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Educational policy analysis archives
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1 of 20 Education Policy Analysis Archives Volume 7 Number 3February 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 Effects on Students of a 4 X 4 Junior High School Block Scheduling Program R. Brian Cobb Stacey Abate Research and Development Center for the Advancement of Student Learning Colorado State University Dennis Baker Poudre (CO) School DistrictAbstractThe effects of a 4 X 4 block scheduling program in a middle school on a variety of student measures were investigated. These measures included standardized achievement tests in mathematics, reading, and writing, cumulat ive and semester grades in middle school and high school, attendance rates, and enrol lment rates in advanced high school courses (in mathematics only). The block scheduling program had been in effect for four years allowing analyses of current middle and high school students who had experienced a minimum of one and one-half years of block scheduling while in middle school. The primary research design was a post-test only, matched pairs design. Students were matched on school characteristics, ge nder, ethnicity, grade level, and 5th grade standardized reading scores. Results were rel atively consistent with the extant literature and generally positive.Introduction

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2 of 20 With the advent of the public school reform movement in the 1980s, schools and school districts were barraged with criticisms and demands for educational reforms. Murphy (1990) categorized the variety of these crit icisms driving the educational reform agendas into three major groups: (a) Macro-level co nditions, such as the failure of the United States to maintain its competitive edge in a global marketplace, (b) school outcomes, such as declining student achievement or increasing dropout rates, and (c) school conditions, such as lack of adequate standar ds for students or poor quality or commitment by staff. Responses to these various criticisms also have been clustered, for example, in federal initiatives, state mandates and policies, a nd local efforts as school improvements (Firestone, 1990). Often, reform initiatives are th e result of interactions between two or more sources of these initiatives (c.f. Wills & Pet erson, 1992; Odden & Marsh, 1990), particularly if those reforms originate at state or federal levels. The systemic reform initiatives of the 1990s, for example the school-to -work initiative (Agency for Educational Development, 1995), originated with fed eral legislation but impacts both state-level and ultimately local-level schooling (F uhrman & Massell, 1992; Goertz, Floden, & O'Day, 1995). Reforms that originate at t he local level, however, can be driven by levels higher up or standalone efforts. Block scheduling and school-based management, for example, are two such stand-alone r eform initiatives whose locus has been strictly from grass roots level. While schoolbased management has had a relatively robust examination in the literature in recent years (Wohlstetter, Smyer, & Mohrman, 1994; Center on Educational Governance, 19 95), the literature on block scheduling remains relatively scant and underpowere d. Although the variations of block scheduling are endless and idiosyncratic to the schools that implement them, all forms of block sch eduling carry one common feature-extended classroom periods of time beyond the tradi tional 50-minute class period. Although block scheduling has been in existence and reported in the contemporary literature since the late 1960s, it gained momentum in the late 1980s as a viable scheduling model in response to the literature on c ognition supporting deeper learning by students through sustained and uninterrupted int eractions with their subject matter. Recently Cawelti (1994) estimated that nearly 40% o f American high schools had implemented or intended to implement some form of b lock scheduling, attesting to its popularity as a flexible scheduling option. The purpose of this research was to add to the literature base on block scheduling by combining several advantageous features of resea rch on educational innovations in general which are not typical of the block scheduli ng empirical literature base. These were the use of multiple measures of student effect s, the use of a high-quality matched control group sampling design, and the use of a sch ool in which block scheduling had been in place for several years.Models of Block Scheduling Although idiosyncratic modifications to any block scheduling model are typically implemented at any school using block scheduling, t here are five general models of block scheduling that appear in the literature. One of these -parallel block scheduling -is used exclusively at the elementary school lev el and thus will not be further mentioned in this article. The other four models ar e used exclusively at the middle school, high school, and postsecondary levels. Thes e are the 4 X 4 Semester Plan, the Alternative Day Plan, the Trimester Plan, and the E xtended-Time Plan. Table 1 gives a brief description of these models and includes any alternative names for these models that have appeared in the recent literature.

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3 of 20 Effects of Block Scheduling Interest in extended periods of classroom t ime beyond the traditional 50-minute period first appeared in the contemporary literatur e under the concept of "modular scheduling", "flexible scheduling", or "modular fle xible scheduling" (Polos, 1969; Stewart & Shank, 1971; Thomson, 1971; Wood, 1970). These descriptive articles arguably derived from what is considered the progen itor of block scheduling -Carroll’s (1963) seminal treatise on the theoretical advantag es of extended time in school classroom periods based upon early learning theory. Presently, some 35 years later, the descriptive literature still abounds both supportin g and decrying the merits of block scheduling. Fortunately the empirical literature ha s gradually evolved as well, and it is this literature that will be summarized briefly bel ow. Within this empirical literature base it is most common to encounter research on student effects and opinions of block scheduling, t eacher effects and perceptions of effects of block scheduling, and parent opinions of block scheduling. Since this study focused exclusively on student effects and opinions of block scheduling, only that literature will be reported below. Table 1 Four General Models of Block Scheduling ModelAlternative Names FeaturesUnique Advantages and Disadvantages4 X 4 SemesterPlan Accelerated ScheduleCopernicanPlan Students enroll in four 90-minute courses that meet every day of the week for asemester, allowing completion of four year-long eq uivalent courses in asemester. Teachers typicallyteach three courses e achsemester. Advantages are that teachers work with fewer students, ha ve fewer preps, and a fresh startwith new students in the middle of the year.Students have only four courses to concentrate on at any one time; they havegreater opportunities for acceleration.Disadvantages are less opportunity to give homework. There seems to b e less time tocomplete the curriculum coverage. Coursestaken in the Fa ll semester may not befollowed by a course in the same discipline for 9 months. Year-long programs/coursessuch as band, orchestra, and choir ca n be cutshort. AlternativeDay Plan A/BOdd/EvenDay1/Day2 Students and teachers meet in three-four 90-120 minute cl asses on alternating daysfor the entire year. Advantages are that teachers have the entire year for eac h course, with a class intensity of90-120 minutes per course; greater opportunity to give homework due to alternating schedule; no extended laten cy periodbetween courses.Disadvantages are the unevenness of scheduling with classes alternatin g eachweek as to which are on Mondays and which

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4 of 20 are on Tuesdays can be c onfusing. TrimesterPlan NoneStudents take two-three 120 minute classes for 60 days, along with two-three traditional-length classes for the entire year. Advantages are that it accommodates those programs/course s such as band, choir, andorchestra that need year-long contact with students, while maintaining weekly intensity of 4 X 4 Semester PlanDisadvantages are similar to 4 X 4 Semester Plan Extended-Time Plan Reconfiguringthe Year Schools usually partition their school year into three se gments, generallyincluding two 75-day blocks, and one 30-day block (some times betweenthe 75-day blocks, sometimes at the end of the year). Then during the75-day blocks, students enroll in three-four 90-120 minute co urses daily.During the 30-day segment, students work in concentrated re mediation orenrichment activities. Advantages are that there is more flexibility inherent in the model.Disadvantages are similar to the 4 X 4 Semester Plan The earliest empirical studies published un der any of the model names described in Table 1 appeared in the late 1960s and early 1970s. The first (Steagall, 1968) compared outcomes in high school business education programs across the state of Ohio for students enrolled in 18 "block-intime" schools wi th students enrolled in 18 "conventional" demographically matched schools. Aft er adjusting for I. Q. differences in the students from both groups, results showed no significant main-effect differences in both knowledge and performance test scores as me asured by the National Business Entrance Tests. When adding a three-level ability f actor and a three-level urbanicity factor, only two significant results emerged -one favoring block scheduling for urban students and one favoring conventional scheduling f or suburban students. Slightly more negative effects of block sch eduling were found by Van Mondfrans, Schott, & French (1972) in an experimental study of block scheduling on performance on English tests and student attitudes toward schoo l. Overall, conventional format students performed significantly better on the Engl ish tests and no significant differences were found on student attitudes. One co nsistent interaction effect was found favoring block scheduling with senior-level student s over freshman, sophomores, and juniors. The design of this study seems somewhat fl awed, however, since all 12 teachers participating in this study taught both traditional format and block scheduling classes on an alternating basis each day. A decade later Sigurdson (1981; 1982) condu cted two studies of the same junior high school in Canada. While his 1981 study showed little differences between block student’s achievement compared to traditionally-sch ooled students, his second year study (Sigurdson, 1982) showed dramatically more po sitive results in favor of block scheduling. One conclusion that has been echoed rep eatedly in subsequent studies pointed to the necessity of waiting at least a year or more. For example, Schroth and

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5 of 20Dixon (1995) and Meadows (1995) both advocated stro ngly that at least three to five years of experience with block scheduling should oc cur in schools before valid and justifiable judgments should be drawn about effects on students. It was not until the mid-1990s that a resur gence of empirical studies were published on block scheduling -probably due at le ast in part to the exponential increase in interest -what Shortt and Thayer (199 7) call a rediscovery and redefinition. Again, many of these studies focused on teacher, ad ministrator, and/or parent effects or opinions (i.e. Davis-Wiley, George, & Cozart; Hurle y, 1997). Our interest in this review is to focus on only student effects, however, so th ose studies will be omitted from this review. A number of studies have been published rec ently which focused on both generalized student effects (Cox, 1994; Guskey, & K ifer, 1995; Hurley, 1997; Mistretta & Polansky, 1997; Queen, Algozzine, & Eaddy, 1997; Meadows, 1995) and discipline-specific effects (Queen, Algozzine, & Ea ddy, 1996; Reid, 1995; Schroth & Dixon, 1995; Wronkovich, Hess, & Robinson, 1997) of block scheduling. The results, on balance, were generally positive, but the negati ve findings which were reported cannot be overlooked. On the positive side, the mos t consistent findings that were reported were students’ favorable opinions of block scheduling, particularly with teachers who found it easy to mix lecture and group -work instruction. Students also liked the fact that block scheduling seemed to redu ce homework loads, although this finding would be construed as negative from other p erspectives. Beyond these kinds of qualitative student o pinion effects, the findings on student achievement, attendance, and behavior/disruptions/s uspensions were more equivocal. Reductions in behavior problems appeared to be rela tively consistent, as were increases in attendance rates, yet if a student missed a sequ ence of classes for any reason, it appeared more difficult to catch up with the conten t and make-up assignments. At-risk students in block scheduling appeared to benefit th e most consistently across the curriculum, but standardized scores on mathematics examinations were consistently lower with block scheduling. This study adds to the literature base in s everal important ways. First, the great majority of empirical studies focused on the high s chool level, while this study reports effects on junior high school students. Second, mos t studies reported effects from one to two years of operating block scheduling, while the present study was implemented after four years of operation. Finally, many studies used non-probability sampling designs -typically convenience or cluster sampling processes while this study used a matched sampling design. None of the currently reported stu dies at the junior high or middle school levels used both a high quality sampling des ign and analysis after multiple years of operation.Method To implement this research, a between-group s (matched control group) design was used, with some variations depending on the particu lar hypothesis to be addressed. Below, each of the research hypotheses is elaborate d followed by a specification of the dependent variable(s) and the specific sampling pro cedures for the hypothesis. Hypotheses This evaluation looked at effects of block scheduling on two groups of students -junior high school students and beginning senior hi gh school students -along four major dependent variables -grade point average, s tandardized achievement scores, attendance rates, and preparation for advanced cour sework. The major research question

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6 of 20 guiding the study was: "What are the effects of blo ck scheduling on a variety of student outcome variables?" The six hypotheses associated w ith this research question were: Hypothesis 1 (H1 ): Junior high school students who experience block scheduling will evidence significantly higher grade point aver ages than their matched control group counterparts. 1. Hypothesis 2 (H2 ): Senior high school students who experienced block scheduling in junior high school will evidence sign ificantly higher grade point averages than their matched control group counterpa rts. 2. Hypothesis 3 (H3 ): Junior high school students who experience block scheduling will evidence significantly higher standardized tes t scores than their matched control group counterparts. 3. Hypothesis 4 (H4 ): Senior high school students who experienced block scheduling in junior high school will evidence sign ificantly higher standardized test scores than their matched control group counte rparts. 4. Hypothesis 5 (H5 ): Attendance rates at Block Junio r High School during the years in which they experienced block scheduling wi ll not differ significantly from their attendance rates prior to block scheduli ng and will not differ significantly from same-year attendance rates at ma tched control junior high schools. 5. Hypothesis 6 (H6 ): Senior high school students who experienced block scheduling in junior high school will enroll in adv anced coursework at significantly higher rates than their matched contr ol group counterparts. 6. Table 2 presents a matrix delineating these dependent variables along with the hypotheses that were examined for each of the two g roups of students. Table 2 Dependent Variables and Associated Research Hypothe ses for Each Grade Level Dependent VariableJunior HighSenior High Grade Point AverageH1H2Standardized Achievement Test ScoresH3H4Attendance RatesH5* Preparation for Advanced Coursework*H6Dependent Variables and Sampling Procedures Hypotheses 1 and 3. The grade point average dependent variable for the Block Junior High School students and their matched controls was their Fall 1996 cumulative grade point average as maintained in the school district student data system. The standardized achievement test dependent variable was represented by three scores from the Iowa Test of Basic Skills (TAP version). These scores were the r eading, mathematics, and written

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7 of 20expression raw scores. These two research hypotheses involved the use of a matched control group sampling design. Block Junior High School students who were in the 8th and 9th grades in the Spring of 1997, and who had experienced a minimum o f three semesters of block scheduling by January 1997, were designated as the experimental group. Each of these students was then matched with students from two ot her junior high schools in the same geographic quadrant of the city whose school size, ethnic, and socio-economic make-up were comparable. The matching process was conducted in two stages. First, each experimental group student was matched on three dem ographic attributes, each with two levels: grade level (8th or 9th), gender (female or male), and ethnicity (white [non-Hispanic] or other). Once a pool of potential matched control students were identified based on these criteria, a second level of matching occurred using 5th grade Iowa Test of Basic Skills (Reading) scores. Matches were thus ma de with the control student whose ITBS Reading score was the closest to the experimen tal group student. Hypotheses 2 and 4. For Senior High School students and their matched c ontrols, the grade point average dependent variable was their first (Fall) term grad e point average in high school. Thus, for 10th graders, it was their Fall 1996 grade point av erage; for 11th graders, it was their Fall 1995 grade point average. The standardized achievem ent test dependent variable was represented by three scores from the Iowa Test of B asic Skills (TAP version). These scores are the reading, mathematics, and written expressio n raw scores. The sampling design (matched control group) and matching criteria that was used for the second and fourth hypotheses was essentially th e same as was used for the first and third hypotheses above, with one key difference -experimental group participants were 10th and 11th grade Senior High School students who had earlier experienced at least three semesters of block scheduling while attending Block Junior High School. Their respective matched controls could have attended junior high sc hool at any of the districts’ six junior high schools which were not under block scheduling. Hypothesis 5. The dependent variable of att endance rates was the annual school-reported average daily attendance rates as r eported to the Colorado Department of Education. Block Junior High School began its block scheduling in the Fall of 1993. Annual school-reported rates for attendance were ob tained from the previous four years (1989-90 to 1992-93) without block scheduling and t he first three active years of block scheduling (1993-94 to 1995-96). Similar rates for attendance of three comparable junior high schools were also sampled for comparison purpo ses. Hypothesis 6. The dependent variable for th is hypothesis was a ranking of the relative difficulty level of the mathematics course for whic h entering 10th graders had registered upon entry into high school. Registration rate for mathematics coursework was selected to represent the construct of "registration for advanc ed coursework" for two reasons. First, mathematics is a curricular area where almost every student registers during their first semester in senior high school. Second, while other areas such as science, English, social studies, and foreign languages are also high freque ncy registration areas, it is far more difficult to judge consistently what is advanced an d what is not in these curricular areas. In mathematics, it is relatively easy to rank order th e difficulty of classes in order to determine consistently advanced registration rates. Table 3 d elineates the names of the various mathematics course options for which entering 10th grade students could enroll and the rankings for those course options as verified by ma thematics teachers and counselors in the high school. The sampling design for this hypothesi s was the same sampling design as that for H2 and H4 with one additional step. From the ex perimental group roster of students, those participants who did not sign up for a mathem atics course (and their matched control counterparts) were eliminated from the sample. Table 3

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8 of 20 Name and Ranking of Mathematics Courses RankingMath Course(s) Title(s) 1Math Concepts2PTC Math (remedial math for alternative placemen t students) 3Algebraic Concepts4Applied Math 15Integrated Algebra IA6PTC Algebra7Accounting I8Algebra I Saxon Algebra I (taught in a different manner tha n traditionally) Integrated Algebra I (taught in a different manne r than traditionally) 9Geometry Saxon Algebra IIIntegrated Geometry (taught in a different manner than traditionally) 10Algebra II Pre-International Baccalaureate Math 11Pre-Calculus International Baccalaureate MathResults The results of this program evaluation are given in two major sections. First, a demographic profile for each of the samples is provided, to the extent that demographic data were collected on them. Second, the six research hypotheses are exami ned with statistical tables and charts provided as appropriate.Demographic Profiles Table 4 presents demographic and sampling d ata for those students who were included in the analyses associated with grade point averages and s tandardized achievement scores. Table 4 Demographic Information on student samples for Grad e Point Averages and Standardized

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9 of 20 Achievement Scores GenderEthnicity Grade Leveln Mean 5th Gr Read.-Block Mean 5thGr Read.-Trad # Girls # Boys #aAs-Am #bLat. #cAf-Am #dNa-Am #eCauc Eighth9659.3459.383858151-89Ninth10961.0361.11565326-299Tenth8859.7359.82503825--81Eleventh6262.1162.19303212--59TOTALS35560.5560.6317418161812328 Note. a Asian-American or Pacific Islander b Latino/Hispanic/Mexican American c African-American d Native American or Alaskan e CaucasianFor each of the 355 block scheduling students, a pe rfect match was located relative to grade level, gender, and ethnicity. For the final matchin g criterion, 5th grade standardized test score in reading, it was not possible in all cases to locate a matched control with exactly the same score. However, no experimental/control matche d pair differed by more than 0.09 points. In addition, boys and girls were relatively equally distributed across the grades. The samples were overwhelmingly made up of Caucasian st udents, which is reflective both of the community and the neighborhoods in which the school s were located. Research Hypotheses The analyses for Hypotheses 1-4, which exam ined the effects of block scheduling on student grade point average (both semester and cumu lative) and standardized test scores (mathematics, reading, and writing), were completed using five ANOVA’s with repeated measures on the matching variable. Student gender ( two levels) and student grade level (four levels) represented the between groups variables, a nd the block schedule students scores compared with the matched control group students sc ores represented the third within subjects variable. Table 5 presents the results of these five ANOVA’s in abbreviated form. As can be seen in Table 5, significant main effects for the matching variable (at p. < .10) existed for both Experimental/Control contrast s and for the mathematics achievement test scores. Significant first order interactions ( at p < .05) existed for the semester GPA contrast. All other effects were not significant. Table 5

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10 of 20 ANOVA Results for GPA and Achievement Test Contrast s Criterion Variablep. value Semester GPAExp/Cont* Exp/Cont x Gender ***Exp/Cont x Grade Level ***Exp/Cont x Gender x Grade Level 2.757.514.260.67 .098.006.006.568 Cumulative GPAExp/Cont ** Exp/Cont x GenderExp/Cont x Grade LevelExp/Cont x Gender x Grade Level 5.360.590.061.28 .022.441.804.259 Standardized MathTest Test x GenderTest x Grade LevelTest x Gender x Grade Level 3.470.270.031.70 .064.761.873.184 Standardized Reading TestTest x GenderTest x Grade LevelTest x Gender x Grade Level 0.011.360.370.15 .915.246.545.697 Standardized Writing TestTest x GenderTest x Grade LevelTest x Gender x Grade Level 0.491.680.650.03 .483.197.422.863 p < .10 **p < .05 ***p < .01 Table 6 presents the sample sizes, means an d standard deviations (for the significant

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11 of 20 within or between group contrasts only), and corres ponding effect sizes for those contrasts in Table 5 that proved statistically significant at th e p < .10 level. As can be seen from Table 6, the four significant Experimental/Control group con trasts all favored block scheduling, whereas the single significant standardized test co ntrast (in math) favored traditional scheduling. It is also evident that the magnitude o f the significant effects, particularly those with p < .05, ranges relatively consistently betwee n one quarter to one third of a standard deviation. Table 6 Descriptive Results for GPA and Achievement Test Ef fects Criterion Variable Effects n pairs Mean (s.d.) Block Schedule Mean (s.d.) Trad. Schedule ES Semester GPAExp/Cont* Exp/Cont x Gender*** (males) Exp/Cont x GradeLevel***(10th graders) (11th graders) 346177 8761 2.91(.87)2.80(.92) 2.82(.95)2.82(.82) 2.82(1.00)2.56(1.04) 2.50(1.06)2.57(0.96) 0.090.22 0.300.25 Cumulative GPA Exp/Cont**1502.98(.76)2.80(0.74)0.24 Achievement Test Math* 23661.63(26.18)65.36(25.24)-0.15 Finally, Table 7 gives all of the direction s of the mean differences for each of the five sets of contrasts regardless of their statistical s ignificance. As can be seen in Table 7, the totals are exactly equal in terms of the number of instances where the block scheduling means were greater than and less than the tradition al scheduling means. It should be remembered, however, that only three of the contras ts were statistically different from each other at the p < .05 level, and all three favored t he block scheduling students. Table 7 Numbers of Contrasts in which the Mean of the Block Sch eduling Students is Greater than and Less than the Mean of the Traditiona l Sch eduling Students Criterion Variable Effects (# of possible contrasts) # contrasts when# contrasts when

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12 of 20 mean block > trad.mean trad. > block Semester GPA Exp/Cont (1)Exp/Cont x Gender (2)Exp/Cont x Grade Level (4) 113 011 Cumulative GPA Exp/Cont (1)Exp/Cont x Gender (2)Exp/Cont x Grade Level (2) 122 000 Standardized Math Test (1)Test x Gender (2)Test x Grade Level (3) 000 .1 23 Standardized Reading Test (1)Test x Gender (2)Test x Grade Level (2) 101 021 Standardized Writing Test (1)Test x Gender (2)Test x Grade Level (2) 011 111 TOTALS28 possible contrasts1414 Hypothesis 5 focused on an examination of c omparative attendance rates at Block Junior High School before and after initiating bloc k scheduling, and with three comparable schools. Figure 1 displays the average daily attend ance rates of all four schools for the four years prior and three years after initiating block scheduling. As can be seen in Figure 1, Block Junior High School’s ADA rate had been declin ing slightly each year (from a 94.5 attendance rate in 1989-90 to a 93.7 rate in 1992-9 3) prior to implementing block scheduling. Attendance rates reversed this trend be ginning in the Fall of 1993 and climbed consistently about 0.1 percentage point each of the three years of block scheduling. Figure 1 does not show any clear patterns of attendance rate s for the other three junior high schools during the four pre-block scheduling and three post -block scheduling years.

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13 of 20 Figure 1. Comparison of average daily attendance ra tes of Block Junior High School with rates at three comparable junior high schools with traditional scheduling. (The vertical line in the center of the figure signifies the point at which block scheduling was implemente d at Block Junior High School.) Hypothesis 6 was concerned with comparing a dvanced coursework registration rates in 10th grade mathematics by students who had experien ced block scheduling versus those who had not. Referring back to Table 3, it can be seen that there were eleven possible mathematics courses into which students could regis ter at the high school to which they matriculated upon completion of their junior high s chool experience. Table 8 presents the results of a Wilcoxin signed ranks matched pairs te st. As can be seen, the mean ranked mathematics course registration was virtually ident ical for both block scheduled and traditionally scheduled students. Table 8 t-test of Advanced Mathematics Course Registration Descriptive StatisticsInferential StatisticsComparison GroupMeanNStd. Dev. T-ValueP-Value Junior High School Students8.572382.120.189.851Comparison School Students8.542381.83 Discussion In prior literature, there were only three empirical studies examining student effects of

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14 of 20block scheduling at the middle/junior high school l evel. Two of them were sequential examinations of a variety of student effects at the same junior high school (Sigurdson, 1981; Sigurdson, 1982) in Alberta, Canada; Schroth and Di xon (1995) looked strictly at mathematics achievement effects of block scheduling on students at two middle schools in Texas. In both locations, a 4 X 4 schedule was used ; none of the three looked at effects past two years of implementation, and both sets of autho rs indicated that more time was necessary in order to ascertain true effects of this scheduli ng intervention. Schroth and Dixon (1995) did find, however, that mathematics achievement did not differ significantly between those students prepare d using block scheduling compared with those prepared under traditional scheduling. Sigurd son (1982) also found no differences between block-scheduled and traditionally-scheduled students in their attitudes towards mathematics nor in their mathematics achievement sc ores. Similar "no differences" findings relative to mathematics effects were found by Lockw ood (1995) and the North Carolina Department of Public Instruction (1995), although t hese studies were both conducted at the high school level. The findings of this study confirm the "no differences" conclusion in registration for advanced mathematics courses, but conflict with the "no differences" finding on mathematics achievement. The block-scheduled students in this s tudy performed significantly less well on standardized mathematics tests compared with their traditionallyscheduled peers. The "no differences" findings in this study in standardized reading and writing test scores also is consistent with the findings of Holmberg (1996). Ag ain, Holmberg was studying standardized test scores of high school students ra ther than junior high school students. On the positive side, this study found cons istently higher grade point averages, both semester and cumulative, in favor of block schedule d students. These kinds of nonstandardized achievement effects have been reported in the high school literature on block scheduling (Buckman, King, & Ryan, 1995; Reid, Hier ck, & Veregin, 1994; Payne & Jordan, 1996), but have been contradicted by Parkin son and Parkinson (1995) at the postsecondary level. At a more complex level this study found st atistically significant interactions suggesting block scheduling has a more positive semester GPA e ffect on male students compared with female students and for 10th and 11th graders compa red with 8th and 9th graders. These interactions did not hold for cumulative GPA. No ot her studies specifically examined differential effects by grade level, and Lockwood’s (1995) study was the only study reporting out gender effects, which were not found to be present. Finally, this study examined attendance dat a. This variable was a popular one in the literature with positive findings at the high schoo l level reported by Buckman et. al., (1995) and Reid et. al., (1994), and no differences report ed by Guskey and Kifer (1995). This study did not test for statistically significant differen ces, but did provide a visual examination of attendance rates over time and across several schoo ls. The findings here are consistent with those cited above. Very little that is definitive can be infer red from this study. As mentioned earlier, its most positive contributions would be that it begins to fill a significant void in the middle/junior high school literature on effects of block scheduling. This contribution is made all the more important given the relatively high-qu ality matched group design, and given that block scheduling had been in effect for four years when the data for this study were collected. As is usually the case, this study points t o as many questions as answers, however. In addition to the necessity for more high-quality stu dies at the middle/junior high school level, two very fertile areas of follow-up research were s uggested from this study. First, variables of gender and ethnicity need to be embedded in ever y design in future studies to clarify as much as possible differential effects. If the gende r or effects found in this study, or other kinds of attributional effects hold up over time, t hese findings may present the most damaging or even fatal flaws in block scheduling re ported to date.

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15 of 20 Just as important is the need for researche rs to ratchet up the "block versus traditional schedule" contrast variable to account for multiple ways of implementing block scheduling. There are sufficient schools, for example, which ar e implementing alternate day scheduling now to allow for multiple block scheduling contrast s to be made instead of the typical single contrast between block scheduling and traditional s cheduling. Similarly, there are now enough modifications that have been reported to 4 X 4 semester block scheduling to allow for multiple contrasts even within this single mode l of block scheduling. This research venue may ultimately provide the most illuminating information about the potential for block scheduling in the schools. Many 4 X 4 semester block scheduled schools are currently leaving room in the schedule for year -long, 50-minute classes which can accommodate the needs for year-long attention to ma thematics, band, chorus, and advanced placement classes. These kinds of modifications pro bably hold the key in the long run to establishing the flexibility in scheduling to make the best use of the characteristics educators like of both traditional and block scheduling.ReferencesAgency for Educational Development, (1995). Study of school-towork reform initiatives. Volume I: Findings and Conclusions Washingt on, DC: Author. Buckman, D., King, B., & Ryan, S. (1995). Block sch eduling: A means to improve school climate NASSP Bulletin, 79 (571), 9-18. Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64, 723-733. Cawelti, G. (1994). High school restructuring: A national survey. Arlington, VA: Educational Research Service.Center on Educational Governance, (1995). The schoo l-based management study. Volume I: Findings and conclusions. Los Angeles, CA: Universi ty of Southern California, School of Education.Cox, L. (1994). A study of the effects of a block s cheduling program with high school students who are "at-risk". Unpublished doctoral di ssertation, University of Houston. Davis-Wiley, P., George, M., & Cozart, A. (1995, No vember). Block scheduling in the secondary arena: Perceptions from the inside. Paper presented at the annual conference of the Mid-South Educational Research Ass ociation, Bi loxi, MS. Firestone, W. A. (1990). Continuity and incremental ism after all: State responses to the excellence movement. In J. Murphy (Ed.). The educat ional reform movement of the 1980s: Perspectives and cases. Berkeley, CA: McCutchan. Fuhrman, S. H., & Massell, D. (1992). Issues and st rategies in systemic reform. New Brunswick, NJ: Rutgers University, Consortium for P olicy Research in Education. Goertz, M. E., Floden, R. E., & O"Day, J. (1995). S tudies of education reform: Systemic reform. Volume I: Findings and conclusions. New Bru nswick, NJ: Rutgers University, Consortium for Policy Research in Education.Guskey, T. R., & Kifer, E. (1995, April). Evaluatio n of a high school block schedule restructuring program. Paper presented at the annua l meeting of the American Educational Research Association, San Francisco, CA.

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16 of 20Holmberg, T. (1996). Block scheduling versus tradit ional education: A comparison of grade-point averages and ACT scores. Unpublished do ctoral dissertation, University of Wisconsin, Eau Claire.Hurley, J. C. (1997b). The 4 x 4 block scheduling m odel: What do students have to say about it? NASSP Bulletin, 81 (593), 64-72. Lockwood, S. (1995). Semesterizing the high school schedule: The impact on student achievement in algebra and geometry in Dothan City Schools, Dothan, Alabama. Unpublished doctoral dissertation, University of Al abama, Tuscaloosa. Meadows, M. E. (1995). A preliminary program review of the four-period day as implemented in four high schools. Unpublished docto ral dissertation, University of Maryland, College Park.Mistretta, G. M., & Polansky, H. B. (1997). Prisone rs of time: Implementing a block schedule in a high school. NASSP Bulletin, 81 (593), 23-31. Murphy, J. (1990). The educational reform movement of the 1980s: A comprehensive analysis. In J. Murphy (Ed.). The educational reform movement of the 1980s: Persp ectives and cases. Berkeley, CA: McCutchan. North Carolina Department of Public Instruction. (1 99 ). Block scheduled high school achievement: Comparison of 1995 end-of-course test scores for blocked and non-Blocked high schools [On-line]. Available: http://www.dpi.s tate.nc.us/block_ block_scheduling_report/#4Parkinson, S., & Parkinson, C. (1995). Examination scores: Block teaching versus traditional teaching. Nurse Educator, 20 (4), 14. Payne, D., & Jordan, M. (1996). The evaluation of a high school block schedule: Convergence of teacher and student data. American Secondary Education, 25 (2), 16-19. Queen, J. A., Algozzine, B., & Eaddy, M. (1996). Th e success of 4 x 4 block scheduling in the social studies. The Social Studies, 87 (6), 249-253. Queen, J. A., Algozzine, R. F., & Eaddy, M. A., (19 97). The road we traveled: Scheduling in the 4 x 4 block. NASSP Bulletin, 81 (588), 88-99. Odden, A., & Marsh, D. (1990). Local response to th e 1980s state education reforms: New patterns of local and state interactions. In J. Mur phy (Ed.). The educational reform movement of the 1980s: Perspectives and cases. Berkeley, CA: McCutchan. Polos, N. C. (1969). Flexible scheduling: Advantage s and disadvantages. Education, 89, 315-319.Reid, L. (1995). Perceived effects of block schedul ing on the teaching of English. (ERIC Document Reproduction Service No. ED 382 950).Reid, W., Hierck, T., & Veregin, L. (1994). Measura ble gains of block scheduling. School Administrator, 51 32-33. Shortt, T. L., & Thayer, Y. V. (1997). A vision for block scheduling: Where are we now? Where are we going? NASSP Bulletin, 81 (593), 1-15.

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17 of 20Schroth, G., & Dixon, J. (1995). The effects of blo ck scheduling on seventh grade math students. East Texas State University. (ERIC Docume nt Reproduction Service No. ED 387 887)Sigurdson, S. (1981). The block plan. An alternativ e approach to the needs of junior high school students. Final report, 1981. Alberta, Canada. (ERIC Document Reproduction Service No. ED 221 623.Sigurdson, S. (1982). Two years on the block plan. Meeting the needs of junior high school students. Final report, 1982. Alberta, Canada. (ERI C Document Reproduction Service No. ED 225 946.Steagall, P. H. (1968). A study of the bolck-of-tim e scheduling in the secondary business and office education program in Ohio. Unpublished docto ral dissertation, The Ohio State University, Columbus.Stewart, J. W., & Shank, J. (1971). Daily demand of modular flexible scheduling for small schools. Educational Leadership, 29, 537-544. Van Mondfrans, A. P., Schott, J. L., & French, D. G (1972). Comparing block scheduling and traditional scheduling on student achievement a nd attitudes. Paper presented at the annual convention of the American Educational Resea rch Association, Chicago, IL. Wills, F. G., & Peterson, K. D. (1992). External pr essures for reform and strategy formation at the district level: Superintendent's interpretat ions of state demands. Educational Evaluation and Policy Analysis, 14 (3), 241-260. Wohlstetter, P., Smyer, R., & Mohrman, S. A. (1994) New boundaries for school-based management: The high involvement model. Educational Evaluation and Policy Analysis, 16 (3), Wood, C. L. (1970). Modular scheduling? Yes but --. Journal of Secondary Education, 45, 40-42.Wronkovich, M., Hess, C. A., & Robinson, J. E. (199 7). An objective look a math outcomes based on new research into block scheduling. NASSP Bulletin, 81 (593), 33-41.About the AuthorsBrian CobbSchool of EducationColorado State University Email: Cobb@CAHS.Colostate.edu Brian Cobb is a Professor in the School of Educatio n at Colorado State University and Co-Director of the Research and Development Center for the Advancement of Student Learning, a community research collaborative in Ft. Collins, Colorado. His research interests presently focus on a variety of educational reform topics including charter schools, high stakes testing, and block scheduling.Dennis BakerPoudre (CO) School DistrictFt. Collins, Colorado

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18 of 20 Dennis Baker has been involved with education for m any years as a teacher, counselor, and administrator. Since 1995, he has been a principal with the Poudre School District in Ft. Collins, CO. In the fall of 1998, he assumed his cu rrent position as principal at Fort Collins High School. Presently, he is completing his doctor al program at the University of Northern Colorado in Educational Leadership.Stacey AbatePoudre (CO) School DistrictFt. Collins, ColoradoStacey Abate, M.Ed., has been the Grants Coordinato r for the Poudre School District for the past 2 years. She also serves as a research assista nt at the Research and Development Center for the Advancement of Student Learning. Her prior educational experience includes appointments as an interpreter and teacher of the D eaf in New Mexico and Australia.Copyright 1999 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is http://epaa.asu.edu 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 University, Tempe, AZ 85287-0211. (602-965-96 44). The Book Review Editor is Walter E. Shepherd: shepherd@asu.edu The Commentary Editor is Casey D. Cobb: casey.cobb@unh.edu .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Andrew Coulson a_coulson@msn.com Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov hmwkhelp@scott.net 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 North Carolina--Greensboro Daniel Kalls Ume University Benjamin Levin University of Manitoba

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19 of 20 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 apembert@pen.k12.va.us Hugh G. Petrie SUNY Buffalo Richard C. Richardson Arizona State University Anthony G. Rud Jr. Purdue University Dennis Sayers Ann Leavenworth Centerfor Accelerated Learning Jay D. Scribner University of Texas at Austin Michael Scriven scriven@aol.com 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 roberto@servidor.unam.mx 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 Docencia Econmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma de Mxicocanalesa@servidor.unam.mx Ursula Casanova (U.S.A.) Arizona State Universitycasanova@asu.edu Jos Contreras Domingo

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20 of 20 Universitat de Barcelona Jose.Contreras@doe.d5.ub.es Erwin Epstein (U.S.A.) Loyola University of ChicagoEepstein@luc.edu Josu Gonzlez (U.S.A.) Arizona State Universityjosue@asu.edu Rollin Kent (Mxico)Departamento de Investigacin Educativa-DIE/CINVEST AV rkent@gemtel.com.mx kentr@data.net.mx Mara Beatriz Luce (Brazil)Universidad Federal de Rio Grande do Sul-UFRGSlucemb@orion.ufrgs.brJavier Mendoza Rojas (Mxico)Universidad Nacional Autnoma de Mxicojaviermr@servidor.unam.mxMarcela Mollis (Argentina)Universidad de Buenos Airesmmollis@filo.uba.ar Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma de Mxicohumberto@servidor.unam.mxAngel Ignacio Prez Gmez (Spain)Universidad de Mlagaaiperez@uma.es Daniel Schugurensky (Argentina-Canad)OISE/UT, Canadadschugurensky@oise.utoronto.ca Simon Schwartzman (Brazil)Fundao Instituto Brasileiro e Geografia e Estats tica simon@openlink.com.br Jurjo Torres Santom (Spain)Universidad de A Coruajurjo@udc.es Carlos Alberto Torres (U.S.A.)University of California, Los Angelestorres@gseisucla.edu


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