xml version 1.0 encoding UTF-8 standalone no
record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd
leader nam a22 u 4500
controlfield tag 008 c20019999azu 000 0 eng d
datafield ind1 8 ind2 024
subfield code a E11-00243
Educational policy analysis archives.
n Vol. 9, no. 46 (November 14, 2001).
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c November 14, 2001
Second year analysis of a hybrid schedule high school / James B. Schreiber, William R. Veal, David J. Flinders, [and] Sherry Churchill.
Arizona State University.
University of South Florida.
t Education Policy Analysis Archives (EPAA)
xml version 1.0 encoding UTF-8 standalone no
mods:mods xmlns:mods http:www.loc.govmodsv3 xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govmodsv3mods-3-1.xsd
mods:relatedItem type host
mods:identifier issn 1068-2341mods:part
mods:detail volume mods:number 9issue 46series Year mods:caption 20012001Month November11Day 1414mods:originInfo mods:dateIssued iso8601 2001-11-14
1 of 19 Education Policy Analysis Archives Volume 9 Number 46November 14, 2001ISSN 1068-2341 A peer-reviewed scholarly journal Editor: Gene V Glass, College of Education Arizona State University Copyright 2001, the EDUCATION POLICY ANALYSIS ARCHIVES Permission is hereby granted to copy any article if EPAA is credited and copies are not sold. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education .Second Year Analysis of a Hybrid Schedule High Scho ol James B. Schreiber Southern Illinois University-Carbondale William R. Veal University of North Carolina-Chapel Hill David J. Flinders Indiana University Sherry Churchill Indiana UniversityCitation: Schreiber, J.B., Veal, W.R., Flinders, D. J., and Churchill, S. (2001, November 14). Second Year Analysis of a Hybrid Schedule High Scho ol, Education Policy Analysis Archives 9 (46). Retrieved [date] from http://epaa.asu.edu/epa a/v9n46/.Abstract The current study examined two independent sophomor e cohorts from a mid-western high school that had implemented a multi-schedule system (i.e., traditional, block, hybrid). The purp ose of the study was to examine differences among the schedule types, gende r, and GPA group
2 of 19on a state mandated standardized test. Analysis of covariance was used to examine the differences. Results indicate that a significant difference among schedule types was observed for only one coho rt and for only one test (mathematics-computation). Results also indica te that schedule type did not significantly interact with gender or GPA g roup. The authors conclude that for these cohorts the type of schedul e does not negatively or positively influence achievement. The reorganization of class scheduling is one curre nt trend in education designed to increase student achievement. One particular ref orm, called block scheduling, has drawn a great deal of attention over the past decad e (Canady & Rettig, 1995). Specifically, questions have been raised concerning the effects of block scheduling on student performance. Survey research has reported t hat many teachers, students, and parents support the block reform initiative, but su rvey data only offer evidence regarding the perceived impact of block scheduling. Lacking i n the educational research journals are studies that directly compare the effects of sc hedule types on student achievement. In addition, previous studies have not systematically investigated which students benefit from the implementation of block scheduling. Respon ding to these relatively neglected areas, this study uses state mandated achievement t ests in specific subject areas to examine the overall effects of schedule type and po tentially differential effects by gender and grade point average.Literature Review The move to block scheduling has found its way into all types of high schools and some middle schools in the United States and Canada (Canady & Rettig, 1995; Cobb, Abate, & Baker, 1999). For this reason, educators, administrators, teachers, and parents have vigorously argued the merits and pitfalls of b lock scheduling. Supporting evidence on both sides is often drawn from surveys (Salvater ra, Lare, Gnall, & Adams, 1999; Sessoms, 1995; Tanner, 1996; Veal & Flinders, 1999) or from trend data (Buckman, King, & Ryan, 1995; Edwards, 1993; Holmberg, 1996; Schoenstein, 1995). However, there have been only a handful of comparative studi es (Bateson, 1990; Cobb, Abate, & Baker, 1999; Hess, Wronkovich & Robinson, 1998; Vea l & Schreiber, 1999), and some of these studies have focused on the outcomes of st andardized tests (see also, Lockwood, 1985; Wild, 1998). As with survey and trend observa tions, results of comparison studies sometimes report benefits for block scheduling, som etimes report no difference, and sometimes report lower achievement than found in tr aditional scheduling. Only a handful of studies have examined the effects of block scheduling on academic achievement by gender, again with inconclu sive results (Cobb, Abate, & Baker, 1999; Lockwood, 1995). Outside the literatur e on block scheduling, however, gender differences in achievement are one of the mo st hotly debated topics in education. In mathematics, for example, Freidman (1989) conduc ted a meta-analysis of 98 studies, concluding that there was little evidence of gender difference in achievement for students up to the age of ten (e.g., Callahan & Cle ments, 1984; Dossey, Mullis, Lindquist, & Chamber, 1988). If differences were fo und at this level, the differences favored females (e.g., Hawn, Elliot, & Des Jardines 1981; Potter & Levy, 1968). At the middle school level, Friedman found widely mixed re sults. Some results favored females (Tsai & Walber, 1979), others favored males (Hilton & Berglund, 1974), and some were conflicting (Circicelli, 1967; Fennema & Sherman, 1 978). At the high school level, Friedman examined seven studies on mathematics achi evement and gender. Five of the
3 of 19seven studies reported males outperforming females with the remaining two studies showing no difference. A host of theories have been offered to explain this trend across grade levels, most of which focus on societal facto rs and/or school practices (e.g., Brophy & Good, 1974; Fennema & Sherman, 1977; Leder 1986; Linn & Peterson, 1986; Lee & Bryk, 1986). In the areas of reading and language, studies of ge nder and achievement across grade levels suggest a different pattern. Thorndike (1973) analyzed international reading achievement data, finding that high school female a chievement was slightly higher than achievement for males but not strong enough to be c onclusive. Other studies suggest that malesÂ’ reading and verbal skills were lower through out and after high school (Backman, 1972; Droege, 1967; Mondary, Hout, & Luntz, 1967; R osenberg & Sutton-Smith, 1969; Very, 1967). Hogrebe, Nist, and Newman (1985) using the High School and Beyond data observed that by the time students reach high school, the magnitude of reading achievement differences between males and females i s small and accounts for less than one percent of the variation in scores. More recent ly, differences favoring female students in the areas of spelling, (Stanley, Benbow Brody, Dauber, and Lupkowski, 1992), reading comprehension (Hedges and Newell, 19 95), and writing (U.S. Department of Education, 1997) have been observed.Purpose The main purpose of the following study is to compa re student achievement on state mandated achievement tests at a unique high s chool currently using three different schedule types (traditional, block, hybrid). In par ticular, the data and analyses focus on how scheduling differentially influence achievement in the areas reviewed above: mathematics, reading, and language. An important el ement in the design of this study was the building of a replication. Two different gr oups of similar sophomore students took the same achievement test in consecutive years Specific research questions are: Is student achievement in the three subject areas i nfluenced by the type of schedule? 1. Is student achievement in the three subject areas r elated to gender? 2. Is student achievement in the three subject areas i nfluenced by GPA? 3. Is there an interaction between gender and schedule type in the three subject areas? 4. Is there an interaction between GPA and schedule ty pe in the three subject areas? 5. Are the results observed on research questions 1-5 consistent across cohorts? 6.Methods This study was conducted at a large, four-year high school located in a medium-sized city in Indiana. The student populatio n consists of approximately 1800 is mostly white children from the town and rural areas of the county. In the fall of 1997, the school began a tri-schedule format running at t he same time during the school day. The tri-schedule format includes three schedule typ es: 4 X 4 block, traditional schedule, and hybrid. The 4 X 4 block schedule consists of fo ur, 87-minute daily classes taught for one semester. The traditional schedule consists of six, 55-minute daily classes that meet for the entire school year. The hybrid schedule con sists of three traditional and two block classes each day. Under this format, both traditional and block cours es were offered in all subject
4 of 19 areas except the performing arts and Advance Placem ent classes. The total contact time in a block course is approximately 2,000 minutes le ss than for a year-long traditional course, or 37 fewer class meetings (see Table 1). T his reduced contact time per course allows block students to complete up to eight rathe r than six courses per year.Table 1 Descriptive information for classes under block and traditional schedulesSchedule DescriptorsTraditionalHybrid4X4 Block Class Time (mins./day)5555 and 8787 Number of Days of Instruction180180 and 9090 Class Time (mins./school year)99009900 and 78307830 Classes/Day654 Classes/Year678 Hours/Day220.127.116.11 Credits121416 State Mandated Test of Basic Skills The Indiana Statewide Testing for Educational Progr ess (ISTEP+) is a state mandated test of basic skills and academic aptitude that is administered to all students in Grades 3, 6, 8, and 10 (Sophomores). The academic s ubject areas tested are reading, language, and mathematics. The sub-areas of reading are comprehension and vocabulary The sub-areas of language are mechanics and expression The sub-areas of mathematics are concepts and applications and computation In addition to these sub-areas, each area has a total score, which is th e composite of the two sub-areas, and a battery score that is a composite of the six sub-ar eas. For the purposes of this study, Normal Curve Equivalent (NCE) scores and the Cognit ive Skills Index (CSI) were used. The NCE and CSI scores are norm-referenced. The NCE scores are based on an equal-interval scale (1-99). Using NCE scores permi ts comparisons among schedule groups. The CSI describes an individualÂ’s overall p erformance on the aptitude questions of the ISTEP+. This score compares the studentÂ’s co gnitive ability with that of students who are the same age. The CSI is a normalized stand ard score with a mean of 100 and a standard deviation of 16.Sample All sophomores are required to take the three secti ons of the ISTEP+ in September. The test is administered to the sophomor es over a four-day period for three hours per day. If a student did not reside in the s tate of Indiana the year before or attended a different school in Indiana, the student is still required to complete the test. Due to absences, some students did not take certain portions of the test. Transfer and absent students were not included in the analyses. The sample for this study consists of two cohorts; students who were sophomores in 1997 a nd 1998. The first sophomore
5 of 19 cohort has 332 students and took the ISTEP+ in Sept ember 1997. The second sophomore cohort has 318 students and took the ISTE P+ in the September 1998. These two cohorts are independent.Analysis All ISTEP+ dependent variables (i.e., test scores) were analyzed using a three factor fixed effect analysis of covariance (ANCOVA) with schedule type, gender, and GPA-group as the independent variables, and CSI as the covariate. Analysis of covariance was used because students were not rando mly assigned to schedule types; i.e., there is reaaason to believe that students co gnitive aptitude varied systematically as a function of their schedule type (Table 2). The de pendent variables were the test scores for each sub-area of the standardized test. For eac h cohort studentsÂ’ cumulative freshman GPAs were divided into four categories (qu artiles) based on boxplots of the grade point averages. The first category, "Low," in cludes those students whose GPAs range from 0.00 to 2.24. The second category, "Midd le," consists of students whose GPAs range from 2.25 to 2.99. The next category, "M id-High," includes students whose GPAs range from 3.00 to 3.59. The final category, high," includes students whose GPAs range from 3.60 to 4.00.Table 2 Cognitive Skills Index for Schedule TypeSchedule TypeCSI (1997)CSI (1998) Traditional113.06109.63Block113.11110.68Hybrid116.99116.03Table 3 Significant Main and Interaction Effects From Cohor t 1 and Cohort 2 Cohort 1Cohort 2 RDGCRDGVLANELANMMAT CA MAT C RDGCRDGVLAN E LAN M MAT CA MATC Gender X XXX XXXXX Schedule X GPA Group X XXXXXXXXXX Gender Schedule
6 of 19 Gender GPA Group XXX Schedule GPA Group Gender Schedule GPA Group X X indicates significant at the .05 level RDGC = reading comprehension RDGV = reading vocabul ary LAN E = language expression LAN M = language mechan ics MAT CA = mathematics concepts and applications MAT C = mathematics computationResults Due to the nature that the sample populations were different, the results are separated into two cohorts to show the replication of the study. This allowed for the results to be analyzed in an attempt to see if the differences or gains were consistent over two years. The results and mean differences of the cohorts on each section of the ISTEP+ are found in Tables 4, 5, 6, and 7. All sign ificant values are reported as p < 0.05.Cohort 1: 1997 SophomoresReading In the readingvocabulary sub-area, males scored significantly higher than females. The difference between the average test sc ores was 5.702. No other main effects or interactions were significant, i.e., GPA and schedule type. In readingcomprehension significant differences were found only for GPA gr oup. High, mid-high, and middle GPA groups all scored signific antly better than the low GPA group. No significant interactions were observed.Table 4 Gender Differences in Test ScoresCohort 1 Reading Vocabulary Language Mechanics Language Expression Math Computation Math Concepts & Application GenderAdjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error
7 of 19 Male68.21.560.41.463.91.218.104.22.168.2 Female62.51.467.71.366.11.263.31.066.11.1 Cohort 2 GenderAdjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Male66.01.461.01.166.31.066.91.072.01.0 Female60.12.067.31.672.11.562.01.465.91.5Table 5 GPA Group DifferencesCohort 1 Reading Comprehension Reading Vocabulary Language Mechanics Language Expression Math Computation Math Concepts & Application GPA Group Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error High72.7101.48267.7261.74272.3872.41372.3661.48973. 4781.98878.3361.405 Mid-High70.9711.40166.5621.64765.5502.06665.0041.40 767.3021.70269.1191.335 Middle70.1551.87864.9602.20763.8231.54965.2411.8866 2.7501.27762.9971.781 Low59.3152.19262.3322.57754.4311.63057.3802.20357.1 521.34461.1682.080 Cohort 2 GPA Group Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error Adjust. Mean Std. Error High73.4942.03070.4212.20673.2611.95678.3601.80472. 5471.72479.5371.626 Mid-High66.4642.60359.9613.03464.9562.50969.7582.31 465.3772.21170.6301.655 Middle66.0541.93364.3472.25364.3771.86471.1071.7196 3.0611.64366.5552.227 Low58.3301.89370.4212.20653.9781.83257.6281.69056.9 321.61559.2321.736Language For the languagemechanics sub-area, females scored significantly higher than males with an average difference of 7.28. High GPA students scored significantly better than Mid-High, Middle, and Low GPA students, with a verage differences of 6.837, 8.564, and 17.956 respectively. Mid-High GPA studen ts scored significantly higher than Low GPA students with an average difference of 11.1 19 and Middle GPA students
8 of 19 scored significantly higher than Low GPA students w ith a average difference of 9.393. No significant interactions were observed. Only GPA differences were significant on the langua geexpression sub-area. High GPA students scored significantly better than Mid-H igh, Middle, and Low GPA students, with average differences of 7.362, 7.125, and 1.985 respectively. Mid-High GPA students scored significantly higher than Low G PA students with an average difference of 7.623 and Middle GPA students scored significantly higher than Low GPA students with an average difference of 7.860. No si gnificant interactions were observed. MathematicsMales scored significantly higher on mathematics -computation than females. The average difference was 3.811. The traditional sched ule students scored significantly higher than block and hybrid schedule students. Hig h GPA students scored significantly higher than Mid-High, Middle, and Low GPA students, with average differences of 6.176, 10.728, and 16.326 respectively. Mid-High GP A students scored significantly higher than Middle and Low GPA students with averag e differences of 4.552 and 10.150 respectively. Middle GPA students scored significan tly higher than Low GPA students did with an average difference of 5.598. No signifi cant interactions were observed.Table 6 Mathematics Computation for Schedule TypeAdjusted MeanStd. Error Schedule Type Traditional68.1191.117 Block64.4011.144 Hybrid63.8061.650 For mathematics -concepts and applications males scored significantly higher than females with an average difference of 3.518. H igh GPA students scored significantly better than Mid-High, Middle, and Low GPA students, with average differences of 9.217, 15.359, and 17.168 respective ly. Mid-High GPA students scored significantly higher than Middle and Low GPA studen ts with average differences of 6.142 and 7.952 respectively. No significant intera ctions were observed. Cohort 2: 1998 SophomoresReading For readingvocabulary males scored significantly higher than females. T he difference between the average test scores was 5.89 8. High GPA students scored significantly better than Mid-High and Low GPA stud ents, with average differences of 10.460 and 12.845 respectively. Middle GPA students scored significantly higher than Low GPA students with an average difference of 6.77 1. Significant interactions were observed for gender by GPA group (F(3,293) = 4.505 p <.05) and schedule type by gender by GPA group (F(6,293) = 3.421 p < .05). The plots of the interactions showed
9 of 19disordinal pattens indicatingvarying achievement le vels as schedule type, gender, and GPA group changed. On the readingcomprehension portion of the test, significant differences were found only for GPA Group. High GPA students scored significantly better than Mid-High, and Low GPA students, with average differ ences of 7.030, 7.440, and 15.164 respectively. Mid-High GPA students scored signific antly higher than Low GPA students with an average difference of 8.134. Middl e GPA students scored significantly higher Low GPA students with an average difference of 7.724. One significant interaction was observed--that for gender by GPA gr oup F (3,317) = 3.875, p = .01. Figure 1 provides a graphic display of the interact ion. The interaction indicates that females perform better than males until they reach the Mid-High GPA level. The performance by Low and Middle GPA students is compa rable with female scores above those of males. Yet, the performance for females in the Mid-High GPA group decreases dramatically compared to males. The scores then reb ound to comparable levels and are slightly below those of males in the High GPA group Figure 1. Reading Comprehension Gender by GPA Group for Cohort 2 Language For the languagemechanics sub-area, females scored significantly higher than males with an average difference of 6.346. High GPA students scored significantly better than Mid-High, Middle, and Low GPA students, with average differences of 8.305, 8.884, and 19.283 respectively. Mid-High GPA students scored significantly higher than Low GPA students with an average differ ence of 10.987, and Middle GPA students scored significantly higher than Low GPA s tudents with an average difference of 10.399. No significant interactions were observe d. For the languageexpression sub-area, females scored significantly higher than males with an average difference of 5.849. High GPA students scored significantly better than Mid-High, Middle, and Low GPA students, with average differences of
10 of 198.602, 7.253, and 20.733 respectively. Mid-High GPA students scored significantly higher than Low GPA students with an average differ ence of 12.131, and Middle GPA students scored significantly higher than Low GPA s tudents with a average difference of 13.480. One significant interaction was observed fo r gender by GPA group. Figure 2 provides a graphic display of the interaction, whic h reveals that males in the Low and Middle GPA groups perform at a lower level than fem ales. The difference in ability is negated with students in the Mid-High and High GPA groups. Females in the Mid-High GPA group perform worse, and subsequently match tho se of the male Mid-High GPA group. Figure 2. Language Expression Gender by GPA Group f or Cohort 2 Mathematics Males scored significantly higher on mathematics -computation than females. The average difference was 4.882. High GPA students sco red significantly better than Mid-High, Middle, and Low GPA students, with averag e differences of 7.170, 9.486, and 15.615 respectively. Mid-High GPA students scor ed significantly higher than Low GPA students with an average difference of 8.445. M iddle GPA students scored significantly higher than Low GPA students with an average difference of 6.129. No significant interactions were observed. For mathematics -concepts and applications males scored significantly higher than females with an average difference of 6.100. H igh GPA students scored significantly better than Mid-High, Middle, and Low GPA students, with average differences of 8.907, 12.982, and 20.304 respective ly. Mid-High GPA students scored significantly higher than Low GPA students with ave rage differences of 11.398. Middle GPA students scored significantly higher than Low G PA students with average differences of 11.398. No significant interactions were observed.Discussion
11 of 19 The discussion is organized by research question an d focuses on areas related to the covariate used in the analysis. These covariate s were chosen because they were accessible in the database, and they answered salie nt practical questions that have not been answered in the literature about block schedul ing effects on ability level students and gender. Block scheduling had essentially no positive impact on academic achievement as measured on the ISTEP+. Only one cohort (1997 sopho mores) showed better performance across various schedule formats and onl y on one of the six sub-tests across the ISTEP+ (mathcomputation ). Surprisingly, this single difference favored the traditional schedule group.Schedule Type Only mathematicscomputation for Cohort 1 had a significant difference in achievement among schedule types. It is possible th at the difference can be attributed to the overall amount of time and the daily class meet ings for an entire year. Recall that the block schedule had the equivalent of 37 fewer class meetings. However, the difference was only observed with one of the six tests, and th e observed difference was not replicated with the second cohort. The replication failure is particularly noteworthy in that the teachers for the second cohort had another year of the scheduling experience behind them allowing them to become familiar with t he block system and make instructional adjustments. Thus, taken as a whole, these findings leave open the possibility that the single statistical signpost re sult may be an anomaly. Overall, schedule type does not appear to improve or decrease student achievement. Gender For Cohort 1, males outperformed females on mathema ticscomputation mathematicsconcepts and applications and readingvocabulary Females outperformed males on languagemechanics No gender differences were found on readingcomprehension and languageexpression For Cohort 2 the same differences were observed on readingvocabulary with male students outperforming female students The vocabulary result was unexpected and originall y it was thought the first difference may have been an artifact of the cohort because males have been observed to perform lower than females in reading achievement ( e.g., Backman, 1972). The replication seems to indicate that this observation may be more consistent than previously thought and warrants further investigati on. The observations for mathematicscomputation and concepts and applications tests are consistent with earlier research on gender inequities at the high school le vel (e.g., Friedman, 1989). Evidence for the pattern of males outperforming females is d isappointing in recent studies such as our own. It suggests that after decades of research the problem of gender disparity has yet to be solved. Overall, excluding the vocabulary observation, the results are consistent with previous gender difference observations.GPA Due to the purposeful categorization of the four GP A groups, the significant differences found in this area are not surprising. It was expected that the highest GPA group would perform significantly better than the o ther GPA groups. One interesting
12 of 19aspect of the GPA groups was the complete lack of a difference for readingvocabulary for cohort one. A second interesting aspect was the lack of significant difference was between the Middle GPA and Mid-High GPA students in a few cases and across cohorts (e.g., languageexpression ). One could speculate that the involvement in extr acurricular activities may influence how the students in the ca tegory performed. Those who may need extra time to study may not be getting it at t hese GPA groups if they are involved in extracurricular activities.Gender and Schedule Type The observations indicate that for both cohorts sc hedule type does not interact with gender and cause differential performance on t he tests. This appears to indicate that schedule type does not hinder or assist one gender over the other, though future studies may or may not support this finding. This finding i s important if it is to inform policy. Schedule type has not been reported as a factor inf luencing gender achievement. Decisions whether to adopt block scheduling should not be made based upon perceived performance by gender.GPA and Gender Interaction. Two interactions for gender by GPA Group were obse rved for readingcomprehension and languageexpression for cohort 2 only. The interaction appears to be driven by differences in female stude nts performance by GPA group. The male students have a more linear trend by GPA, wher e as the female student performance fluctuates. The reason or reasons for t he fluctuation is (are) unknown and warrant follow up investigation.GPA and Schedule Type No significant interactions were observed for sche dule type and GPA group indicating that schedule type does not positively o r negatively impact one GPA group over another. There have been unsubstantiated repor ts that the 4x4-semester schedule allows the lower achieving students to perform bett er since they have fewer courses on which to focus. On the other hand, arguments agains t the intensity and increased amount of content in a short period of time of the 4x4-sem ester schedule are unsubstantiated. The results of this study show otherwise. Neither s chedule (block nor hybrid) appear to harm, lower, or decrease the academic achievement o f students compared to those in a traditional schedule.Consistency from Cohort 1 to Cohort 2 Table 3 provides a quick graphic view of similarit ies and differences observed between the cohorts. As can be seen in the table th e observations are quite consistent from Cohort 1 to Cohort 2. Out of all the possible changes from one cohort examination to the next only seven were observed. The consisten t results provide support for the argument that the different schedule types are not impacting achievement, either positively or negatively, for these students. The c onsistency also increases ones ability to generalize the results with similar high school pop ulation parameters.
13 of 19Conclusions The findings of this study are important in several ways. Most importantly, schedule type was not an influential factor in stud ent achievement as it pertained to gender and GPA group. First, the results of this st udy indicate that schedule type does not interact with gender. This finding informs the debate over block scheduling because it supports the possibility that if other benefits of block courses are found, either achievement benefits in other subject areas or bene fits in areas such as student attitudes, then educators may have the opportunity to secure t hese benefits without increasing whatever gender disparities already exist. Schedule type, also, does not interact with GPA gro up. This result informs those considering block scheduling that the type of sched ule does not appear to differentially impact students at different academic levels. It se ems obvious that a school would not want to implement a program that systematically hel ps one group of students while at the same time systematically hurts another group. I f a school desires to implement block scheduling, gender, academic level, and scheduling should not influence the decision. Rather other items that are more contextual should influence the decision to move to a block or differentiated schedule. For example, with increasing state standards for graduation, the move to a block schedule might allo w those college tract students to take more electives such as AP courses, music, art, work study, business, and physical education. Second, studies like the one we have described can alert parents and educators to gender differences and possible biases that work ag ainst large numbers of students. The gender disparities found in mathematics and reading vocabulary achievement signal that more needs to be done to explore the antecedents of these inequities. Moreover, comparing this study with previous research suggest s that gender differences in mathematics are persistent, and may thus require ev en more concerted efforts than are currently in place. Finally, the observation that achievement differenc es across schedule type were significant in only one area, mathematicscomputation and for only one cohort suggests variations in the effects of block scheduling acros s academic skills and subjects is not consistent. Given that it was the only observation for a difference in achievement based on schedule type, the overall results indicate that the schedule type does not influence achievement on these tests. Therefore, those school s considering block scheduling may want to determine other reasons for implementing th e schedule. Such reasons may be class flexibility, more classes offered during the year, or attitudes towards having a block schedule. The reader is reminded that only reading, language, and mathematics were examined and the cohort make up. Different results may exist for science or the arts.ReferencesBackman, M. E. (1972). Patterns of mental abilities : Ethnic, socioeconomic, and sex differences. American Educational Research Journal, 9, 1-12. Bateson, D. J. (1990). Science achievement in semes ter and all-year courses. Journal of Research in Science Teaching, 27 (3), 233-240. Brophy, J., & Good, T. L. (1974). Teacher student relationships: Causes and consequences. New York: Holt, Rinehart, and Winston.
14 of 19Buckman, D., King, B., and Ryan, S. (1995). Block s cheduling: A means to improve school climate. NASSP Bulletin, 79 (571), 9-18. Callahan, L. G., & Clements, D. H. (1984). Sex diff erences in rote counting ability on entry to first grade: Some observations. Journal for Research in Mathematics< Education, 15, 378-382. Canady, R. L. & Rettig, M. D. (1995). Block scheduling: A catalyst for change in high school Princeton, NJ: Eye on Education. Canady, R. L. & Rettig, M. D., (Eds.). (1996). Teaching in the block: Strategies for engaging active learners Princeton, NJ: Eye on Education. Circicelli, V. G. (1967). Sibling constellation, cr eativity, IQ, and academic achievement. Child Development, 38, 481-490. Dossey, J. A., Mullis, I. V. S., Lindquist, M. M., & Chambers, D. L. (1988). The mathematics report card: Are we measuring up? Trend s and achievement based on the 1986 national assessment. (Report No. 17-M-01). Pri nceton, NJ: National Assessment of Educational Progress, Educational Testing Service. (ERIC Document Reproduction Service No. ED300206)Droege, R. C. (1967) Sex differences in aptitude ma turation during high school. Journal of Counseling Psychology, 14 407-411. Edwards, C. (1993). The 4 X 4 plan. Educational Leadership, 53 (3), 16-19. Fennema, E. & Peterson, P. (1985). Autonomous learn ing behavior: A possible explanation of gender-related differences in mathem atics. In L. C. Wilkinson & C. Marrett (Eds.), Gender influences in classroom interaction (pp. 17-35). Orlando, FL: Academic.Fennema, E., & Sherman, J. (1977). Sex-related diff erences in mathematics achievement, spatial visualization, and affective f actors. American Education Research Journa, 14, 51-71. Fennema, E., & Sherman, J. (1978). Sex-related diff erences in mathematics achievement and related factors: A further study. Journal of Research in Mathematics Education, 9 189-203.Friedman, L. (1989). Mathematics and the gender gap : A meta-analysis of recent studies on sex differences in mathematics tasks. Review of Educational Research, 59 185-213. Hawn, H. C., Ellet, C. D., & Desjardines, L. (1981, March). Differences in mathematics achievement between males and females in grades 1-3 Paper presented at the annual meetings of the Eastern Educational Research Associ ation, Philadelphia, PA. (ERIC Document Reproduction Service No. ED 209094).Hedges, L. V., & Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science 269 : 41-45 Hess, C., Wronkovich, M., & Robinson, J. (2001). Me asured outcomes of learning in the
15 of 19block. Manuscript submitted for publication.Hilton, T. L., & Berglund, G. W. (1974). Sex differ ences in mathematics achievementÂ—a longitudinal study. Journal of Educational Research, 67, 232-237. Hogrebe, M. C., Nist, S. L., & Newman, I. (1985). A re there gender differences in reading achievement? An investigation using the hig h school and beyond data. Journal of Educational Psychology, 6 716-724. Holmberg, T. (1996). Block scheduling versus tradit ional education: A comparison of grade-point averages and ACT scores. Unpublished do ctoral dissertation, University of Wisconsin, Eau Claire.Leder, G. C. (1986, April). Gender linked differenc es in mathematics learning: Further explorations. Paper presented at the Research Proce ssion to the annual meeting of the National Council of Teachers of Mathematics, Washin gton, DC. Lee, V. E., & Bryk, A. S. (1989). A multilevel mode l of the distribution high school achievement. Sociology of Education, 62 (3), 172-192. Linn, M. C., & Peterson, A. C. (1986). A meta-analy sis of gender differences in spatial ability: Implications for mathematics and science a chievement. In J.S. Hyde and M.C. Linn (Eds.), The psychology of gender. Advances through meta-ana lysis. (pp. 67-101). Baltimore, MD: The Johns Hopkins University Press.Lockwood, S. (1995). Semesterizing the high school schedule: The impact of student achievement in algebra and geometry. NASSP Bulletin, 79 (575), 102-108. McLeod, D. B. (1992). Research on affect in mathema tics education: A reconceptualization. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 575-596). New York: Macmillan. Monday, L. A., Hout, D. P., & Lutz, S.W. (1966) College student profiles: American college testing program Iowa City, IA: ACT Publications. Potter, M. C., & Levy, E. (1968). Spatial enumerati on without counting. Child Development, 39, 265-272. Rosenberg, B. G., & Sutton-Smith, B. (1969). Siblin g association, family size, and cognitive abilities. Journal of Genetic Psychology, 109, 271-279. Salvaterra, M., Lare, D., Gnall, J., & Adams, D. (1 999). Block scheduling: StudentsÂ’ perceptions of readiness for college math, science, and foreign language. American Secondary Education, 27 (4), 13-38. Schoenstein, R. (1995). The new school on the block schedule. The Executive Educator, 17 (8):18-21. Sessoms, J. C. (1995). Teachers perceptions of thre e models of high school block scheduling. Unpublished doctoral dissertation, Univ ersity of Virginia, Charlottesville. Stanley, J.C., Benbow, C. P., Brody, L. E., Dauber, S., and Lupkowski, A. (1992).
16 of 19Gender differences in eighty-six nationally standar dized aptitude and achievement tests. In Colangelo, N., Assouline, S. G., and Ambrosen, D L. (Eds.). Talent Development, Vol. 1: Proceedings from the 1991 Henry B. and Joce lyn Wallace National Research Symposium on Talent Development. Trillium Press, Unionville, NY, pp. 42-65. Tanner, B. M. (1996). Perceived staff needs of teac hers in high schools with block schedules. Unpublished doctoral dissertation, Unive rsity of Virginia, Charlottesville Thorndike, R. L. (1973). Reading comprehension education in fifteen countrie s: An empirical study Stockholm: Halsted Press. Tsai, S. L., & Walberg, H. J. (1983). Mathematics a chievement and attitude productivity in junior high school. Journal of Educational Research, 76 (5), 267-272. U. S. Department of Education. (1997). National assessment of educational progress (Indicator 32: Writing Proficiency: Prepared by the Educational Testing Service). Washington, DC.Very, P. S. (1967). Differential factor structures in mathematical abilities. Genetic Psychological Monographs, 75, 169-207. Veal, W. & Flinders, D. J. (1999). Block scheduling and the practice of teaching. In Flinders, D. J. (Ed.) Research on block scheduling (132-156). Bloomington, IN: Phi Delta Kappa International.Veal, W. & Schreiber, J.B. (1999, September 19). Bl ock scheduling effects on state mandated test of basic skills. Education Policy Analysis Archives, 7 (29). Available at: http://epaa.asu.edu/epaa/v7n29.html Wild, R. D. (1998, April). Science achievement and block schedules. Paper presented at the annual meeting of the National Association for Research in Science Teaching, San Diego, CA.About the AuthorsJames B. Schreiber Educational Psychology and Special Education Southern Illinois University-Carbondale Mailcode 4618 Carbondale, IL 62901-4618 Email: email@example.com James B. Schreiber is Assistant Professor of Human Learning and Development at Southern Illinois University-Carbondale. His resear ch interests include factors impacting academic achievement, beliefs and reasoning in acad emic and non-academic settings, and secondary education.William Veal is an Assistant Professor of Science Education at the University of North Carolina at Chapel Hill. His areas of research inte rest are preservice science education, pedagogical content knowledge, cultural science, an d block scheduling. He currently teaches in the secondary Masters of Arts in Teachin g program.
17 of 19 David J. Flinders is Associate Professor of education at Indiana Uni versity, Bloomington. His research interest focus on seconda ry education and school restructuring.Sherry Churchill recently completed a Master's Degree in Public Aff airs from Indiana University's School of Public and Environmental Aff airs. She is currently residing in Maine where she is working in the area of policy is sues for resource management.Copyright 2001 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, firstname.lastname@example.org or reach him at College of Education, Arizona State University, Tempe, AZ 8 5287-0211. (602-965-9644). The Commentary Editor is Casey D. C obb: email@example.com .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov firstname.lastname@example.org Thomas F. Green Syracuse University Alison I. Griffith York University Arlen Gullickson Western Michigan University Ernest R. House University of Colorado Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Calgary Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Dewayne Matthews Education Commission of the States William McInerney Purdue University Mary McKeown-Moak MGT of America (Austin, TX) Les McLean University of Toronto Susan Bobbitt Nolen University of Washington Anne L. Pemberton email@example.com Hugh G. Petrie SUNY Buffalo
18 of 19 Richard C. Richardson New York University Anthony G. Rud Jr. Purdue University Dennis Sayers California State UniversityÂ—Stanislaus Jay D. Scribner University of Texas at Austin Michael Scriven firstname.lastname@example.org Robert E. Stake University of IllinoisÂ—UC Robert Stonehill U.S. Department of Education David D. Williams Brigham Young University EPAA Spanish Language Editorial BoardAssociate Editor for Spanish Language Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico email@example.com Adrin Acosta (Mxico) Universidad de Guadalajaraadrianacosta@compuserve.com J. Flix Angulo Rasco (Spain) Universidad de Cdizfelix.firstname.lastname@example.org Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma deMxicocanalesa@servidor.unam.mx Ursula Casanova (U.S.A.) Arizona State Universitycasanova@asu.edu Jos Contreras Domingo Universitat de Barcelona Jose.Contreras@doe.d5.ub.es Erwin Epstein (U.S.A.) Loyola University of ChicagoEepstein@luc.edu Josu Gonzlez (U.S.A.) Arizona State Universityjosue@asu.edu Rollin Kent (Mxico)Departamento de InvestigacinEducativa-DIE/CINVESTAVrkent@gemtel.com.mx email@example.com Mara Beatriz Luce (Brazil)Universidad Federal de Rio Grande do Sul-UFRGSlucemb@orion.ufrgs.brJavier Mendoza Rojas (Mxico)Universidad Nacional Autnoma deMxicojaviermr@servidor.unam.mxMarcela Mollis (Argentina)Universidad de Buenos Airesmmollis@filo.uba.ar Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma deMxicohumberto@servidor.unam.mxAngel Ignacio Prez Gmez (Spain)Universidad de Mlagaaiperez@uma.es Daniel Schugurensky (Argentina-Canad)OISE/UT, Canadadschugurensky@oise.utoronto.ca Simon Schwartzman (Brazil)Fundao Instituto Brasileiro e Geografiae Estatstica firstname.lastname@example.org
19 of 19 Jurjo Torres Santom (Spain)Universidad de A Coruajurjo@udc.es Carlos Alberto Torres (U.S.A.)University of California, Los Angelestorres@gseisucla.edu