Educational policy analysis archives

Educational policy analysis archives

Material Information

Educational policy analysis archives
Arizona State University
University of South Florida
Place of Publication:
Tempe, Ariz
Tampa, Fla
Arizona State University
University of South Florida.
Publication Date:


Subjects / Keywords:
Education -- Research -- Periodicals ( lcsh )
non-fiction ( marcgt )
serial ( sobekcm )

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
E11-00132 ( USFLDC DOI )
e11.132 ( USFLDC Handle )

Postcard Information



This item has the following downloads:

Full Text
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 c19999999azu 000 0 eng d
datafield ind1 8 ind2 024
subfield code a E11-00132
0 245
Educational policy analysis archives.
n Vol. 7, no. 20 (June 08, 1999).
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c June 08, 1999
Testing on computers : a follow-up study comparing performance on computer and on paper / Michael Russell.
x Research
v Periodicals.
2 710
Arizona State University.
University of South Florida.
1 773
t Education Policy Analysis Archives (EPAA)
4 856

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 7issue 20series Year mods:caption 19991999Month June6Day 88mods:originInfo mods:dateIssued iso8601 1999-06-08


1 of 47 Education Policy Analysis Archives Volume 7 Number 20June 8, 1999ISSN 1068-2341 A peer-reviewed scholarly electronic journal Editor: Gene V Glass, College of Education Arizona State University Copyright 1999, 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 Testing On Computers: A Follow-up Study Comparing Performance On Computer and On Paper Michael Russell Boston CollegeAbstract Russell and Haney (1997) reported that open-ended test items administered o n paper may underestimate the achievement of students accus tomed to writing on computers. This study builds on Russell and Haney's work by ex amining the effect of taking open-ended tests on computers and on paper for stud ents with different levels of computer skill. Using items from the Massachusetts Comprehensive Assessment System (MCAS) and the National Assessment of Educational P rogress (NAEP), this study focuses on language arts, science and math tests ad ministered to eighth grade students. In addition, information on students' prior compute r use and keyboarding speed was collected. Unlike the previous study that found lar ge effects for open-ended writing and science items, this study reports mixed results. Fo r the science test, performance on computers had a positive group effect. For the two language arts tests, an overall group effect was not found. However, for students whose k eyboarding speed is at least 0.5 or one-half of a standard deviation above the mean, pe rforming the language arts test on computer had a moderate positive effect. Conversely for students whose keyboarding speed was 0.5 standard deviations below the mean, p erforming the tests on computer had a substantial negative effect. For the math tes t, performing the test on computer had an overall negative effect, but this effect became less pronounced as keyboarding speed increased. Implications are discussed in terms of t esting policies and future research.


2 of 47Introduction Recently, Walt Haney and I (Russell & Haney, 1997) reported that open-ended tests administered on paper to students accustomed to wor king on computer may seriously underestimate students' achievement. Although previ ous research on multiple-choice items suggests that the mode of administration, tha t is paper versus computer administration, does not significantly affect the t est taker's performance (Bunderson, Inouye & Olsen, 1989), our study suggests that the mode of administration may have an extraordinarily large effect on students' performan ce on open-ended items. Focusing on students participating in a project tha t placed heavy emphasis on computers, the study indicates that approximately 60% of the s tudents in the Advanced Learning Laboratory (ALL School) were performing adequately on writing tests before the project began. Nearly two years after the program was imple mented the same writing tests taken on paper indicated that only 30% of the students we re writing adequately, an apparent decline of approximately 30% points. Yet, when the same tests were administered on computer (without student access to word processing tools such as spell checking or grammar checking), nearly 70% of students performed adequately. This significant and startling difference also occurred on National Asse ssment of Educational Progress (NAEP) reading and science items, which required st udents to respond to open-ended items (similar to items used in the Third Internati onal Math and Science Study, the Massachusetts Comprehensive Assessment System and o ther state level testing programs). The study concludes that for the student s in the ALL School, most of whom are accustomed to working on computers, open-ended test questions administered on paper severely underestimated students' achievement Although our findings raise questions about the val idity of open-ended test results for students accustomed to working on computer but who completed tests on paper, our study had several shortcomings. As we noted, only o ne extended writing item was used. Furthermore, no information regarding the extent to which students used computers or the proficiency with which students used computers was available. All of the examinees included in the study were accustomed to working on computers. Thus it was not possible to study the mode of administration effect across varied levels of previous computer use. Finally, beyond scores for a set of o pen-ended items performed by both groups on paper, no other information about prior a cademic achievement, such as standardized test scores or grades, was considered.Despite these shortcomings, the results raise impor tant questions about the extent to which scores for open-ended items administered on p aper can be used to make inferences about individual students (or their scho ols) who are accustomed to working on computers. Moreover, if test scores are used to evaluate the effect increased expenditures for computers have on student achievem ent, the use of open-ended items administered on paper may also undermine the growin g emphasis on educational technology.In this study, I build on our prior work and overco me the shortcomings of our previous study. Specifically, I improved the study design in five ways. First, the sample of students was broadened to cover a range of prior co mputer experience. Second, information about students' prior use of computers, preference for writing on computer or on paper, and an indicator of students' keyboard ing skill was collected. Third, rather


3 of 47Background For three decades, educational theorists have propo sed many ways in which computers might influence education. Although it was not unti l the 1970's that computers began having a presence in schools, since then the use of computers in education has increased dramatically (Zandvliet & Farragher, 1997). The Nat ional Center for Education Statistics reports that the percentage of students in grades 1 to 8 using computers in school more than doubled from 31.5 in 1984 to 68.9 in 1993 (Sny der & Hoffman, 1990; 1994). Similarly, the availability of computers to student s in school increased from one computer for every 125 students in 1983 to one comp uter for every 9 students in 1995 (Glennan & Melmed, 1996). As the number of computer s has increased, theories about how computers might benefit students' writing have proliferated. To a lesser extent, some researchers have carried out formal studies to examine whether writing on computer actually leads to better writing. Many of these studies have reported that writing on computers leads to measurable increases in students' motivation to write, the quantity of their work and the number of revisions made. Some of these studies also indicate that writing on computers improved the qua lity of writing. In a meta-analysis of 32 computer writing studies, Bangert-Drowns (1993) reports that about two-thirds of the studies indicated improved quality for text produce d on computer. However, the extent to which writing on computers leads to higher quali ty writing seems to be related to the type of students examined. Generally, improvements in the quality of writing produced on a computer are found for learning disabled stude nts, early elementary students, low-achieving students and college-aged students. D ifferences generally are not found for middle school and high school students.Learning Disabled, Early Elementary Students and Co llege-Aged Students Although neither Kerchner and Kistinger (1984) nor Sitko and Crealock (1986) included a comparison group in their studies, both noted sig nificant increases in motivation, quantity and quality of work produced by learning d isabled students when they began writing on the computer. After teaching learning di sabled students strategies for revising opinion essays, MacArthur and Graham (1987) reporte d gains in the number of revisions made on computer and the proportion of those revisi ons that affected the meaning of the passage. They also noted that essays produced on co mputer were longer and of higher quality. In a separate study, MacArthur again repor ted that when writing on a computer, learning disabled students tended to write and revi se more (1988). At the first grade level, Phoenix and Hannan (1984) report similar dif ferences in the quality of writing produced on computer.Williamson and Pence (1989) found that the quality of writing produced by college freshman increased when produced on computer. Also focusing on college age students, Robinson-Stavely and Cooper (1990) report that sent ence length and complexity increased when a group of remedial students produce d text on the computer. Hass and Hayes (1986a) also found that experienced writers p roduced papers of greater length and quality on computer as compared to those who create d them on paper. Middle and High School Students In a study of non-learning disabled middle school s tudents, Dauite (1986) reported that although writing performed on the computer was long er and contained fewer mechanical errors, the overall quality of the writing was not better than that generated on paper. In a


4 of 47similar study, Vacc (1987) found that students who worked on the computer spent more time writing, wrote more and revised more, but that holistic ratings of the quality of their writing did not differ from text produced with pape r-and-pencil. At the middle school level, Grejda (1992) did not f ind any difference in the quality of text produced on the two mediums. Although Etchison (1989) found that text produced on computer tended to be longer, there was no notic eable difference in quality. Nichols (1996) also found that text produced on computer by sixth graders tended to be longer, but was not any better in quality than text produce d on paper. Yet, for a group of eighth grade students, Owston (1991) found that compositio ns created on computer were rated significantly higher than those produced on paper.Focusing on high school freshman, Kurth (1987) repo rts that there was no significant difference in the length of text produced on comput er or on paper. Hawisher (1986) and Hawisher and Fortune (1989) also found no significa nt differences in the quality of writing produced by teenagers on paper and on compu ter. Hannafin and Dalton (1987) also found that for high achieving students, writin g on computer did not lead to better quality writing. But for low-achieving students, te xts produced on the computer were of a higher quality than those produced on paper.Summary of Studies The research summarized above suggests many ways in which writing on computer may help students produce better work. Most formal stud ies report that when students write on computer they tend to produce more text and make more revisions. Studies that compare student work produced on computer with work produced on paper find that for some groups of students, writing on computer also h as a positive effect on the quality of student writing. This positive effect is strongest for students with learning disabilities, early elementaryaged students and college-aged st udents. All of the studies described above focus on student work produced in class under un-timed conditions. These studies also focus on work typically produced for English o r Language Arts class, such as short stories or essays. However, the study presented her e focuses on writing produced under formal timed testing conditions in three subject ar eas, namely language arts, math and science. Specifically, this study addresses the ext ent to which producing open-ended responses on computer or on paper effects students' performance, particularly for students with different levels of computer use.Study DesignTo better understand whether open-ended test items administered on paper underestimate the achievement of students accustome d to working on computers, six open-ended math, six science, and six language arts items were converted to a computer format and then administered in two modes, paper an d computer. In addition, all students completed a computer use survey and perfor med a short keyboarding test. Finally, an indicator of prior achievement, namely Grade 7 Stanford Achievement Test version 9 (SAT 9) scores, was collected for each st udent. As is explained in detail below, the indicator of achievement was used to str atify and randomly assign representative sample groups and is used as a covar iate for some analyses. The study focuses on three subject areas: math, lan guage arts, and science. For each subject area, a total of six open-ended items were administered. To decrease the amount


5 of 47of testing time required for each student, students were divided into four groups. Two of these four groups performed the six science items a nd three of the language arts items, only. For ease of reference I call these groups of students SL and LS. The remaining two groups of students performed the six math items and the other three language arts items, only. These groups of students are referred to as M L and LM. All students completed the computer use survey and performed the keyboarding t est. In addition, an indicator of prior achievement was collected for each student.The study occurred in three stages. During stage 1, SAT 9 scores were collected for each student. In total, four SAT 9 scores were collected for each student: Comprehensive Normal Curve Equivalent (NCE), Math NCE, Language A rts NCE and Science NCE. Once collected, the Comprehensive NCE was used to s tratify and randomly assign four groups of students. Two of these groups formed the SL and LS students while the remaining two groups formed the ML and LM students.During stage 2, all students completed the computer use survey and performed the keyboarding test. During stage 3, a crossed design was used to administer the open-ended items to each group. In this crossed des ign, the SL students first performed the science items on computer and then performed th ree language arts items on paper. The LS students first performed the three language arts items on computer and then performed the science items on paper. Similarly, th e ML students first performed the math items on computer and then performed the three language arts items on paper. Finally, the LM students first performed the langua ge arts items on computer and then performed the math items on paper. Below, the instr uments, sampling method and scoring method are discussed in greater detail.Instruments The instruments used in this study fall into three categories: indicators of prior achievement; computer experience; and open-ended te sts. Indicator of Prior Achievement As described in greater detail below, an indicator of prior achievement was used to assign students to experimental groups and as a cov ariate during analyses. Since the sample of students was limited to students in grade eight, the students' grade 7 SAT 9 NCE scores were used as the indicator of prior achi evement. Computer Experience Two instruments were used to estimate students' lev el of computer experience. First, a survey that focused on prior computer use was admin istered to all students. Second, all students completed a brief keyboarding test adminis tered on computer. Student QuestionnaireThe survey was designed to collect information abou t how much experience students had working with computers and, in particular, how they used computers during their writing process. The survey included questions that asked: how long the student has had a computer in his/her home: 1. how many years they have used a computer; 2.


6 of 47how often they currently use a computer in school a nd at home; 3. how often they use a computer during different stag es of their writing process (e.g., brainstorming, outlining, composing a first draft, editing, writing the final draft); and 4. whether they prefer to write papers on paper or on computer. 5. In addition, the survey asked students to draw a pi cture of their writing process and to then describe what they had drawn. The purpose of t he drawing prompt was to collect information about if and when computers enter the s tudent's writing process. Finally, the student questionnaire asked students to indicate th eir gender and their race/ethnicity. To code student drawings, the following guide was u sed: 0 No computer visible 1 Computer used for final draft only 2 Computer used prior to creating the final draft When coding drawings, both the drawing and the stud ent's description of their drawing were reviewed prior to assigning a score. All drawi ngs were coded by one rater. However, to examine inter-rater reliability, a samp le of 20 drawings was coded by a second rater. For these 20 drawings, there was no d iscrepancy between the two raters' scores.Keyboarding TestTo measure keyboarding skills, all students perform ed a computer based keyboarding test. The keyboarding test contained two passages w hich students had two minutes apiece to type verbatim into the computer. Words pe r minute unadjusted for errors was averaged across the two passages and was used to es timate students' keyboarding speed. Both keyboarding passages were taken directly from encyclopedia articles to assure that the reading level was not too difficult.Although there is considerable debate about how to quantify keyboarding ability (see West, 1968, 1983; Russon & Wanous, 1973; Arnold, et al, 1997; and Robinson, et al, 1979), for the purposes of this study, students ave rage words per minute (WPM) uncorrected for errors was recorded. In each of the scoring guidelines used to rate student responses to the open-ended test items, spe lling was not explicitly listed as a criterion raters should consider when scoring stude nt responses. For this reason, students keyboarding errors did not seem to be directly rele vant to this study. Open-Ended Tests This study examines the mode of administration effe ct on student performance in three subject areas: science, math, and language arts. To restrict testing time to 60 minutes per test, 6 science items, 6 math items and two sets of 3 language arts items were administered. All items included in this study were taken directly from open-ended test instruments used previously. Sources for items incl ude the National Assessment of Educational Progress (NAEP) and the Massachusetts C omprehensive Assessment System (MCAS).Language Arts Items


7 of 47In total, six language arts items were used in this study. Three of the language arts items were taken from the 1999 Spring administration of M CAS. Two of the items were taken directly from the 1988 Grade 8 NAEP Writing Assessm ent. And the final language arts item was taken from the 1992 Grade 8 NAEP Writing A ssessment. The three MCAS language arts items focus on reading comprehension. For each of these items, students read a brief passage and then answe rs an open-ended question about the passage. The passages include a poem titled "The Ca ged Bird", a speech titled "Sojourner Truth's Speech From the 1850s", and a sh ort story titled "The Lion's Share". The three NAEP language arts items focus on writing The first writing item asks students to create a narrative piece that describes an embarrassing experience they have had. The second writing item prompt focuses on crea tive writing and asks students to write a good, scary ghost story. The final writing item tests students' expository writing skills and asks students to write about their favor ite story, telling why they like it and what it means to them.When selecting the items, two criteria were used. F irst, the time required to respond to the item could not exceed 30 minutes. Second, the a mount of reading (if any) students had to complete before responding to the item could not exceed 1 page. The reason for this second criterion was to maximize the amount of time students spent actually responding to the item. It should be noted that all three MCAS items required students to read a short body of text before responding to a qu estion while none of the NAEP items required students to read any text. For this reason the MCAS items can be classified as primarily measuring reading comprehension and the N AEP items can be classified as measuring writing ability.After the six items were selected, they were placed into one of two booklets. Two MCAS items and the 1992 NAEP item formed the test b ooklet titled Language Arts 1. The remaining MCAS item and the two 1988 NAEP items formed the second test booklet titled Language Arts 2.MathematicsThe mathematics test booklet contained six items. T hree of the items were taken from the 1998 grade 8 spring MCAS test and three items w ere taken from the 1996 grade 8 NAEP Assessment. Two of the math items tested fract ions and proportions. Two items focused on students' ability to read and interpret a graph. One item tested students' ability to calculate and interpret means and median s. And the final item focused on students' problem solving skills.When selecting mathematics items, two criteria were applied. First, the item had to require students to generate an extended (a minimum of one sentence) written response. Second, the item could not require students to draw a picture, diagram or graph. The first criterion was used to assure that students had to c ompose text in order to perform well on the item. The second criterion was used to preve nt students working on computer from having to access drawing or graphing programs.Science


8 of 47Like the mathematics items, three of the science it ems came from the 1998 grade 8 spring MCAS test and three items came from the 1996 grade 8 NAEP assessment. Similarly, all of the items required students to ge nerate a substantial amount of text (more than a sentence) in order to succeed and none of the items required students to draw pictures or graphs. Two of the items tested st udents understanding of the physical sciences. Two items focused on human biology. One i tem tested students understanding of electricity. And the final item tested students' ability to design an experiment. Scoring CriteriaFor all of the items, the scoring criteria develope d by MCAS or NAEP were used. All of the MCAS scoring guidelines used a scale tha t ranged from 0 to 4. For the MCAS items, a score of 0 indicated that the item was lef t blank or that the student's response was completely incorrect. Scores of 1 to 4 represen ted increasingly higher levels of performance.The scales for the NAEP scoring guidelines varied f rom 1-3, 1-4, 1-5, and 1-6. A code of 9 was awarded to items that were left blank. For al l items, a 1 indicated that the student's response was completely incorrect. Scores of 2 to 6 represented increasingly higher levels of performance. To make the scores for the M CAS and the NAEP items more comparable, all blank responses were re-coded as a zero. The resulting NAEP scales ranged from 0-3, 0-4, 0-5, or 0-6.Converting Paper Versions to Computer VersionsBefore the tests could be administered on computer, the paper versions were converted to a computerized format. Several studies suggest t hat slight changes in the appearance of an item can affect performance on that item. Som ething as simple as changing the font in which a question is written, the order item s are presented, or the order of response options can affect performance on that ite m (Beaton & Zwick, 1990; Cizek, 1991). Other studies have shown that people become more fatigued when reading text on a computer screen than when they read the same t ext on paper (Mourant, Lakshmanan & Chantadisai, 1981). One study (Haas & Hayes, 1986b) found that when dealing with passages that covered more than one pa ge, computer administration yielded lower scores than paper-and-pencil administration, apparently due to the difficulty of reading extended text on screen. Clearly, by conver ting items from paper to computer, the appearance of items is altered.To minimize such effects, students taking a test on computer were given a hard copy of the test booklet. The only difference between the h ard copy of the test booklets received by students taking the test on computer and the ori ginal paper version was that the blank lines on which students recorded their responses we re replaced by instructions to write answers on the computer.Prior to beginning a test, students in the computer group launched a computer program that performed four tasks. First, the program promp ted students to record their name and identification number. Second, the program presente d the same directions that appeared in their hard copy. Third, the program allowed stud ents to navigate between text boxes in which they recorded their responses to the openended questions presented in their


9 of 47test booklet. Finally, after a student completed th e test, the program presented two questions about the taking the test on computer (de scribed more fully below). To assist students in recording their responses in the proper text box, the program placed the question number and accompanying prompt above e ach text box. To help avoid confusion, only one text box appeared on the screen at a time. To move between text boxes, two buttons appeared on the bottom of the sc reen. The button labeled "Next" allowed students to navigate to the text box for th e next question and the button labeled "Back" allowed students to move to the previous que stion. Below the last text box, a button labeled "I'm Finished" appeared. Once studen ts felt they were finished with the test, they clicked on the "I'm Finished" button. To assure that they were in fact finished, students were asked again if they were done. If so, they clicked the "I'm Finished" button again. Otherwise, they clicked the "Back" button to continue working on their responses. When students were finished taking the test and had selected the "I'm Finished" button twice, they were prompted with two questions about the test. The first asked students: "Do you think you would have done better on this te st if you took it on paper. Why?" The second question asked: "Besides not knowing the answer to a question, what problems did you have while taking this test on com puter?" Students were required to answer these questions before they could quit the p rogram. To create a computerized version of the test bookle ts, the following steps were taken: An appropriate authoring tool, namely Macromedia Di rector, was selected to create software that would allow students to naviga te between questions and to write data to a text file. 1. A data file was created to store student input, inc luding name, ID number, and responses to each item. 2. A prototype of each test was created, integrating t he text and database into a seamless application. As described earlier, navigat ional buttons were placed along the lower edge of the screen. In addition, a "cover page was created in which students entered their name and id numbers. 3. The prototype was tested on a class of ninth grade students to assure that all navigational buttons functioned properly, that data was stored accurately, and that items were easy to read. 4. Finally, the prototype was revised as needed and th e final versions of the computer tests were installed on twenty computers in the ALL School and twentyfour computers in the Sullivan Middle School. 5. For all questions, examinees used a keyboard to typ e their answers into text boxes that appeared on the screen. To enable students to write as much as they desired, scrolling text boxes were used for all items. Although they c ould edit using the keyboard and mouse, examinees did not have access to word proces sing tools such as spell-checker or grammar-checker.Sampling Method The sample of students was drawn from two Worcester Public Middle Schools, namely The Advanced Learning Laboratory (ALL School) and t he Sullivan Middle School. Since the analyses focus on how the mode of adminis tration effect varies across achievement levels and, more importantly, computer use/proficiency levels, the


10 of 47population of students was pooled across the two sc hools. Before sampling began, a list of all grade eight students in the ALL School and a ll grade eight students from one team in the Sullivan Middle School was generated. In tot al, this yielded 327 students. For each student on the list, an indicator of prior ach ievement, namely grade 7 SAT 9 scores, was collected. Since some of the students were new to the district or had not taken the SAT 9 the previous year, SAT 9 scores were only ava ilable for 287 students. Using a stratified random assignment procedure, students we re then assigned to one of two groups. Group 1 was then assigned to the Language A rts 1 and Math tests and group 2 was assigned to the Science and Language Arts 2 tes ts. For each group, this process was repeated again, this time assigning half of the stu dents in group 1 to take the Language Arts 1 test on computer first and the remaining hal f to take the Math test on computer first. Similarly, half of the second group of stude nts was assigned to take the Language Arts 2 test on computer first and the remaining hal f took the Science test on computer first.Those students for whom SAT 9 scores were not avail able were randomly assigned to one of the four groups. Although their scores are n ot included in the analyses below, their responses were used to train raters prior to scoring the test booklets for students included in the analyses.Due to absences and refusals to perform one or more instruments, complete data records were available for 229 students. To be clear, a com plete data record was defined as one containing a student's SAT 9 scores, their response s to the student questionnaire, the results of the keyboarding test, and results from a t least one of the open-ended tests.ScoringTo reduce the influence hand writing has on raters' scores (Powers, Fowles, Farnum & Ramsey, 1994), all responses to the open-ended item s administered on paper were transcribed verbatim into computer text. The transc ribed responses were randomly intermixed with the computer responses. All student responses were formatted with the same font, font size, line spacing and line width. In this way, the influence mode of response might have on the scoring process was elim inated. Scoring guidelines designed for each item were used to score student responses. To increase the accuracy of the resulting scores, all responses were double-scored. When discrepancies between raters' scores arose, an adju dicator awarded the final score. At the conclusion of the scoring process, one score was re corded for each student response. To estimate inter-rater reliability, the original s cores from both raters were used. The resulting scores were compared both via correlation and percent agreement methods. Table 1 shows that for most items the correlation b etween the two raters' scores was above .8 and for many items the correlation was abo ve .9. For two of the items on the first language arts test, however, correlations wer e closer to .7. Nonetheless, this represents an adequate level of inter-rater reliabi lity.Table 1 Inter-rater Reliability for Open-Ended Items


11 of 47 Correlation % Exact Agreement % Within 1 Point Language Arts 1 Item 1 .80 .68 1.00 Item 2 .74 .50 1.00 Item 3 .72 .59 .95 Language Arts 2 Item 1 .95 .84 1.00 Item 2 .94 .88 1.00 Item 3 .91 .76 1.00 Math Item 1 .91 .60 1.00 Item 2 .83 .65 .90 Item 3 .88 .80 .95 Item 4 .94 .80 1.00 Item 5 .84 .90 1.00 Item 6 .70 .75 .95 Item 1.80 .64 .95 Item 2 .86 .73 1.00 Item 3 .88 .82 1.00 Item 4 .88 .86 1.00 Item 5 .92 .82 1.00 Item 6 .85 .73 1.00 To estimate intra-rater reliability, one rater doub le-scored 20% of the responses. The resulting scores for this rater were compared via c orrelation and percent agreement methods. Table 2 shows high correlations between th e two sets of scores. Moreover, where discrepancies occurred, the difference betwee n the two scores was never more than one point.Table 2 Intra-rater Reliability for Open-Ended Items Correlation% Exact Agreement % Within 1 Point Language Arts 1 Item 1 .92 .86 1.00 Item 2 .95 .95 1.00


12 of 47 Item 3 .88 .77 1.00 Language Arts 2 Item 1 .91 .82 1.00 Item 2 .93 .91 1.00 Item 3 .94 .86 1.00 Math Item 1 .92 .77 1.00 Item 2 .97 .91 1.00 Item 3 .98 .95 1.00 Item 4 .96 .82 1.00 Item 5 .88 .91 1.00 Item 6 .84 .82 1.00 Science Item 1 .94 .86 1.00 Item 2 .96 .91 1.00 Item 3 .93 .91 1.00 Item 4 .92 .91 1.00 Item 5 .98 .95 1.00 Item 6 .94 .86 1.00 Note that the adjudicated scores were produced for all students and that the adjudicated scores were used for all analyses described below.Results This study explores the relationships between prior computer use and performance on four open-ended test booklets. To examine this rela tionship, three types of analyses were performed. First, independent samples t-tests were employed to compare group performance. Second, total group regression analyse s were performed to estimate the mode of administration effect controlling for diffe rences in prior achievement. And third, sub-group regression analyses were performed to exa mine the group effect at different levels of keyboarding speed. However, before the re sults of these analyses are described, summary statistics are presented.Summary Statistics Summary statistics are presented for each of the in struments included in this study. The raw data are also available from this point. These original data are presented by the author for othes who may wish to perform secondary data an alyses; anyone publishing analyses


13 of 47 of these data should cite this article as the origi nal source. For the student questionnaire, keyboarding test, and the SAT 9 scores, summary sta tistics are based on all 229 students included in the study. For the language arts, math and science open-ended tests, summary statistics are based on the sub-set of students tha t performed each test. When between group analyses are presented, summary statistics fo r select variables are presented for each sub-set of students that performed a given tes t. Keyboarding Test The keyboarding test contained two passages. Table 3 shows that the mean number of words typed for passage 1 and passage 2 was 31.2 an d 35.0, respectively. As described above, the number of words typed for each passage w as summed and divided by 4 to yield the number of words typed per minute for each student. Across all 229 students included in this study, the mean WPM was 16.5. Cons idering that the minimum WPM required by most employers when hiring a secretary is at least 40, an average of 16.5 WPM suggests that most students included in this st udy were novice keyboarders.Table 3 Summary Statistics for the Keyboarding TestN=229 Mean Std Dev Min Max Passage 1 31.2 11.2 5 71 Passage 2 35.0 10.6 9 80 WPM 16.5 5.3 4.8 37.8 Student Questionnaire The student questionnaire contained 11 questions. T he maximum score for the Survey was 46 and the minimum score was 10. The scale for each item varied from 0 to 2, 1 to 2, 1 to 3 and 1 to 6. To aid in interpreting the summa ry statistics presented in table 4, the scale for each item is also listed. In addition to the Survey total score, summary statistics are presented for the Comp-Writing sub-score.Although comparative data is not available, Table 4 suggests that on average students included in this study do not have a great deal of experience working with computers. The average student reports using a computer for be tween two and three years, having had a computer in the home for less than a year, an d using a computer in school and in their home less than 1-2 hours a week. Furthermore, most students report that they do not use a computer when brainstorming, creating an outl ine or writing a first draft. Slightly more students report using a computer to edit the f irst draft. Most students, however, report using a computer at least sometimes to write the final draft. Similarly, most students indicate that if given the choice, they wo uld prefer to write a paper on computer than on paper. Yet, when asked to draw a picture of their writing process, less than half the students included a computer in their drawing.Again, the divergence between students' preference and their reported use of a computer


14 of 47 in the writing process may indicate that when recor ding their preference some students provided a socially desirable response. If students did provide socially desirable responses, estimating the effect preference had on students' performance will be less precise.Table 4 Summary Statistics for the Student QuesitonnaireN=229 Scale Mean Std Dev Min Max Years having computer at home 1-6 2.81 1.86 1 6 Years using computer 1-6 4.75 1.51 1 6 Use computer in school 1-6 2.74 1.10 1 6 Use computer at home 1-6 2.75 1 6 Brainstorm with computer 1-3 1.35 .55 1 3 Outline with computer 1-3 1.50 .57 1 3 First draft with computer 1-3 1.72 .75 1 3 Edit with computer 1-3 1.85 .73 1 3 Final draft with computer 1-3 2.50 .65 1 3 Preference 1-2 1.80 .40 1 2 Computer in drawing 0-2 .59 .72 0 2 Survey 10-43 24.37 5.68 12 41 Comp-Writing 5-17 9.51 2.77 5 17 Indicator of Prior Achievement Four indicators of prior achievement were collected prior to the study. Specifically, SAT 9 Composite, Reading, Math and Science NCE scores w ere collected for each student. Note that the NCE scores provided for this study ha d been multiplied by 10 before they were supplied by the district office. Thus, the ran ge for the NCE scores was 10 to 990 with a mean of 500 and a standard deviation of appr oximately 210 (see Crocker and Algina, 1986 for a fuller description of NCE scores ). Table 5 displays the mean and standard deviation for each SAT 9 score. The mean s core for each subject area and for the composite score for students included in this study is approximately .5 standard deviations below the national average. However, wit hin this sample of students there is a substantial variation.Table 5 Summary Statistics for Indicators of Prior Achievem entN=229MeanStd DevMinMaxComposite NCE402150.2119888


15 of 47 Reading NCE39117810990Math NCE38917410990Science NCE43417267896 Open-Ended Tests Four open-ended tests were administered in three su bject areas: math, science and language arts. As is described more fully above, tw o versions of the language arts test were administered. Each of the tests was administer ed to a sample of students. The number of students who performed each test ranged f rom a high of 117 for Language Arts 1 to a low of 100 for Language Arts 2. Within each sample, approximately half of the students performed the test on computer and hal f of the students performed the test on paper.The summary statistics for the total sample of stud ents who performed each test are presented in tables 6 through 9. Since each test co ntained some items from NAEP and some items from MCAS, it is not possible to directl y compare the total test scores to the performance of students in other settings. However, to aid in interpreting the test scores, summary statistics are presented for each item alon g with the national or state average performance for each item. Note that comparison dat a for the MCAS items represents the mean score on a 0-4 point scale for all students in the state. Comparison data for the NAEP items represents the percentage of students na tionally performing adequately or better on the item.Table 6 presents the summary statistics for Languag e Arts 1 and Table 7 presents the summary statistics for Language Arts 2. For all ite ms but one item included in the language arts tests, a score below 3 indicates inad equate performance. For item 2 on language arts test 1, a score below 4 indicates ina dequate performance. For all items, many students failed to perform adequately. For all items, the mean performance was below 3 and for four items the mean performance was below 2. This low level of performance suggests these items were difficult for these samples of students.Table 6 Summary Statistics for Language Arts 1N=117 Scale Mean Std Dev % Adequate* Mean on MCAS % Adequate on NAEP Item 1 0-4 1.42 1.04 19 1.99 NA Item 2 0-4 1.50 1.05 16 1.73 NA Item 3 0-6 2.91 1.41 31 NA 29 Total 0-14 5.84 2.85 For items 1 and 2, a score of 3 or higher was con sidered adequate performance. For item 3, a score of 4 or higher was considered a dequate performance.


16 of 47 Table 7 Summary Statistics for Language Arts 2N=100 Scale Mean Std Dev % Adequate* Mean on MCAS % Adequate on NAEP Item 1 0-4 1.23 0.98 10 1.74 NA Item 2 0-4 1.67 1.07 31 NA 51 Item 3 0-4 2.12 0.81 22 NA 25 Total 0-12 5.02 2.29 For all three items, a score of 3 or higher was c onsidered adequate performance. Table 8 displays the summary statistics for the Mat h test. Again, for all items except number 4, a score below 3 indicates inadequate perf ormance. For item 4, a score below 4 indicates inadequate performance. For all items, th e mean performance for this sample of students indicates that on average students perform ed below the adequate level.Table 8 Summary Statistics for MathN=110 Scale Mean Std Dev % Adequate* Mean on MCAS % Adequate on NAEP Item 1 0-4 1.18 1.17 15 2.05 NA Item 2 0-4 1.26 1.23 18 1.83 NA Item 3 0-4 1.64 1.03 22 1.84 NA Item 4 0-5 2.45 1.28 33 NA 28 Item 5 0-3 1.44 .53 2 NA 11 Item 6 0-4 1.99 .89 30 NA 26 Total 0-24 9.96 4.18 For items 1, 2, 3 and 6, a score of 3 or higher w as considered adequate. For item 4, a score of 4 or higher was considered a dequate. For item 5, a score of 3 was considered adequate.Table 9 displays the summary statistics for the Sci ence test. For all items, a score below 3 indicates inadequate performance. For all items, the mean performance was below the adequate level.Table 9


17 of 47 Summary Statistics for ScienceN=102 Scale Mean Std Dev % Adequate* Mean on MCAS % Adequate on NAEP Item 1 0-4 1.89 1.02 32 1.49 NA Item 2 0-4 1.71 1.21 26 1.70 NA Item 3 0-3 1.50 .75 8 NA 19 Item 4 0-3 1.21 .67 3 NA 9 Item 5 0-3 1.78 .90 22 NA 52 Item 6 0-4 1.57 1.09 20 1.81 NA Total 0-24 9.66 4.14 For all items, a score of 3 or higher was conside red adequate performance. Clearly, students had difficulty with all four of t hese tests. For all MCAS items, this sample of students performed at a level below that of other students in the state of Massachusetts. For the NAEP items, students perform ed about as well or worse than other students in the nation.Comparing Performance on Computer and on Paper For each test, approximately one half of the sample of students was randomly assigned to perform the test on computer while the other hal f performed the test on paper. Tables 10 through 13 present the results of between group comparisons for each test. For each test, an independent samples t-test (assuming equal variances for the two samples and hence using a pooled variance estimate) was perform ed for the total test score. The null hypothesis for each of these tests was that the mea n performance of the computer and the paper groups did not differ from each other. Th us, these analyses test whether performance on computer had a statistically signifi cant effect on students' test scores. To examine whether prior achievement, computer use or keyboarding skills differed between the two groups of students who performed ea ch test, independent samples t-tests were also performed for students' SAT 9 Com posite score, the corresponding SAT 9 sub-test score, Survey, Comp-Writing and WPM. The results of these tests are also presented in tables 10 through 13.Table 10 shows that on average students who perform ed the first language arts test on paper performed the same as students who performed the test on computer. Similarly, differences between the two groups' SAT 9 Comprehen sive scores, SAT 9 Reading scores, Survey scores, and WPM were not statistical ly significant. However, Table 10 shows that the mean Comp-Writing score for students who performed the language arts 1 test on computer was larger than the mean for stu dents who performed the test on computer.Table 10


18 of 47 Between Group Comparisons for Language Arts 1Paper N = 57Computer N = 60 Mean Std Dev SE of Mean t-value Sig. LA 1 Paper 5.84 2.65 .35 Computer 5.83 3.04 .39 .02 .99 SAT 9 Comp. Paper 379 145 19 Computer 421 159 21 -1.49 .14 SAT 9 Reading Paper 360 168 22 Computer 426 189 24 -1.98 .05 Survey Paper 24.6 5.6 .75 Computer 23.9 5.9 .76 .67 .51 Comp-Writing Paper 10.1 2.8 .37 Computer 9.0 2.6 .33 2.09 .04* WPM Paper 17.5 4.3 .56 Computer 16.4 5.3 .68 1.17 .24 *Significant at the .05 level.On the second language arts test, table 11 shows th at Comp-Writing was the only measure on which the two groups differed. However, for the second language arts test, students who performed the test on computer had hig her Comp-Writing scores on average than did those students who performed the t est on paper. For all other instruments, the two groups did not differ signific antly.


19 of 47 Table 11 Between Group Comparisons for Language Arts 2Paper N = 45Computer N = 55 Mean Std Dev SE of Mean t-value Sig. LA 2 Paper 5.07 1.70 .25 Computer 4.98 2.70 .36 .18 .86 SAT 9 Comp. Paper 413 138 20 Computer 393 146 19 .59 .55 SAT 9 Reading Paper 402 173 26 Computer 376 172 23 .74 .46 Survey Paper 24.4 5.8 .87 Computer 25.1 5.7 .76 -.63 .53 Comp-Writing Paper 8.9 3.1 .46 Computer 10.1 2.6 .36 -2.09 .04* WPM Paper 15.7 4.9 .73 Computer 17.2 6.5 .88 -1.23 .22 *Significant at the .05 level.With a few exceptions, the students who performed t he first language arts test also performed the math test. However, those students wh o performed the first language arts test on computer performed the math test on paper a nd vice versa. For this reason, table 12 indicates that the mean Comp-Writing score for t he computer group was higher than


20 of 47 that of the paper group. Again, this difference is statistically significant. For all other instruments, Table 12 indicates that differences be tween the two groups' scores were not statistically significant.Table 12 Between Group Comparisons for MathPaper N = 54Computer N = 56 Mean Std Dev SE of Mean t-value Sig. Math Paper 10.70 4.34 .59 Computer 9.25 3.90 .52 1.84 .07 SAT 9 Comp. Paper 414 155 21 Computer 407 154 21 .23 .82 SAT 9 Math Paper 401 179 24 Computer 406 190 25 -.16 .87 Survey Paper 23.6 5.98 .81 Computer 25.1 5.72 .76 -1.29 .20 Comp-Writing Paper 9.0 2.66 .36 Computer 10.1 2.65 .35 -2.22 .03* WPM Paper 16.0 4.7 .64 Computer 17.9 5.2 .69 -2.01 .05 *Significant at the .05 level.The open-ended science test was the only test for w hich there was a statistically


21 of 47 significant difference in the two groups' test perf ormance. Table 13 shows that on average the computer group performed better than th e paper group. There were no other statistically significant differences between the t wo groups.Table 13 Between Group Comparisons for SciencePaper N = 51Computer N = 51 Mean Std Dev SE of Mean t-value Sig. Science Paper8.55 3.88 .54 Computer 10.76 4.14 .58 -2.79 .006* SAT 9 Comp. Paper 388 134 19 Computer 426 152 21 -1.33 .19 SAT 9 Science Paper 414 154 21 Computer 466 181 25 -1.57 .12 Survey Paper 25.4 5.5 .77 Computer 24.0 5.7 .80 1.22 .23 Comp-Writing Paper 9.9 2.6 .37 Computer 9.1 3.0 .43 1.39 .17 WPM Paper 17.1 6.3 .88 Computer 15.9 5.1 .72 1.01 .32 *Significant at the .05 level.Note that statistical significance for the t-tests reported above was not adjusted to


22 of 47 account for multiple comparisons. Given that six co mparisons were made for each group, there is an increased probability that repor ted differences occurred by chance. Employing the Dunn approach to multiple comparisons (see Glass & Hopkins, 1984), a for c multiple comparisons, a pc, is related to simple a for a single comparison as follows: a pc = 1 (1a )1/ cHence, for six comparisons the adjusted value of a simple 0.05 alpha level becomes 0.009. Analogously, a simple alpha level of 0.01 fo r a simple comparison becomes 0.001.Once the level of significance is adjusted for mult iple comparisons, the open-ended science test is the only instrument for which there is a statistically significant group difference. This difference represents an effect si ze of .57 (Glass's delta effect size was employed). Although this effect size is about half of that reported by Russell and Haney (1997), it suggests that while half of the students in the computer group scored above 10.76, approximately 30% of students performing the test on paper scored above 10.76. The difference between the two groups' open-ended s cience scores, however, may be due in part to differences in their prior achieveme nt as measured by SAT 9 Science scores.To control for differences in prior achievement, a multiple regression was performed for each open-ended test. Tables 14 through 17 present the results of each test score regressed on the corresponding SAT 9 score and grou p membership. For all four regression analyses, the regression coefficient (B) for group membership indicates the effect group membership has on students' performanc e when the effect of SAT 9 scores is controlled. Group membership was coded 0 for the paper group and 1 for the computer group. A positive regression coefficient i ndicates that performing the test on computer has a positive effect on students' test pe rformance. A negative regression coefficient suggests that on average students who p erformed the test on computer scored lower than students who performed the test on paper Table 14 indicates that SAT 9 Reading scores are a significant predictor of students' scores on the first open-ended language arts test. For each one standard score unit increase in SAT 9 Reading scores, on average studen ts experience a .42 standard score increase in their test score. Table 14 also indicat es that after controlling for differences in SAT 9 Reading scores, performing the first langu age arts test on computer has a negative impact on students scores. This effect, ho wever, is not statistically significant.*Table 14 Language Arts 1 Regressed on SAT 9 Reading and Grou p Membership B SE B Beta T Signif. SAT 9 Reading .007 .001 .42 4.87 <.0001 Group -.443 .492 -.08 -.90 .37 F 11.85 <.0001


23 of 47 N 117 R2.17 Adjusted R2.16 The results for the second language arts test are s imilar to those for the first language arts test. Table 15 shows that a one point standard score increase in SAT 9 Reading score is associated with a .4 point standard score increase in language arts 2 score and that this effect is statistically significant. Cont rolling for SAT 9 Reading scores, group membership does not have a significant effect on st udents' test score. Table 15 Language Arts 2 Regressed on SAT 9 Reading and Grou p Membership B SE B Beta T Signif. SAT 9 Reading .005 .001 .40 4.3 <.0001 Group .051 .428 .01 .1 .91 F 9.12 .0002 N 100 R2.16 Adjusted R2.14 For both the math and science tests, SAT 9 scores a nd group membership have statistically significant effects on students' scor es. The direction of the effect, however, is different for each test. Table 16 indicates that performing the open-ended math test on computer has a negative effect on students' test sc ores when SAT 9 Math scores are controlled. For science, this effect is reversed. T able 17 shows that after controlling for differences in SAT 9 Science scores, performing the open-ended science test on computer leads to higher scores than performing the same test on paper. For both tests, the effects are equivalent to just less than .2 sta ndard score units. Table 16 Math Regressed on SAT 9 Math and Group Membership B SE B Beta T Signif. SAT 9 Math .016 .001 .72 11.03 <.0001 Group -1.546 .541 -.19 -2.86 .005 F 64.41 <.0001 N 110 R2.54


24 of 47 Adjusted R2.54 Table 17 Science Regressed on SAT 9 Science and Group Member ship B SE B Beta T Signif. SAT 9 Science 0.014 .002 .59 7.50 <.0001 Group 1.466 .645 .18 2.28 .025 F 34.19 <.0001 N 102 R2.41 Adjusted R2.40 Sub-Group Analyses The regression analyses presented above indicate th at mode of administration did not have a significant effect on students' performance on either language arts test. For the science test, performing the test on computer had a positive effect on students' scores. And for the math test, performing the test on compu ter led to lower performance. For all four of these analyses, the effect was examined acr oss levels of computer use. To test whether the effect of mode of administration varied for students with different levels of computer skill, students' WPM was used to form thre e groups. The first group contained students whose WPM was .5 standard deviations below the mean, or less than 13.8. The second group contained students whose WPM was betwe en .5 standard deviations below the mean and .5 standard deviations above the mean, or between 13.8 and 19.2. The third group contained students whose WPM was .5 sta ndard deviations above the mean or greater than 19.2. For each group, the open-ende d test scores were regressed on SAT 9 scores and group membership.Table 18 displays the results of the three separate regressions for the first language arts test. For students whose WPM is .5 standard deviati ons below the mean and for students whose WPM is within .5 standard deviations of the m ean, performing the test on computer has a negative effect on their scores. How ever, for these two groups of students, neither SAT 9 Reading nor group membershi p is a statistically significant predictor of language arts 1 score. However, for st udents whose keyboarding speed is one-half of standard deviation above the mean, or g reater than 19.2 words per minute, performing the test on computer has a statistically significant positive effect on their performance. This effect is also three times strong er than the relationship between their SAT 9 reading score and their performance on the fi rst language arts test. For the first language arts test, performing the test on computer seems to hurt students whose WPM is near or well below the mean and helps students w hose WPM is well above the mean. Table 18


25 of 47 Language Arts 1 Regressed on SAT 9 Reading and Grou p for Three Sub-Groups WPM <13.8 N=30 B SE B Beta TSignif. SAT 9 Reading 0.006 0.003 .36 1.98 .06 Group -1.115 1.001 -.20 -1.12 .27 Adjusted R2 .08 13.819.2 N=33 B SE B Beta TSignif. SAT 9 Reading .003 .002 .19 1.32 .20 Group 2.946 .764 .56 3.86 .0006 Adjusted R2 .38 The same relationship was found for the second lang uage arts test (Table 19). However, for this test, the negative effect of taking the te st on computer was statistically significant for students whose WPM was .5 standard deviations below the mean. For students whose WPM was within .5 standard deviation s of the mean, performing the test on computer also had a negative effect on test perf ormance, but this effect was not statistically significant. For students whose WPM w as .5 standard deviations above the mean, performing the test on computer had a positiv e effect of nearly a half standard score on their language arts 2 test scores. This ef fect was statistically significant. Table 19 Language Arts 2 Regressed on SAT 9 Reading and Grou p for Three Sub-Groups WPM <13.8 N=35 B SE B Beta TSignif. SAT 9 Reading -.0001 .002 -.02 -.10 .92 Group -1.48 .601 -.41 -2.47 .02


26 of 47 Adjusted R2 .11 13.819.2 N=37 B SE B Beta TSignif. SAT 9 Reading .002 .002 .17 1.02 .31 Group -.974 .631 -.26 -1.54 .13 Adjusted R2 .08 WPM >19.2 N=28 B SE B Beta TSignif. SAT 9 Reading .006 .002 .37 2.32 .03 Group 2.068 .71 .46 2.90 .008 Adjusted R2 .43 For the open-ended math test, performing the test o n computer had a negative effect on students' scores at all levels of keyboarding (Tabl e 20). However, as keyboarding speed increased, this effect became less pronounced. For students whose WPM was .5 standard deviations below the mean, taking the test on compu ter had an effect of -.39 standard score units. For students whose WPM was within .5 s tandard deviations of the mean, this effect was -.24 standard score units. Both of these effects were statistically significant. However, for students whose WPM was .5 standard deviations above the mean, the effect was -.17 standard units and was no t statistically significant. Table 20 Math Regressed on SAT 9 Math and Group for Three Sub-Groups WPM <13.8 N=30 B SE B Beta TSignif. SAT 9 Math .015 .003 .68 5.58 <.0001 Group -3.068 .970 -.39 -3.16 .004 Adjusted R2 .57 13.819.2 N=49 B SE B Beta TSignif. SAT 9 Math .010 .003 .50 4.09 .0002 Group -1.55 .796 -.24 -1.96 .05


27 of 47 Adjusted R2 .30 WPM >19.2 N=31 B SE B Beta TSignif. SAT 9 Math .019 .003 .73 5.96 <.0001 Group -1.499 1.070 -.17 -1.40 .17 Adjusted R2 .56 Conversely, taking the science test on computer had a positive effect on students' scores at all levels of keyboarding speed (Table 21). Howe ver, this effect was only statistically significant for students whose WPM was within .5 st andard deviations of the mean. For students whose WPM was .5 standard deviation units above the mean, this effect is less pronounced and is not statistically significant. Table 21 Science Regressed on SAT 9 Science and Group for Three Sub-Groups WPM <13.8 N=35 B SE B Beta TSignif. SAT 9 Science .011 .003 .50 3.33 .002 Group .909 1.020 .13 .89 .38 Adjusted R2 .24 13.819.2 N=40 B SE B Beta TSignif. SAT 9 Science .010 .002 .47 4.00 .0003 Group 3.368 .893 .45 3.77 .0006 Adjusted R2 .48 WPM >19.2 N=27 B SE B Beta TSignif. SAT 9 Science .021 .004 .70 4.71 .0001 Group .170 1.204 .02 .14 .89


28 of 47 Adjusted R2 .45 DiscussionThe experiment described here extends the work of R ussell and Haney (1997) and improved upon their study in five ways. First, this study included students whose prior computer experience varied more broadly. Second, ma ny more open-ended items in the area of language arts, math and science were admini stered. Third, all of the open-ended test items included in this study had been used in state or national testing programs and had been validated previously. Fourth, an indicator of academic achievement was collected prior to the study and was used both to r andomly assign students to groups and as a covariate during regression analyses. And fift h, information on students' prior computer use and keyboarding speed was collected an d used during analyses. In their study, Russell and Haney (1997) reported l arge, positive group differences which were consistent for all writing, math and science o pen-ended items administered on computer. In this study, a significant positive gro up difference was found only for the open-ended science test. This effect was about half the size reported by Russell and Haney (1997). However, in this study students' leve l of prior computer use varied more than it did in the previous study. Although Russell and Haney did not collect a formal measure of computer use, the students included in t heir study were so accustomed to working on computer that when standardized tests we re given, the school had difficulty finding enough pencils for all students. Although t hree years have passed since the previous study, it may be possible to estimate the difference in the level of prior computer use of the students included in both studi es. This study includes students from two schools, one of which was the focus of the previous study. Table 22 compares the WPM and surve y scores for students in the ALL School and Sullivan Middle School. For both measure s of computer use and for keyboarding speed, students in the ALL School have significantly higher scores. For the ALL School the mean WPM was nearly .5 standard devi ations above the mean for the total sample while the mean for students from the S ullivan Middle School was below the total sample mean. By including Sullivan Middle Sch ool students in this study, a broader range and lower levels of computer use were represe nted. Including students with low levels of computer use and poor keyboarding skills seems to have counteracted the effect described in the previous study since these student s performed less well on the language arts computer tests than on the paper tests. Table 22 Comparison of Computer Use across Participating Sch ools ALL N = 35Sullivan N = 194 Mean Std Dev SE of Mean t-value Sig. WPM ALL 18.9 5.1 .36 Sullivan 16.1 6.2 1.05 2.94 .004


29 of 47 Survey ALL 27.5 5.8 .99 Sullivan 23.8 5.5 .39 3.69 <.0001 Comp-Writing ALL 11.1 2.6 .43 Sullivan 9.2 2.7 .20 3.84 <.0001 To examine the effect the mode of administration ha d on student performance at different levels of computer use, sub-group analyse s were performed. Figure 1 summarizes the effects found for three sub-groups: a. students whose WPM was .5 standard deviations below the mean; b. students who se WPM was within .5 standard deviations of the mean; and c. students whose WPM w as .5 standard deviations above the mean. Figure 1 Effect of Performing Test on Computer When Prior Achievement is Controlled Figure 1 shows that across three of the four tests, performing the test on computer had an adverse effect on the performance of students whose WPM was .5 standard deviations below the mean. Conversely, for students whose WPM was at least .5 standard deviations above the mean, performing the language arts tests on compute r had a moderate positive effect. While performing the math test on computer had a negative effect for all students, this negative


30 of 47 effect became less pronounced as students' keyboard ing speed increased. For the Science test, performing the test on computer had a positive effe ct across levels of computer use. However, the effect was much larger for students wh ose WPM was within .5 standard deviations of the mean than it was for students who se keyboarding speed was either .5 standard deviations above the mean or .5 standard d eviations below the mean. Explaining the Effects To explore the reasons why some students had diffic ulty working on computers, students were asked to answer the following two questions af ter they completed the computer version of the test: 1. Do you think you would have done be tter on this test if you took it on paper? Why?; and 2. Besides not knowing the answer to a qu estion, what problems did you have while taking this test on computer? Students' responses to these questions were coded i n two ways. First, the following numerical code was used:0 No, I would not perform better on paper 1 I would perform the same or it didn't matter 2 Yes, I would perform better on paper In addition to these codes, an emergent coding sche me was used to tabulate the reasons students provided for their answers. While coding r esponses to the post-test questions, it became apparent that when read together, the two qu estions provided more information about students' experience than reading them separately. Some students would simply write yes or no for the first question, but their reasoning beca me apparent in their response to the second question. Other students explained the problems the y encountered for the first question and wrote little for the second question. For this reas on, responses to both questions were read during the emergent coding.Table 23 presents the numerical codings for the fir st question. Across all tests, only 10% of students indicated that they would have performed b etter if they had taken the test on paper. However, over half of those who indicated they woul d have performed better on paper took the math test on computer. To explore why more stud ents who took the math test on computer felt they would perform better on paper, t he full responses to the two follow-up questions were examined. Table 23 Frequency of Students Responses to Post-Test Questi on 1: Do you think you would have done better on this tes t if you took it on paper? Frequency Percent Language Arts 1 Not better on paper 38 63.3 Same on paper 19 31.7 Better on paper 3 5.0 Language Arts 2


31 of 47 Not better on paper 36 65.5 Same on paper 15 27.3 Better on paper 4 7.3 Math Not better on paper 20 35.7 Same on paper 18 32.1 Better on paper 18 32.1 Science Not better on paper 32 62.7 Same on paper 16 31.4 Better on paper 3 5.9 Table 24 presents the frequency of student response s by test. Clearly, the most frequently sited problem related to students' keyboarding skil ls.* Across all tests, about 25% of the students who performed the test on computer indicat ed they had difficulty "finding the keys," "pressing the wrong key" or simply said they "could n't type." Twenty percent of the students who performed the math test on computer also compla ined that it was difficult to show their work on the computer or that they had to solve prob lems on paper and then transfer it to the computer. Several students who performed the langua ge arts tests on computer mentioned that they preferred the computer because it was nea ter and that they didn't have to erase mistakes but could simply delete them. Across all t ests, a few students also stated that they preferred the computer because their hands did not get as tired or that it was faster to write on the computer. Table 24 Frequency of Responses to Post-test Questions 1 and 2 LA 1 LA 2 Math Science Total Difficulty typing 12 12 17 18 59 Neater on computer/can delete 8 9 2 2 21 Can't show work/drawings 10 10 Ran out of time on computer 1 2 3 1 7 Hand doesn't get tired 3 1 2 1 7 Faster on computer 3 1 2 1 7 Can take notes/solve problems on paper 1 4 5 Think better on computer/concentrate better 2 1 1 1 5 Write easier on paper 1 2 3


32 of 47 Hard looking back and forth between paper and computer 2 2 Hard to read screen 2 2 Problems with mouse 1 1 2 Easier to concentrate on paper 1 1 2 Write Poorly on paper 1 1 More comfortable on paper 1 1 More space on paper 1 1 Became confused where to put answers 1 1 Examining these responses, it appears that many mor e students who took the language arts tests recognized the computer's ability to display text that is easy to read and edit as an advantage. Conversely, students who took the math t est felt that the inability to present and manipulate numbers in text was a disadvantage. In p art, these different reactions to performing the tests on computer may explain the ne gative group effect for the math test and the positive group effects for the language arts te sts. However, students' responses provide little insight into the overall group effect for sc ience. The Effect of WPM on Student Performance To further examine the relationship between level o f computer use and students' performance on the language arts tests, separate regression ana lyses were performed for students who performed the tests on paper and those who performe d the tests on computer. For each of these regression analyses, the effect of prior comp uter use on students' performance was estimated controlling for SAT 9 scores. To provide separate estimates for keyboarding speed and for students' survey scores, two sets of regres sions were performed for each subgroup. First, the test score was regressed on SAT 9 score, WPM and Survey. Second, the test score was regressed on SAT 9 score, WPM and Comp-Writing. Since Survey is partially composed of Comp-Writing, effects for each variable are esti mated through separate regressions to avoid redundancy in the data and hence decrease the effects of colinearity. Figures 2 through 5 display the effects each variable had on test per formance for students who took the test on computer and for those who took the test on paper.Figure 2 and 3 show that across all tests, WPM is a weak predictor of students' scores when the test is performed on paper. However, for both l anguage arts tests and the science test, WPM is a good predictor of students' scores when th e test is performed on computer. This suggests that when these tests are performed on com puter, the speed with which a student can type had a significant effect on their performance. However, for the math test, the effect of WPM on students' performance on computer is much le ss pronounced. Figure 2: Effect of WPM on Student Performance Cont rolling for Survey


33 of 47 Figure 3: Effect of WPM on Student Performance Cont rolling for Comp-Writing Figures 4 and 5 indicate that neither the total Sur vey score nor the CompWriting score had a meaningful effect on the performance of stude nts in either group. In fact, when the effect of WPM is considered, both the amount of pri or computer use and use of


34 of 47computers during the writing process have slightly lower effects when the test is taken on computer for the language arts and science tests. Y et, for the math test, the effect is larger and positive. This pattern is difficult to explain. Nonetheless, the weak relationship between either Survey or Comp-Writing and students' performance on computer suggests that students' level of computer use is not as impo rtant as their keyboarding proficiency in predicting their performance on open-ended tests. I n future studies it is highly recommended that measures of keyboarding speed rath er than self-reported levels of computer use are collected and used to examine effe cts of computer and paper administration. Figure 4: Effect of Survey on Student Performance C ontrolling for WPM Figure 5: Effect of Comp-Writing on Student Perform ance Controlling for WPM


35 of 47 Preference and Performance One of the questions this experiment was designed t o address was whether students who performed the test via their preferred medium perfo rmed better than predicted and whether those who did not perform the test on their preferred medium performed worse than predicted. Prior to performing either test, st udents responded to the following survey question: If forced to choose, would you rat her write a paper on computer or on paper? To examine the relationship between preferen ce and performance, a dummy variable was coded 1 if the students' preference wa s the same as the medium on which they performed the test and 0 if their preference a nd performance medium did not match. For each test, students' test scores were regressed on their SAT 9 scores and Match. Table 25 shows that for the science test, students who took the test on their preferred medium did perform significantly better after contr olling for prior achievement. Matching preference with medium of performance did not have a significant effect for the other three tests. Table 25 Test Score Regressed on SAT 9 and Match Language Arts 1 Match=43 NoMatch=74 B SE B Beta TSignif. SAT 9 Reading .007 .001 .42 4.83 <.0001 Match (1=yes) -.367 .510 -.06 .72 .47 Adjusted R2 .15


36 of 47 Language Arts 2 Match=53 NoMatch=47 B SE B Beta TSignif. SAT 9 Reading .005 .001 .40 4.27 <.0001 Match (1=yes) -.522 .422 -.11 1.24 .22 Adjusted R2 .15 Math Match=63 NoMatch=47 B SE B Beta TSignif. SAT 9 Math .016 .002 .72 10.80 <.0001 Match (1=yes) -.948 .561 -.11 -1.69 .09 Adjusted R2 .52 Science Match=51 NoMatch=51 B SE B Beta TSignif. SAT 9 Science .014 .002 .58 7.46 <.0001 Match (1=yes) 1.487 .646 .18 2.30 .02 Adjusted R2 .40 As discussed above, preference for some students se ems to have been influenced by social desirablity. As a result, the relationship b etween preference and performance on the preferred medium may be poorly estimated. Simpl y giving students the alternative to perform open-ended test questions via their "prefer red" medium may not reduce the effect of medium found in this study. Rather, befor e students are given the choice, it might be useful to explain the apparent relationshi p between keyboarding speed and performance.Gender, Keyboarding and Performance on Computers Resent research suggests that females do not use co mputers in school as frequently as males (ETS, 1998). If this research is accurate, it is possible that the keyboarding skill of females is less developed than males. Given the rel ationship between WPM and performance on computer, performing tests on comput er may have an adverse impact on the scores for females.To examine the relationship between gender and WPM, an independent samples t-test was performed using all 229 students included in th e study. To examine whether there were gender differences on computer use and prior a chievement, t-tests were also performed for Survey, Comp-Writing and the SAT 9 co mprehensive NCE. Table 26 indicates that WPM was the only variable for which there was a gender difference. However, on average, it was males' keyboarding spee d that was 3 words per minute


37 of 47 slower than females. This represents an effect size of approximately .68. This difference, however, does not seem to be caused by less compute r experience or less use of computers in the writing process since there were n egligible differences for either Survey or Comp-Writing. Table 26 Gender Differences for WPM, Survey, Comp-Writing and SAT 9 Comprehensive Males=97Females=132 Mean Std. Dev. SE T-value Signif. WPM Males 14.8 4.4 .45 Females 17.8 5.6 .49 4.41 <.001 Survey Males 24.2 5.5 .56 Females 24.5 5.8 .51 .37 .72 Comp-Writing Males 9.4 3.0 .31 Females 9.6 2.6 .22 .56 .58 SAT 9 Comp. Males 408 160 16.3 Females 398 143 12.4 .48 .63 As described above, WPM was a significant predictor for students' performance on computer in all subject areas. But given that males were on average slower keyboarders, one might expect their scores in all tests to be lo wer when performed on computer. Table 27 shows that this was the case for all four tests but that the difference was only significant for the first language arts test. Table 27 Gender Differences for Test Performance on Computer s Mean Std. Dev. SE T-value Signif. LA 1 Males (26) 4.96 2.60 .51 Females (34) 6.50 3.22 .55 1.99 .05


38 of 47 LA 2 Males (24) 4.33 2.57 .52 Females (31) 5.48 .273 .49 1.59 .12 Math Males (21) 8.24 4.38 .96 Females (35) 9.86 3.51 .59 1.52 .13 Science Males (21) 10.48 4.62 1.01 Females (30) 10.97 3.83 .70 .41 .68 Table 28 shows that gender differences were not fou nd for any tests when prior achievement and WPM were controlled. In part, this finding suggests that although males included in this study tended to be slower ke yboarders, they performed as well as females with similar keyboarding and SAT 9 scores. This finding provides further evidence that keyboarding skills play an important role in how well students, regardless of their sex, perform on computers. Table 28 Test Score Regressed on SAT 9, WPM and Gender for Computer Groups Only Language Arts 1 N=60 B SE B Beta TSignif. SAT 9 Reading .004 .002 .28 2.28 .03 WPM .258 .072 .45 3.573 .0007 Sex (1=Male) -.888 .648 -.15 1.37 .18 Adjusted R2 .43 Language Arts 2 N=55 B SE B Beta TSignif. SAT 9 Reading .003 .002 .20 1.51 .14 WPM .246 .059 .59 4.15 .0001 Sex (1=Male) .311 .605 .06 .51 .61 Adjusted R2 .48


39 of 47 Math N=56 B SE B Beta TSignif. SAT 9 Math .011 .002 .53 4.67 .0001 WPM .177 .089 .23 2.01 .05 Sex (1=Male) -.605 .833 -.08 .73 .47 Adjusted R2 .45 Science N=51 B SE B Beta TSignif. SAT 9 Science .011 .003 .49 4.25 .0001 WPM .266 .095 .33 2.79 .008 Sex (1=Male) -.379 .935 -.05 .41 .69 Adjusted R2 .42 Reading Comprehension vs. Writing Items For students whose WPM was at least .5 standard dev iations below the mean, performing either language arts test on computer had a negativ e effect on students' test scores. The negative effect was much larger for the second lang uage arts test than it was for the first. To explore why the effect was larger for the second language arts test, the content of the two tests was examined.Recall that the language arts tests contained two t ypes of items, namely reading comprehension and writing. The first language arts test contained two reading comprehension items and only one writing item while the second language arts test contained two writing items and only one reading co mprehension item. To examine the relationship between item type and the effect at ea ch level of keyboarding speed, separate regressions were performed for each item. Figure 6 indicates that the effect for all items are about the same for students whose WPM is within .5 standard deviations of the mean or at least .5 standard deviations above the mean. However, for students whose WPM is at least .5 standard deviations below the mean, the re seem to be two different effects. Language arts 1 item 1 and language arts 2 items 2 and 3 all seem to have an effect of about -.4. Language arts 1 item 2 and 3 and languag e arts 2 item 1 have effects between 0 and -.1. This pattern, however, does not seem to be related to item format. Although language arts 1 item 1 addresses reading comprehens ion, the two other items showing a similar effect test writing skills. Similarly, alth ough language arts 1 item 2 and language arts 2 item 2 are both reading comprehension, the t hird item in this triad is a writing item. Thus, item format does not seem to explain the diff erences in the effect sizes for the two language arts tests at the low level of keyboarding speed. Figure 6: Effects by Language Arts Item


40 of 47 Explaining Smaller Effect SizesAs noted above, the magnitude of the effects in thi s study are about half the size reported by Russell and Haney (1997). While these positive e ffects are still quite large and represent approximately one half of a standard devi ation difference in test scores, there are three observations that may shed some light on why the effects in this study were less pronounced than in the 1997 study.First, the test scores used in Russell and Haney's study were part of a formal testing program. In the study reported here, the tests were described to students as practice for the spring MCAS administration and thus may not have be en taken as seriously by students, especially those unaccustomed to working on compute r. This was particularly evident during the computer administration. Whereas the aut hor noted only one student being disciplined during four paper administration sessio ns that he observed, nearly 40 behavioral problems (e.g., students talking, studen ts touching each other, or students moving around the room without permission) were add ressed during the seven computer administration sessions that he observed. This incr eased level of disruptions may have occurred in part because students were frustrated b y their inability to type. These disruptions also may have distracted students who d id not experience difficulty keyboarding. Between not being as motivated during a practice test and being distracted more often, students' performance on computer may h ave suffered. In turn, this may have led to under-estimates of the positive effects and overestimates of the negative effects. Second, when the previous study occurred, the ALL s chool was in its third year of reform


41 of 47and was receiving full external support for its tec hnology reforms. For this reason, there was great enthusiasm for the use of technology by t eachers and students. As noted in the previous study, at the time students performed almo st all of their work on computer. Since then, three years have passed, there has been a turn-over in teachers, and the external support for the ALL school has largely dis appeared. For these reasons, it is possible that students in the ALL School are not us ing technology as extensively as they did three years ago. This is supported to some exte nt by the relatively low mean keyboarding speed for students in the ALL school. A lthough the ALL School's mean WPM was significantly higher than that of the Sulli van Middle School, it was still less than 20 WPM. This low keyboarding speed suggests th at although students in the ALL School use computers more often than students in th e Sullivan Middle School, their keyboarding skills are not as developed as one migh t expect if students are using computers on a daily basis. Although there is no di rect data to confirm possible decrease use of computers in the ALL School, a decreased use might partially account for the smaller effect.Finally, given the findings of the previous study a nd the heavy emphasis the Massachusetts Department of Education has placed on schools' performance on MCAS, it is possible that teachers require students to write more on paper in the ALL School now than three years ago in order to improve their perf ormance on open-ended items. Sadly, after sharing this hypothesis with the ALL School's principal, Carol Shilinsky confirmed that in preparation for MCAS, teachers now require students to perform most of their writing on paper. If this was a successful strategy then it would have improved students' scores on paper. In turn, the size of the effect of performing the tests on computer would be decreased.Limitations Despite efforts to create equivalent groups and to control for the confounding effects of scoring handwritten and computer printed responses, reading extensive passages of text on screen, and only using items that had been forma lly validated, this study still had several limitations. First, only a small group of s tudents from one urban district were included. Recent research suggests that computers a re not used the same way in all schools and that there are meaningful differences i n the way students in urban and suburban schools use computers, particularly for ma th (ETS, 1998). These differences may lead to different effects for students in diffe rent settings. Second, the tests were not administered under forma l, controlled testing conditions. This may have decreased motivation, increased distractio ns and led to under-performance for many students. As noted above, this may be particul arly true for students with better keyboarding skills who performed the tests on compu ter. Third, although this study included many more openended items than did the previous study, testing time for each test was limited to si xty minutes. In order to increase the number of items included in the study, the time req uired to respond to items was limited, on average, to 10 minutes for math and science and 20 minutes for language arts. This time limit precluded extended writing and extended math items (requiring more than 10 minutes) from the study. However, MCAS and other te sting programs include more extended open-ended items. And the effect of perfor ming these types of items on computer may be larger given that in order to perfo rm well, students generally need to


42 of 47produce more text.Fourth, the sample of students included in this stu dy had relatively slow keyboarding skills. For this reason, it was not possible to est imate the effect of taking open-ended tests on computer for students who are proficient or adva nced keyboarders. Given the sharp increase in the size of the effect as keyboarding s peed increases from near the mean to .5 standard deviations above the mean, it is possible that the effect of performing tests on computer is even larger for students with more adva nced keyboarding skills.Implications This study suggests that for students who keyboard about 20 words per minute or more, performing open-ended language arts tests on paper substantially underestimates their level of achievement. However, for slower keyboarde rs, performing open-ended tests on computer adversely affects their performance. To pr ovide more accurate estimates of students' achievement, these findings suggest that students who can keyboard at a moderate level should be allowed to compose their r esponses to open-ended items on computers. Conversely, students with weak keyboardi ng speed should compose their responses on paper.This study also demonstrates that for math tests, p erformance on computer underestimates students' achievement regardless of their level of keyboarding speed. This occurred despite efforts to include items that did not require students to draw pictures or graphs to receive credit. Nonetheless, about 20% of the students who performed the math test on computer indicated that they had difficulty showing their work and/or needed scrap paper to work out their solutions. For these reasons, it is likely that the negative effect found in this study underestimates the effec t that would occur if a full range of openended math items were included.This study also re-emphasizes the danger of making inferences about students or schools based solely on paper-and-pencil tests. Similarly, as the public investigates the impact computers have on student learning (Oppenheimer, 19 97), caution should be taken when student learning is measured by tests containing op en-ended items. As found in the previous study, scores on paper and pencil tests fo r students accustomed to working on computer may substantially underestimate students achievement. As computer use in schools and at home continues to increase rapidly, it is likely that more students will develop solid keyboarding skills and, thus, will be adversely affected by taking open-ended tests on paper.Finally, this study provides further evidence that the validity of open-ended tests should be considered in terms of both content and medium o f learning. Until all students have access to and use computers regularly, open-ended t ests administered via a single medium, either paper or computer, will likely under estimate performance of students accustomed to working in the alternate medium. Base d on this study, further research on a larger scale into computers and openended tests i s clearly warranted. Until then, we should exercise caution when drawing inferences abo ut students based on open-ended test scores when the medium of assessment does not match their medium of learning.References


43 of 47Bangert-Drowns, R. L. (1993). The word processor as an instructional tool: A meta-analysis of word processing in writing instruc tion. Review of Educational Research 63(1), 69-93. Beaton, A. E. & Zwick, R. (1990). The Effect of Changes in the National Assessment: Disentangling the NAEP 1985-86 Reading Anomaly Princeton, NJ: Educational Testing Service, ETS.Bunderson, C. V., Inouye, D. K. & Olsen, J. B. (198 9). The four generations of computerized educational measurement. In Linn, R. L ., Educational Measurement (3rd Ed.), Washington, D.C.: American Council on Educati on, pp. 367-407. Burstein, J., Kaplan, R., Wolff, S., & Lu, C. (1997 ). Using lexical semantic techniques to classify free-responses A report issued by Educational Testing Service. P rinceton, NJ.Cizek, G. J. (1991). The effect of altering the pos ition of options in a multiple-choice Examination. Paper presented at NCME, April 1991. ( ERIC) Crocker, L. & Algina, J. (1986). Introduction to Classical and Modern Test Theory Orlando, FL: Harcourt Brace Jovanovich College Publ ishers. Daiute, C. (1986). Physical and cognitive factors i n revising: insights from studies with computers. Research in the Teaching of English 20 (May), p. 141-59. Educational Testing Service. (1998). Does it compute? The relationship between educational technology and student achievement in m athematics Princeton, NJ: Policy Information Center Research Division Educational Te sting Service. Etchison, C. (1989). Word processing: A helpful too l for basic writers. Computers and Composition 6(2), 33-43. Glass, G. & Hopkins, K. (1984). Statistical Methods in Education and Psychology Boston, MA: Allyn and Bacon.Glennan, T. K., & Melmed, A. (1996). Fostering the use of educational technology: Elements of a national strategy Santa Monica, CA: RAND. Grejda, G. F. (1992). Effects of word processing on sixth graders' holistic writing and revision. Journal of Educational Research 85(3), 144-149. Haas, C. & Hayes, J. R. (1986b). What did I just sa y? Reading problems in writing with the machine. Research in the Teaching of English 20(1), 2235. Haas, C. & Hayes, J. R. (1986a). Pen and paper vers us the machine: Writers composing in hard-copy and computer conditions (CDC Technical Report No. 16). Pittsburgh, PA: Carnegie-Mellon University, Communication Design Ce nter. (ERIC ED 265 563). Hannafin, M. J. & Dalton, D. W. (1987). The effects of word processing on written composition. The Journal of Educational Research 80 (July/Aug.) p. 338-42.


44 of 47Hawisher, G. E. & Fortune, R. (1989). Word processi ng and the basic writer. Collegiate Microcomputer 7(3), 275-287. Hawisher, G. E. (1986, April). The effect of word p rocessing on the revision strategies of college students. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. (ERIC ED 2 68 546) Kerchner, L. B. & Kistinger, B. J. (1984). Language processing/word processing: Written expression, computers, and learning disable d students. Learning Disability Quarterly 7(4), 329-335. Kurth, R. J. (1987). Using word processing to enhan ce revision strategies during student writing activities. Educational Technology 27(1), 13-19. MacArthur C. & Graham, S. (1987). Learning disabled students' composing under three methods of text production: Handwriting, word proce ssing and dictation. Journal of Special Education 21(3), 22-42. MacArthur, C. A. (1988). The impact of computers on the writing process. Exceptional Children 54(6), 536-542. Mourant, R. R., Lakshmanan, R. & Chantadisai, R. (1 981). Visual Fatigue and Cathode Ray Rube Display Terminals. Human Factors 23(5), 529-540. Nichols, L. M. (1996). Pencil and paper versus word processing: a comparative study of creative writing in the elementary school. Journal of Research on Computing in Education 29(2), 159-166. Oppenheimer, T. (1997). The computer delusion. The Atlantic Monthly 280(1), 45-62. Owston, R. D. (1991). Effects of word processing on student writing in a high-computer-access environment (Technical Report 91-3). North York, Ontario: York University, Centre for the Study of Computers in Ed ucation. Phoenix, J. & Hannan, E. (1984). Word processing in the grade 1 classroom. Language Arts 61(8), 804-812. Powers, D., Fowles, M, Farnum, M, & Ramsey, P. (199 4). Will they think less of my handwritten essay if others word process theirs? Ef fects on essay scores of intermingling handwritten and word-processed essays. Journal of Educational Measurement 31(3), 220-233.Robinson-Stavely, K. & Cooper, J. (1990). The use o f computers for writing: Effects on an English composition class. Journal of Educational Computing Research 6(1), 41-48. Russell, M. & Haney, W. (1997). Testing writing on computers: an experiment comparing student performance on tests conducted vi a computer and via paper-and-pencil. Education Policy Analysis Archives 5(3). Available online at, M.C. & Crealock, C. M. (1986, June). A longi tudinal study of the efficacy of


45 of 47 computer technology for improving the writing skill s of mildly handicapped adolescents. Paper presented at at the Invitational Research Sym posium on Special Education Technology, Washington, DC.Snyder, T. D. & Hoffman, C. (1990). Digest of Education Statistics Washington, DC: U. W. Department of Education.Snyder, T. D. & Hoffman, C. (1994). Digest of Education Statistics Washington, DC: U. W. Department of Education.Vacc, N. N. (1987). Word processor versus handwriti ng: A comparative study of writing samples produced by mildly mentally handicapped stu dents. Exceptional Children 54(2), 156-165.Williamson, M. L. & Pence, P. (1989). Word processi ng and student writers. In B. K. Briten & S. M. Glynn (Eds.), Computer Writing Environments: Theory, Research, an d Design (pp. 96-127). Hillsdale, NJ: Lawrence Erlbaum & As sociates. Zandvliet, D. & Farragher, P. (1997). A comparison of computeradministered and written tests. Journal of Research on Computing in Education 29(4), 423-438.About the AuthorMichael Russell Email: Michael Russell is a research fellow for the Nation al Board on Educational Testing and Public Policy and a research associate in the Cente r for the Study of Testing, Evaluation and Educational Policy at Boston College. His resea rch interests include standards based reform, assessment, and educational technology.Copyright 1999 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, or reach him at College of Education, Arizona State University, Tempe, AZ 85287-0211. (602-965-96 44). The Book Review Editor is Walter E. Shepherd: The Commentary Editor is Casey D. Cobb: .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Andrew Coulson


46 of 47 Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov 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 Richard M. Jaeger University of NorthCarolina—Greensboro Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Dewayne Matthews Western Interstate Commission for Higher Education 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 Hugh G. Petrie SUNY Buffalo Richard C. Richardson New York University Anthony G. Rud Jr. Purdue University Dennis Sayers Ann Leavenworth Centerfor Accelerated Learning Jay D. Scribner University of Texas at Austin Michael Scriven Robert E. Stake University of Illinois—UC Robert Stonehill U.S. Department of Education Robert T. Stout Arizona State University David D. Williams Brigham Young University EPAA Spanish Language Editorial BoardAssociate Editor for Spanish Language Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico Adrin Acosta (Mxico) Universidad de Guadalajara J. Flix Angulo Rasco (Spain) Universidad de Cdiz


47 of 47 Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho Alejandro Canales (Mxico) Universidad Nacional Autnoma Ursula Casanova (U.S.A.) Arizona State Jos Contreras Domingo Universitat de Barcelona Erwin Epstein (U.S.A.) Loyola University of Josu Gonzlez (U.S.A.) Arizona State Rollin Kent (Mxico)Departamento de InvestigacinEducativa-DIE/ 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 Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma deMxicohumberto@servidor.unam.mxAngel Ignacio Prez Gmez (Spain)Universidad de Daniel Schugurensky (Argentina-Canad)OISE/UT, Simon Schwartzman (Brazil)Fundao Instituto Brasileiro e Geografiae Estatstica Jurjo Torres Santom (Spain)Universidad de A Carlos Alberto Torres (U.S.A.)University of California, Los


Download Options

Choose Size
Choose file type
Cite this item close


Cras ut cursus ante, a fringilla nunc. Mauris lorem nunc, cursus sit amet enim ac, vehicula vestibulum mi. Mauris viverra nisl vel enim faucibus porta. Praesent sit amet ornare diam, non finibus nulla.


Cras efficitur magna et sapien varius, luctus ullamcorper dolor convallis. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Fusce sit amet justo ut erat laoreet congue sed a ante.


Phasellus ornare in augue eu imperdiet. Donec malesuada sapien ante, at vehicula orci tempor molestie. Proin vitae urna elit. Pellentesque vitae nisi et diam euismod malesuada aliquet non erat.


Nunc fringilla dolor ut dictum placerat. Proin ac neque rutrum, consectetur ligula id, laoreet ligula. Nulla lorem massa, consectetur vitae consequat in, lobortis at dolor. Nunc sed leo odio.