The 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
Colorado State University
Nancy Leech
University of Colorado--Denver
Michael Viney
Wendy White
Poudre School District
Citation: Lewis, C. W., Cobb, R.B., Winokur, M., Leech, N., Viney, M. & White, W.
(2003, November 11). The effects of full and alternative day 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/.
|
Abstract
The effects of a full (4 X 4) block scheduling program and an
alternate day (AB) block scheduling program in a junior high
school were under investigation in this study through 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 been in place
for several years, and one teacher in science and one teacher in
language arts had taught students under both forms of scheduling.
Because the sampling designs and analyses were different for the
science and the language arts areas, 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 consistently show
students in both forms of block scheduling outperforming students
in traditional scheduling, and that AB block scheduling 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 fastest growing educational reform initiatives in
secondary public education during the last two decades. Although
block scheduling has been a viable scheduling choice for many
schools for over forty years, it was not until the late 1980s that
block scheduling became more widespread throughout secondary
schools in the United 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
Rettig 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 implemented in many different ways with numerous
modifications, often called “hybrids” 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 increasing the time available for instruction
by extending classes beyond the traditional 50-minute class
(Weller & McLeskey, 2000).
Full Block (4 x 4) Semester Plan
The 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 every day of the week. By
attending each class every day, a student can complete four
yearlong equivalent courses in one semester, although the amount
of time spent in the course may be slightly less than in
traditional scheduling (Queen, Algozzine, & Eddy, 1997). The
plan offers teachers a manageable timetable, as they teach three
classes with a daily planning period rather than five or six
classes with a planning period every other day (Edwards,
1995).
Many researchers have explored student, teacher, and
administrator perceptions of the 4 x 4 semester plan.
Specifically, researchers have offered findings on classroom
climate, instructional approaches, student/teacher relationships,
and overall satisfaction with block scheduling. As for perceptions
regarding the overall effectiveness of the 4 x 4 semester 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, Edwards (1995) found
that after one semester with a 4 x 4 schedule, they reported
significant improvements in teaching effectiveness. Staunton
(1997) found that teachers with five or more years of teaching in
the 4 x 4 semester plan had significantly higher perceived ratings
of assessment techniques than did teachers in a traditional
scheduling environment. In a survey of four 4 x 4 block scheduling
programs, Wilson and Stokes (2000) found that, overall and over
time, students perceived block scheduling to be an effective
approach, especially if they thought that teachers used a greater
variety of teaching strategies in class. Thomas and
O’Connell (1997b) found that students felt 4 x4 block
classes offered fewer chances to cheat and increased fairness in
grading. Additionally, Edwards (1995) found that a majority of
students found it easier to focus 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 perceived the 4 x 4 semester plan to offer a
better instructional environment than in traditional scheduling
(e.g., teachers get to know them better, greater variety of
instruction). In addition, teachers perceived student/teacher
relations to be better with 4 x 4 semester plan as there was more
time for concentrated interactions (Eineder & Bishop, 1997;
Skrobarcek et al., 1997; Thomas & O’Connell, 1997b).
O’ Neill (1995) also argued that discipline problems have
dropped at many of the schools using block schedules because of
this enhanced climate. These findings suggest that the 4 x 4
semester plan format may increase the number of students per class
while creating a more productive learning environment.
The 4 x 4 semester plan is designed to create a new and
different teaching and learning experience for students and
teachers. Staunton (1997) found that teachers with more years of
experience were significantly more satisfied with instruction in 4
x 4 semester plan scheduling than in traditional scheduling.
However, Baker and Bowman (2000) found that teachers with less
experience were more likely to view block scheduling positively
than were more experienced teachers, as they appeared more willing
to make the necessary instructional changes. Using direct
observations and in-depth interviews, Queen, Algozzine, and Eddy
(1997), found that teachers appreciated the flexibility in
classroom instruction, longer planning periods, greater course
offerings, and more time for in-depth study that block scheduling
provided.
Alternate Day Block (AB) Plan
The Alternate Day Plan for block scheduling is also known as
AB, Odd/Even, and Day 1/Day 2 respectively. With AB scheduling,
students take three or four 90-120 minute classes on alternating
days for an entire school year. Many school districts have found
this mode of block scheduling conducive to school environments
versus the 4 x 4 block schedule and the traditional 45-55 minute
class schedule.
The research literature much more sparse on AB scheduling.
However, Buchman, King, and Ryan (1995) found that both
scheduling formats produced very positive perceptions regarding
the impact of AB block scheduling on safety, success, involvement,
commitment, interpersonal competency, and satisfaction. According
to Payne and Jordan’s (1996) study on the instructional
impact of AB block scheduling, “teachers reported that they
enjoyed having more time to give students individual assistance;
opportunities to get to know the students personally; time for
more creative and meaningful student work; and the ability to
structure a full lesson” (p. 18). Thus, these advantages of
an 85-minute block period led to a less stressful and more
flexible classroom 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 (1996) did not
find significant differences in students’ perceptions
regarding the efficacy of the AB scheduling plan as compared with
traditional scheduling. On the 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 Compared
As many school districts look to take on some form of block
scheduling, it is necessary to look at literature for analysis of
which block scheduling option seems most favorable by students,
teachers, and administrators. The majority of the literature on
these two forms of block scheduling has focused on the following
characteristics: student grades, class size, classroom climate,
time issues, instruction, and dropout and attendance rates.
Pisapia and Westfall (1997a; 1997b) found that teachers 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 teachers’ perceptions of class size and
classroom climate in both block scheduling formats compared with
traditional scheduling and found teachers did not think class size
was reduced with block schedules nor was classroom climate
perceived more favorably with either block schedule formats.
The way class time is used has also been an important factor in
implementing either 4 x 4 semester plan or AB block plans.
Teachers often perceived a greater need to change the pace and
type of instruction (e.g., group learning) with both the 4 x 4
semester plan and AB plans (Pisapia & Westfall, 1997b; Swope,
Fritz, & Goins, 1998).
Research has also shown mixed findings in attendance and
dropout rates for students in both the 4 x 4 semester and AB
plans. Pisapia & Westfall (1997a, 1997c) found that teachers
perceive better attendance with the 4 x 4 semester plan than with
the AB plan. Conversely, other studies 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., Skrobarcek
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 scheduling (Pisapia & Westfall,
1997b). According to Lapkin, Harley, and Hart (1997),
three-quarters of students believed that the longer periods in
both the 4 x 4 semester and AB plans made it easier to speak
French and to interact 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.
Conclusion
It is difficult to produce any consistent conclusions 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 scheduling that have been
identified through both quantitative and qualitative research on
the subject.
Advantages
Overall, the research provides modest support for the presumed
advantages of block scheduling (Payne & Jordan, 1996).
According to Lapkin et al. (1997), block scheduling may promote
higher levels of reading and writing proficiency. Students
participating in block scheduling plans are also appear to show
greater gains in grade point average as compared with traditional
instructional formats (Edwards, 1995). Nichols (2000) concluded
that longer class periods encourage teachers to develop more
effective behavioral management techniques rather than relying on
administrative disciplinarians. In addition, Nichols (2000)
argued that the decrease in quantitative minutes of classroom
instruction is more than offset in the quality of student-teacher
interaction in a block scheduling format.
Disadvantages
Adopting a block schedule has its disadvantages, especially
around designing instruction appropriate for the longer classes
(O’Neill, 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 percent (Queen et al., 1997). As
a result, some teachers will inevitably cover less material
because of the reduced number of total instructional minutes
(O’Neill, 1995). Advocates of Advancement Placement programs
have also expressed concerns about the preparation of students who
take fall class and spring exams (O’Neill, 1995), a concern
associated with 4 x 4 block scheduling in particular. In addition
to concerns about the scheduling of advanced placement courses,
the sequencing of foreign language and music are also challenges
to the block scheduling format (Shortt & Thayer, 1995).
Furthermore, there are concerns about the effectiveness of
block scheduling for all student populations. Specifically,
transfer students and lower-achieving students may not garner the
same benefits as the other students because of the faster pace and
tighter structure of block scheduling (Nichols, 2000; Shortt &
Thayer, 1995). These students may actually experience lower levels
of achievement and success “in schools where block
scheduling was poorly planned for and quickly implemented”
(Nichols, 2000, p. 145). Students may also have difficulty in
keeping track of their books, 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
limited time within the schedule for students to contact teachers
to see what 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 academic
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, overemphasis on lecture, and
teacher fatigue toward the middle of the second semester.
Research Questions
Because of the equivocation in the research literature on
achievement effects of block scheduling a priori research
hypotheses were not posed. Instead research questions were
developed around the major main effects and interactions of the
instructional format variable (4 x 4, AB, and traditional
schedules) attribute variables (gender and achievement level) and
outcome variables (science process, science content, and language
arts).
- What is the effect on science content, science process, 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 gender 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
analyses to merit separate methodological and results
sections.
Language Arts Study
Method
Population, Sample, and Sampling Design
The theoretical population for this ex post facto study
is students who attend either junior high school or middle school
in moderately sized cities in the mountain west. The actual sample
for this study was 111 students who attended two different junior
high schools in a city of approximately 125,000 in Colorado. In an
attempt to overcome some of the weaknesses 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 block scheduling simultaneously for several
years, representing a relatively 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 taught the same curriculum across both modalities,
adding important internal controls for both curriculum and
instruction across both conditions. Finally, this teacher taught
both 4 x 4 and AB block scheduling modalities in the same academic
years of this study (i.e. 4 x 4 classes in the morning; AB classes
in the 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 School (TSS) was selected.
Although TSS was considerably smaller in total number of students
(450 students versus 750 for BSS), the similarities of these two
schools on other important features were quite close. Both had a
relatively mature teaching force; both were located on the same
sector of the city, that 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 41%
respectively); and mathematics (51% versus 52% respectively)
proficiency ratings on the state’s high stakes examinations.
TSS also averaged approximately 4 students less across all grade
levels in student/teacher ratio than BSS.
Student selection stage of the sampling design. The
sample of students from BSS in the 4 x 4 block scheduling and AB
block scheduling groups were those students in the 1998-1999 and
1999-2000 academic years who were taught their language arts
courses by the instructor associated with this study. These
students numbered 131 students in 4 x 4 block scheduling and 134
in AB scheduling groups. From these initial numbers, data were
collected from the school district database on these
students’ 6th grade language arts Iowa Test of
Basic Skills (ITBS) test scores, converted into Normal 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 in 4 x 4 and AB block scheduling
respectively.
A random sample of approximately 60 students from each of the
two years was then drawn from TSS and 6th grade
language arts ITBS test scores, converted into NCE’s were
collected on each of these students. Again, missing ITBS test
score data reduced these TSS samples down to 97 students. A
Pearson correlation was then conducted for these 294 correlating
these ITBS scores with the outcome variable of interest –
the students’ 9th grade language arts RIT score
(described below). The correlation was .75, which suggested the
6th grade ITBS score was an excellent matching variable
on which to equate individual students.
The final student sampling process then, involved matching
individual students in each of the three instructional format
groups by gender and by 6th grade ITBS scores. To do
this matching process the students 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 was 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 33.2. This matching process was
followed producing a 100% match on gender, and a better than 90%
exact match on ITBS scores. In those cases where there was not a
perfect 3-group match on ITBS scores, at least two of the three
scores were an identical match, and the off-matched score was
never more that +/- 2 NCE’s. In order to maintain this high
level of matching, however, a significant attrition occurred in
each database. The final language arts sample ended up having 37
complete cases with the dataset organized for a mixed ANOVA
analysis.
Interventions
Students in the BSS science and language arts classes were
enrolled in either a 4 x 4 block format or an AB block format.
Students 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 semester. 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 format took the outcome tests in second week of April.
The curriculum, instructional formats, laboratory activities,
projects, and other in-class activities were identical for
students in both block formats and within each of the science and
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.
Variables
The dependent variable for this study was the students’
9th grade language arts RIT score on the
criterion-referenced levels test which was administered in the
late fall and late spring of each year of this study. The levels
test (Northwest Evaluation Association, 1997) is a
well-established achievement test battery that allows school
districts to measure growth in student learning from one year to
the next
The within subjects variable was the instructional format used
to teach language arts – with three levels – 4 x 4
block scheduling, AB block scheduling, and traditional scheduling.
The between groups variables for this study were the
students’ gender, and the students achievement levels in
language arts as they entered junior high school. To create this
achievement level variable, the students’ 6th
grade ITBS scores were sorted above and below the median level of
this ITBS score creating a two-level variable.
Analysis
The data for this study were analyzed using a 3 x 2 x 2 mixed
ANOVA with repeated measures on the first factor (instructional
format). Gliner & Morgan (2000) asserted that the appropriate
analytic technique 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.
Results
Table 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 instructional formats and
Mauchly’s test for sphericity also proved non-significant
(X2 = 0.129, df = 2, p. =
.938).
Table 2 presents the ANOVA source table for this language arts
analysis. As can be seen, achievement in language arts at the
9th grade level varied significantly across
instructional format, F (2, 66) = 4.89, p = .01. The
strongly significant differences on the main effect of achievement
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 on 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 Grade Language Arts Achievement Broken out by Instructional
Format, Gender, and Achievement Level
| |
Males |
Females |
| Instructional Format |
M |
n |
SD |
M |
n |
SD |
| |
Low Achieving
Group |
| 4 x 4 Block Scheduling |
222.50 |
8 |
4.44 |
218.50 |
8 |
8.18 |
| AB Block Scheduling |
225.38 |
8 |
4.14 |
226.50 |
8 |
7.09 |
| Traditional Scheduling |
212.00 |
8 |
7.87 |
220.13 |
8 |
7.49 |
| |
High Achieving
Group |
| 4 x 4 Block Scheduling |
230.78 |
9 |
7.31 |
227.42 |
12 |
5.76 |
| AB Block Scheduling |
230.00 |
9 |
7.26 |
230.83 |
12 |
7.71 |
| Traditional Scheduling |
232.56 |
9 |
7.33 |
231.17 |
12 |
5.69 |
effects, and the instructional format by gender interaction
– see Figure 1 and corresponding narrative for explanation)
along with effect sizes.
As can be seen in Table 3 and Figure 1, AB block scheduling
generated a small to moderate main effect over traditional
scheduling (Cohen, 1988); 4 x 4 block scheduling did not. Much
more interesting and powerful, 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 Language Arts Achievement as a Function of
Instructional Format, Achievement Level, and Gender
| Source |
df |
SS |
MS |
F |
p |
| Instructional Format |
2 |
385.60 |
179.30 |
4.89 |
.01 |
| Achievement Level |
1 |
2501.30 |
2501.30 |
38.58 |
.00 |
| Gender |
1 |
1.33 |
1.33 |
0.02 |
.89 |
| Instructional Format x Gender |
2 |
231.31 |
115.65 |
3.16 |
.045 |
| Instructional Format x Achievement Level |
2 |
590.85 |
295.42 |
8.06 |
.001 |
| Achievement Level x Gender |
1 |
63.02 |
63.02 |
0.97 |
.33 |
| Instructional Format x Achievement Level x
Gender |
2 |
141.75 |
70.87 |
1.93 |
.15 |
| Error (Instructional Format |
66 |
2419.22 |
36.66 |
|
|
Table 3. Statistically Significant Pairwise
Comparisons and Effect Sizes for the Language Arts
Analyses
| Pairwise
Comparisons |
M diff |
p |
da |
| AB Block Schedule versus
Traditional Schedule |
3.54 |
.006 |
.40 |
| Low Achievers in 4 x 4 Block Schedule versus
Low Achievers in Traditional Schedule |
3.90 |
.025 |
.51 |
| Low Achievers in AB Block Schedule versus Low
Achievers in Traditional Schedule |
7.09 |
.000 |
1.39 |
acalculated using weighted, pooled standard
deviation formula
Figure 1. Mean language arts RIT
Scores for students in 4 x 4, AB, and traditional schedules broken
out by gender and achievement levels.
of language arts achievement distribution. Here, both forms of
block scheduling had significant advantages over traditional
scheduling, with 4 x 4 block scheduling generating a moderately
strong effect size, and AB block scheduling demonstrating a very
large effect size – nearly three times that of 4 x 4 block
scheduling. Looking at Figure 1, it appears that the significant
format by gender interaction was due in large part to the
disparity of scores in the traditionally-scheduled school and
hence does not appear to be a genuine effect worth reporting.
Science Content and Process Study
Method
Population, Sample, and Sampling Design
The theoretical population for this ex post facto study
is identical to the language arts study above. The actual sample
for both of these science analyses was 340 students. These
students were drawn from the same two schools as the language arts
study; however, the lack of a well-suited matching variable
changed the sampling design and subsequent analyses. Hence the
school selection stage of the two stage sampling design described
in the language arts study was exactly the same for this science
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 curriculum across both modalities and in the same
academic years of this study.
Student selection stage of the sampling design. The
sample of students from BSS in the 4 x 4 block scheduling and AB
block scheduling groups were those students in the 1998-1999 and
1999-2000 academic years that were taught their science courses by
the instructor associated with this study. These students numbered
114 students in 4 x 4 block scheduling and 102 in AB scheduling
groups. Similar to the language arts study, data were collected
from the school district database on these students’
6th grade mathematics ITBS test scores, converted into
Normal Curve Equivalents (NCE’s). Due to missing data on
these ITBS scores, these initial sample sizes were reduced to 88
and 83 students in 4 x 4 and AB block scheduling respectively.
A random sample of approximately 60 students from each of the
two years was then drawn from TSS and 6th grade
mathematics ITBS test scores, converted into Normal Curve
Equivalents (NCE’s) were collected on each of these
students. Again, missing ITBS test score data reduced 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
students correlating these ITBS scores with the outcome variables
of interest – the students’ 9th grade
science process and content RIT scores. These correlations were
.39 for the science content/ITBS math correlation and .41 for the
science process/ITBS math correlation, which, although both
statistically significant at the p < .01 level, did not
correlate well enough to justify their use as a criterion for a
matched sampling design. Hence, this study was conducted and
analyzed as a factorial between groups design.
Variables
The dependent variables for this sub-study were the
students’ 9th grade science content and process
RIT scores on the criterion-referenced levels test which was
administered in the late spring of each year of this study. The
three between groups variables for this study were: (a) the
grouping variable, with three levels (4 x 4 block scheduling, AB
block scheduling, and traditional scheduling); (b) students’
gender; and (c) the students achievement levels in 6th
grade mathematics as they entered junior high school, blocked into
two groups—those at the median and above, and those below
the median.
Analyses
A preliminary one-way ANOVA was conducted using the mathematics
ITBS NCE scores as the dependent variable and the instructional
formats as the grouping variable. This analysis tested the
viability of the assumption that the matching process using math
ITBS scores served to equate, 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 using 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 dependent variables (science 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 consistently in
the same direction, and when there is a high correlation between
the two dependent variables.
Science content. Table 4 presents descriptive
information about the science content sample. Levene’s
statistic, testing the assumption of equality of variances across
the various
Table 4. Means and Standard Deviations for
9th Grade Science Content Achievement Broken out by
Instructional Format, Gender, and Achievement Level
| |
Males |
Females |
| Instructional
Format |
M |
n |
SD |
M |
n |
SD |
| |
Low
Achieving Group |
| 4 x 4 Block Scheduling |
218.74 |
19 |
15.11 |
218.59 |
22 |
10.81 |
| AB Block Scheduling |
214.76 |
17 |
13.95 |
217.29 |
21 |
6.07 |
| Traditional Scheduling |
210.83 |
24 |
9.30 |
207.85 |
20 |
6.17 |
| |
High
Achieving Group |
| 4 x 4 Block Scheduling |
223.30 |
30 |
11.50 |
220.94 |
17 |
9.59 |
| AB Block Scheduling |
221.33 |
15 |
8.15 |
222.00 |
30 |
9.62 |
| Traditional Scheduling |
222.54 |
26 |
8.36 |
219.68 |
28 |
8.41 |
levels of the three independent variables proved statistically
significant, F (11, 257) = 2.04, p < .03. Hence,
for all post hoc tests the Games-Howell test was used which Field
(2000, p. 276) recommended to be used when samples sizes are
relatively small and unequal, and the assumption of homogeneity of
variance has been violated.
Table 5 presents the ANOVA source table for this science
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
interaction between instructional format and achievement level,
F (2, 257) = 4.22, p < .016. Of course, the
statistically significant main effect for achievement 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 Gender
| Source |
df |
SS |
MS |
F |
p |
| Instructional Format |
2 |
1279.65 |
639.82 |
6.40 |
.002 |
| Achievement Level |
1 |
3095.66 |
3095.66 |
30.96 |
.000 |
| Gender |
1 |
47.34 |
47.34 |
0.47 |
.49 |
| Instructional Format x Gender |
2 |
220.73 |
110.37 |
1.10 |
.33 |
| Instructional Format x Achievement Level |
2 |
844.69 |
422.35 |
4.22 |
.016 |
| Achievement Level x Gender |
1 |
27.65 |
27.65 |
0.28 |
.60 |
| Instructional Format x Achievement Level x
Gender |
2 |
18.01 |
9.01 |
0.09 |
.91 |
| Error (Instructional Format) |
257 |
25693.37 |
99.99 |
|
|
analysis; no significant main or interaction effects 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 statistically significant pairwise comparisons; Figure 2
graphically displays these comparisons.
Table 6. Statistically Significant Pairwise
Comparisons and Effect Sizes for Science Content Analyses
| Pairwise
Comparisons |
M diff |
p |
da |
| 4 x 4 Block Schedule versus
Traditional Schedule |
4.82 |
.009 |
.44 |
| Low Achievers in 4 x 4 Block
Schedule versus Low Achievers in Traditional Schedule |
9.18 |
.003 |
.89 |
| Low Achievers in AB Block Schedule
versus Low Achievers in Traditional Schedule |
6.68 |
.022 |
1.01 |
acalculated using weighted, pooled
standard deviation formula
As can be seen in Table 6 and Figure 2, 4 x 4 block scheduling
generated a moderately strong main effect over traditional
scheduling; AB block scheduling did not. This finding is a
reversal of the block scheduling main effect found in the language
arts analysis above, but still suggests 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 entered junior high school in the bottom half of
mathematics achievement distribution. Here, both forms of block
scheduling had significant advantages over traditional scheduling,
with both forms of scheduling demonstrating large effect sizes
over traditional scheduling. Figure 2 displays this pattern of
findings. Of equal interest,
Figure 2. Mean science content RIT
scores for students in 4 x 4, AB, and traditional schedules broken
out by gender and achievement levels.
although not reported below in Table 6 is that the only
subgroup that the high achievers in traditional scheduling format
outperformed with statistically significant differences was the
traditionally scheduled low achievers. Thus it appears that
block scheduling may be capable of
bringing low achieving students to the levels of their high
achieving counterparts who receive science instruction in
traditional scheduling formats.
Science process. Table 7 presents descriptive
information about the science process sample. Levene’s
statistic, testing the assumption of equality of variances across
the various levels of the three independent 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 Grade Science Process Achievement Broken out by
Instructional Format, Gender, and Achievement Level
| |
Males |
Females |
| Instructional
Format |
M |
n |
SD |
M |
n |
SD |
| |
Low
Achieving Group |
| 4 x 4 Block Scheduling |
220.63 |
19 |
9.62 |
222.09 |
22 |
12.56 |
| AB Block Scheduling |
213.35 |
17 |
17.69 |
219.86 |
21 |
10.86 |
| Traditional Scheduling |
210.54 |
24 |
11.75 |
206.15 |
20 |
7.80 |
| |
High
Achieving Group |
| 4 x 4 Block Scheduling |
223.43 |
>30 |
13.13 |
220.00 |
17 |
8.78 |
| AB Block Scheduling |
220.40 |
15 |
7.89 |
224.77 |
31 |
10.55 |
| Traditional Scheduling |
223.35 |
26 |
7.58 |
222.82 |
28 |
7.41 |
Table 8 presents the ANOVA source table for the science 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 interaction 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
achievement 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 Gender
| Source |
df |
SS |
MS |
F |
p |
| Instructional Format |
2 |
1598.50 |
799.25 |
6.77 |
.001 |
| Achievement Level |
1 |
3164.46 |
3164.46 |
26.79 |
.000 |
| Gender |
1 |
28.33 |
28.33 |
0.24 |
.63 |
| Instructional Format x Gender |
2 |
732.08 |
366.04 |
3.10 |
.047 |
| Instructional Format x Achievement Level |
2 |
2381.28 |
1190.64 |
10.08 |
.000 |
| Achievement Level x Gender |
1 |
17.74 |
17.74 |
0.15 |
.70 |
| Instructional Format x Achievement Level x
Gender |
2 |
227.99 |
113.99 |
0.97 |
.38 |
| Error (Instructional Format) |
258 |
30477.58 |
118.13 |
|
|
analysis. Post hoc analyses using the Games-Howell statistic
along with effect size estimates are presented in Table 9 for all
statistically significant pairwise comparisons; Figure 3
graphically displays these comparisons.
As can be seen in Table 9 and Figure 3, 4 x 4 block scheduling
generated a moderately strong main effect over traditional
scheduling, almost identical to the effect seen in the science
content analysis in Table 6. As with the science content analysis,
AB block scheduling did not produce a statistically significant
advantage over traditional scheduling. As with the science content
effect size analysis, the interactions between instructional
format and entering
Table 9. Statistically Significant Pairwise
Comparisons and Effect Sizes for Science Content Analyses
| Pairwise
Comparisons |
Mdiff |
p |
da |
| 4 x 4 Block Schedule versus
Traditional Schedule |
5.28 |
.006 |
.46 |
| Low Achievers in AB Block Schedule
versus Low Achievers in Traditional Schedule |
12.87 |
.000 |
1.20 |
| Females in AB Block Schedule
versus Females in Traditional Schedule |
8.40 |
.044 |
.69 |
| Low Achievers in 4 x 4 Block
Schedule versus Low Achievers in Traditional Schedule |
7.56 |
.015 |
.69 |
acalculated using weighted, pooled
standard deviation formula
achievement level strongly favored both block scheduling
formats over traditional scheduling although the relative strength
of the particular block format was reversed in this analysis. The
gender x instructional format interaction was the first time a
gender effect appeared in any of these analyses. Looking at the
graphs in Figures 1 and 2 compared with Figure 3, this effect
would seem to be related to the relatively wide dispersion 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 confidence can be placed in the veracity of
this interaction. Again, as with the science content analysis, the
only subgroup that the high achievers in traditional scheduling
format outperformed with statistically significant differences 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 achievement levels.
Discussion
What is to be made of these findings, especially given Veal and
Schreiber’s (1999) recent statement “The literature is
consistent on the inconsistency of achievement of students within
the block schedule (p. 3)” in their review of literature? It
probably makes the most sense to start by considering the
non-achievement literature on block scheduling as well. Here, the
findings are much more consistent (although not uniformly so), and
they to tend favor both forms of block scheduling over traditional
scheduling on such things a school climate (i.e. Bickel, 1999),
student satisfaction with school (Lapkin et. al, 1997; Knight, De
Leon, & Smith, 1999)—except students in AP classes
(Knight et al, 1999), and teacher, parent, and counselor
satisfaction with school (Edwards, 1999; Wilson & Stokes,
1999; Deuel, 1999). Hence, if the main and interaction effects of
block scheduling on student achievement can be “held
harmless” versus traditional scheduling then the relatively
consistent 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) found no
adverse effects on mathematics, reading, and language arts of
attending block scheduled high school classes. Their findings,
then, are in conflict 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
traditional scheduling (and a 4 x 4/traditional hybrid) and did
not look at effects of AB block scheduling. Bickel (1999) found no
differences between block scheduling and traditional scheduling on
mathematics achievement. Wallinger (1998) 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 positive
effects of block scheduling on science achievement.
Thus it would seem that the sum of this prior research (our own
prior research notwithstanding – see Cobb, Abate, &
Baker, 1999) and the findings of this current study would tend to
support the use of block 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 one to be viewed
with caution. First, most of the studies cited that looked at
student achievement were at best causal-comparative in design, and
in some cases, purely correlational. Many of these studies did not
even exercise the attempt at controls that this present study did
– that is equating schools and students in 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 longer) and hence there are a whole host of additional
variables about the integrity of the block scheduling intervention
that are unreported and uncontrolled in these causal-comparative
research studies and that bring unmeasured effects into the
research results. Also, with multi-school studies, the variable of
instructional quality of the teachers independent of the
scheduling format, adds a significant unmeasured dimension to the
research results.
These limitations notwithstanding, we believe we have findings
worth adding to the theoretical mix, and paradoxically, they are
characterized by consistency. Looking at the consistency in three
graphs and in the magnitude of the corresponding effect size
tables, we consistently found sizeable gains in favor of block
scheduling. These gains persevered across both the language arts
and science domains of achievement; and these gains were largest
consistently in favor of lower achieving students while
consistently holding harmless upper achieving students. These are
the findings of one junior high school, however, and need to be
replicated with high quality quasi-experiments and ex post
facto studies in order to be generalized to other
settings.
In future research efforts we have a number of observations and
recommendations that seem particularly germane with the escalating
demands for “scientific rigor” in educational research
associated with current federal education legislation. First, we
recommend qualitative research—particularly case studies,
ethnographies, or grounded theory research—that explores
several very likely and important sources of variation in prior
research results. For example, differing instructional practices
by teachers 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 block scheduling and
traditional scheduling is delivered also has much to do with
efficacy 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 theory about successful
implementation of whole school reform. Exploring how schools move
to block scheduling by focusing on the attributes of specificity,
consistency, authority, stability, and context, case study or
grounded theory research can add immense understandings as to how
and why achievement results vary as they do across implementation
sites.
From the quantitative paradigm, we recommend that university
and school-based researchers aim as high as possible within the
methodological boundaries they attach to their studies. With
group-based studies, we recommend that research begin, if at all
possible with schools that are planning a move to block scheduling
in order to move the design characteristics from ex post
facto to quasi-experimentation, with attention to the
attendant improvements to controls over threats to internal
validity. Documentation of the adoption processes and
implementation activities will 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 earlier
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 validity features within this design, credible
research results can weigh into the discussion 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 schools are going to hybridized versions of either 4 x 4
or AB block scheduling, and these hybrids must be documented and
described. Second, we recommend that outcome measures focus on
math, science, and language arts—domains that 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 decade of the 21st century and the
“social validity” of this research will be enhanced by
attention to these domains. Third, ex post facto studies
absolutely must measure pre-block scheduled achievement levels of
students on measures that are highly correlated with the outcome
measures. Whether or not these pre-measures result in matched
sampling designs or ANCOVA statistical designs will be more of a
judgment call of the researchers, but these pre-measures must be
included in the research. Fourth, researchers have to measure the
length of time students attended block scheduled and traditional
(comparison) schools and eliminate students who were not in those
school long enough (i.e. 1-2 years) to demonstrate the effects of
those schools’ scheduling formats. Finally recommend
longitudinal follow up of students, if possible to explore the
durability of block scheduling effects (see, for example, the Veal
& Schreiber, 1999 and the Schreiber et. al, 2001 studies as an
example). We can envision, for example, a methodologically
appealing line of research that looked at students in
middle/junior high school who were in block scheduled and
traditional formats and who then attended, differentially, block
scheduled or traditionally scheduled formats in high school.
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About the Authors
Chance W. Lewis is an Assistant Professor in the School
of Education at Colorado State University and a Research Associate
at the Research and Development Center for the Advancement of
Student Learning.
Brian Cobb is a Professor in the School of Education at
Colorado State University and Co-Director of the Research and
Development 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 Center for Learning and Teaching in the West at Colorado
State University.
Nancy L. Leech is an Assistant Professor in the School
of Education 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
Professional 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
Poudre School District, Fort Collins, Colorado.
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