 | 7 | 7 012 3 | 6 577 3 | 6 333 3 +-----+ 5 58 2 | | 5 00124 5 | | 4 5678 4 *--+--* 4 23334 5 | | 3 55666788 8 +-----+ 3 224 3 | 2 59 2 | 2 3 1 | ----+----+----+----+ Multiply Stem Leaf by 10**+2 Figure 34. Stem-leaf graph of the combined score of the competency course on the first drive through of the Concrete Sequential learners. Abstract Random/Concrete Random Stem Leaf # Boxplot 8 1 1 0 7 7 6 6 5 677 3 +-----+ 5 0 1 *--+--* 4 88 2 +-----+ 4 0 1 | ----+----+----+----+ Multiply Stem Leaf by 10**+2 Figure 35. Stem-leaf graph of the combined score of the competency course on the first drive through of the Abstract Random/Concrete Random learners. The first main effect was the various learning styles on the competency course. As predicted, there was no main effect of expectations on performance between learning 139

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styles, F (9, 86) = 1.90, p=0.0625. The mean square was 40502.43 and the R-square was 0.166. The mean score was 516.27 with a Root MSE of 146.01. The second main effect was the control group versus the treatment group. There was a significant main effect of the two groups F(1,4) = 10.65, p = 0.0016, which further emphasizes the statistical significance of the group who received the treatment as discussed in research question one. The interaction tested was to determine if there was an interaction between learning styles and driving scores. There was no interaction between learning styles and driving scores, F(1,4) = 0.38, p = 0.820. The results are provided in Table 34. 140

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Table 34. Results of ANOVA for competency course scores by learning styles. Source DF Sum of Squares Mean Square F Value Pr > F Model 9 384521.91 40502.43 1.90 0.0625 Error 86 1833383.05 2138.41 Corrected Total 95 2197904.96 R-Square Coeff Var Root MSE SCORE Mean 0.166 28.28 146.01 516.27 Source DF ANOVA SS Mean Square F Value Pr > F Group 1 226981.50 226981.50 10.65 0.0016 LS 4 104788.02 26197.01 1.23 0.305 LS*Group 4 32752.38 8188.10 0.38 0.8195 The mean for the AR (n=21) on the driving score was 549.24 seconds with a standard deviation of 164.11. The mean for the AS (n=12) group was 476.58 seconds with a standard deviation of 140.58. The mean for the CR (n=14) was 561.43 seconds with a standard deviation of 141.06. The CS (n=41) group had a mean of 490.07 seconds with a standard deviation of 156.09. The results are provided in Table 35. Figure 36 illustrates the mean of the combined driving score on the competency course for each of the learning styles by using a box plot graph. The + sign indicates the mean on each chart. 141

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Table 35. Learning styles descriptive statistics competency course scores of ANOVA analysis. n Mean Standard Deviation AR 21 549.24 164.11 AS 12 476.58 140.58 CR 14 561.43 141.06 CS 41 490.07 156.09 AR/CR 8 544.50 121.81 | 1000 + | | | 0 900 + | | | | | | | | | | | 800 + | | | 0 | | | | | | | | | | | | 700 + | | | | | | | +-----+ | | | | | | | | | | | | +-----+ 600 + +-----+ +-----+ | | | | | | | | | | | | | +-----+ | | + | | | *--+--* | | | + | | *-----* | | | | | | *-----* 500 + | | | | | | | + | | | | | | | + | | | | | +-----+ | | | | | | | *-----* | | +-----+ *-----* +-----+ | | | 400 + | | | | | | | | | | | | | | | +-----+ +-----+ | | | | 300 + | | | | | | | | | | 200 + ------------+-----------+-----------+-----------+-----------+----------LS AR AS CR CS AR/CR Figure 36. Learning styles descriptive statistics competency course mean scores of ANOVA analysis. 142

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The mean of the control group was 564.90 seconds, and the standard deviation was 164.00. The mean of the treatment group was 467.65 seconds, and the standard deviation was 122.63. The results are provided in Table 36. The results of Table 36 are similar to the results of research question one. In research question one the results of the first run through the competency course were analyzed by time and penalty points individually. In this analysis, the combined scores of the first run through the competency course were analyzed, with the results showing that the treatment group was statistically significant to the control group. Figure 37 illustrates the mean of the combined driving score on the competency course for the control and treatment groups by using a box plot graph. The + sign indicates the mean on each chart. Table 36. Competency course mean and standard deviation statistics of ANOVA analysis. Level of Group N Mean Standard Deviation Control 48 564.90 164.00 Treatment 48 467.65 122.63 143

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| 1000 + | | | | 900 + | | | | | 0 | | 800 + | | | | | | | 700 + | | +-----+ | | | | | | | | 600 + | | | | *--+--* | | | | +-----+ | | | | | 500 + | | | | | | | | + | | | | *-----* | +-----+ | | 400 + | | | | | +-----+ | | | | | | 300 + | | | | | | | | | | 200 + ------------+-----------+----------Control Treatment Figure 37. Competency course mean scores of the control and treatment analysis. Random Sequential Grouping The learning style sample population was then categorized into Random or Sequential groups for the next analysis. A t-test was conducted to determine if there was a significant difference between the random group of learners and the sequential group of learners. 144

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The t-test conducted for research question three was based on three assumptions: normality, homogeneity of variance, and independence of the observations. Normality was tested by using a stem-leaf graph for each of the groups for the t-test. The Random group had a normal distribution with one outlier. The sequential group had positive skewed distribution. The homogeneity of variance is based upon the following. The two groups were not equal; therefore, the variance for each group was analyzed. The random group (n=43) had a variance of 21,501, and the sequential group (n=53) had a variance of 22,954. The variances were reasonably close and could be said to be homogeneous in variance. Independence of the observations is when the two groups were not paired, dependent, correlated, or associated in any way, which is the case in this analysis. The stem-leaf graph illustrates the distribution of the combined score, time and points, of the first drive through the competency course. 145

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Random Group Stem Leaf # Boxplot 9 2 1 0 8 7 1 | 8 14 2 | 7 56 2 | 7 | 6 799 3 | 6 0114 4 +-----+ 5 6777888 7 | + | 5 00002244 8 *-----* 4 6688 4 | | 4 00111224 8 +-----+ 3 59 2 | 3 | 2 6 1 | ----+----+----+----+ Multiply Stem Leaf by 10 Figure 38. Stem-leaf graph for the combined driving scores of the Random group. Sequential Group Stem Leaf # Boxplot 8 6 1 | 8 4 1 | 7 | 7 0012 4 | 6 55777 5 | 6 333 3 +-----+ 5 5558 4 | | 5 00124 5 | | 4 55678 5 *--+--* 4 123334 6 | | 3 5566678889 10 +-----+ 3 122234 6 | 2 59 2 | 2 3 1 | ----+----+----+----+ Multiply Stem Leaf by 10 Figure 39. Stem-leaf graph for the combined driving scores of the Sequential group. 146

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The data collected for the random group (n=43) yielded the following descriptive statistics for the total score, drive time plus penalty points, the first time through the competency course. The results are provided in Table 37. The mean was 552.33 seconds; the standard deviation was 146.63; the median was 538 seconds; the mode was 406 seconds; a kurtosis of 0.288 and a skewness of 0.694 were obtained. The data collected for the sequential group (n=53) yielded the following descriptive statistics for the total score, drive time plus penalty points, the first time through the competency course. The results are provided in Table 37. The mean was 487.02 seconds; the standard deviation was 151.51; the median was 487.02 seconds; the mode was 434 seconds; a kurtosis of -0.417 and a skewness of 0.552 were obtained. Table 37. Mean, median, mode, standard deviation, Kurtois, and skewness for Random versus Sequential learning style of competency course scores. n Mean Median Mode Standard Deviation Kurtosis Skewness Random 43 552.33 seconds 538 seconds 406 seconds 146.63 0.288 0.694 Sequential 53 487.02 seconds 487.02 seconds 434 seconds 151.51 -0.417 0.552 147

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| 1000 + | | | 0 900 + | | | | | | | | | 800 + | | | | | | | | | | | 700 + | | | | | | | | | +-----+ +-----+ 600 + | | | | | | | | | | *--+--* | | | | | | | 500 + | | | | | | | | + | | | | *-----* | +-----+ | | 400 + | | | | | | | | | +-----+ | | | 300 + | | | | | | | | | | 200 + ------------+-----------+----------LS R S Figure 40. Mean scores for Random versus Sequential learning style on competency course scores. To test the effect of the random versus sequential learners, scores were analyzed using an independent t-test. Table 38 provides a summary of these results. The t-test was significant, t=2.13, p=0.0357. On average, the sequential learning style group accumulated less total time to drive the competency course on the first run. 148

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Table 38. T-test results of total points the first run through the competency course for random versus sequential learners. Mean Difference Std Dev Difference df t value p Time 65.307 149.35 94 2.13 0.0357 An ANOVA was conducted to compare the relationship of the competency course scores of the Random versus Sequential learning styles with or without the treatment. The ANOVA conducted for this portion of research question three was based on three assumptions: normality, homogeneity of variance, and independence of the observations. Normality was tested by using a stem-leaf graph for each group. The Random learning style control group had a normal distribution. The Random treatment group and the Sequential treatment group had a normal distribution that was positively skewed. The Sequential control group had a normal distribution that was negatively skewed. The homogeneity of variance is based upon the following. The groups were not equal among the five groups. Therefore, the variance for each group was analyzed. The Random control group (n=24) had a variance of 26,847; the Random treatment group (n=19) had a variance of 26,846; the Sequential control group (n=19) had a variance of 11,204; and the Sequential treatment group (n=24) had a variance of 12,916. Independence of the observations is when the five groups are not paired, dependent, correlated, or associated in any way, which is the case in this analysis. The distribution and independence assumptions were met on this test. The stem-leaf graph shows the distribution of the competency course scores of the four groups. 149

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Random Control Group Stem Leaf # Boxplot 9 2 1 | 8 17 2 | 7 56 2 | 6 014799 6 +-----+ 5 00047788 8 *--+--* 4 112 3 | 3 5 1 | 2 6 1 | ----+----+----+----+ Multiply Stem.Leaf by 10**+2 Figure 41. Stem-leaf graph of the competency course scores for the Random control group. Random Treatment Group Stem Leaf # Boxplot 8 4 1 0 7 6 1 1 | 5 0224678 7 +--+--+ 4 001246688 9 *-----* 3 9 1 | ----+----+----+----+ Multiply Stem.Leaf by 10**+2 Figure 42. Stem-leaf graph of the competency course scores for the Random treatment group. 150

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Sequential Control Group Stem Leaf # Boxplot 8 6 1 | 7 0012 4 | 6 35777 5 +-----+ 5 00258 5 *--+--* 4 248 3 +-----+ 3 2568 4 | 2 59 2 | ----+----+----+----+ Multiply Stem.Leaf by 10**+2 Figure 43. Stem-leaf graph of the competency course scores for the Sequential control group. Sequential Treatment Group Stem Leaf # Boxplot 8 4 1 0 7 7 6 5 1 | 6 33 2 | 5 55 2 | 5 14 2 +-----+ 4 5567 4 | | 4 1333 4 *--+--* 3 5667889 7 +-----+ 3 12234 5 | 2 | 2 3 1 | ----+----+----+----+ Multiply Stem.Leaf by 10**+2 Figure 44. Stem-leaf graph of the competency course scores for the Sequential treatment group. The first main effect using a Type I error was the treatment group versus the control group on the competency course. There was a main effect of expectations on performance between the groups, F 11, p=0.0013. 151

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The second main effect was the Learning Style group, Random versus Sequential. There was not a significant main effect of the two groups F = 3.53, p = 0.0635. The interaction tested was to determine if there was an interaction between random versus sequential learning styles with or without simulation. There was no interaction between learning styles random versus sequential with or without simulation, F = 0.01, p = 0.9392. The interaction shows an almost parallel line with no interaction. The results are provided in Table 39. Table 39. Results of ANOVA for competency course scores by learning styles with or without simulation. Source DF Sum of Squares Mean Square F Value Pr > F Model 3 299883 99961 4.85 0.0036 Error 92 1898021 20630 Corrected Total 95 2197904 R-Square Coeff Var Root MSE SCORE Mean 0.1364 27.821 143.634 516.271 Source DF Type I SS Mean Square F Value Pr > F Group 1 226982 226982 11 0.0013 LS 1 72781 72781 3.53 0.0635 LS*Group 1 120.73 120.73 0.01 0.9392 The mean for the Random control (n=24) on the driving score was 591.63 seconds with a standard deviation of 163.85. The mean for the Random treatment (n=24) group was 538.17 seconds with a standard deviation of 163.17. The mean for the Sequential control (n=19) group was 502.68 seconds with a standard deviation of 105.85. The Sequential treatment (n=29) group was a mean of 444.69 seconds with a standard 152

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deviation of 129.07. The results are provided in Table 40. Figure 45 illustrates the mean of the combined driving score on the competency course for each of the learning styles by using a box plot graph. The + sign indicates the mean on each chart. Table 40. Learning styles with or without simulation mean and standard deviation of the competency course scores of ANOVA analysis. n Mean Standard Deviation Random control (R1) 24 591.63 163.85 Random treatment (R2) 24 538.17 163.17 Sequential control (S1) 19 502.68 105.85 Sequential treatment (S2) 29 444.69 129.07 153

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1000 + | | | | 900 + | | | | | | 0 | 0 | | | 800 + | | | | | | | | | | | 700 + +-----+ | | | | +-----+ | | | | | | | | | | | | 600 + | + | | | | | | *-----* | | | | | | | +-----+ | + | | | | | | | *-----* | 500 + +-----+ | + | | | +-----+ | | *-----* | | | | | | | | | | | + | | | +-----+ | | *-----* 400 + | | +-----+ | | | | | | | | | | | +-----+ | | | | 300 + | | | | | | | | | | | | | 200 + ------------+-----------+-----------+-----------+----------R1 R2 S1 S2 Figure 45. Learning styles with or without simulation mean of the competency course scores of ANOVA analysis. Qualitative Results Research Question Four The qualitative research question investigated the emergency vehicle operators perceptions of using a driving simulator as part of an emergency vehicle training course. Separate surveys were administered to the treatment group and control group, as 154

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described earlier. A thematic approach was used to analyze the responses to each of the questions. Each response was categorized into one theme for each group. Describe how the simulator did or did not help you prepare you for the driving course. The treatment group was asked to describe how the simulator did or did not help them to prepare for the driving course. There were four themes established from the responses. The overwhelming theme was, The simulator prepared them to drive and gain a better understanding of the competency course layout. Another positive theme was, It was easier than driving the actual vehicle. There were a few who did not think the simulator helped them, and a few who had no response. The results are provided in Table 41. 155

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Table 41. Thematic responses for simulator preparing treatment group. Theme Percentage of respondents Positive Prepare to drive/course layout knowledge 82% Easier than actual driving 4% Negative Did not help 4% Unambiguous No response 10% Some of the responses from the theme Prepare to drive/course layout knowledge were: It helped in knowing about the course set up. Helped you learn the track but was not like the actual driving of the vehicle. and It helped me a lot to prepare for the actual driving course. It made me more prepared for what I had to do. The theme Easier than actual driving had such responses as, It was a little easier than driving. A negative theme response from the theme Did not help was, The simulator gave me a false sense of security. I felt over confident when I hit the course, but [then] reality hit. They work well together. What is your opinion of simulators teaching emergency vehicle operators to drive an emergency vehicle? The remaining questions were asked of both groups. The first question asked what their opinion of using simulators to teach emergency vehicle operators to drive an emergency vehicle. Although both groups answered the same question, the data and themes are presented separately for the treatment and control groups. 156

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Treatment Group There was a notable theme that a simulator would help an individual to learn to drive an emergency vehicle in both groups. The treatment group also had responses of needing both simulator and actual driving time, the simulator needs to be harder, the simulator was easier than the ambulance, no opinion, and the simulator did not help. The results are provided in Table 42. Table 42. Treatment groups thematic responses using simulator to teach driving. Theme Percentage of respondents Positive Simulator helpful 50% Need both simulator and actual driving 30% Negative Simulator does not help 8% Simulator needs to be harder 4% Easier in ambulance 2% Ambiguous No opinion 6% Some of the positive responses from the treatment group Simulator helpful theme included, I think it is an excellent way of teaching. Its [a] lot cheaper to make mistakes in a simulator. Responses from the Need both simulator and actual driving theme included, It should definitely be used in training to operate emergency vehicle. As a matter of fact, I believe the simulator should be a mandatory part in certifying drivers who operate emergency vehicles. I think it is very important, a better one would be driving on streets with traffic with and intersections. Maybe even running hot! A response from the negative theme Simulator needs to be harder was, It is almost the same but doing it for real is a little harder. 157

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Control Group The control group thought the simulator would be helpful. There were also responses of good idea, the actual driving is better, and the question was not applicable to them. The results are provided in Table 43. Table 43. Control groups thematic responses using simulator to teach driving. Theme Percentage of respondents Positive Simulator is helpful 79% Negative Actual driving is better 4% Ambiguous Question not applicable 17% The positive responses from the theme Simulator would be helpful included, I think it would be beneficial, I think the simulator would be a great way of teaching. It can be taught rain or shine and is close to the real thing, I never used the simulator. I think its beneficial because it builds your confidence on what the course would be like, and I believe they are a good training device. A response from the negative theme Actual driving is better was I think the real thing is a better teacher. Do you feel the simulator was beneficial as part of your training? The next question asked the treatment group if the simulator was a beneficial part of their driver training program and the control group if they thought the simulator would have been beneficial. 158

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Treatment Group The treatment group responded overwhelmingly (82%) in favor that the simulator was beneficial in their training. The results are provided in Figure 46. 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Yes No Unknown Figure 46. Treatment group responses to whether simulator was beneficial as part of their training. The recurring theme emerging as the reason to the benefit was the ability to drive the course and gain an understanding of how the course was laid out. The additional themes were that it provided practice time, it was fun, not like the actual driving, it was confusing, and no response. The results are provided in Table 44. 159

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Table 44. Treatment groups thematic response to benefits of driver training program. Theme Percentage of respondents Positive Driving and course lay out 76% Time to practice 2% It was fun 2% Negative Not like actual driving 8% Confusing 2% Ambiguous No response 10% Some of the positive responses from the theme Driving and course lay out were: I remembered the course tracks easier, so I knew where I was supposed to go; plus, I was more confident. I believe the simulator was a confidence builder but not a true to life guide. It made me more comfortable and helped with mirror usage. A negative response from the Not like actual driving theme was, It is not the same as the real thing. From the Confusing theme was, Confusing altered my depth perception. Control Group The control group responded that the simulator would have been beneficial to them (85%). The results are provided in Figure 47. The control group responded with the following themes: it would have been helpful with different learning points was the most dominant response, vehicle dynamics, actual driving is better, reduce the number of penalty points, unknown, did OK without simulator, it is safer, and the simulator would have been less stressful. The results are provided in Table 45. 160

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Figure 47. Control group responses to whether simulator would be beneficial as part of their training. Table 45. Control groups thematic response to benefits of simulator in driver training program. Theme Percentage of respondents Positive Simulator provides more learning points 56% Helped with vehicle dynamics 22% Reduce penalty points 4% Simulator would be less stress 2% Simulator would be safer 2% Negative Actual driving better 8% Did ok without simulator 2% Ambiguous Unknown 4% Some of the positive responses from the various themes: Simulator provides more learning points It would have helped me pass it in lesser tries. Reduce penalty points I think it would have given me a chance to get used to the course before getting behind the 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Yes No 161

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wheel of the ambulance. Simulator would be safer More experience without being concerned with safety of outside hazards (people, cars, etc.). Helped with vehicle dynamics Help with the use of backing up and use of mirrors. A response from the negative theme Actual driving better was, I learn better in real life and real time. Should the simulator be incorporated into the driver training program? Both groups were asked if a driving simulator should be incorporated into the emergency vehicle driving program. Treatment Group The treatment group responded (86%) in favor of incorporating the simulator in the emergency vehicle driver program. The results are provided in Figure 48. The overwhelming theme was, the simulator helps to prepare them to drive. The other themes were no response, excellent tool, safe alternative, cost effective, need to use the simulator longer, simulator was unrealistic, and needs to be ambulance size. The results are provided in Table 46. 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Yes No Unknown Figure 48. Treatment group responses to whether the simulator should be incorporated into the driver training program. 162

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Table 46. Treatment groups thematic response to incorporating simulator into driver training program. Theme Percentage of respondents Positive Prepares a person to drive 64% Excellent tool 8% Safe alternative 6% Cost effective 2% Negative Need to use simulator longer 2% Simulator is unreal 2% Need it to be size of ambulance 2% Ambiguous No response 14% The positive responses from the theme Safe alternative included: Excellent safe alternative. You can make mistakes on a simulator but not on the street. From the theme Prepares a person to drive were, I got a lot out using the mirrors on the simulator to prepare me. It might help some people; therefore, it should be optional. From the negative responses, the theme Simulator is unreal was, You realize how big and clunky these things really are. A response from the theme Need to use simulator longer The simulator used longer than we did could be more beneficial. Control Group The control group responded (87%) in favor of including the simulator as part of the emergency vehicle driving program. The results are provided in Figure 49. The themes that emerged from this question were, the benefits of understanding the competency course, the benefit for non-experienced drivers, unknown, more 163

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training, safer, different insight, more practice, save money, and actual driving is better. The results are provided in Table 47. 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Yes No Unknown Figure 49. Control group responses to whether the simulator should be incorporated into the driver training program. Table 47. Control group's thematic response to incorporating simulator into driver training program. Theme Percentage of respondents Positive Better understanding of competency course 33% Benefits for non-experienced drivers 23% More training 8% Safer 5% Different insight 5% More practice 2% Save money 2% Negative Actual driving better 2% Ambiguous Unknown 20% 164

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The positive responses from the theme Better understanding of competency course were: It would not only give a different situation but would better prepare you for the course. You can see your errors before actually driving on the course. Different insight was, Help focus on the basics of the dos and donts of the truck. Benefits for non-experienced drivers was, Because your overall expectations and anticipation of the actual driving experience [are] enhanced. Save money was, It may be cheaper and less use of industrial equipment. And it may be a lot safer. A negative response from the Actual driving better theme was, Hands on is better. Should the simulator be used instead of the competency course in the driver training program? The final question asked both groups if the simulator should be used instead of the competency course. Treatment Group The treatment group responded overwhelmingly (90%) that the simulator should not replace the driving portion of the emergency vehicle driver training program. The results are provided in Figure 50. The majority theme was, actual driving experience was needed and nothing could replace the actual driving. There were also themes of competency course did not help, the simulator did not help, the simulator was stressful, and no response. The results are provided in Table 48. 165

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90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Yes No Unknown Figure 50. Treatment group responses to whether the simulator should be used instead of the competency course. Table 48. Treatment group's thematic response to whether the simulator should be used instead of the competency course. Theme Percentage of respondents Positive Actual driving also needed 80% Simulator less stressful 4% Negative Competency course did not help 4% Simulator did not help 4% Ambiguous No response 8% The positive responses to the theme Actual driving included: I think that they are both helpful but the competency course is as realistic as a student could possibly get. A mixture of both would be necessary to educate driver to drive safely in real work. Keep 166

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using both. The more training the more comfortable you will feel. A negative response from the Simulator did not help was, I believe the simulator is too much like a video game; it is not till you run over some cones do you understand the driving requirements. Control Group The control group also responded overwhelmingly (83%) that the simulator should not replace the driving portion of the emergency vehicle driver training program. The results are provided in Figure 51. The majority theme for this group was the same as the treatment group, actual driving experience was needed and nothing could replace the actual driving. The other themes were actual driving only, simulator only, more time on simulator, and unknown. The results are provided in Table 49. 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Yes No Unknown Figure 51. Control group responses to whether the simulator should be used instead of the competency course. 167

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Table 49. Control group's thematic response to whether the simulator should be used instead of the competency course. Theme Percentage of respondents Positive Need actual driving also 62% More time on simulator 2% Negative Actual driving only 20% Simulator only 4% Ambiguous Unknown 12% The positive responses from the theme Need actual driving also included: Nothing can replace actual hands on training. You have to feel the vehicle. You need actual driving time to make it all hit home. Both would be very beneficial. The actual vehicle moves a lot different than a simulator. because you need the feel of the heaviness of the vehicle. I think you need both teaching techniques to accommodate different learning styles. Qualitative Summary The results of surveying both groups answered the question of the emergency vehicle operators perceptions of using a driving simulator as part of an emergency vehicle training course. The simulator allowed the treatment group to understand the course prior to actually driving the course. The control group thought the simulator would have afforded them the opportunity to learn the course before actually driving the course. Both groups thought the simulator needs to be a part of the driver training course, but do not see the simulator replacing actual driving experience. These results did not 168

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substantiate the statement that there is no difference in an emergency vehicle operators perception of using a driving simulator compared with not using a simulator as part of an emergency vehicle training course. In both instances, the emergency vehicle operators thought the simulator would improve their driving ability. 169

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Chapter 5 Discussion Summary of Findings Statement of the Problem Emergency vehicles are operated by drivers who may or may not receive training in their safe operation. The number of accidents has remained an issue over the past decade. The literature suggests that human error continues to be the primary reason for the number of emergency vehicle accidents (What you don't know at the wheel can hurt, 2003). The statistics gathered illustrate the number of emergency responders who are injured or killed as a result of an emergency vehicle accident ("Ambulance crash-related injuries among emergency medical services workers United States, 1991-2002," 2003; Firefighter Fatality Retrospective Study, 2002; The U.S. fire service, 2003). Purpose Statement Although computer simulator-based training for emergency vehicle drivers has intuitive appeal, little is known about its effectiveness. Thus, this study examined the effectiveness of the simulator-based learning environment in comparison with conducting training in a more traditional, non-simulated learning environment. 170

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Research Questions This study investigated some of the obvious, yet essential, questions related to the effectiveness of computer simulation-based training for emergency vehicle drivers. The first hypothesis states that there is no significant difference in competency course scores of emergency vehicle drivers who utilize a simulator before driving through a competency course and those who do not use a simulator. However, there was a significant difference in the competency course scores of the participants who used the simulator and those who did not use the simulator; therefore, the null hypothesis was rejected. The findings indicated that the treatment group took significantly less time to drive through the competency course on the first run (t=3.74, p=0.0003), acquired significantly fewer penalty points on the first run (t=2.41, p=0.0178), and required significantly fewer total runs to successfully complete the course (t=3.53, p=0.0006). This evidence would suggest that using a simulator improves the individuals ability to drive an ambulance on the required competency course. The second hypothesis states that there is no significant relationship between a students learning style and his or her performance on the written post-test. The results from this analysis show a significant difference (F (4)=2.56, p=0.044) in the relationship of an individuals learning style to the score on the written test. A Duncan multiple range test (p<.05) post hoc test shows that the Abstract Random (AR) group was significantly superior to the Abstract Random/Concrete Random (AR/CR) and the Abstract Sequential (AS) groups; therefore, the null hypothesis was rejected. The third hypothesis states that there is no significant relationship between a students learning style and his or her performance on the standardized competency 171

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course (with or without the simulation segment). This relationship was not significant across all groups (F(1,4) = 0.38, p = 0.820). However, when the students scores were grouped as either Random or Sequential learners, a difference emerged. A t-test was performed to determine if there was a statistical significance between these two learning style groups. The results showed a statistical significance (t=2.13, p=0.0357); therefore, the null hypothesis was rejected. The sequential learning style group required less total time to drive the competency course on the first run than the random learners. This finding was based on all students those who used the simulator and those who did not. An ANOVA was conducted to compare the relationship of the competency course scores of the Random versus Sequential learning styles with or without the treatment. There was no interaction between learning styles random versus sequential with or without simulation, (F=0.01, p=0.9392). Additionally, the qualitative component of this study investigated the emergency vehicle operators perceptions of using a driving simulator compared with not using a simulator as part of an emergency vehicle training course. The majority of the participants indicated that they felt simulators would be very beneficial to training. Conclusion of Findings Time of First Run The first research question asked if there was a significant difference in competency course scores of emergency vehicle operators who were trained to drive an emergency vehicle via a simulator prior to driving on a standardized competency course and those of emergency vehicle operators who were not trained using a simulator. The 172

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results were significant when conducting a one-tailed t-test on the time on the first run through the driving course. The difference in the mean scores was 92 seconds; the standard deviation difference was 124. A statistical significance (t=3.74, p=0.0003) was found between the two variables. These findings indicate that the drivers who had used a simulator were more familiar with the vehicle and the course. As a driver of an emergency vehicle, it is important to know your vehicle and the path of travel you are going to take to reach the scene of an emergency. It is not practical to practice on a simulator prior to running an emergency call; however, it does illustrate that using a simulator to practice driving in the areas that an emergency vehicle driver will likely respond to calls could help the driver learn the routes and respond in a more safe and efficient manner to emergencies. These results also relate to a study conducted by Kiser (2000) that showed an increase in the confidence level of students who perform anesthesia. Performing anesthesia is a high-risk activity, much like emergency vehicle driving. Kiser (2000) found that the participants confidence level rose from 55% prior to using the simulator to perform anesthesia to 75% after using the simulator to perform anesthesia after four days. The confidence level of the participants was not measured in this study, but the increase in speed on the first run through the competency course may indicate higher confidence as well as competence in the participants. Penalty Points on First Run The next set of data analyzed was the total penalty points. A one-tailed t-test was conducted on the penalty points on the first run through the driving course. A significant difference was noted when using the t-test. The difference in the mean scores was 25 173

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seconds; the standard deviation difference was 52. A significant difference (t=2.41, p=0.0178) was found between the two variables. The significant findings illustrate that the drivers knew their vehicle better by the reduction in penalty points. Penalty points are assessed for crossing over lines or knocking cones over. If an individual knows his/her vehicle and the characteristics of their vehicle, they are going to be less apt to incur penalty points. These findings correspond to a study by Mills and Hubal (2001) that tested a group of police cadets on the Profiler system. The cadets used the computer-based simulator, and then drove on a coned competency course. The results of that study showed that the cadets who had higher test scores on the Profiler system had fewer driving errors on the track. Number of Runs through Competency Course A one-tailed t-test was conducted on the total runs through the competency course. A significant difference was noted when using the t-test. The difference in the mean scores was 0.577 runs; the standard deviation difference was 0.826. A significant difference (t=3.53, p=0.0006) was found between the two variables. This test, being significant, emphasized the driver knowing his/her vehicle and the characteristics of the vehicle and knowing the route they were to take. The results of the treatment group versus the control group in each of these areas showed that the treatment group performed better on the first run through the competency course than the control group. Similar findings were found in the research study conducted by Brock, Jacobs, and Buchter (2001). The success rate of the individuals in their study who used the simulator versus their traditional course to teach bus drivers realized a 95% pass rate. The study did not cite the pass rate of the other drivers. 174

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Learning Styles and the Written Test The findings of the learning style as it relates to his or her performance on the written post-test showed a significant difference (F (4) = 2.56, p=0.044). These findings indicated that an individuals learning style may predict how well they will perform on a written exam. The AR group had the highest mean score of 88.76 with a standard deviation of 9.60. The lowest scoring group was the AS with a mean of 81.67 and a standard deviation of 7.90. A Duncan multiple range post hoc test (p<0.05) showed that the AR group is significantly superior to the AR/CR and the AS groups. The other groups do not appear to have a significant difference. The results of the learning style as it relates to the performance on the written post-test seem to be opposite what is typically expected using the Gregorc Mind Style Delineator (Gregorc, 1982b). According to the characteristics of learners as described by Gregorc, the AS typically would do well on written tests, and the AR typically do not perform as well on written tests nor do they like written tests (Gregorc, 1982b). A number of studies show no significant difference when analyzing learner styles and the effect they have post-test written scores. This is validated by Stahl (1999) in his document Different strokes for different folks? A critique of learning. Few studies showed a significant difference when comparing the effects of learning styles as they relate to the scores on a written post-test and demonstrate the typical characteristics as described by Gregorc (1982b). Ross (1997) conducted a study on the effects of cognitive learning styles on human-computer interaction. In his study, he did an ANOVA on the pre-/post-test scores by dominant learning styles. The learning style inventory Ross used was the Gregorc 175

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Mind Style Delineator. The study found that the AR group had a high mean score on the pre-test; however, the AR group dropped to the lowest mean score on the post-test. This study had the greatest increase between the pre-test and the post-test with an average of 3.64 gain in points. He concluded that the results indicated that the tutorial program led to significant gains in knowledge from pre-test to post-test. It was only when groups were distilled by learning style groups that difference in performance became apparent (J. L. Ross, 1997). The current study did not compare the pre-test to the post-test; however, it was interesting to note that the post-test results in the study conducted by Ross (1997) showed the opposite effects that this study showed. The AR group in Rosss study had the lowest score, while the AR had the highest mean score in this study. Likewise the AS had the highest mean score in the Ross (1997) study and the AR had the lowest mean score in this study. The results of this study and the Ross (1998) demonstrate that the characteristics of an Abstract learner whether they are Sequential or Random are not always predictable regarding how Gregorc (1982b) characterizes these learners. A contributing factor may be that the reliability of the test scores for the post-test that was administered in this study was very low. The assumptions for the statistical analysis of the ANOVA were not met. The combination of these two critical elements may have skewed the data for this analysis. Therefore, the results of this test may not be reflective of a significant finding. Learning Styles and the Competency Course Score The findings of the third research question were designed to determine if there is a relationship between a students learning style and his or her performance on the standardized competency course (with or without the simulation segment). The data 176

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collected for each participant were his/her combined driving score on the competency course. To test the effect of learning styles on the first run, total points scored on the competency course, a one-way ANOVA was used. The first main effect was the various learning styles on the competency course. As predicted, there was no main effect of expectations on performance among learning styles, (F (9, 86) = 1.90, p=0.0625). The mean square was 40,502.43 and the R-square was 0.166. The mean score was 516.27 seconds with a Root MSE of 146.01. To test the effect of the random versus sequential learners, scores were analyzed using an independent t-test. The t-test was significant, (t=2.13, p=0.0357). The sequential learning style group accumulated less total time to drive the competency course on the first run. However, this finding was based on all students, whether or not they used a simulator. The ANOVA that was conducted to test for the relationship between learning style (random vs. sequential) and treatment (simulator vs. non-simulator) found there was no interaction between learning styles random versus sequential with or without simulation, (F=0.01, p=0.9392). According to the ordering abilities of Gregorc, sequential learners allow their mind to organize information in a linear, step-by-step manner. When using sequential ability, the learner follows a logical train of thought, a traditional approach to dealing with information. They also prefer to have a plan and to follow it, rather than relying on impulse (D. W. Mills, 2002). The driving simulator and the driving competency course had a planned series of obstacles the driver maneuvered through in order to complete. The plan was laid out in an organized fashion with a map of the course given to each participant. This corresponds to the finding that the sequential learners did better 177

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compared to the random learners on the competency course, with or without the treatment. Random learners let their mind organize information by chunks, and in no particular order. When they use random ability, they may often be able to skip steps in a procedure and still produce the desired result. They may even start in the middle, or at the end, and work backward. They may also prefer life to be more impulsive, or spur of the moment, than planned (D. W. Mills, 2002). These learners contradict the entire philosophy of the driving course, hence the reasoning why they may have more points on the first run through the competency course. Qualitative Findings The implications of the qualitative findings of this study illustrate that there is a perception that using a simulator has a positive effect on your driving ability. The participants also expressed an overall positive attitude toward using the simulator as part of emergency vehicle driver training. A study conducted by Brock, Jacobs, and Buchter (2001) asked participants in their study about the satisfaction of using driver simulators to train to drive a bus. There was a high level of satisfaction (92%) reported from all locations of respondents for training purposes. Furthermore, 58% of the respondents reported that the simulator was more effective than traditional training methods. Conclusion of Findings The findings of this study indicate simulators can be effective training tools for teaching emergency vehicle drivers. Simulators would be beneficial to include in the emergency vehicle driver training program for a couple of reasons. The first benefit is the cost of the simulator versus the cost of an actual ambulance. The simulator used in this study retails for $70,000 (Craft, 2004). As noted in an article in the Fort Myers News 178

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Press, the cost of an ambulance is about $100,000 ("Medics injured in ambulance crash released from hospital," 2003). If an ambulance is damaged during a training evolution, there is a cost to repair the damage and a time period for which the ambulance is not available to respond on calls. If a driver wrecks an ambulance on the simulator, no actual damage occurs. In addition, if a driver is involved in an ambulance accident, he or she may be injured; whereas, injury is unlikely to occur in a simulator. In addition to the cost of the emergency vehicle, there are many other costs associated with conducting a competency course training program on a training site. There is the cost of the instructors. At least two instructors should be on the competency course at all times, along with a safety officer. In contrast, an instructor is not required to be present when the driver is training on a simulator. Additionally, the simulator can be used at any time of any day. In order to set up the driving course, it takes approximately 1 hours to lay out and mark the course. There are 100 cones that need to be used, and these orange traffic cones typically become damaged and destroyed over time from the ambulances running over them. There were approximately 15 traffic cones destroyed during this study. The weather is another factor. The course cannot be conducted during inclement weather. The simulator can be used during any type of weather, at any time of the day. According to a USA Today article, the cost of fuel is increasing and prices are at record highs (Kenworthy, 2004). Using an ambulance for driver training is becoming more expensive just by the increase of fuel cost. Therefore, it may be much more economical to have the driver practice on a simulator before they drive an actual vehicle on the course. This study showed that the number of required runs through the competency 179

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course decreased with the treatment group; this decrease can help in reducing fuel and other associated costs. Recommendations for Future Research Use of Simulators As noted in the theoretical framework of this research, a four-step training method was developed for the analysis of learning by doing. The method encompassed four steps in the learning process for each task to be performed. These steps are familiarize, acquire skills, practice skills and validate skills -otherwise known as the FAPV (Familiarize, Acquire skills, Practice skills, Validate skills) method (Frank et al., 2000). Familiarize is the passive process the student learns. In this study, the student completed an eight-hour classroom portion to acquire the knowledge by absorbing information through the presentation. The next step is acquiring skill. This is when the student learns the technique and procedure by being tutored. In this study, the treatment group had the simulator as the acquiring skill step. The control group was not afforded the same level of acquiring the skill. The third step is practicing skill. The control group and the treatment group both performed the actual driving of the vehicle on the competency course. The last step is validating the skill. This step was not accomplished during this study. At this level, the individual would actually drive the emergency vehicle in a real setting. The individuals in this study were never subjected to a real streetscape in either the simulator or the real environment. Future research needs to look at the effects a simulator has on emergency vehicle drivers in real scenarios. The addition of flashing 180

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lights and sirens adds another dimension that was not assessed in this study. As discussed in Chapter 1, the leading cause of accidents is using flashing lights and sirens responding to calls. For emergency vehicle drivers, the greatest number of and the most severe accidents occur at intersections Future studies should include scenarios using street driving on the simulator and then street driving on the road. The emergency vehicle driving training program recommends that the fourth component of a driver training program be street driving time. The simulator could be incorporated in a fashion similar to that used in this study. Using simulation for training individuals to drive on highways streets may also show a cost reduction. These cost reductions could come in the form of reducing maintenance costs and decreasing accident rates. Return on investment studies could prove to be beneficial for promoting the use of simulators in training. In this study, the simulator was used by rookie ambulance drivers. Future research needs to evaluate the effect of simulators on drivers who are experienced emergency vehicles drivers and drive on a regular basis. An experienced driver adds new dimensions to the study. In this study, the level of the drivers ability in general was not measured. Future research needs to establish a benchmark of the drivers ability to account for any driving experience that may be similar to what is being tested. For example, some participants in this study had used mirrors to drive other vehicles in the past. This was not taken into account. The researcher did an informal survey of the group verbally to determine if the participant had driven vehicles in the past that required the use of mirrors to back the vehicle. It appeared to be about the same number of participants in both 181

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groups who had used mirrors to back other vehicles. The total count was minimal, but it is a factor to consider for future research. The direct observation by the researcher and those who did the scoring on the competency course raised the possibility that the simulator can be a predictor of the drivers behavior in a real environment. If this could be substantiated through research, it could help in the recruitment process of individuals to drive emergency vehicles. Perhaps the weak areas could be identified before the person gets behind the wheel of a vehicle. These weaknesses could be used to provide concentrated training to correct the weaknesses and prevent an individual from driving a vehicle of this size until he/she is ready. Mills and Hubal (2001) realized similar findings in their study. They concluded that pre-testing of driving skills in a controlled environment on a computer simulator had some capacity to assess and predict driving skills in the real world for police cadets. Future research should be formalized for both of these observations. Further studies need to be conducted to validate these observations. If the predictions are true that the characteristics of the driver can be predicted before driving an actual vehicle, the simulator could have an additional teaching component. The simulator could be designed to detect those obstacles the driver is having the most difficulties with and switch to a scenario in which the driver could receive additional training on that particular skill. For example, if the driver is having difficulty with obstacles on a straight line, the simulator could switch to a scenario that would give the driver more practice time and additional help in learning this skill. Future studies also need to take into account the demographics of the drivers to determine if there is a difference in gender. It was observed that females had more 182

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difficulty driving the ambulance on the competency course than did the males. Additional demographic information would be insightful in future research studies to determine if there is any significant difference when investigating not only gender, but age, physical characteristics (including height), and ethnicity. Design of Simulators The design of this study used a simulator that emulated the driving competency course. This resulted in a near transfer learning environment for the participant. A near transfer is when the participant is placed into an environment very similar to the environment in which the participant will be functioning in the real world (Alessi & Trollip, 2001). Future studies should use a far-transfer learning environment. The term far transfer refers to applying what is learned to somewhat different circumstances, or generalization of what is learned (Alessi & Trollip, 2001). The simulator used in this study had other scenarios that could be used for this purpose. The simulator has a siren that can be turned on as if the driver is on an emergency response. There is also a scenario that depicts a typical suburban environment with intersections and other vehicles that the driver encounters during the response. Currently, a simulation of highway response is being completed and tested for emergency drivers to respond on an Interstate highway, encountering other vehicles and requiring the drivers to make decisions that they would encounter during an emergency response on a highway. Road Safety International is the maker of the Black Box, which is a device that detects speed, braking, and vehicle maneuvering. If the driver exceeds the speed limit, brakes too hard, or maneuvers in such a way that it may cause an accident, an alarm sounds from the Black Box. In addition, a printout can be obtained at the end of the 183

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response to detect what the driver did improperly during the response. Future studies could include the use of the Black Box. Further, the addition of the sounds from the siren and the Black Box could be added stressors placed upon the driver, which could be measured. Recommendations for Future Practice Ross (2002) listed three advantages to adopting the approach of using simulators in training. They are: (a) according to a 1990 national survey of the United Kingdom, companies found training time was reduced by 30%; (b) automatic logging of individuals performance eliminates manual marking. Retraining then can be accurately targeted, as participation in training can be easily tracked and monitored, according to a 1995 study; and, (c) technology-based training can achieve similar results at lower cost compared to conventional methods. This research demonstrated that emergency vehicle operators who use a simulator to learn driving skills before they drive an actual vehicle in a similar environment perform significantly better. It is the researchers recommendation to incorporate the simulator into the driving course to train emergency vehicle operators. Additionally, the researcher recommends additional studies to determine if the use of the simulator can assist in screening and recruiting personnel to be future drivers of emergency vehicles. Summary In conclusion, the results of this study have illustrated that computer simulations have a positive significant effect on training emergency vehicle drivers. This essentially means that drivers who are trained using the simulator before driving an actual vehicle tend to do better. Future studies are required to determine if the simulator may be a good 184

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predictor to identify the problem areas the driver will have when driving an actual vehicle. Additionally, using a learning style assessment may be a predictor of those individuals who will be more successful on a written test and the driving course. The majority of the participants in this study expressed the opinion that using simulation as part of a driving training program is an important component of the training. 185

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References Accident analysis. (2003, June 4). Retrieved December 18, 2003, from http://www.flightdeckautomation.com/studies/study-analysis.aspx Ackerman, C. M., & Willson, V. L. (1997). Learning styles and student achievement in the Texas A&M freshman foundation coalition program. Paper presented at the Southwest educational research association annual meeting, Austin, Texas. Alessi, S. M., & Trollip, S. R. (2001). Simulations. In Multimedia for learning (pp. 213-269). Needham Heights, MA: Pearson Education Company. Allen, R. W., Cook, M., & Rosenthal, T. J. (2001, August 14-17). Low cost PC simulation technology applied to novice driver training. Paper presented at the International driving symposium on human factors in driver assessment, training and vehicle design, Snowmass Village at Aspen, Colorado. Allen, R. W., Park, G., Cook, M., & Rosenthal, T. J. (2003, July 21-24). Novice driver training results and experience with a PC based simulator. Paper presented at the 2nd International driving symposium on human factors in driver assessment, training and vehicle design, The Grand Summit Resort Hotel and Conference Center Park City, Utah, USA. 186

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Appendix A Competency Course Instrument 202

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Appendix B Simulation Evaluation Survey 205

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Survey for Simulator Participants Thank you for completing the emergency vehicle driving course. The following survey is to assist in evaluating your opinion of the program as it relates to having used or not used the driving simulator. Describe how the simulator did or did not help you prepare you for the driving course. ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ What is your opinion of simulators teaching emergency vehicle operators to drive an emergency vehicle? ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ 206

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Do you feel the simulator was beneficial as part of your training? ____ Yes ____ No Why? __________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ Should the simulator be incorporated into the driver training program? ____ Yes ____ No Why? __________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ Should the simulator be used instead of the competency course in the driver training program? ____ Yes ____ No Why? __________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ 207

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Survey for Non-Simulator Participants What is your opinion of simulators teaching emergency vehicle operators to drive an emergency vehicle? ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ Do you think the simulator would have been beneficial to you in your EVOC training? ____ Yes ____ No Why? __________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ Should the simulator be incorporated into the driver training program? ____ Yes ____ No Why? __________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ Should the simulator be used instead of the competency course in the driver training program? ____ Yes ____ No Why?___________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________ 208

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Appendix C Informed Consent 209

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Informed Consent Form The Effects of Competency Course Testing and Learning Styles Using Computer Simulation to Train Emergency Vehicle Drivers I HAVE BEEN INFORMED THAT: 1. Jeffrey Lindsey who is a doctoral student has requested my participation in a research study at this institution. 2. The purpose of the research is to study the use of computer simulation for training emergency vehicle drivers versus traditional emergency vehicle driver training in order to reduce emergency accidents. Traditionally emergency vehicle drivers complete an eight-hour didactic class followed by driving a vehicle on a competency course. The accident rates of emergency vehicles continue to rise. Does the education to train emergency vehicle drivers aid in the reduction of accident rates? Computer simulation may be the needed component to train emergency vehicle drivers. 3. My participation will involve participating in an eight-hour didactic training session on emergency vehicle driving that includes a preand post-test, an eight-hour session driving an emergency vehicle on a competency course, and, if selected, using a computer simulator to test my driving ability. My participation will also involve completing the Gregorc Style Delineator, a learning style assessment. The scores of the competency course and post-test will be used in accordance with the program as to issuing a successful completion certificate, otherwise all other scores including the pre-test, Gregorc Style Delineator assessment, and simulator scores will be used for this study. There are no foreseeable risks or discomforts if I agree to participate in this study. 4. The possible benefits of my participation in this research study are to identify the benefits of using a computer simulation to train emergency vehicle drivers and assist in the reduction of the number of emergency vehicle accidents and deaths of emergency service responders as a result of vehicle accidents. I will not be paid for my participation. 5. Any questions I have concerning the research study or my participation in it, before or after my consent, will be answered by Jeffrey Lindsey 19850 Breckenridge Drive, Estero, FL 33928 239-947-3473, jtsafety@aol.com 6. If you have questions about your rights as a person who is taking part in a research study, you may contact the Division of Research Compliance of the University of South Florida at (813) 974-5638. 210

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Your privacy and research records will be kept confidential to the extent of the law. Authorized research personnel, employees of the Department of Health and Human Services, and the USF Institutional Review Board and its staff, and other individuals, acting on behalf of USF may inspect the records from this research project. The results of this study may be published. However, the data obtained from you will be combined with data from others in the publication. The published results will not include your name or any other information that would personally identify you in any way. I have read the above informed consent form. I understand that I may withdraw my consent and discontinue participation at any time without penalty or loss of benefits to which I may otherwise be entitled. In signing this consent form, I am not waiving any legal claims, rights or remedies. A copy of this consent form will be offered to me. Subject's Signature _________________________________ (Date) _________________ Signature of Person Obtaining Informed Consent (Date) 211

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About the Author Jeffrey Lindsey has served in the fire and EMS arena for the past 25 years. Mr. Lindsey serves on various advisory boards, state and national committees, and also writes a monthly article for JEMS a national EMS journal. He is currently the Executive Fire Officer for Estero Fire Rescue. In 1985 Mr. Lindsey pioneered the first advanced lif e support service in Cumberland County, PA. Additionally, Mr. Lindsey is a Senior Part ner in the Internat ional Consulting And Training Specialist firm. He holds an Associates degree in Paramedicine from Harrisburg Area Community College, a Bachelors degree in Fi re and Safety from the University of Cincinnati, and a Masters degree in Instru ctional Technology from the University of South Florida. He is completing disser tation for his Ph.D. in Instructional Technology/Adult Education from University of South Florida. Mr. Lindsey is completing the Executive Fire Officer Program at the National Fire Academy.