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Educational policy analysis archives
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Educational policy analysis archives.
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260
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c November 11, 2003
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Effects of full and alternative day block scheduling on language arts and science achievement in a junior high school / Chance W. Lewis, R. Brian Cobb, Marc Winokur, Nancy Leech, Michael Viney [and] Wendy White.
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Arizona State University.
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1 of 25 A peer-reviewed scholarly journal Editor: Gene V Glass College of Education Arizona State University Copyright is retained by the first or sole author, who grants right of first publication to the EDUCATION POLICY ANALYSIS ARCHIVES EPAA is a project of the Education Policy Studies Laboratory. 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 Volume 11 Number 41November 11, 2003ISSN 1068-2341The Effects of Full and Alternative Day Block Sched uling on Language Arts and Science Achievement in a Junior H igh School Chance W. Lewis R. Brian Cobb Marc Winokur Colorado State University Nancy Leech University of Colorado--Denver Michael Viney Wendy White Poudre School DistrictCitation: Lewis, C. W., Cobb, R.B., Winokur, M., Le ech, N., Viney, M. & White, W. (2003, November 11). The effects of full and alternative d ay block scheduling on language arts and science achievement in a junior high school Education Policy Analysis Archives, 11 (41). Retrieved [Date] from http://epaa.asu.edu/epaa/v11n41/.AbstractThe effects of a full (4 X 4) block scheduling prog ram and an alternate day (AB) block scheduling program in a ju nior high

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2 of 25 school were under investigation in this study throu gh the use of an ex post facto matched sampling design. Measures investigated were standardized achievement tests in science and language arts. Both forms of block scheduling had b een in place for several years, and one teacher in science and o ne teacher in language arts had taught students under both forms of scheduling. Because the sampling designs and analys es were different for the science and the language arts are as, two studies are reported here—each examining the effects of 4 x 4, AB, and traditional scheduling with attribute variables of gender and student skill levels in each analysis. Results cons istently show students in both forms of block scheduling outperfo rming students in traditional scheduling, and that AB block schedu ling has the largest positive impact on low-achieving students. Defined as 90 – 120 minute class periods versus the traditional 45 – 55 minute class period, block scheduling is one of the fastes t growing educational reform initiatives in secondary public education during th e last two decades. Although block scheduling has been a viable scheduling choic e for many schools for over forty years, it was not until the late 1980s that b lock scheduling became more widespread throughout secondary schools in the Unit ed States. The growth in block scheduling was a reaction to the notion that “close, personal relationships among students and teachers had become less likely in traditional environments as student numbers and student-teacher ratios increased” (Nichols, 2000, p. 135). As of 1995, Canady and Ret tig estimated that 50% of American high schools had implemented some form of block scheduling, with some states (e.g., North Carolina, Virginia) having much higher rates. In the literature, block scheduling first appeared as modular scheduling, flexible scheduling, or modular flexible scheduling (Stewart & Shank, 1971; Wood, 1970). Accordingly, block scheduling can be impleme nted in many different ways with numerous modifications, often called “hyb rids” in current literature. Whether called an intensive block, 4 x 4 block, AB plan, or modified block—all types of block scheduling have the commonality of i ncreasing the time available for instruction by extending classes beyond the tra ditional 50-minute class (Weller & McLeskey, 2000). Full Block (4 x 4) Semester PlanThe most popular method of block scheduling is the 4 x 4 semester plan, also known as “Accelerated Schedule” or “Copernican.” In a 4 x 4 semester plan, students attend the same four 90-minute classes eve ry day of the week. By attending each class every day, a student can compl ete four yearlong equivalent courses in one semester, although the am ount of time spent in the course may be slightly less than in traditional sch eduling (Queen, Algozzine, & Eddy, 1997). The plan offers teachers a manageable timetable, as they teach three classes with a daily planning period rather t han five or six classes with a planning period every other day (Edwards, 1995).Many researchers have explored student, teacher, an d administrator perceptions of the 4 x 4 semester plan. Specificall y, researchers have offered

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3 of 25 findings on classroom climate, instructional approa ches, student/teacher relationships, and overall satisfaction with block scheduling. As for perceptions regarding the overall effectiveness of the 4 x 4 se mester plan, parents have consistently perceived improvement in the academic and social outcomes of students participating in a block scheduling format (Eineder & Bishop, 1997; Thomas & O’Connell, 1997a). As for teachers, Edward s (1995) found that after one semester with a 4 x 4 schedule, they reported s ignificant improvements in teaching effectiveness. Staunton (1997) found that teachers with five or more years of teaching in the 4 x 4 semester plan had si gnificantly higher perceived ratings of assessment techniques than did teachers in a traditional scheduling environment. In a survey of four 4 x 4 block schedu ling programs, Wilson and Stokes (2000) found that, overall and over time, st udents perceived block scheduling to be an effective approach, especially if they thought that teachers used a greater variety of teaching strategies in cl ass. Thomas and O’Connell (1997b) found that students felt 4 x4 block classe s offered fewer chances to cheat and increased fairness in grading. Additional ly, Edwards (1995) found that a majority of students found it easier to focu s on assignments and understand the lessons better.As for class size and classroom climate, two recent studies have found that teachers perceived an increase in class size with 4 x 4 semester plan scheduling (Limback & Jewell, 1998; Moore, Kirby, & Becton, 1997). However, Wilson and Stokes (2000) found that students percei ved the 4 x 4 semester plan to offer a better instructional environment th an in traditional scheduling (e.g., teachers get to know them better, greater va riety of instruction). In addition, teachers perceived student/teacher relati ons to be better with 4 x 4 semester plan as there was more time for concentrat ed interactions (Eineder & Bishop, 1997; Skrobarcek et al., 1997; Thomas & O’C onnell, 1997b). O’ Neill (1995) also argued that discipline problems have dr opped at many of the schools using block schedules because of this enhan ced climate. These findings suggest that the 4 x 4 semester plan forma t may increase the number of students per class while creating a more product ive learning environment. The 4 x 4 semester plan is designed to create a new and different teaching and learning experience for students and teachers. Stau nton (1997) found that teachers with more years of experience were signifi cantly more satisfied with instruction in 4 x 4 semester plan scheduling than in traditional scheduling. However, Baker and Bowman (2000) found that teacher s with less experience were more likely to view block scheduling positivel y than were more experienced teachers, as they appeared more willing to make the necessary instructional changes. Using direct observations an d in-depth interviews, Queen, Algozzine, and Eddy (1997), found that teach ers appreciated the flexibility in classroom instruction, longer planni ng periods, greater course offerings, and more time for in-depth study that bl ock scheduling provided.Alternate Day Block (AB) PlanThe Alternate Day Plan for block scheduling is also known as AB, Odd/Even, and Day 1/Day 2 respectively. With AB scheduling, s tudents take three or four 90-120 minute classes on alternating days for an en tire school year. Many school districts have found this mode of block sche duling conducive to school

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4 of 25 environments versus the 4 x 4 block schedule and th e traditional 45-55 minute class schedule.The research literature much more sparse on AB sche duling. However, Buchman, King, and Ryan (1995) found that both sche duling formats produced very positive perceptions regarding the impact of A B block scheduling on safety, success, involvement, commitment, interpersonal com petency, and satisfaction. According to Payne and Jordan’s (1996) study on the instructional impact of AB block scheduling, “teachers reported that they enjo yed having more time to give students individual assistance; opportunities to ge t to know the students personally; time for more creative and meaningful s tudent work; and the ability to structure a full lesson” (p. 18). Thus, these ad vantages of an 85-minute block period led to a less stressful and more flexible cl assroom climate (Payne & Jordan, 1996; Weller & McLeskey, 2000). As a result supportive teachers working under this type of block scheduling develop curricula focused on cooperative learning exercises to take advantage of the longer blocks of time (Weller & McLeskey, 2000). Payne and Jordan (1996) also found that teachers were positive about the way classes were scheduled, staff development, and planning time afforded by an AB schedule.As for the impact on learning Payne and Jordan (199 6) did not find significant differences in students’ perceptions regarding the efficacy of the AB scheduling plan as compared with traditional scheduling. On th e downside, Payne and Jordan (1996) found that teachers reported needing more resources for varying instruction and more time for planning. Shortt and Thayer (1995) found that teachers believed that students needed instruction in a subject every day to maximize the learning process. 4 x 4 Semester Plan and AB Plan ComparedAs many school districts look to take on some form of block scheduling, it is necessary to look at literature for analysis of whi ch block scheduling option seems most favorable by students, teachers, and adm inistrators. The majority of the literature on these two forms of block sched uling has focused on the following characteristics: student grades, class si ze, classroom climate, time issues, instruction, and dropout and attendance rat es. Pisapia and Westfall (1997a; 1997b) found that teac hers in the 4 x 4 semester plan were more satisfied with students’ grades than were teachers in AB schedule. However Hamdy and Urich (1998) explored t eachers’ perceptions of class size and classroom climate in both block sche duling formats compared with traditional scheduling and found teachers did not think class size was reduced with block schedules nor was classroom clim ate perceived more favorably with either block schedule formats.The way class time is used has also been an importa nt factor in implementing either 4 x 4 semester plan or AB block plans. Teach ers often perceived a greater need to change the pace and type of instruc tion (e.g., group learning) with both the 4 x 4 semester plan and AB plans (Pis apia & Westfall, 1997b; Swope, Fritz, & Goins, 1998).

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5 of 25 Research has also shown mixed findings in attendanc e and dropout rates for students in both the 4 x 4 semester and AB plans. P isapia & Westfall (1997a, 1997c) found that teachers perceive better attendan ce with the 4 x 4 semester plan than with the AB plan. Conversely, other studi es show no reduction in dropouts or increased attendance with either the 4 x 4 semester or AB plans as compared to traditional scheduling (e.g., Skrobarce k et al., 1997). As for overall satisfaction, students in the 4 x 4 semester plan were found to be more satisfied with the number of courses available for them in which to enroll than both students in AB plan and in traditional sc heduling (Pisapia & Westfall, 1997b). According to Lapkin, Harley, and Hart (1997 ), three-quarters of students believed that the longer periods in both t he 4 x 4 semester and AB plans made it easier to speak French and to interac t with the teacher. However, a similar majority of students reported being more tired, less attentive, and more bored in the longer French periods as compared with the shorter classes in traditional scheduling plans.ConclusionIt is difficult to produce any consistent conclusio ns from the recently published literature on block scheduling as most researchers disagree about the positive and negative effects of 4 x 4 semester plan and AB scheduling. However, there are certain advantages and disadvantages to block s cheduling that have been identified through both quantitative and qualitativ e research on the subject. AdvantagesOverall, the research provides modest support for t he presumed advantages of block scheduling (Payne & Jordan, 1996). According to Lapkin et al. (1997), block scheduling may promote higher levels of readi ng and writing proficiency. Students participating in block scheduling plans ar e also appear to show greater gains in grade point average as compared with tradi tional instructional formats (Edwards, 1995). Nichols (2000) concluded that lon ger class periods encourage teachers to develop more effective behavi oral management techniques rather than relying on administrative di sciplinarians. In addition, Nichols (2000) argued that the decrease in quantita tive minutes of classroom instruction is more than offset in the quality of s tudent-teacher interaction in a block scheduling format.DisadvantagesAdopting a block schedule has its disadvantages, es pecially around designing instruction appropriate for the longer classes (O’N eill, 1995). Additionally, although time is extended on a daily basis for all types of block scheduling, the actual class time may actually drop around ten perc ent (Queen et al., 1997). As a result, some teachers will inevitably cover less material because of the reduced number of total instructional minutes (O’Ne ill, 1995). Advocates of Advancement Placement programs have also expressed concerns about the preparation of students who take fall class and spr ing exams (O’Neill, 1995), a

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6 of 25 concern associated with 4 x 4 block scheduling in p articular. In addition to concerns about the scheduling of advanced placement courses, the sequencing of foreign language and music are also challenges t o the block scheduling format (Shortt & Thayer, 1995).Furthermore, there are concerns about the effective ness of block scheduling for all student populations. Specifically, transfer stu dents and lower-achieving students may not garner the same benefits as the ot her students because of the faster pace and tighter structure of block sche duling (Nichols, 2000; Shortt & Thayer, 1995). These students may actually experien ce lower levels of achievement and success “in schools where block sch eduling was poorly planned for and quickly implemented” (Nichols, 2000 p. 145). Students may also have difficulty in keeping track of their book s, due dates for assignments, and when quizzes and exams are scheduled (Weller & McLeskey, 2000). In addition, “absences are magnified within the block schedule because of the time between class periods and because there is lim ited time within the schedule for students to contact teachers to see wh at work they have missed” (Weller & McLeskey, 2000, p. 215). Thus, “students who miss class or do not keep up with their studies are more likely to fail” (Edwards, 1995, p. 27). Finally, Shortt and Thayer (1995) concluded that ac ademic pacing is a concern when switching to block scheduling, as teachers may struggle with meeting instructional objectives and curriculum standards. According to Queen et al. (1997), other negative aspects of block scheduling include too much independent study, limited number of electives, ove remphasis on lecture, and teacher fatigue toward the middle of the second sem ester.Research QuestionsBecause of the equivocation in the research literat ure on achievement effects of block scheduling a priori research hypotheses were not posed. Instead resear ch questions were developed around the major main effe cts and interactions of the instructional format variable (4 x 4, AB, and tradi tional schedules) attribute variables (gender and achievement level) and outcom e variables (science process, science content, and language arts). What is the effect on science content, science proc ess, and language arts achievement of learning that content in 4 x 4 block scheduling, AB block scheduling, or traditional scheduling? How do those effects vary depending on student gend er and prior student achievement levels? Although these research questions are posed across all outcome and independent variables, the language arts study and the science studies were sufficiently different in sampling designs and anal yses to merit separate methodological and results sections. Language Arts StudyMethod

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7 of 25 Population, Sample, and Sampling DesignThe theoretical population for this ex post facto study is students who attend either junior high school or middle school in moder ately sized cities in the mountain west. The actual sample for this study was 111 students who attended two different junior high schools in a cit y of approximately 125,000 in Colorado. In an attempt to overcome some of the wea knesses in causal inferences associated with ex post facto designs, a two-stage sampling design was used and is described below.School selection stage of the sampling design. Block Schedule School (BSS) was the school of interest in this study primarily because the school had implemented both 4 x 4 block scheduling and AB bloc k scheduling simultaneously for several years, representing a re latively unique opportunity to examine differential effects of both forms of block scheduling while controlling for school effects. Additionally, the same language arts teacher taught in both the 4 x 4 and AB block scheduling modalities, and t aught the same curriculum across both modalities, adding important internal c ontrols for both curriculum and instruction across both conditions. Finally, th is teacher taught both 4 x 4 and AB block scheduling modalities in the same acad emic years of this study (i.e. 4 x 4 classes in the morning; AB classes in t he afternoon) thus helping to control for differential historical threats across these two conditions. To generate a comparison sample of students from a traditionally-scheduled school, a comparable school—Traditional Schedule Sc hool (TSS) was selected. Although TSS was considerably smaller in total numb er of students (450 students versus 750 for BSS), the similarities of t hese two schools on other important features were quite close. Both had a rel atively mature teaching force; both were located on the same sector of the city, t hat is very heterogeneous in the types of families that live there; and both had very similar 2001 reading (72% BSS versus 69% TSS); writing (48% versus 4 1% respectively); and mathematics (51% versus 52% respectively) proficien cy ratings on the state’s high stakes examinations. TSS also averaged approxi mately 4 students less across all grade levels in student/teacher ratio th an BSS. Student selection stage of the sampling design. The sample of students from BSS in the 4 x 4 block scheduling and AB block sche duling groups were those students in the 1998-1999 and 1999-2000 academic ye ars who were taught their language arts courses by the instructor assoc iated with this study. These students numbered 131 students in 4 x 4 block sched uling and 134 in AB scheduling groups. From these initial numbers, data were collected from the school district database on these students’ 6th gra de language arts Iowa Test of Basic Skills (ITBS) test scores, converted into Nor mal Curve Equivalents (NCE’s). Due to missing data on these ITBS scores, these initial sample sizes were reduced to 102 and 95 language arts students i n 4 x 4 and AB block scheduling respectively.A random sample of approximately 60 students from e ach of the two years was then drawn from TSS and 6th grade language arts ITB S test scores, converted into NCE’s were collected on each of these students Again, missing ITBS test

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8 of 25 score data reduced these TSS samples down to 97 stu dents. A Pearson correlation was then conducted for these 294 correl ating these ITBS scores with the outcome variable of interest – the students’ 9t h grade language arts RIT score (described below). The correlation was .75, w hich suggested the 6th grade ITBS score was an excellent matching variable on which to equate individual students.The final student sampling process then, involved m atching individual students in each of the three instructional format groups by gender and by 6th grade ITBS scores. To do this matching process the studen ts were sorted by group, and then by gender and ITBS scores in language arts The process was then followed wherein, for example, a male student who w as in the 4 x 4 block scheduling group and who might have had a language arts 6th grade ITBS score of 33.2 was matched with a male from each of the other two instructional groups who also had a language arts ITBS score of 3 3.2. This matching process was followed producing a 100% match on gend er, and a better than 90% exact match on ITBS scores. In those cases wher e there was not a perfect 3-group match on ITBS scores, at least two of the t hree scores were an identical match, and the off-matched score was neve r more that +/2 NCE’s. In order to maintain this high level of matching, howe ver, a significant attrition occurred in each database. The final language arts sample ended up having 37 complete cases with the dataset organized for a mix ed ANOVA analysis. InterventionsStudents in the BSS science and language arts class es were enrolled in either a 4 x 4 block format or an AB block format. Student s in the AB block format met every other day throughout the entire school year. Students in the 4 x 4 block format met every day of the week for a single semes ter. Those students in the 4 x 4 block format who enrolled in the fall semester took the outcome achievement tests in language arts and science (see description below) in the first week of December. Students in the 4 x 4 block format who enrolled in the spring semester and all students in the AB block fo rmat took the outcome tests in second week of April. The curriculum, instructio nal formats, laboratory activities, projects, and other in-class activities were identical for students in both block formats and within each of the science a nd language arts curricula. Students in the TSS received instruction in science and language arts for the entire academic year in 50 minute classes every day of the week. VariablesThe dependent variable for this study was the stude nts’ 9th grade language arts RIT score on the criterion-referenced levels test w hich was administered in the late fall and late spring of each year of this stud y. The levels test (Northwest Evaluation Association, 1997) is a well-established achievement test battery that allows school districts to measure growth in s tudent learning from one year to the nextThe within subjects variable was the instructional format used to teach language arts – with three levels – 4 x 4 block scheduling, AB block scheduling, and

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9 of 25 traditional scheduling. The between groups variable s for this study were the students’ gender, and the students achievement leve ls in language arts as they entered junior high school. To create this achievem ent level variable, the students’ 6th grade ITBS scores were sorted above a nd below the median level of this ITBS score creating a two-level variable.AnalysisThe data for this study were analyzed using a 3 x 2 x 2 mixed ANOVA with repeated measures on the first factor (instructiona l format). Gliner & Morgan (2000) asserted that the appropriate analytic techn ique for matched student sampling designs is to treat the grouping variable as a within subjects variable and use repeated measures analyses as the statistic A total of 37 complete cases were used in this analysis.ResultsTable 1 presents descriptive information about the samples of students who were included in the language arts analysis. Levene ’s test for equality of variances proved non-significant for all three inst ructional formats and Mauchly’s test for sphericity also proved non-signi ficant (X2 = 0.129, df = 2, p = .938).Table 2 presents the ANOVA source table for this la nguage arts analysis. As can be seen, achievement in language arts at the 9t h grade level varied significantly across instructional format, F (2, 66) = 4.89, p = .01. The strongly significant differences on the main effect of achie vement level suggest appropriate separation between the two achievement level groups; similarly, the negligible main effect on gender suggests relative equality of these two groups. Statistically significant effects were also found o n the interaction between instructional format and gender, F (2, 66) = 3.16, p < .05, and between instructional format and achievement level F (2, 66) = 8.06, p < .01. Post hoc analyses using a Tukey HSD are presented in Table 3 for all statistically significant pairwise comparisons (excluding gender and achievement level main Table 1. Means and Standard Deviations for 9th Grad e Language Arts Achievement Broken out by Instructional Format, Gen der, and Achievement Level MalesFemales Instructional FormatMnSDMnSD Low Achieving Group 4 x 4 Block Scheduling222.5084.44218.5088.18AB Block Scheduling225.3884.14226.5087.09Traditional Scheduling212.0087.87220.1387.49 High Achieving Group 4 x 4 Block Scheduling230.7897.31227.42125.76

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10 of 25 AB Block Scheduling230.0097.26230.83127.71Traditional Scheduling 232.5697.33231.17125.69effects, and the instructional format by gender int eraction – see Figure 1 and corresponding narrative for explanation) along with effect sizes. As can be seen in Table 3 and Figure 1, AB block sc heduling generated a small to moderate main effect over traditional scheduling (Cohen, 1988); 4 x 4 block scheduling did not. Much more interesting and power ful, however, were the effects that both forms of block scheduling seemed to hold for those students who entered junior high school in the bottom half Table 2. Analysis of Variance for 9th Grade Languag e Arts Achievement as a Function of Instructional Format, Achievement Lev el, and GenderSourcedfSSMSFp Instructional Format 2385.60179.304.89.01 Achievement Level 12501.302501.3038.58.00 Gender 11.331.330.02.89 Instructional Format x Gender2231.31115.653.16.045Instructional Format x Achievement Level2590.85295. 428.06.001 Achievement Level x Gender163.0263.020.97.33Instructional Format x Achievement Level x Gender21 41.7570.871.93.15 Error (Instructional Format662419.2236.66 Table 3. Statistically Significant Pairwise Compari sons and Effect Sizes for the Language Arts AnalysesPairwise ComparisonsM diffp daAB Block Schedule versus Traditional Schedule3.54.0 06.40 Low Achievers in 4 x 4 Block Schedule versus Low Ac hievers in Traditional Schedule 3.90.025.51 Low Achievers in AB Block Schedule versus Low Achie vers in Traditional Schedule 7.09.0001.39acalculated using weighted, pooled standard deviatio n formula

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11 of 25 Figure 1. Mean language arts RIT Scores for students in 4 x 4, AB, and traditional schedules broken out by gender and achi evement levels. of language arts achievement distribution. Here, bo th forms of block scheduling had significant advantages over traditional schedul ing, with 4 x 4 block scheduling generating a moderately strong effect si ze, and AB block scheduling demonstrating a very large effect size – nearly thr ee times that of 4 x 4 block scheduling. Looking at Figure 1, it appears that th e significant format by gender interaction was due in large part to the disparity of scores in the traditionally-scheduled school and hence does not a ppear to be a genuine effect worth reporting. Science Content and Process StudyMethodPopulation, Sample, and Sampling DesignThe theoretical population for this ex post facto study is identical to the language arts study above. The actual sample for bo th of these science analyses was 340 students. These students were draw n from the same two schools as the language arts study; however, the la ck of a well-suited matching variable changed the sampling design and subsequent analyses. Hence the school selection stage of the two stage sampling de sign described in the language arts study was exactly the same for this s cience content and process study. For example, the same science teacher taught in both the 4 x 4 and AB block scheduling modalities, and taught the same cu rriculum across both

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12 of 25 modalities and in the same academic years of this s tudy. Student selection stage of the sampling design. The sample of students from BSS in the 4 x 4 block scheduling and AB block sche duling groups were those students in the 1998-1999 and 1999-2000 academic ye ars that were taught their science courses by the instructor associated with this study. These students numbered 114 students in 4 x 4 block sched uling and 102 in AB scheduling groups. Similar to the language arts stu dy, data were collected from the school district database on these students’ 6th grade mathematics ITBS test scores, converted into Normal Curve Equivalents (NC E’s). Due to missing data on these ITBS scores, these initial sample sizes we re reduced to 88 and 83 students in 4 x 4 and AB block scheduling respectiv ely. A random sample of approximately 60 students from e ach of the two years was then drawn from TSS and 6th grade mathematics ITBS test scores, converted into Normal Curve Equivalents (NCE’s) were collecte d on each of these students. Again, missing ITBS test score data reduc ed these TSS samples down to a total of 97 students across the two years of the study. Then, two Pearson correlations were conducted for these 264 s tudents correlating these ITBS scores with the outcome variables of interest – the students’ 9th grade science process and content RIT scores. These corre lations were .39 for the science content/ITBS math correlation and .41 for t he science process/ITBS math correlation, which, although both statisticall y significant at the p < .01 level, did not correlate well enough to justify the ir use as a criterion for a matched sampling design. Hence, this study was cond ucted and analyzed as a factorial between groups design.VariablesThe dependent variables for this sub-study were the students’ 9th grade science content and process RIT scores on the crite rion-referenced levels test which was administered in the late spring of each y ear of this study. The three between groups variables for this study were: (a) t he grouping variable, with three levels (4 x 4 block scheduling, AB block sche duling, and traditional scheduling); (b) students’ gender; and (c) the stud ents achievement levels in 6th grade mathematics as they entered junior high s chool, blocked into two groups—those at the median and above, and those bel ow the median. AnalysesA preliminary one-way ANOVA was conducted using the mathematics ITBS NCE scores as the dependent variable and the instru ctional formats as the grouping variable. This analysis tested the viabili ty of the assumption that the matching process using math ITBS scores served to e quate, in some measure, the students in the three groups. This ANOVA proved non-significant F (2, 267) = .149, p = .862, lending some credibility to the assumption of equality of groups.The outcome data for this sub-study were analyzed u sing two 3 x 2 x 2 between groups ANOVA’s. The decision was made not to use a single MANOVA due to the high correlation (r = .70) between the two depe ndent variables (science

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13 of 25 process and science content). Cole, Maxwell, Arvey, & Salas (1994) recently recommended using separate univariate ANOVA’s when the expected effect sizes for both analyses are reasonably large and co nsistently in the same direction, and when there is a high correlation bet ween the two dependent variables. Science content. Table 4 presents descriptive information about the science content sample. Levene’s statistic, testing the ass umption of equality of variances across the various Table 4. Means and Standard Deviations for 9th Grad e Science Content Achievement Broken out by Instructional Format, Gen der, and Achievement Level MalesFemales Instructional FormatMnSDMnSD Low Achieving Group 4 x 4 Block Scheduling218.741915.11218.592210.81AB Block Scheduling214.761713.95217.29216.07Traditional Scheduling210.83249.30207.85206.17 High Achieving Group 4 x 4 Block Scheduling223.303011.50220.94179.59AB Block Scheduling221.33158.15222.00309.62Traditional Scheduling222.54268.36219.68288.41levels of the three independent variables proved st atistically significant, F (11, 257) = 2.04, p < .03. Hence, for all post hoc tests the Games-How ell test was used which Field (2000, p. 276) recommended to be u sed when samples sizes are relatively small and unequal, and the assumptio n of homogeneity of variance has been violated.Table 5 presents the ANOVA source table for this sc ience content analysis. As can be seen, achievement in science content at the 9th grade level varied significantly by instructional format, F (2, 257) = 6.40, p = .002. Statistically significant effects were also found on the interact ion between instructional format and achievement level, F (2, 257) = 4.22, p < .016. Of course, the statistically significant main effect for achieveme nt level was not of interest in this Table 5. Analysis of Variance for 9th Grade Science Content Achievement as a Function of Instructional Format, Achievement Level, and GenderSourcedfSSMSFp Instructional Format 21279.65639.826.40.002 Achievement Level 13095.663095.6630.96.000 Gender 147.3447.340.47.49

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14 of 25 Instructional Format x Gender2220.73110.371.10.33Instructional Format x Achievement Level2844.69422. 354.22.016 Achievement Level x Gender127.6527.650.28.60Instructional Format x Achievement Level x Gender21 8.019.010.09.91 Error (Instructional Format)25725693.3799.99 analysis; no significant main or interaction effect s were found on the gender variable. Post hoc analyses using the Games-Howell statistic along with effect size estimates are presented in Table 6 for all sta tistically significant pairwise comparisons; Figure 2 graphically displays these co mparisons. Table 6. Statistically Significant Pairwise Compari sons and Effect Sizes for Science Content AnalysesPairwise ComparisonsM diff p da4 x 4 Block Schedule versus Traditional Schedule4.8 2.009.44 Low Achievers in 4 x 4 Block Schedule versus Low Ac hievers in Traditional Schedule 9.18.003.89 Low Achievers in AB Block Schedule versus Low Achie vers in Traditional Schedule 6.68.0221.01acalculated using weighted, pooled standard deviatio n formula As can be seen in Table 6 and Figure 2, 4 x 4 block scheduling generated a moderately strong main effect over traditional sche duling; AB block scheduling did not. This finding is a reversal of the block sc heduling main effect found in the language arts analysis above, but still suggest s an advantage for block scheduling formats over traditional scheduling. As with the language arts analysis, the more interesting and powerful effects were found in favor of both forms of block scheduling for those students who en tered junior high school in the bottom half of mathematics achievement distribu tion. Here, both forms of block scheduling had significant advantages over tr aditional scheduling, with both forms of scheduling demonstrating large effect sizes over traditional scheduling. Figure 2 displays this pattern of findi ngs. Of equal interest,

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15 of 25 Figure 2. Mean science content RIT scores for students in 4 x 4, AB, and traditional schedules broken out by gender and achi evement levels. although not reported below in Table 6 is that the only subgroup that the high achievers in traditional scheduling format outperfo rmed with statistically significant differences was the traditionally sched uled low achievers. Thus it appears that block scheduling may be capable ofbringing low achieving students to the levels of th eir high achieving counterparts who receive science instruction in traditional sche duling formats. Science process. Table 7 presents descriptive information about the science process sample. Levene’s statistic, testing the ass umption of equality of variances across the various levels of the three in dependent variables proved statistically significant, F (11, 255) = 2.68, p < .003. Again, then, all post hoc tests used the Games-Howell statistic. Table 7. Means and Standard Deviations for 9th Grad e Science Process Achievement Broken out by Instructional Format, Gen der, and Achievement Level MalesFemales Instructional FormatMnSDMnSD Low Achieving Group 4 x 4 Block Scheduling220.63199.62222.092212.56AB Block Scheduling213.351717.69219.862110.86

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16 of 25 Traditional Scheduling210.542411.75206.15207.80 High Achieving Group 4 x 4 Block Scheduling223.43>3013.13220.00178.78AB Block Scheduling220.40157.89224.773110.55Traditional Scheduling223.35267.58222.82287.41Table 8 presents the ANOVA source table for the sci ence process analysis. As can be seen, achievement in science process at the 9th grade level varied significantly by instructional format, F (2, 258) = 6.77, p = .001. Statistically significant effects were also found on the interact ion between instructional format and gender, F (2, 258) = 3.10, p = .047, and on the interaction between instructional format and achievement level, F (2, 258) = 10.08, p < .000. Again, the statistically significant main effect for achie vement level was not of interest in this Table 8. Analysis of Variance for 9th Grade Science Process Achievement as a Function of Instructional Format, Achievement Level, and GenderSourcedfSSMSFp Instructional Format 21598.50799.256.77.001 Achievement Level 13164.463164.4626.79.000 Gender 128.3328.330.24.63 Instructional Format x Gender2732.08366.043.10.047Instructional Format x Achievement Level22381.28119 0.6410.08.000 Achievement Level x Gender117.7417.740.15.70Instructional Format x Achievement Level x Gender22 27.99113.990.97.38 Error (Instructional Format)25830477.58118.13 analysis. Post hoc analyses using the Games-Howell statistic along with effect size estimates are presented in Table 9 for all sta tistically significant pairwise comparisons; Figure 3 graphically displays these co mparisons. As can be seen in Table 9 and Figure 3, 4 x 4 block scheduling generated a moderately strong main effect over traditional sche duling, almost identical to the effect seen in the science content analysis in Tabl e 6. As with the science content analysis, AB block scheduling did not produ ce a statistically significant advantage over traditional scheduling. As with the science content effect size analysis, the interactions between instructional fo rmat and entering Table 9. Statistically Significant Pairwise Compari sons and Effect Sizes for Science Content AnalysesPairwise ComparisonsMdiffp da4 x 4 Block Schedule versus Traditional Schedule5.2 8.006.46 Low Achievers in AB Block Schedule versus Low Achie vers in Traditional Schedule 12.87.0001.20

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17 of 25 Females in AB Block Schedule versus Females in Trad itional Schedule8.40.044.69 Low Achievers in 4 x 4 Block Schedule versus Low Ac hievers in Traditional Schedule 7.56.015.69acalculated using weighted, pooled standard deviatio n formula achievement level strongly favored both block sched uling formats over traditional scheduling although the relative streng th of the particular block format was reversed in this analysis. The gender x instructional format interaction was the first time a gender effect appe ared in any of these analyses. Looking at the graphs in Figures 1 and 2 compared w ith Figure 3, this effect would seem to be related to the relatively wide dis persion of mean scores by gender for the AB format in Figure 3 compared with this dispersion in Figures 1 and 2. Given the lack of consistency in this gender x instructional format interaction, however, it is questionable how much c onfidence can be placed in the veracity of this interaction. Again, as with th e science content analysis, the only subgroup that the high achievers in traditiona l scheduling format outperformed with statistically significant differe nces was the traditionally scheduled low achievers Figure 3. Mean science process RIT scores for students in 4 x 4, AB, and traditional schedules broken out by gender and achi evement levels.DiscussionWhat is to be made of these findings, especially gi ven Veal and Schreiber’s (1999) recent statement “The literature is consiste nt on the inconsistency of

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18 of 25 achievement of students within the block schedule ( p. 3)” in their review of literature? It probably makes the most sense to sta rt by considering the non-achievement literature on block scheduling as w ell. Here, the findings are much more consistent (although not uniformly so), a nd they to tend favor both forms of block scheduling over traditional scheduli ng on such things a school climate (i.e. Bickel, 1999), student satisfaction w ith school (Lapkin et. al, 1997; Knight, De Leon, & Smith, 1999)—except students in AP classes (Knight et al, 1999), and teacher, parent, and counselor satisfact ion with school (Edwards, 1999; Wilson & Stokes, 1999; Deuel, 1999). Hence, i f the main and interaction effects of block scheduling on student achievement can be “held harmless” versus traditional scheduling then the relatively c onsistent results on these kinds of “softer” measures above might tip the scales in favor of block scheduling. Thus, if the “standard” for consistent findings on student achievement of block scheduling is that it does not produce worse outcomes rather than that it does produce positive outcomes then the consistency picture does clear up somewhat. Here, Veal and Schreiber (1999) and their follow up study (Schreiber, Veal, Flinders, & Churchill, 2001) foun d no adverse effects on mathematics, reading, and language arts of attendin g block scheduled high school classes. Their findings, then, are in confli ct with this study on language arts, but at least are consistent on a “no adverse effect” criterion. These researchers also looked only at 4 x 4 and tradition al scheduling (and a 4 x 4/traditional hybrid) and did not look at effects o f AB block scheduling. Bickel (1999) found no differences between block schedulin g and traditional scheduling on mathematics achievement. Wallinger (1 998) found no differences on foreign language achievement although Lapkin et al, (1997) found differences in favor of block scheduling on foreign language achievement. Finally, Edwards (1999) found very cautious positiv e effects of block scheduling on science achievement.Thus it would seem that the sum of this prior resea rch (our own prior research notwithstanding – see Cobb, Abate, & Baker, 1999) a nd the findings of this current study would tend to support the use of bloc k scheduling. However, there are a number of limitations both with this current study and with the empirical literature set in general that make this judgment o ne to be viewed with caution. First, most of the studies cited that looked at stu dent achievement were at best causal-comparative in design, and in some cases, pu rely correlational. Many of these studies did not even exercise the attempt at controls that this present study did – that is equating schools and students i n them. Seldom was there a reporting of the procedures of the block scheduling intervention (except of course, that the length of the class period was lon ger) and hence there are a whole host of additional variables about the integr ity of the block scheduling intervention that are unreported and uncontrolled i n these causal-comparative research studies and that bring unmeasured effects into the research results. Also, with multi-school studies, the variable of in structional quality of the teachers independent of the scheduling format, adds a significant unmeasured dimension to the research results.These limitations notwithstanding, we believe we ha ve findings worth adding to the theoretical mix, and paradoxically, they are ch aracterized by consistency. Looking at the consistency in three graphs and in t he magnitude of the

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19 of 25 corresponding effect size tables, we consistently f ound sizeable gains in favor of block scheduling. These gains persevered across both the language arts and science domains of achievement; and these gains wer e largest consistently in favor of lower achieving students while consistentl y holding harmless upper achieving students. These are the findings of one j unior high school, however, and need to be replicated with high quality quasi-e xperiments and ex post facto studies in order to be generalized to other setting s. In future research efforts we have a number of obse rvations and recommendations that seem particularly germane with the escalating demands for “scientific rigor” in educational research asso ciated with current federal education legislation. First, we recommend qualitat ive research—particularly case studies, ethnographies, or grounded theory res earch—that explores several very likely and important sources of variat ion in prior research results. For example, differing instructional practices by t eachers in blocked and traditional scheduled classes are doubtless sources of error variance in achievement results, especially in studies that use only one or a few schools and teachers. Clearly, the context within which blo ck scheduling and traditional scheduling is delivered also has much to do with ef ficacy of achievement results. Desimone (2002), for example, has recently affirmed in her review of comprehensive school reform model literature Porter Floden, Freeman, Schmidt, & Schwille’s (1988) policy attributes theo ry about successful implementation of whole school reform. Exploring ho w schools move to block scheduling by focusing on the attributes of specifi city, consistency, authority, stability, and context, case study or grounded theo ry research can add immense understandings as to how and why achievemen t results vary as they do across implementation sites.From the quantitative paradigm, we recommend that u niversity and school-based researchers aim as high as possible wi thin the methodological boundaries they attach to their studies. With group -based studies, we recommend that research begin, if at all possible w ith schools that are planning a move to block scheduling in order to move the des ign characteristics from ex post facto to quasi-experimentation, with attention to the at tendant improvements to controls over threats to internal v alidity. Documentation of the adoption processes and implementation activities wi ll go a long way to remove and explain sources of error variance that plague ex post facto designs and are likely to be the source of the inconsistencies in e arlier research. Nonetheless, we recognize that the preponderance of future quantitative research on block scheduling is likely to be causal -comparative. As such we believe that with careful attention to internal val idity features within this design, credible research results can weigh into the discus sion about block scheduling such that evidence-based judgments can be made that draw, in part from these kinds of research studies. First, more and more sch ools are going to hybridized versions of either 4 x 4 or AB block scheduling, an d these hybrids must be documented and described. Second, we recommend that outcome measures focus on math, science, and language arts—domains t hat are likely to be tested more and more with the implementation of No Child Left Behind legislation. These are the achievement domains of the first deca de of the 21st century and the “social validity” of this research will be enha nced by attention to these

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20 of 25 domains. Third, ex post facto studies absolutely must measure pre-block scheduled achievement levels of students on measure s that are highly correlated with the outcome measures. Whether or no t these pre-measures result in matched sampling designs or ANCOVA statis tical designs will be more of a judgment call of the researchers, but these pr e-measures must be included in the research. Fourth, researchers have to measur e the length of time students attended block scheduled and traditional ( comparison) schools and eliminate students who were not in those school lon g enough (i.e. 1-2 years) to demonstrate the effects of those schools’ schedulin g formats. Finally recommend longitudinal follow up of students, if po ssible to explore the durability of block scheduling effects (see, for ex ample, the Veal & Schreiber, 1999 and the Schreiber et. al, 2001 studies as an e xample). We can envision, for example, a methodologically appealing line of r esearch that looked at students in middle/junior high school who were in b lock scheduled and traditional formats and who then attended, differen tially, block scheduled or traditionally scheduled formats in high school.ReferencesBaker, A. J., Bowman, K. (2000). Attitudes and perc eptions toward block scheduling in rural Kentucky agricultural programs. Rural Educator, 22 (1), 26-30. Bickel, S. (1999). Block scheduling versus traditional scheduling: A c omparison of learning climate, student achievement, and instructional methods in t wo Colorado junior high schools. Unpublished doctoral dissertation, Colorado State U niversity, Ft. Collins. Buckman, D. C., King, B. B., & Ryan, S. (1995). Blo ck scheduling: A means to improve school climate. NASSP Bulletin, 79 (571), 9-18. Canady, R. and Rettig, M. (1995). Block scheduling: A catalyst for change in high sch ool Princeton, NJ: Eye on Education. Cobb, R. B., Abate, S., & Baker, D. (1999). Effects on students of a 4 x 4 junior high school block scheduling program. Educational Policy Analysis Archives. 7 (3), Retrieved May 15, 2003 from http://epaa.asu.edu/epaa /v7n3.html Cohen, J. (1988). Statistical power analysis for the behavioral scien ces (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. Cole, D. A., Maxwell, S. E., Arvey, R., & Salas, E. (1994). How the power of MANOVA can both increase and decrease as a function of the intercor relations among the dependent variables. Psychological Bulletin, 115 (3), 465-474. Desimone, L. (2002). How can comprehensive school r eform models be successfully implemented? Review of Educational Research, 72 (3), 433-479. Deuel, L.-L., S. (1999). Block scheduling in large, urban high schools: Effects on academic achievement, student behavior, and staff perception s. The High School Journal, 83 (1), 14-25. Edwards, M. C., Jr. (1995). Virginia’s 4X4 high sch ools: High school, college, and more. NASSP Bulletin, 79 (571), 23-41. Edwards, M. C. (1999). A comparison of student achievement in three school -day scheduling patterns for secondary students enrolled in the agr iscience course, animal science. Unpublished doctoral dissertation, Texas A & M University, Coll ege Station. Eineder, D. V., & Bishop, H. L. (1997). Block sched uling the high school: The effects on achievement, behavior, and student-teacher relation ships. NASSP Bulletin, 81 (589), 45-54. Field, A. (2000). Discovering statistics using SPSS for Windows: Adva nced techniques for the

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21 of 25 beginner Thousand Oaks, CA: Sage Publications Gliner, J. A., & Morgan, G. A. (2000). Research methods in applied settings: An integrated approach to design and analysis. New York: Lawrence Erlbaum. Hamdy, M., & Urich, T. (1998). Perceptions of teach ers in south Florida toward block scheduling. NASSP Bulletin, 82 (596), 79-82. Knight, S. L., De Leon, N. J., & Smith, R. G. (1999 ). Using multiple data sources to evaluate an alternative scheduling model. The High School Journal, 83 (1), 1-13. Limback, E. R., & Jewell, C. S. (1998). Impact of b lock scheduling on business education in Missouri. Business Education Forum, 52 (4), 6-10. Lapkin, S., Harley, B., & Hart, D. (1997). Block sc heduling for language study in middle grades: A summary of the Carleton Case Study. Learning Languages, 2 (3), 4-8. Moore, G., Kirby, B., & Becton, L. K. (1997). Block scheduling’s impact on instruction, FFA, and SAE in agricultural education. Journal of Agricultural Education, 38 (4), 1-10. Nichols, J. D. (2000). Scheduling reform: A longitu dinal exploration of high school block scheduling structures. International Journal of Educational Reform, 9 (2), 134-147. Northwest Evaluation Association. (1997). Achievement levels tests: Technical manual Portland, OR: Author. O’Neil, J. (1995). Finding time to learn. Educational Leadership, 53 (3), 11-15. Payne, D. A., & Jordan, M. M. (1996). The evaluatio n of a high school block schedule: Convergence of teacher and student data. American Secondary Education, 25 (2), 16-19. International Conference of Social Sciences. (ERIC Document Reproduction Service No. ED 421 328) Pisapia, J., & Westfall, A. L. (1997a). Alternative high school scheduling. A view from the teacher’s desk. Research report (Report No. UD031866). Richmond, VA: Metropolitan Educational Research Consortium. (ERIC Document Reproduction Se rvice No. ED 411 335) Pisapia, J., & Westfall, A. L. (1997b). Alternative high school scheduling. A view from the student’s desk. Research report (Report No. UD031867). Richmond, VA: Metropolitan Educational Research Consortium. (ERIC Document Reproduction Se rvice No. ED 411 336). Pisapia, J., & Westfall, A. L. (1997c). Alternative high school scheduling. Student achieve ment and behavior. Research report (Report No. UD031868). Richmond, VA: Metropolitan Educational Research Consortium. (ERIC Document Reproduction Se rvice No. ED 411 337). Porter, A. C., Floden, R., Freeman, D., Schmidt, W. & Schwille, J. (1988). Content determinants in elementary school mathematics. In D. Grouws & T. Co oney (Eds.). Perspectives on research on effective mathematics teaching. Reston, VA: National Council of Teachers of Mathem atics. Queen, J. A., Algozzine, B., & Eddy, M. (1997). Imp lementing 4X4 block scheduling: Pitfalls, promises, and provisos. High School Journal, 81 (588), 107-114. Schreiber, J. B., Veal, W. R., Flinders, D. J., & C hurchill, S. (2110). Second year analysis of a hybr id schedule high school. Educational Policy Analysis Archives, 9 (46), 1-18. Shortt, T. L., & Thayer, Y. (1995). What can we exp ect to see in the next generation of block scheduling? NASSP Bulletin, 79 (571), 53-62. Skrobarcek, S. A., Chang, H-W. M., Thompson, C., Jo hnson, J., Atteberry, R., Westbrook, R., & Manus, A. (1997). Collaboration for instructional i mprovement: Analyzing the academic impact of a block scheduling plan. NASSP Bulletin, 81 (589), 104-11. Staunton, J. (1997). A study of teacher beliefs on the efficacy of block scheduling. NASSP Bulletin, 81 (593), 73-80.

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22 of 25 Stewart, J. W., & Shank, J. (1971). Daily demand of modular flexible scheduling for small schools. Educational Leadership, 29 537-544. Swope, J. A., Fritz, R. L., & Goins, L. K. (1998). What are marketing teachers and principals saying about block schedules? Business Education Forum, 53 (2), 36-37, 61. Thomas, C., & O’Connell, R. W. (1997a). Parent perceptions of block scheduling in a New Yor k State public high school (Report No. EA028507). New York, USA: Educational Research Organization. (ERIC Document Reproduction Service N o. ED 409 644). Thomas, C., & O’Connell, R. W. (1997b). Student perceptions of block scheduling in a New Yo rk State public high school (Report No. SP037833). New York, USA: Northeastern Educational Research Association (ERIC Document Reproduction Se rvice No. ED 417 186). Veal, W. R., & Schreiber, J. (1999). Block scheduli ng effects on a state mandated test of basic skills Educational Policy Analysis Archives, 7 (29), 1-13. Wallinger, L. M. (1998). The impact of alternative scheduling practices on s tudent performance in French I. Unpublished doctoral dissertation, The College of William and Mary. Weller, D. R., & McLeskey, J. (2000). Block schedul ing and inclusion in a high school. Remedial and Special Education, 21 209-218. Wilson, J. W., & Stokes, L. C. (1999a). Teachers’ p erceptions of the advantages and measurable outcomes of the 4 x 4 block scheduling design. The High School Journal, 83 (1), 44-54. Wilson, J. W., & Stokes, L. C. (2000). Students’ pe rceptions of the effectiveness of block versus traditional scheduling. American Secondary Education, 28 (3), 3-12. Wood, C. L. (1970). Modular scheduling? Yes, but -. Journal of Secondary Education, 45 40-42.About the AuthorsChance W. Lewis is an Assistant Professor in the School of Educati on at Colorado State University and a Research Associate at the Research and Development Center for the Advancement of Student L earning. Brian Cobb is a Professor in the School of Education at Color ado State University and Co-Director of the Research and Deve lopment Center for the Advancement of Student Learning.Marc Winokur is a doctoral candidate in Educational Leadership at Colorado State University and an Evaluation Fellow for the C enter for Learning and Teaching in the West at Colorado State University.Nancy L. Leech is an Assistant Professor in the School of Educati on at the University of Colorado at Denver.Mike Viney is the Science Chair at Blevins Junior High School in Poudre School District, Ft. Collins, CO and the Profession al Development Science Instructor for the Center for Learning and Teaching in the West at Colorado State University.Wendy White is a secondary Language Arts teacher and Language Arts Department Chair at Blevins Junior High School in P oudre School District, Fort Collins, Colorado.

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23 of 25 The World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu Editor: Gene V Glass, Arizona State UniversityProduction Assistant: Chris Murrell, Arizona State University General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, glass@asu.edu or reach him at College of Education, Arizona State Un iversity, Tempe, AZ 85287-2411. The Commentary Editor is Casey D. Cobb: casey.cobb@unh.edu .EPAA Editorial Board Michael W. Apple University of Wisconsin David C. Berliner Arizona State University Greg Camilli Rutgers University Linda Darling-Hammond Stanford University Sherman Dorn University of South Florida Mark E. Fetler California Commission on TeacherCredentialing Gustavo E. Fischman Arizona State Univeristy Richard Garlikov Birmingham, Alabama Thomas F. Green Syracuse University Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Ontario Institute ofTechnology Patricia Fey Jarvis Seattle, Washington Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Les McLean University of Toronto Heinrich Mintrop University of California, Los Angeles Michele Moses Arizona State University Gary Orfield Harvard University Anthony G. Rud Jr. Purdue University Jay Paredes Scribner University of Missouri Michael Scriven University of Auckland Lorrie A. Shepard University of Colorado, Boulder Robert E. Stake University of Illinois—UC Kevin Welner University of Colorado, Boulder Terrence G. Wiley Arizona State University John Willinsky University of British Columbia

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24 of 25 EPAA Spanish and Portuguese Language Editorial BoardAssociate Editors for Spanish & Portuguese Gustavo E. Fischman Arizona State Universityfischman@asu.eduPablo Gentili Laboratrio de Polticas Pblicas Universidade do Estado do Rio de Janeiro pablo@lpp-uerj.netFounding Associate Editor for Spanish Language (199 8-2003) Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico Adrin Acosta (Mxico) Universidad de Guadalajaraadrianacosta@compuserve.com J. Flix Angulo Rasco (Spain) Universidad de Cdizfelix.angulo@uca.es Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma deMxicocanalesa@servidor.unam.mx Ursula Casanova (U.S.A.) Arizona State Universitycasanova@asu.edu Jos Contreras Domingo Universitat de Barcelona Jose.Contreras@doe.d5.ub.es Erwin Epstein (U.S.A.) Loyola University of ChicagoEepstein@luc.edu Josu Gonzlez (U.S.A.) Arizona State Universityjosue@asu.edu Rollin Kent (Mxico) Universidad Autnoma de Puebla rkent@puebla.megared.net.mx Mara Beatriz Luce (Brazil) Universidad Federal de Rio Grande do Sul-UFRGSlucemb@orion.ufrgs.br Javier Mendoza Rojas (Mxico)Universidad Nacional Autnoma deMxicojaviermr@servidor.unam.mx Marcela Mollis (Argentina)Universidad de Buenos Airesmmollis@filo.uba.ar Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma deMxicohumberto@servidor.unam.mx Angel Ignacio Prez Gmez (Spain)Universidad de Mlagaaiperez@uma.es DanielSchugurensky (Argentina-Canad) OISE/UT, Canadadschugurensky@oise.utoronto.ca Simon Schwartzman (Brazil) American Institutes forResesarch–Brazil (AIRBrasil) simon@sman.com.br

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25 of 25 Jurjo Torres Santom (Spain) Universidad de A Coruajurjo@udc.es Carlos Alberto Torres (U.S.A.) University of California, Los Angelestorres@gseisucla.edu EPAA is published by the Education Policy Studies Laboratory, Arizona State University


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