Evaluating the Impact of Block Scheduling Mathematics at the Palmerton Area
Junior High School

A Doctoral Capstone Project
Submitted to the School of Graduate Studies and Research
Department of Education

In Partial Fulfillment of the
Requirements for the Degree of
Doctor of Education

Daniel J. Heaney
Pennsylvania Western University
June 2024

EVALUATING THE IMPACT OF BLOCK SCHEDULING
Copyright

© Copyright by
Daniel J. Heaney
All Rights Reserved
August 2024

ii

EVALUATING THE IMPACT OF BLOCK SCHEDULING

Pennsylvania Western University
School of Graduate Studies and Research

Department of Education and Administrative Leadership

We hereby approve the capstone of
Daniel J. Heaney
Candidate for the Degree of Doctor of Education

‘Dr. Todd E. Kerfskin
Doctoral Capstone Faculty Advisor
Doctoral Capstone Faculty Committee Chair

a\a\ an

Ost

Sp aM RON

Dr. Jodi
Frankelli
Executive
Director, Allentown School District
Doctoral Capstone External Committee Chair

ili

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iv

Acknowledgements
I am deeply grateful to my wife, Jenn, and my sons, Gabe and Cael, for all their
support and patience during this journey. Your love and encouragement have been my
greatest strength.
I also want to give a heartfelt thank you to my Doctoral Capstone Committee, Dr.
Todd Keruskin and Dr. Jodi Frankelli. Your guidance and encouragement throughout the
doctoral process were invaluable. Your support has been crucial to the success of this
project.
I must also thank Mr. William Denny for his assistance throughout the research
and writing process.

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Table of Contents
Copyright ............................................................................................................................ ii
Acknowledgements ............................................................................................................ iv
Table of Contents .................................................................................................................v
List of Tables .......................................................................................................................x
List of Figures .................................................................................................................... xi
Abstract ............................................................................................................................. xii
CHAPTER I .........................................................................................................................1
CHAPTER II........................................................................................................................6
Literature Review.................................................................................................................6
Introduction ..................................................................................................................... 6
History of Scheduling ..................................................................................................... 7
Scheduling Models........................................................................................................ 10
Traditional Schedule ................................................................................................. 11
Table 1 .......................................................................................................................... 12
Table 2 .......................................................................................................................... 13
Block Schedule ......................................................................................................... 13
Table 3 .......................................................................................................................... 15
Table 4 .......................................................................................................................... 16
Hybrid Block............................................................................................................. 16
Table 5 .......................................................................................................................... 17
Table 6 .......................................................................................................................... 17
Traditional Schedule vs Block Schedule ...................................................................... 17
Standard Traditional Schedule .................................................................................. 18

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vi

Rotating Traditional Schedule .................................................................................. 20
4x4 Block Schedule .................................................................................................. 21
Table 7 .......................................................................................................................... 22
Alternating Block Schedule ...................................................................................... 22
Impact on Student Achievement ................................................................................... 24
Traditional Schedule vs Block Schedule .................................................................. 26
Impact on Student Discipline ........................................................................................ 27
Traditional Schedule vs Block Schedule .................................................................. 28
Impact on Student Attendance ...................................................................................... 29
Traditional Schedule vs Block Schedule .................................................................. 30
Summary ....................................................................................................................... 32
CHAPTER III ....................................................................................................................36
Methodology ......................................................................................................................36
Purpose.......................................................................................................................... 37
Setting and Participants................................................................................................. 38
Research Plan ................................................................................................................ 39
Research Methods & Data Collection........................................................................... 40
Table 8 .......................................................................................................................... 43
Table 9 .......................................................................................................................... 44
Table 10 ........................................................................................................................ 44
Table 11 ........................................................................................................................ 46

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vii

Table 12 ........................................................................................................................ 47
Table 13 ........................................................................................................................ 50
Table 14 ........................................................................................................................ 50
Validity ......................................................................................................................... 51
Fiscal Implications ........................................................................................................ 53
Summary ....................................................................................................................... 54
CHAPTER IV ....................................................................................................................56
Data Analysis and Results .................................................................................................56
Data Analysis ................................................................................................................ 57
Capstone Research Question 1.................................................................................. 57
Table 15 ........................................................................................................................ 58
Figure 1 ......................................................................................................................... 59
Figure 2 ......................................................................................................................... 61
Figure 3 ......................................................................................................................... 62
Figure 4 ......................................................................................................................... 63
Figure 5 ......................................................................................................................... 64
Table 16 ........................................................................................................................ 66
Capstone Research Question 2.................................................................................. 66
Table 17 ........................................................................................................................ 67
Figure 6 ......................................................................................................................... 68
Figure 7 ......................................................................................................................... 70

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viii

Figure 8 ......................................................................................................................... 71
Table 18 ........................................................................................................................ 72
Capstone Research Question 3.................................................................................. 73
Table 19 ........................................................................................................................ 73
Figure 9 ......................................................................................................................... 75
Figure 10 ....................................................................................................................... 76
Figure 11 ....................................................................................................................... 77
Figure 12 ....................................................................................................................... 79
Figure 13 ....................................................................................................................... 80
Table 20 ........................................................................................................................ 81
Figure 14 ....................................................................................................................... 83
Figure 15 ....................................................................................................................... 84
Figure 16 ....................................................................................................................... 85
Figure 17 ....................................................................................................................... 86
Figure 18 ....................................................................................................................... 87
Table 21 ........................................................................................................................ 88
Capstone Research Question 4.................................................................................. 89
Figure 19 ....................................................................................................................... 90
Figure 20 ....................................................................................................................... 92
Figure 21 ....................................................................................................................... 93

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Figure 22 ....................................................................................................................... 94
Summary ....................................................................................................................... 95
CHAPTER V .....................................................................................................................98
Conclusions and Recommendations ..................................................................................98
Conclusions ................................................................................................................... 99
Capstone Research Question 1.................................................................................. 99
Capstone Research Question 2................................................................................ 100
Capstone Research Question 3................................................................................ 101
Capstone Research Question 4................................................................................ 101
Limitations .................................................................................................................. 102
Recommendations for Future Research ...................................................................... 104
Summary ..................................................................................................................... 105
References ........................................................................................................................107
Appendix A ......................................................................................................................115
Appendix B ......................................................................................................................116
Appendix C ......................................................................................................................117
Appendix D ......................................................................................................................122
Appendix E ......................................................................................................................124

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List of Tables
Table 1. Sample Traditional Seven-Period Schedule ....................................................... 12
Table 2. Sample Rotating Traditional Seven-Day Schedule ……………………………13
Table 3. Sample 4x4 Block Schedule............................................................................... 15
Table 4. Sample Alternating Block Schedule .................................................................. 16
Table 5. Modified A/B Block with Standard Period Day ................................................ 17
Table 6. Standard Periods Modified with Block………..……………………………….17
Table 7. Example: Math Gap ........................................................................................... 22
Table 8. Collection Timeline for Student Mathematics Grades ....................................... 43
Table 9. Collection Timeline for Student Science and Social Studies Grades ................ 44
Table 10. Raw Academic Data Collection Form………………………………………..44
Table 11. PSSA Raw Data Collection Form .................................................................... 46
Table 12. Collection Timeline for PSSA Mathematics Data……………….…………...47
Table 13. Collection Timeline for Student Discipline and Attendance Data ................... 50
Table 14. Raw Nonacademic Data Collection Form…………………………………….50
Table 15. Raw Mathematics Data ………………………………………………………58
Table 16. Marking Period T-test Data…………………………………………………..66
Table 17. PSSA Raw Data ............................................................................................... 67
Table 18. PSSA T-test Data ............................................................................................. 72
Table 19. Raw Academic Data ......................................................................................... 73
Table 20. Marking Period T-test Science Data……………………………………………….81
Table 21. Marking Period T-test Social Studies Data ...................................................... 88

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List of Figures
Figure 1. Mean comparison of marking period and year-end grades .............................. 59
Figure 2. Grade Distribution Q1………………………………………………………...61
Figure 3. Grade Distribution Q2 ...................................................................................... 62
Figure 4. Grade Distribution Q3 ...................................................................................... 63
Figure 5. Grade Distribution Q4 ...................................................................................... 64
Figure 6. Comparison of the mean number of students by performance level ................ 68
Figure 7. Mean Percent of Students Scoring at each Performance Level……………….70
Figure 8. Performance Level Distribution ....................................................................... 71
Figure 9. Mean Comparison Group A vs Group B - Science .......................................... 75
Figure 10. Grade Distribution Q1 – Science .................................................................... 76
Figure 11. Grade Distribution Q2 – Science .................................................................... 77
Figure 12. Grade Distribution Q3 – Science .................................................................... 79
Figure 13. Grade Distribution Q4 – Science .................................................................... 80
Figure 14. Mean Comparison Group A vs Group B - Social Studies .............................. 83
Figure 15. Grade Distribution Q1 - Social Studies .......................................................... 84
Figure 16. Grade Distribution Q2 - Social Studies .......................................................... 85
Figure 17. Grade Distribution Q3 - Social Studies .......................................................... 85
Figure 18. Grade Distribution Q4 - Social Studies .......................................................... 86
Figure 19. Average Number of Absences Group A vs Group B………………………..90
Figure 20. Annual Attendance Rate ................................................................................. 92
Figure 21. Average Discipline Rates Group A vs Group B……………………………..93

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Abstract
This study evaluates the effectiveness of block scheduling in mathematics at Palmerton
Area Junior High School (PJHS). Implemented in 2018-2019 to address poor student
performance, the modified Block Schedule provides students with 90 minutes of daily
mathematics instruction. Despite this significant change, no formal evaluation has been
conducted until now.
The study aims to answer the following questions: How did the Block Schedule affect
student grades during marking periods and at the end of the year? Did it influence the
number of students scoring Advanced or Proficient on the Mathematics PSSA? How did
math grades compare to other subjects taught traditionally? What was the impact on
student discipline and attendance?
Data from PJHS’s student information system, including grades, PSSA scores,
attendance, and discipline records, were analyzed. The comparison covers three years of
the Traditional Schedule and four years of the Block Schedule, using statistical methods
to evaluate the data.
Results indicated improvements in mathematics achievement under the Block Schedule,
with higher marking period and year-end grades, and an increased number of students
scoring Advanced or Proficient on the Mathematics PSSA. However, no significant
changes were observed in discipline referrals or attendance rates.
The study provides insights into the academic benefits of block scheduling in
mathematics and offers recommendations for further research.

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CHAPTER I
The purpose of this study is to evaluate the effectiveness of block scheduling
mathematics at the Palmerton Area Junior High School (PJHS).
In response to poor performance in mathematics at the junior high level, the
Palmerton Area Junior High School (PJHS) implemented a modified Block Schedule in
the 2018-2019 school year. Since then, students in grades 7 and 8 have received 90
minutes of mathematics instruction per day for the full length of the school year. PJHS
has recently completed the fourth year using this schedule, and as of this proposal, there
has been no formal evaluation of its overall effectiveness.
Background
In my role as the Director of Curriculum, Instruction, and Technology, I played a
significant role in the development and implementation of this plan. The planning process
required a team of stakeholders to assist in its development, all of whom have a vested
interest in the results. The team had planned to review the data to measure the
implementation’s effectiveness, but COVID-19 and administrative restructuring created a
disconnect, and the evaluation has yet to be completed. Although curriculum and
instruction no longer fall under my jurisdiction, I still work closely with that department
and believe that evaluating the schedule’s effectiveness is the responsible course of action
for all the stakeholders involved. The purpose of this study is to evaluate the impact of
block scheduling mathematics at the PJHS.
The research method used will be comparative using quantitative data. This study
will compare student performance data from the past seven years, the last three years that
PJHS used a Traditional Schedule, and the first four years using the modified Block

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2

Schedule. The academic data used will include marking period and year-end grades, in
addition to the percentage of students who scored Advanced or Proficient on the
Mathematics PSSA. Nonacademic data will also be compared and that will be comprised
of student discipline and absence information.
Capstone Research Questions
1. How did the Block Schedule affect student marking period and year end grades?
2. How did the Block Schedule affect the number of students receiving Advanced or
Proficient on the Mathematics PSSA?
3. How did the Block Schedule affect students’ mathematics grades compared to
courses still taught using a Traditional Schedule?
4. How did the Block Schedule affect student discipline and attendance?
Data Collection
Raw data will be gathered from the PJHS student information system (SIS).
PJHS’s Block Schedule differs from the typical block in that students receive instruction
for the entire year rather than a semester. The data collected for use in this study will
include all four quarters as well as the final grade. (Q1, Q2, Q3, Q4 and Y1). The data
will be sorted and categorized by academic and nonacademic indicators as well as the
grading period and school year.
Data Analysis
The data analysis will compare student achievement data and other performance
indicators from the first four years of using the modified Block Schedule against the last
three years of using the Traditional Schedule. The data will be analyzed to determine if
there are any significant changes as a result of the implementation of the modified Block
Schedule.

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3

Academic Data Analysis
The academic data will be sorted and categorized by grading period and year. The
analysis will compare each grading period (Q1, Q2, Q3, Q4 and Y1), across the sevenyear span, to determine if there are any significant changes in number of student’s
earning failing or high achieving grades. In addition, mathematics data from the four
years using the modified Block Schedule will also be compared to science and social
studies data, which were taught using a Traditional Schedule, to determine if the
increases in instructional minutes resulted in improved student performance.
Seven years of Mathematics PSSA data will also be compared to identify if there
are any significant changes in the number of students scoring Advanced and Proficient as
a result of the schedule change.
Nonacademic Data Analysis
Attendance and student discipline data will also be compared, across the sevenyear time frame, to determine if the modified Block Schedule can be attributed to any
changes in those areas.
Expected Outcomes
Proponents of block scheduling often highlight its advantages, attributing
increased student achievement, decreased discipline referrals, and improved attendance to
the adoption of this schedule. As no formal evaluation of its overall effectiveness at the
PJHS has been conducted, I can only rely on informal observations to predict the
outcomes of the study. I anticipate the results will show an increase in student
achievement in mathematics during the years associated with the Block Schedule

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4

compared to those on a Traditional Schedule. However, I do not expect any significant
difference in discipline referrals or student attendance.
Fiscal Implications
The fiscal implications associated with implementing a Block Schedule are
substantial. When considering the move to a Block Schedule, one of the largest hurdles
for our school board was the costs associated with it. The change required the hiring of
additional teaching staff, classroom space, and curriculum materials, in addition to
professional development directed at preparing staff for teaching in the block. The
implementation was estimated to come at an annual cost of approximately three hundred
thousand dollars. To date, the total cost is in excess of 1.5 million dollars, further
demonstrating the need for a formal evaluation of its overall effectiveness.
Summary
Due to unsatisfactory performance in mathematics in grades seven and eight, the
school implemented a modified Block Schedule in 2018-2019. This provided students
with an additional fifty minutes of mathematics instruction per day for the entirety of the
school year. It has been four years since the launch of implementation, and thus far, there
has been no formal evaluation of its impact. This research will utilize a comparative
approach using quantitative data, comparing the last four years of the modified Block
Schedule with the preceding three years of a Traditional Schedule. Academic data,
including marking period and year-end grades, as well as the percentage of students
achieving Advanced or Proficient in Mathematics PSSA will be examined. Nonacademic
data, specifically student discipline and attendance information, will also be analyzed.

EVALUATING THE IMPACT OF BLOCK SCHEDULING
The goal of the study is to measure the effectiveness of the block scheduling
implementation for mathematics at Palmerton Area Junior High School.

5

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6

CHAPTER II
Literature Review
Introduction
As a result of poor performance in mathematics at the junior high level, the
Palmerton Area Junior High School (PJHS) implemented a modified block schedule in
the 2018-2019 school year. As part of this new scheduling approach, students in these
grades were provided with ninety minutes of daily mathematics instruction throughout
the entire school year. This change marked a significant departure from the Traditional
Schedule previously in place at the school in which students received only forty minutes
of mathematic instruction per day.
PJHS has recently completed the fourth year of implementing the modified Block
Schedule for mathematics instruction, and despite the passage of time and the
considerable investment in this new approach, no formal assessment has been conducted
to determine its overall effectiveness. The purpose of this study is to comprehensively
evaluate the impact and outcomes of block scheduling mathematics instruction at the
PJHS.
This review of the literature begins with a brief history of schedule designs in the
United States, followed by the most commonly used scheduling models and the leading
theories on their effectiveness. It also includes what current research shows to be the
advantages and disadvantages of each, in addition to their impact on student achievement,
discipline, and attendance.

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History of Scheduling
In the early 19th century, educators of varying backgrounds and with minimal or
even no formal academic training undertook the challenging task of imparting knowledge
to students. Educators provided instruction in any subject, at any moment, to students of
all levels (Schroth, n.d.). This educational frontier, however, lacked the structured rigor
we commonly associate with contemporary schooling. Formal schedules were noticeably
absent from the field of education, which led to variability in the way instruction was
delivered and introducing a degree of inconsistency into the teaching process. It was
during this period of academic inconsistency that a pressing need arose in the field of
education. High school principals began to voice their concerns about the lack of
standardized prerequisites for college admission. This call for uniformity in college
entrance requirements gave birth to what has since become widely recognized as the
"Committee of Ten" (Kliebard, 2004). The Committee of Ten, also known as the
Committee on Secondary School Studies, was an educational committee convened by
National Education Association, with the primary purpose to examine and reform the
secondary education system, specifically the high school curriculum.
In 1892, the Committee of Ten was officially convened and tasked with
evaluating the curriculum in American high schools. Their mandate was significant, as it
carried the responsibility of shaping the future of American education (National
Education Association of the United States. Committee of Ten on Secondary School
Studies, 1894).
In 1893, the committee published an innovative report, presenting an outline for
the subjects that should comprise the high school curriculum as well as their sequence

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length (Adrian, 2009). This report “determined the course of American secondary
education for a generation following its publication" (Butts & Cremin, 1953).
By the end of the 19th century, as efforts were being made to standardize
education, the Carnegie Unit was introduced to standardize the awarding of academic
credit and to establish a consistent measure for course completion. It was developed in
1906 by the Carnegie Foundation for the Advancement of Teaching and is the basis of
the Traditional Schedule used by many schools today (Williams, 2011).
The Carnegie Unit is the measure of time a student must study a subject in order
to receive one “unit” of high school credit. It operates on the principle that a student must
dedicate a specific amount of time to studying a particular subject to earn a "unit" of high
school credit. The Carnegie Unit is equal to 120 hours of study (Carnegie Foundation,
2014).
The traditional school schedule, which most students and educators are familiar
with, finds its origins closely connected with the Carnegie Unit. In the Traditional
Schedule, a typical class period generally spans 40 to 50 minutes. The reason for this is
so that students can attain the required 120 hours needed within a twenty-six-week school
year. In essence, the very length of each class period is strategically designed to ensure
students reach the required hours of study needed to earn a Carnegie Unit (Carnegie
Foundation, 2014).
During the late 1960s and early 1970s, the individualized instruction movement
gained momentum, leading educators to look for ways to provide students with
customized learning experiences (Berg & Others, 1970). As a result, a notable increase in
the adoption of a new flexible modular schedule emerged.

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Flexible modular scheduling, often referred to as Flex-Mod, represents a
scheduling methodology that divides the school day into classes of varying length. With
this model, the school day is broken down into many 10-20 minutes modules or "Mods."
Classes can then convene for one or more modules to accommodate the students' and/or
courses' time requirements (Canady & Rettig, 1995).
This scheduling approach allows students to access diverse modes of instruction,
offering a range of time patterns, period lengths, and meeting frequencies within the
school curriculum for both students and teachers (Valencia, 1969). At its peak, an
estimated 15% of American high schools were implementing this approach (Canady &
Rettig, 1995).
Nevertheless, since the 1970s, the utilization of Flexible-Modular scheduling has
experienced a decline, primarily attributed to concerns about discipline that arose from
the increased unscheduled and unsupervised time afforded to students throughout the day
(Hackmann, 2004). In addition, an article titled "FlexMod scheduling redux" noted that
some believe alternative scheduling methods, such as block scheduling, have also
contributed to the declining popularity of Flex-Mod scheduling (Murray, 2008).
By the late 80’s and into the early 90’s many schools began to re-examine their
scheduling practice. The primary goal was to move away from the conventional "lockstep, daily, single-period schedule" that had long been the norm (Canady & Rettig, 1995).
According to Canady and Rettig (1995), scheduling emerged as a critical factor in the
broader efforts being made to restructure American schools. This shift subsequently led
to an increase in the popularity of block scheduling.

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Throughout the 1990s, the Block Schedule was one of only a handful of designs
in the landscape of school scheduling. In fact, according to Canady and Rettig (1995),
during this period, it stood as one of the few scheduling designs that were actively being
implemented or even considered for implementation in American schools.
Scheduling Models
In 1994, the National Education Commission on Time and Learning released a
report which outlined five key obstacles defining the time-related challenges confronting
American schools. According to the report, these issues are considered "insurmountable
barriers" to efforts directed at enhancing learning:
•

The fixed clock and calendar, seen as a fundamental design flaw, needs reevaluation
and change.

•

Academic time has been diverted to accommodate numerous nonacademic activities.

•

Today's school schedules must adapt to the significant societal changes that have
transformed American life outside the classroom.

•

Educators often lack the necessary time to perform their duties effectively.

•

Achieving world-class standards demands additional time for nearly all students.

(National Education Commission on Time and Learning, 1994)
The report highlights that efforts to improve schools have faced considerable
challenges, largely attributed to the inherent design flaw associated with a fixed clock and
calendar. For many, the Traditional Schedule embodies this flaw. During this period,
schools started re-examining their scheduling practices with the goal of departing from
the Traditional Schedule. The report suggests that Block scheduling, which provides
students with additional time to explore more complex subjects, is one approach to

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11

address this perceived design flaw that has been hampering both students and teachers
(Underwood, 2014).
Traditional Schedule
The Traditional Schedule, which can be traced back to the early 20th century,
continues to be the predominant schedule design used in American secondary schools.
(Canady & Rettig, 1995). The Traditional Schedule’s popularity grew out of the apparent
need to standardize education. The need for standardization coupled with the challenge of
efficiently educating a large number of students led to the design currently used by many
schools.
The application of the scientific management theory and the development of the
Carnegie Unit are thought to have been the motivation for the development of the
traditional 7-period schedule (Adrian, 2009). Despite its popularity, the Traditional
Schedule is often criticized for being guided by time rather than the needs of the students
(DeMarrais & LeCompte, 1999). Remarkably, this schedule design has remained largely
unchanged since its inception.
Below are examples of two common variations of the Traditional Schedule.
Standard Traditional Schedule
The Standard Traditional Schedule is a long-standing model commonly used in
American secondary schools. This design typically features six, seven, or eight classes
each day, with each class period ranging from forty to sixty minutes (Canady & Rettig,
1995). Under this schedule, students attend each of their classes daily, following the same
sequence and spending the same amount of time in each class throughout the entire

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12

school year. This structure aims to provide a balanced and comprehensive education by
exposing students to a variety of subjects on a daily basis.
According to data from the National Center for Education Statistics,
approximately 63.3% of public schools in Pennsylvania adhere to a Traditional schedule
(National Center for Education Statistics, n.d.). This statistic underscores the continued
prevalence and acceptance of this scheduling format within the American education
system.
Table 1 illustrates an example of a Standard Traditional Schedule; it offers a clear
visual representation of how students' days are organized in this widely adopted format.
Table 1
Sample Traditional Seven-Period Schedule
Period Monday
Tuesday Wednesday Thursday
Friday
1
Math
Math
Math
Math
Math
2
English
English
English
English
English
3
Art
Art
Art
Art
Art
4
Science
Science
Science
Science
Science
5
Music
Music
Music
Music
Music
6
History
History
History
History
History
7
Computers Computers Computers Computers Computers

Rotating Traditional Schedule
In addition to the Standard Traditional Schedule, another commonly adopted
variation is the Rotating Traditional Schedule, also known as the Slide Schedule (Canady
& Rettig, 1995). This schedule closely resembles the Standard Traditional Schedule in
that students attend all their classes daily, spending the same amount of time on each
subject throughout the school year. However, the distinction lies in the daily sequence,
which rotates or "slides."

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13

For example, if a particular course is scheduled for the first period on Monday, it
will shift to the second period on Tuesday, and then to the third period on Wednesday,
continuing to rotate through a 7-day cycle. This rotation provides students with a
schedule where the order of their classes change regularly, potentially offering benefits
such as minimizing the impact of early morning or late afternoon classes on their overall
learning experience.
Table 2 offers an example of a Rotating Traditional Schedule on a seven-day
cycle. This illustrates how this schedule operates and further highlights its distinctive
features.
Table 2
Sample Rotating Traditional Seven-Day Schedule
Period

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

1

Math

Tech ed.

History

Music

Science

Art

English

2

English

Math

Tech ed.

History

Music

Science

Art

3

Art

English

Math

Tech ed.

History

Music

Science

4

Science

Art

English

Math

Tech ed.

History

Music

5

Music

Science

Art

English

Math

Tech ed.

History

6

History

Music

Science

Art

English

Math

Tech ed.

7

Tech ed.

History

Music

Science

Art

English

Math

Block Schedule
The Block Schedule emerged as a prominent schedule design in American high
schools during the 1990s. Since then, it has gained substantial popularity. As reported by

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14

the Texas Education Agency in 1995, approximately 40% of American high schools had
adopted some form of Block Scheduling (Byers, 2011). This adoption rate underscores
the appeal and potential benefits of this scheduling approach for both educators and
students alike.
Since its inception, Block Scheduling has continued to maintain its popularity in
American secondary education. Approximately 30% of the nation's secondary schools
still utilize some form of Block Scheduling, indicating its sustained relevance and
widespread usage (Rettig & Canady, 1999). Notably, as of 2008, there were a remarkable
52 variations of Block Scheduling in use across secondary schools. Amid this diversity,
the 4x4 Block and Alternating Block models have emerged as the two most common
designs, showcasing their effectiveness and adaptability in meeting the evolving needs of
modern education (Hackmann, 2004).
4x4 Block
In a 4x4 Block Schedule, students are enrolled in four classes per semester,
amounting to a total of eight classes over the course of a school year. This format allows
students to concentrate more on each subject, as they have longer class periods ranging
from 80 to 110 minutes. The extended class periods in a 4x4 Block Schedule provide
students and teachers with the opportunity to spend more time on each subject, engage in
hands-on activities or labs, and explore complicated subjects in greater depth. This format
is valued for its potential to promote more in-depth learning experiences.
Table 3 offers an example of a 4x4 Block Schedule, illustrating how students'
semesters are structured under this model. It provides a visual representation of the way
subjects are sequenced.

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15

Table 3
Sample 4x4 Block Schedule
Block
1
2
3
4
1
2
3
4

Monday

Tuesday Wednesday Thursday
Friday
Semester 1
Math
Math
Math
Math
Math
English
English
English
English
English
Art
Art
Art
Art
Art
Science
Science
Science
Science
Science
Semester 2
Music
Music
Music
Music
Music
History
History
History
History
History
Tech ed.
Tech ed.
Tech ed.
Tech ed.
Tech ed.
PE/Health PE/Health PE/Health PE/Health PE/Health

Alternating Block Schedule
In the Alternating Block design, students follow a scheduling pattern where they
are enrolled in a total of eight courses that span a 2-day cycle. This means that their class
schedule alternates between two different day schedules, typically referred to as Day A
and Day B.
On Day A, students attend four of their scheduled classes, while the other four
classes are scheduled for Day B (Underwood, 2014).
Table 4 offers an example of an Alternating Block Schedule, demonstrating how
students' classes are arranged across the two-day cycle. This visual representation
illustrates how subjects are distributed and rotated.

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16

Table 4
Sample Alternating Block Schedule
Block

Day A

Day B

1

Math

Music

2

English

History

3

Art

Tech ed.

4

Science

PE/Health

Hybrid Block
Differing from the previously discussed variations of the Block Schedule, the
Hybrid Block, also commonly referred to as the 'Modified Block,' combines elements of
both Traditional and Block Scheduling. In a Traditional Schedule, students attend
multiple classes every day, typically between 40-60 minutes per class. Conversely, Block
Scheduling typically involves longer class periods, lasting 80 minutes or more, with
students taking fewer classes each day but for a longer duration. In a Modified or Hybrid
Block, students will attend classes of various lengths, representative of both a traditional
class period and a block period.
Schools commonly adapt a Block Schedule in two fundamental ways: (1) a
weekly Modified Block, wherein four days a week follow the Alternating Block format,
while the fifth day adheres to the Traditional Standard period, or (2) a daily blend of
block and standard traditional periods (Unlocking Time, 2018a).
Table 5 illustrates examples of a Modified A/B Block with a standard period day.
Days 1/4 and 2/5 are representative of a typical alternating A/B Block Schedule, while
Day 3 is representative of a Standard Traditional Schedule.

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Table 5
Modified A/B Block with Standard Period Day
Block/Period Day 1
Day 2
Day 3
Day 4
Day 5
1
Math
Math
Music
Math
Music
2
English
3
Art
English History
English History
4
Science
5
Music
Art
Elective
Art
Elective
6
History
7
Elective
Science Tech ed.
Science Tech ed.
8
Tech ed.
Table 6 is representative of a Hybrid Block schedule featuring standard periods
modified with two blocks. In this schedule, students start their day with a block of
mathematics, then move to more traditional periods for periods 3 through 6, and then
conclude their day with a block of English.
Table 6
Standard Periods Modified with Block
Period Monday Tuesday Wednesday Thursday Friday
1
Math
Math
Math
Math
Math
2
3
Art
Art
Art
Art
Art
4
Science Science
Science
Science
Science
5
History History
History
History History
6
PE
PE
PE
PE
PE
7
English English
English
English English
8
Traditional Schedule vs Block Schedule
In recent years, educational discussions have increasingly centered on the
effectiveness of scheduling models used in schools. There are both advantages and

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18

disadvantages to the use of Traditional and Block Schedules. However, most of the recent
literature focusing on scheduling practice seems to advocate for the use of Block
Scheduling. As expected, many of the advantages and disadvantages for each design
contradict those of the other. Conversely, it is worth noting that contradictory statements
can be seen within a single scheduling design model as well. This section will detail both
the advantages and disadvantages of the most commonly used models of the traditional
and Block Scheduling designs.
Standard Traditional Schedule
Much of the recent literature on school schedule designs seems to be concentrated
on promoting a shift from the Standard Traditional Schedule to some form of Block
Scheduling. Despite the rise in popularity of Block Scheduling in the 90s, American high
schools still predominantly use the Traditional Schedule (Canady & Rettig, 1995).
As with any schedule design, there are both advantages and disadvantages
associated with its use. Some of the advantages of the Standard Traditional Schedule
include familiarity with it; most people have at least some experience with a schedule
consisting of six, seven, or eight classes daily. Another frequently cited advantage is the
reduced costs associated with staffing when using the Standard Traditional Schedule,
especially when compared to 4x4 or Alternating Block design (Williams, 2011).
In addition, proponents believe students on this schedule have increased
student/teacher contact, greater opportunity to make up missed assignments, and
increased instructional minutes (Spence, 2020). According to Ford (2015), operating on
a 7-period day provides students with nine-thousand minutes of instructional time, while
schools on the 4x4 block only have eight-thousand one-hundred minutes of instruction.

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19

Furthermore, Cromwell (1997) states that the Traditional Schedule offers students
the opportunity to acquire experience in time management, balancing schedules,
transitioning from class to class, which are valuable skills in life after high school.
While the Standard Traditional Schedule is the most widely used model in
American schools, it is not without criticism. One of the primary points cited for
opposing its use lies in the schedule's historical origins and the fundamental motivation
for its development. Critics contend that the Traditional Schedule was conceived to serve
the need for efficiency rather than considering the educational needs of students (Adrian,
2009).
The scientific management theory, which was popular at the time, emphasized the
efficient use of time and resources in an effort to increase productivity (DeMarrais &
LeCompte, 1999). This is believed to have had a major influence on the Traditional
Schedule as we know it.
The consequence of this approach is that students find themselves in a rigid daily
routine. Their schedules are carefully planned down to the minute, leaving little room for
flexibility to accommodate the diverse needs of individual students. This one-size-fits-all
approach fails to acknowledge that some students require more time and support, while
others may perform well at a faster pace.
Other frequently referenced faults of the Traditional Schedule include shorter
class periods, a faster instructional pace, more classroom transitions, and increased costs
associated with instructional materials (Williamson, 2010).
Shorter periods create a time crunch for instructors and make completing labs
and/or long projects a challenge for students. The increase in class changes has been

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20

attributed to an increase in discipline referrals. There are often increased costs associated
with instructional materials as more materials are needed, and there are fewer
opportunities to share textbooks, calculators, etc.
Rotating Traditional Schedule
In addition to the aforementioned advantages of the Standard Traditional
Schedule, the Rotating Traditional Schedule has been shown to offer additional benefits.
In the rotating schedule, the student’s course sequence rotates, allowing those students
who perform better at different times of the day to have each course during those
preferred windows at some point during the cycle.
For instance, some students may perform better in math during the afternoon. In a
Standard Traditional Schedule, students who have math in the morning are locked into
having math class at that time for the entirety of the school year, never having the
opportunity to have that class during their more productive hours. A student on a rotating
schedule will have math in the morning on Days 1, 2, and 3 and will then have it in the
afternoon on the remaining days of the cycle. This arrangement allows them the
opportunity to attend class during their higher achieving time.
Furthermore, students on a rotating schedule are not repeatedly impacted by late
starts, early dismissals, athletics, etc. (Unlocking Time, 2018b).
Much like the advantages of the Rotating Traditional Schedule, the disadvantages
are also similar to those associated with the standard version of the design. However,
there are additional disadvantages related directly to the rotation of the courses. Like the
Standard Traditional Schedule, students on a rotating schedule attend each class daily for
the same length of time, with the exception that the daily course sequence slides through

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21

a 7-day cycle. This 7-day cycle is an increase from the 5-day Monday through Friday
cycle common to the standard schedule. Critics believe that the additional two days in the
cycle create a hardship for students and parents, as they now must track which day of the
cycle students are on in order to know the correct daily course sequence. This complexity
makes it more challenging, disrupting the establishment of a regular routine and
potentially hindering students' ability to manage their time effectively.
4x4 Block Schedule
As an alternative to a Traditional Schedule, many districts have moved to the
Block Schedule to help alleviate some of the shortcomings often associated with it. The
most widely used form of Block Scheduling is the 4x4 Block, where students attend four
90-minute classes each day and complete courses in a single semester rather than a full
school year (Comer, 2012).
Proponents of Block Scheduling cite a variety of benefits in support of its use.
Unlocking Time (2018c), a national project aimed at helping K12 school leaders
implement innovative time strategies, highlights several advantages of the 4x4 Block
Schedule. Advantages include increased instructional time, reduced preparation demands
for both students and teachers, stronger connections between staff and students, and
fewer disruptions associated with class changes.
The prolonged instructional periods on the Block Schedule offer greater flexibility
in teaching practices, as noted by Canady and Rettig. Moreover, unlike Traditional
Schedules, the Block Schedule allows students extended class periods of up to 90
minutes, providing them with the additional time necessary to complete experiments,
labs, or more time-intensive projects.

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22

As with all the schedule designs included in this literature review, the 4x4 Block
also has its limitations. On the 4x4 Block, students take four classes every day for the full
length of the semester. This creates a challenge for students who complete classwork that
requires end-of-course exams offered only at the end of the year. For example, AP course
exams are traditionally given in May; students scheduled for these courses in the first
semester will be tested on content long after the conclusion of the course. These students
will have a semester-long hiatus between the course close and the exams (Canady &
Rettig, 1995).
Furthermore, students on the 4x4 Block could also receive uneven schedules,
resulting in an overload of challenging courses in either semester. It can also lead to
potential gaps in content, allowing some students to go an entire year without taking
coursework in a content area. Table 7 provides an example of the potential gap.
Table 7
Example: Math Gap
School Year

Semester 1

Semester 2

Year 1

Algebra 1

No Math Course

Year 2

No Math Course

Algebra 1

Alternating Block Schedule
Like the 4x4 Block Schedule, on the Alternating Block, also known as the A/B
Block Schedule, students attend four classes per day for up to 90 minutes each. However,
on the Alternating Block, students will have a courseload of eight classes, with four per
day, taught on alternating days for the full length of the school year (Underwood, 2014).
The advantages of this schedule are much like those of the 4x4 block. Nevertheless, there

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23

are additional benefits that address some of the limitations often associated with the
standard 4x4 Block Schedule. However, with these additional advantages also come
some added disadvantages. Much like the Traditional Schedules mentioned above,
several of the benefits and limitations associated with both the 4x4 and Alternating Block
are contradictory.
In addition to the aforementioned advantages of the 4x4 schedule, the Alternating
Block also offers additional benefits targeted directly at addressing the inadequacies of
the former. One significant advantage of the Alternating Block is that students are
enrolled in courses for the entire length of the year, thus alleviating the potential content
gaps associated with its semester-based counterpart. The yearlong enrollment of the
Alternating Block also addresses the potential hiatus between the end of Semester 1
courses and the end-of-year exams. In the 4x4 schedule, students may experience a
disconnect between the conclusion of Semester 1 courses and the end-of-year exams, but
the Alternating Block negates this issue.
Furthermore, enrolling students in eight courses for the entirety of the school year
also lessens the potential of receiving an unbalanced schedule. Canady and Rettig (1995)
also state that students on an alternate-day schedule have fewer classes, tests, and
homework assignments on any one day.
While the Alternating Block Schedule addresses some of the limitations of the
4x4 Block, it does have several drawbacks of its own to consider. Many of the criticisms
that are commonly associated with the Traditional Schedule reemerge as concerns for the
alternating block.

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24

The Standard Traditional Schedule is often criticized for the high number of
students teachers work with. Comparably, teachers in schools utilizing the Alternating
Block Schedule find themselves working with similar numbers of students, which only
adds to the demands of the high-class sizes many teachers are already faced with.
Additionally, students also continue to be responsible for the workload associated with
eight courses (Great Schools Partnership, 2013).
Critics also contend that the alternating day cycle disrupts the continuity of
education, particularly in subjects like mathematics, instrumental music, and foreign
languages (Underwood, 2014).
Impact on Student Achievement
Since the beginning of school in America, teachers and administrators have been
investigating ways to improve student achievement. During the early 19th century,
education was inconsistent and unreliable. Students attended schools that were often
overcrowded and had limited resources (US History Scene, 2024). In response to the
need for a uniform way to document and evaluate student achievement, the late 1800s
saw the development of the Carnegie Unit, which is the basis of the Standard Traditional
Schedule still in use today.
Despite its long history, the Traditional Schedule has remained largely
unchanged, drawing criticism for its emphasis on the efficient use of time and not the
needs of the students (Adrian, 2009). Opponents of the Traditional Schedule often cite
the shorter class periods and faster pace as the primary barriers to student achievement
(Williamson, 2010). Alternatives to the Traditional Schedule, like Block Scheduling,
have gained in popularity throughout the years in an attempt to address these obstacles

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25

and increase student achievement. In fact, many schools cited student achievement as the
primary reason they converted from the Traditional Schedule (Childers, 2018).
In 2002, the No Child Left Behind (NCLB) Act was signed into law and focused
on accountability. Since then, states have been required to test students annually in both
reading and math in grades 3 through 8 and once in high school (Klein, 2015). The results
of these standardized tests are used to measure how well schools were progressing toward
the goal of the legislation, which was to have all students reach proficiency, as defined by
their state, by 2014. The results of these high-stakes tests are reported to the public and
also used to hold schools accountable for the quality of the education they provide (Great
Schools Partnership, 2014). This, along with findings in reports like the National
Commission on Time and Learning’s "Prisoners of Time," has compelled educators to
pursue alternative schedules as a means of increasing student achievement.
The National Commission on Time and Learning's report titled "Prisoners of
Time," published in 1994, had a considerable influence on school schedules due it its
focus on time as a resource. The report highlighted several key concerns related to
scheduling, in addition to offering recommendations aimed at improving student
achievement.
In the report, the primary recommendation was to increase student learning time.
It highlighted that the current 6-hour, 180-day school year was insufficient to meet the
needs of students. The results of the report prompted educators to start reevaluating their
current scheduling practices. Block Scheduling was an alternative presented in the report
due to the longer periods employed with this schedule. The report recognized that longer
class periods allow for more in-depth exploration of content and enhance student

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26

engagement and achievement by providing extended periods for focused instruction. It
also noted that it adds instructional time as a result of the reduced number of transitions
between class periods (National Education Commission on Time and Learning, 1994).
Traditional Schedule vs Block Schedule
Throughout this literature review, numerous studies have been cited, comparing
the impact of scheduling on measures of student achievement, with the most common
comparisons being between Block and Traditional Schedules. However, it should be
noted that most of the available studies are directed at either measuring the impact of
Block Scheduling on student achievement or determining whether the bell schedule itself
has any impact on student achievement. A search for "Traditional Schedule impact on
student achievement" was conducted and yielded limited results directly measuring the
impact of the Traditional Schedule. Conversely, a similar search for "Block Schedule
impact on student achievement" produced a much greater number of results. However,
the literature reviewed has presented varying results when it comes to the impact of
Block Scheduling on academic achievement.
While many researchers have demonstrated the efficacy of Block Scheduling,
there are others whose studies have shown little or no positive impact in measures of
student achievement. Overall, the research presents mixed findings on the relationship
between Block Scheduling and academic achievement. Arnold (2002) found no
significant increase in test scores associated with Block Scheduling. However, Lewis et
al. (2005) found that students on a 4x4 Block Scheduling had greater gain scores in
reading and mathematics compared to traditional and A/B Block Scheduling. Veal and
Schreiber, (1999) found no significant difference in test results for reading and language

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27

but did note a statistical difference in mathematics-computation in favor of the
Traditional Schedule, suggesting that Block Scheduling may be beneficial for obtaining
more credits in mathematics but may not improve achievement and conceptual
understanding. Howard (1998), highlights concerns about decreased mathematics
achievement associated with Block Scheduling. Overall, the findings suggest that the
impact of Block Scheduling on academic achievement may vary depending on the
specific scheduling type and subject area.
Impact on Student Discipline
For as long as schools have been in session, student discipline has been an issue
for teachers and administrators. From the early days of the one-room classrooms when
paddling was a common practice, through to today, teachers and administrators have been
searching for ways to improve student discipline. Discipline problems occur in
classrooms, often creating a distraction from learning for both the students and their
peers. When teachers and administrators are required to address these concerns, it takes
additional time away from instruction, ultimately leading to poor academic performance.
One analysis of 160,480 students found that students with one or more discipline
referrals are 2.4 times more likely to score below proficiency in math as opposed to their
peers with no referrals (Whisman & Hammer, 2014). They also found that as the number
of referrals per student increases, as do the odds that they would perform at a level below
proficiency.
Advocates for Block Scheduling frequently cite the lower number of transitions
and improved teacher-student relationships as advantages of the Block Schedule that
result in decreased numbers of discipline referrals.

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28

Traditional Schedule vs Block Schedule
Research related to the impact that scheduling has on student discipline is limited.
Very few studies directly address the impact scheduling has on discipline. In reviewing
the literature, it seems that in most instances student discipline is included as a
component of a larger study investigating the overall effectiveness of the Block Schedule.
Like the results regarding student achievement, the data on student discipline is also
mixed.
In "Effects of Block Scheduling on Academic Achievement Among High School
Students", studies by Carroll (1989) and Matarazzo (1999) are cited, indicating that
student satisfaction, as measured by attendance, dropout rate, discipline referrals, and
suspensions, increased while on the Block Schedule. They conclude that the Block
Schedule positively influences student attitudes toward school, which results in a
decreased number of discipline referrals (Gruber & Onwuegbuzie, 2001, as cited in
Carroll, 1989; Matarazzo, 1999).
Proponents of Block Scheduling frequently cite a decrease in discipline concerns
as one of the advantages of this schedule. Rettig and Canady (1999) state that many case
studies have shown discipline referrals to be reduced by as much as 25 to 50 percent.
Khazzaka (1998) analyzed the data of six secondary schools that transitioned from a
Traditional Schedule to block and found that infractions related to violent behavior
decreased by 45.5%, and referrals for class disruption or insubordination dropped by
57%.
However, much like the findings associated with student achievement, the
research on student discipline offers varied results. Williams (2011) compared discipline

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29

data from two high schools within the same suburban district. One used an A/B Block
schedule, while the other followed a Traditional Schedule, from 2005-2010. The findings
show that the school employing the A/B schedule consistently had a higher average
number of referrals per student, with a 5-year average of 2.23 while the school utilizing
the Traditional Schedule averaged only 1.9 referrals per student during that same 5-year
period. It's also worth noting that discipline referrals overall decreased for both schools
during that time. The school with the Traditional Schedule experienced a greater overall
decrease, reducing by 10.9% compared to a 9.9% decrease for the A/B Block Schedule
school.
Balsimo (2005) conducted a nine-year longitudinal study that compared the
average number of discipline referrals per student, for the last two years on a Traditional
Schedule versus the first seven on a block and found no significant difference in the in
the average number per student.
Impact on Student Attendance
In the United States, compulsory education laws began to emerge in the 19th
century. The first state to enact such a law was Massachusetts in 1852, and by 1918,
every state had some form of attendance law (Yeban, 2024). Along with the adoption of
these new laws came the responsibility of implementing them. This, coupled with the
pressure of increasing student achievement, left administrators with the challenge of
identifying ways to increase attendance rates and reduce truancy.
Research has consistently demonstrated the negative impact attendance has on
measures of student achievement. Absenteeism is often associated with an increase in atrisk behaviors as well as negative academic and life outcomes; those who are absent for

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30

three days a month are missing 15% of their instructional time, leading to poor school
performance and increased dropout rates (Maynard et al., 2017).
In addition, many state funding formulas consider attendance when allocating
resources to schools. Schools with consistently low attendance rates may receive less
funding compared to those with higher attendance rates. Absenteeism not only affects the
immediate learning experience of the absent students but also has broader implications
for administration to consider. As a result, many schools have taken a closer look at all
aspects of the educational experience, including the bell schedule, as a possible means of
improving student attendance.
Traditional Schedule vs Block Schedule
Much like the research associated with the impact scheduling has on student
discipline, research dedicated solely to its impact on student attendance is also limited. A
search for studies associated with Block Scheduling's impact on student attendance
yielded very limited results. It appears that, in many cases, student attendance is
considered as a factor within a broader examination assessing the overall efficacy of the
Block Schedule.
Proponents of Block Scheduling often cite improved attendance as one of its
advantages. They contend that reduced transitions, deeper engagement, fewer classes per
day, and flexibility in planning all contribute to a more engaging and motivating school
environment, which, in turn, encourages students to attend school consistently.
However, a range of studies have explored the impact Block Scheduling has on
various outcomes, including attendance rates, and the results appear to be inconclusive.

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31

Most of the literature reviewed found no significant difference in the attendance rates for
students on the Traditional Schedule compared to those on the Block Schedule.
A study conducted in 2011 comparing the attendance rates for students under the
A/B Block Schedule and students under the Traditional Schedule from 2005 to 2010,
found that the attendance rates did not differ significantly (Williams, 2011). In fact,
Traditional Schedule students saw an increase from 80.76% to 83.70%, indicating a rise
of 2.94%. By contrast, A/B block-scheduled students experienced a decrease from
84.53% to 82.04%, representing a decline of 2.49% over that same time (Williams,
2011).
Another study conducted by Clark (2021) analyzed the mean attendance rates for
each of the three schools before block implementation, during block implementation, and
post block implementation and found there was no significant statistical difference in
attendance rates between students on Block Schedules versus their counterparts on the
Traditional Schedule at any of the three schools.
Those studies that did indicate positive outcomes for attendance seem to infer this
as a result of increases in other metrics measured, such as school climate, student
achievement, or dropout rates. "Block Scheduling: A Catalyst for Change in High
Schools," credits characteristics commonly associated with Block Scheduling, such as
reduced transitions, increased engagement, fewer classes per day, flexibility in
instruction, and personalized learning opportunities, with contributing positively to
school climate (Canady & Rettig, 1995). This, in turn, positively influences student
attendance.

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32

Deuel (1999) reported that students in Block Scheduling schools had higher
grades and fewer absences. However, it is worth noting that the data used were gathered
by administering surveys to a sample of teachers and counselors. Thirty-three percent of
the teachers and thirty-seven percent of counselors surveyed saw improvements in
student attendance.
The current data on Block Scheduling and student attendance presents mixed
results. The overall findings of this literature review suggest that additional research is
needed to truly determine the impact, if any, the Block Schedule has on student
attendance. Many of the variables used to measure the efficacy of Block Scheduling with
regard to student attendance are subjective and may be influenced by factors such as the
type of Block Schedule, how well the Block Schedule is received by the school
community, implementation quality, data interpretations, etc.
Summary
In response to poor mathematics performance at the junior high school level,
Palmerton Area Junior High School (PJHS) implemented a modified Block Schedule,
offering students in those grades ninety minutes of daily mathematics instruction, a
departure from the previous forty-minute periods. This study aims to assess the impact of
the schedule change in subsequent years. The literature review covers a brief history of
scheduling in the United States, an overview of commonly used scheduling models, and
leading theories on their effectiveness. It also investigates the impact of Block
Scheduling on measures of student achievement, discipline, and attendance.
From the early 19th century, when education lacked consistency and structure,
through to the present day, schools have consistently faced new challenges. Whether it's

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33

the call of high school principals voicing concerns about the lack of standardized
prerequisites for college admission or the passage of legislation like the No Child Left
Behind (NCLB) Act, administrators have been forced to seek ways to make their schools
more successful. Oftentimes, they re-examine their scheduling practices as a means of
improvement.
The Traditional Schedule, whose roots can be traced back to the Carnegie Unit, is
the most popular model used in schools today. Most people have experience with this
schedule; students attend anywhere from six to eight classes every day, typically lasting
between 40-60 minutes per class. Despite its popularity, it does draw criticism, as critics
argue that its purpose is to serve the need for efficiency rather than considering the
educational needs of students.
Proponents of the Block Schedule often cite it as an alternative to the Traditional
Schedule, asserting that the advantages of the Block Schedule outweigh the shortcomings
associated with the traditional model. The longer class periods, typically lasting from 80
to 110 minutes, provide students and teachers with the opportunity to spend more time on
each subject, engage in hands-on activities or labs, and explore complicated subjects in
greater depth. This approach is believed to increase student focus and minimize the loss
of instructional time due to the decrease in transitions needed when compared with the
Traditional Schedule.
Since the early days of education, enhancing student achievement has been the
goal of educators. Alternative schedules gained popularity throughout its history, with
many schools making changes in an effort to improve student achievement. The No Child
Left Behind (NCLB) Act in 2002 stressed the need for accountability by mandating

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34

annual standardized tests, whose results are used as a measure of student achievement
and to assess the school’s progress toward the goals of the legislation. This, along with
reports such as the National Commission on Time and Learning's "Prisoners of Time" in
1994, influenced the scheduling landscape. The report argued that the standard 6-hour,
180-day school year was insufficient to meet the diverse needs of students, making the
recommendation to increase student learning time. The emphasis on the deficiencies of
the Traditional Schedule prompted educators to reevaluate their current scheduling
practices.
Student discipline has long been a concern for both teachers and administrators,
creating challenges that disrupt the classroom environment. Since the early days of oneroom classrooms through to the present, educators have searched for effective strategies
to address discipline concerns and the loss of instructional time associated with it.
Advocates of the Block Schedule contend that advantages like fewer transitions and
improved teacher-student relationships result in decreased discipline referrals. However,
research studying the impact of scheduling on discipline presents mixed results.
Much like student discipline, student attendance has also posed challenges for
school administrators. Students with attendance concerns underperform academically and
have a higher likelihood of exhibiting at-risk behaviors. Research consistently shows
negative consequences for those students with high rates of absenteeism. In addition, high
rates of absenteeism can impact funding as well. Some schools have looked to the
schedule to help alleviate these concerns due to potential positive outcomes associated
with Block Scheduling. Despite the assertions of advocates that Block Scheduling

EVALUATING THE IMPACT OF BLOCK SCHEDULING
improves attendance rates, limited research corroborates these claims, and the overall
conclusion indicates the need for additional research.

35

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36

CHAPTER III
Methodology
As a result of poor performance in mathematics at the Palmerton Area Junior
High School, the decision was made to implement a modified Block Schedule. The
schedule added an additional forty-five minutes of mathematics instruction per day for
the full length of the school year for all students. Since the implementation in the 20182019 school year there has been no formal evaluation to determine the effectiveness of
the schedule on measures of student achievement, discipline, and attendance. A team of
stakeholders, consisting of administrators and professional staff, all of whom have a
personal stake in the results, had planned to review the data at the conclusion of year
three to measure the implementation's efficacy; however, COVID-19 and administrative
restructuring created a disconnect, and the evaluation has yet to be completed.
The decision to move forward with the modified Block Schedule at Palmerton
Area Junior High School (PJHS) was largely influenced by the success of Palmerton
Area Senior High School’s 4x4 Block Schedule. Historically, Palmerton Area Senior
High School has scored at or above the state average on the Algebra I Keystone Exam. In
the three years preceding Palmerton Area Junior High's Block Schedule implementation,
the senior high school scored an average of 12.5% higher than the state average in the
number of students reaching Advanced or Proficient. This, in addition to the advantages
associated with Block Scheduling commonly believed to improve student achievement,
such as increased instructional time, reduced preparation demands for both students and
teachers, stronger connections between staff and students, and fewer disruptions

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37

associated with class changes, served as the primary drivers for the transition away from
the Traditional Schedule.
This Comparative Research study aims to address four key questions concerning
the impact of the Block Schedule implementation. First, it aims to understand the effect
the Block Schedule has on student achievement, including both marking period and yearend grades. Second, the study seeks to investigate the relationship between the Block
Schedule and the performance levels on the Mathematics PSSA. Third, it aims to
examine the effect of the Block Schedule on students' mathematics grades in comparison
to those courses still taught using a Traditional Schedule. Lastly, the study will explore
the influence of the Block Schedule on nonacademic indicators such as student discipline
and attendance, aiming to identify any discernible impact on students’ discipline and
attendance rates.
Purpose
The purpose of this study is to compare student performance data from the past
seven years: the last three years when Palmerton Area Junior High School used a
Traditional Schedule and the first four years using the modified Block Schedule. The goal
is to determine if there are any significant changes evident as a result of the schedule
change.
In the four years leading into the implementation of the modified Block Schedule
at Palmerton Area Junior High School, the number of students who scored at or above the
level needed to be considered proficient on the Mathematics PSSA consistently fell
below the state average. During that time, the four-year average for the number of
students who scored proficient or above at the Palmerton Area School District was only

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38

27.9%. During that same four-year period, the state average for 7th and 8th grade was
33.9%, placing them, on average, 6% below the state average during that four-year span.
Additionally, in 2015, only 17.9% of 7th grade students reached Advanced or Proficient,
placing them 15.1% below the state average that year. Students in the 8th grade fared
slightly better during that span; however, in 2016, only 25.5% of students reached
Advanced or Proficient, placing them 5.7% below the state average.
The following Capstone Research Questions will guide this study:
1. How did the Block Schedule affect student marking period and year end grades?
2. How did the Block Schedule affect the number of students receiving Advanced or
Proficient on the Mathematics PSSA?
3. How did the Block Schedule affect students’ mathematics grades compared to
courses still taught using a Traditional Schedule?
4. How did the Block Schedule affect student discipline and attendance?
Setting and Participants
This comparative research study was conducted at Palmerton Area Junior High
School, the only school of its grade span within the Palmerton Area School District. It
consists of two grade levels, 7th and 8th, and is situated in the southernmost part of Carbon
County in Northeast Pennsylvania.
The average school enrollment from 2016 through 2022, the years used to
conduct the study, was 296 students, with an average of 149 in the 8th grade and 147 in
7th. The students who attend Palmerton Area Junior High School were brought together
here for the first time from the district’s two elementary buildings to attend 7th grade.
Towamensing Elementary, which is similar in size to the junior high school, sent an

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39

average of 50 sixth-grade students to the junior high school, while S.S. Palmer, the
district’s largest elementary, sent an average of 94.
The student demographic data for the school has remained largely unchanged
from the start of the study until present; however, it is still worth noting that the data
included in this section is taken from the school’s most recent Future Ready Index
(Pennsylvania Department of Education, 2024). The percentage of enrollment by gender
is 53.2% male and 46.8% female. Furthermore, 41.6% of students are classified as
economically disadvantaged, and 23.6% qualify for special education. In addition, the
school qualifies for Title I services; however, it does not participate in the program. The
percentage of enrollment by races is 91% White, 7.5% Hispanic, 0.4% Black, and 1.1%
two or more races.
The participants chosen for this study consist of the entire student population of
Palmerton Area Junior High School for each of the seven years under consideration. The
students included in the first three years, 2016 through 2018, represent the group that
participated in 45 minutes of mathematics instruction on the Traditional Schedule, while
the students included for the 2019 through 2022 school years represent the group that
participated in 90 minutes of Block Scheduling mathematics instruction. All students for
each of the years are included to have the largest sample possible.
Research Plan
The goal of this study is to compare student performance data over the past seven
years, with a focus on analyzing the impact of a modified Block Schedule at Palmerton
Area Junior High School. The transition to the modified Block Schedule was motivated

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40

by concerns about the low number of students scoring proficient or above on the
Mathematics PSSA, particularly in the three years leading up to the implementation.
The modified Block Schedule, which had been successful at exceeding the state
average in the number of Advanced and Proficient students on the Keystone Exams at the
senior high school level, was considered as a potential solution. In addition, the review of
the literature also identified various advantages associated with the use of Block
Scheduling. For instance, Canady and Rettig (1995) cite increased instructional time,
decreased preparation requirements for both students and teachers, enhanced
relationships between staff and students, and minimized disruptions related to class
transitions as advantages that the Block Scheduling offers. Additionally, Lewis (2005)
found that students following a 4x4 Block Schedule demonstrated higher gain scores in
reading and mathematics in comparison to those under traditional and A/B Block
Scheduling formats.
This study aims to determine the effect of implementing the modified Block
Schedule at Palmerton Area Junior High School, specifically assessing its impact on
student performance. The primary focus is on investigating whether the schedule change
has led to noteworthy improvements in academic outcomes, as well as non-academic
areas such as attendance and discipline.
Research Methods & Data Collection
This research study aims to investigate and identify if any significant changes in
student performance occurred as a result of the shift from a Traditional Schedule to a
modified Block Schedule.

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41

The objectives of this study are threefold: first, to compare and analyze student
performance data over the past seven years, considering Mathematics PSSA scores,
marking period grades, and year-end grades; second, to assess the impact of the modified
Block Schedule on annual attendance rates; and third, to evaluate the number of
discipline referrals issued each year. The ultimate goal is to comprehensively understand
the academic outcomes and potential effects on student behavior resulting from the
change in scheduling.
Quantitative methods form the foundation of this study, providing the framework
for statistical analyses aimed at quantifying the academic impact of the modified Block
Schedule. Specifically, the focus lies on examining Mathematics PSSA scores, marking
period grades, year-end grades, as well as attendance and discipline records. This
decision is supported by findings from Johnson and Onwuegbuzie (2004), who
emphasize the inherent strengths of quantitative methods, particularly in their ability to
uncover patterns and trends within large datasets
The selection of quantitative methods for this study is further justified by their
effectiveness in analyzing large datasets and identifying significant patterns or trends in
student performance. With a dataset spanning seven years and encompassing multiple
performance metrics, quantitative analysis offers a systematic approach to assessing the
impact of the modified Block Schedule at Palmerton Area Junior High School. Through
the application of statistical measures, relationships between the scheduling change and
student performance can be clarified, providing invaluable insights into the efficacy of
the implementation. Furthermore, quantitative analysis furnishes objective measures and
evidence to substantiate the findings.

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42

The data for this study will come from a variety of sources. The raw data
associated with student performance in mathematics, as well as science and social studies
courses, will be gathered from the Palmerton Area School District’s student information
system (SIS). Palmerton Area Junior High School’s Block Schedule differs from the
typical block in that students receive instruction daily for the entire year rather than a
semester. Therefore, the data collected for use in this study will include all four quarters
as well as the final grade (Q1, Q2, Q3, Q4, and Y1) for all years included. The raw data
associated with attendance and discipline will also be collected from the district’s SIS,
and the PSSA data will be gathered from test administration results downloaded from the
DRC Insight website.
Only the raw data will be collected from the student information system and the
DRC data files. No identifiable information will be collected or used for the study. The
dataset will be stripped of all identifying information so there is no way it can be linked
back to the subjects from whom it was collected.
No data was collected prior to receipt of the Institutional Review Board (IRB)
approval. The effective date of the initial IRB approval is September 10, 2021, valid
through September 9, 2022 (See Appendix A for IRB approval). However, an extension
was requested and granted by the IRB to allow the collection window to remain open
until September 15. 2024 (see Appendix B for IRB approval extension).
Seven years of student marking period and year-end mathematics grades will be
collected. The first three years of data represent the last three years using the Traditional
Schedule, and the remaining four represent the first four years using the modified Block
Schedule. This data will be used to investigate Research Question number 1 “How did

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43

the Block Schedule affect student marking period and year-end grades? “ The data
analysis will examine if there are any significant changes in the number of student’s
earning failing or high achieving grades. Table 8 illustrates the collection timeline for
student marking period and year-end mathematics grades.
Table 8
Collection Timeline for Student Mathematics Grades
Research Question

Type of Data and Data Sources

Timeline

Questions 1: How did
the Block Schedule
affect student
marking period and
year end grades?

Quantitative Data

November
2023 –
February 2024

The data will be collected from the
district’s student information system and
placed on the raw academic data
collection form for analysis.

Additionally, student marking period and year-end Science and Social Studies
grades will also be collected for the same seven years period. These courses are still
taught using the Traditional Schedule. This data will be used to investigate research
question number 3 “How did the Block Schedule affect student’s mathematics grades
compared to courses still taught using a Traditional Schedule?” The data analysis will
explore if the increased instructional minutes resulted in improved student performance.
Table 9 illustrates the collection timeline for student marking period and year-end science
and social studies grades.

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44

Table 9
Collection Timeline for Student Science and Social Studies Grades
Research Question

Type of Data Sources

Timeline

Question 3: How did
the Block Schedule
affect student’s
mathematics grades
compared to courses
still taught using a
Traditional
Schedule?

Quantitative Data

November
2023 –
February 2024

The data will be collected from the
district’s student information system and
placed on the raw academic data
collection form for analysis.

The academic data retrieved from the school’s student information system (SIS)
will be recorded on the Raw Academic Data Collection Form. The data will be sorted and
categorized into tables illustrating the number of A's, B's, C's, D's, and F's earned by
students in each grading period as shown in Table 10.
The numeric data pulled from the SIS will be converted into letter grades using
the Palmerton Area Junior High School’s “Numeric Grade Scale”, on this grade scale 90100 corresponds to an A, 80-89 to a B, 70-79 to a C, 60-69 to a D, and 0-59 to an F.
Table 10
Raw Academic Data Collection Form
YEAR:
Subject: Math
Grade
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)

Q1

Q2

Q3

Q4

Y1

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45

To investigate these questions, descriptive statistics (mean) and inferential
statistics (t-test) will be used to establish whether there is a statistically significant
difference in grades before and after the implementation of the Block Schedule. The data
will be categorized into two groups: Group A (Pre-Block Schedule), covering the three
years prior to the block implementation (Years 1-3), and Group B (Post-Block Schedule),
covering the years following the implementation (Years 4-7).
The mean marking period and year-end grades will be calculated for both Group
A and Group B to gain an overview of the central tendency of grades before and after the
schedule change. Additionally, a grade distribution analysis will be employed to analyze
the distribution of letter grades (e.g., A, B, C, D, F) for both Group A (Pre-Block
Schedule) and Group B (Post-Block Schedule).
To assess if there is a significant difference in mean grades between Group A
(Pre-Block Schedule) and Group B (Post-Block Schedule), a t-test will be conducted.
1. Null Hypothesis (H0): There is no significant difference in the mean marking period
grades before and after the schedule change.
2. Alternative Hypothesis (H1): There is a significant difference in the mean marking
period grades before and after the schedule change.
Mean grades will be calculated for each marking period, including year-end scores,
resulting in marking period grades for the three years before the schedule change (Group
A) and the four years after (Group B). The differences in mean grades between Group A
and Group B will be calculated, and the mean of these differences will be determined.
Standard deviation and standard error of the mean difference will be calculated to assess
variability and reliability. Utilizing the t-statistic formula, a t-statistic will be calculated

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46

to gauge the significance of the observed difference. With a predetermined significance
level of 0.05, a critical value from the t-distribution table will be determined. The
calculated t-statistic will then be compared to this critical value to assess if there is a
statistically significant difference in mean grades between the two groups.
Like the academic data, seven years of Mathematics PSSA data will also be
collected. Similarly, the initial three years will correspond to the last three years of the
Traditional Schedule, while the subsequent four years will align with the first four years
under the modified Block Schedule. The data will be recorded on the PSSA Raw Data
Collection Form and organized into tables, identifying the number of students who scored
at Advanced, Proficient, Basic, and Below Basic levels on the Mathematics PSSA as
shown in Table 11.
Table 11
PSSA Raw Data Collection Form
Year:
Subject: PSSA Mathematics
Performance Level

# of Students

% of Students

Advanced
Proficient
Basic
Below Basic
No Score
This data analysis aims to answer research question number 2: "How did the
Block Schedule affect the number of students receiving Advanced or Proficient on the
Mathematics PSSA?" The investigation will concentrate on determining if there are

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47

significant changes in the number of students scoring Advanced and Proficient,
potentially influenced by the schedule change.
Descriptive and inferential statistics will be used to explore this question, in
addition to a performance level distribution analysis. Similar to the marking period and
year-end grades, the PSSA data will be divided into two groups: Group A, representing
the three years before the introduction of Block Scheduling (Years 1-3), and Group B,
covering the years following implementation (Years 4-7).
The data will be further broken down by year and how the students scored
according to the PSSA scoring guidelines. For example, in 2017, ten students scored at
the Advanced level, seven at Proficient, and so on. Table 12 illustrates the collection
timeline for PSSA Mathematics data.
Table 12
Collection Timeline for PSSA Mathematics Data
Research Question

Type of Data Sources

Timeline

Question 2: How did
the Block Schedule
affect the number of
students receiving
Advanced or
Proficient on the
Mathematics PSSA?

Quantitative Data

November
2023 –
February 2024

The data will be collected from the
district's raw data files downloaded from
the DRC Insight website for each test
administration between the years 2016
and 2022.

Descriptive statistics will be used to calculate the mean percentage of students
scoring Advanced or Proficient each year. The results will be used to trend students
scoring Advanced or Proficient over the seven years. The performance level distribution
analysis will be employed to analyze the distribution of performance levels (Advanced,

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48

Proficient, Basic, Below Basic) for both Group A (Pre-Block Schedule) and Group B
(Post-Block Schedule).
To evaluate whether there exists a significant difference in the mean percentage of
students scoring Advanced or Proficient each year between Group A (Pre-Block
Schedule) and Group B (Post-Block Schedule), a t-test will be conducted.
1. Null Hypothesis (H0): There is no significant difference in the mean percentage of
students scoring Advanced or Proficient on the PSSA before and after the schedule
change.
2. Alternative Hypothesis (H1): There is a significant difference in the mean percentage
of students scoring Advanced or Proficient on the PSSA before and after the schedule
change.
The mean percentage of students scoring Advanced or Proficient on the PSSA
will be computed, including the three years pre-implementation and four years postimplementation. The differences in mean percentages between Group A and Group B
will be calculated, along with the mean of these differences. Standard deviation and
standard error of the mean difference will be computed to evaluate variability and
reliability. Utilizing the t-statistic formula, a t-statistic will be derived to assess the
significance of the observed difference. Using a predetermined significance level of 0.05,
a critical value from the t-distribution table will be determined. The calculated t-statistic
will be compared to this critical value to determine if there is a statistically significant
difference in mean grades between the two groups leading to the rejection of the null
hypothesis.

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49

In addition to the aforementioned, this study will also look to see if the schedule
change had a noticeable impact on non-academic areas, particularly in regard to student
attendance and discipline. Proponents of Block Scheduling, such as Rettig and Canady
(1999), argue that by having longer class periods, students can delve deeper into the
subject matter during each session. This, they believe, can lead to increased engagement,
better understanding of the material, and a more meaningful learning experience.
Consequently, students may be more motivated to attend class regularly, contributing to
improved attendance rates.
Student discipline data will also be examined to identify if any meaningful change
has occurred as a result of the schedule switch. Similarly to attendance, proponents of
Block Scheduling frequently attributed a decrease in the number of discipline referrals to
the Block Schedule. Rettig and Canady (1999) states that many case studies have shown
discipline referrals to be reduced by as much as 25 to 50 percent.
This data collection and analysis will be used to answer Research Question
number 4 “How did the Block Schedule affect student discipline and attendance?”. The
primary objective is to gather data on student discipline incidents and attendance rates to
analyze the potential impact of Block Scheduling on these metrics. As with the academic
data, seven years of student daily attendance and discipline referral data will also be
collected. This data will be categorized into two groups: Group A (Pre-Block Schedule),
covering the three years prior to the block implementation (Years 1-3), and Group B
(Post-Block Schedule), covering the years following the implementation (Years 4-7).
Table 13 illustrates the collection timeline for student discipline and attendance data.

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50

Table 13
Collection Timeline for Student Discipline and Attendance Data
Research Question

Type of Data Sources

Timeline

Question 4: How did
the Block Schedule
affect student
discipline and
attendance?

Quantitative Data.

November
2023 –
February 2024

The data will be collected from the
district’s student information system and
placed on the raw non-academic data
collection form.

The data will be sorted and categorized into tables illustrating the number of
absence occurrences and discipline referrals earned by students in each grading period as
well as the year end totals. The data will be collected from the district’s student
information system (SIS) and will be stripped of all identifying information. The
attendance counts will include any absences from the following categories: excused,
unlawful, out of school suspension, unexcused and vacation. The discipline data includes
any referral recorded in the district SIS as a PA State Reportable Incident type coded as a
behavior code. Raw non-academic data will be collected and recorded in tables on the
Raw Nonacademic Data Collection Form as shown in Table 14.
Table 14
Raw Nonacademic Data Collection Form
YEAR:
Absences

Q1

Q2

Q3

Q4

Y1

Discipline
Descriptive statistics will be used to interpret the attendance data. The average
number of absences, as well as the average annual attendance rates, will be calculated for

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51

each group to ascertain the typical level of attendance during each grading period. This
aims to identify the patterns, central tendencies, and variability of student attendance
before and after the implementation of the Block Schedule.
Moreover, the standard deviation of daily attendance within each group will be
examined to assess the variability in attendance patterns. This investigation aims to
determine whether attendance became more consistent or varied following the
implementation of the Block Schedule.
Descriptive statistics will also be employed to analyze discipline data. Similar to
attendance, the average number of discipline referrals and the average annual disciplinary
incident rates will be computed for each group to understand the typical level of
disciplinary issues during each grading period. This analysis intends to uncover patterns,
central tendencies, and variability in student disciplinary incidents before and after the
adoption of the Block Schedule.
Additionally, the standard deviation of daily disciplinary referrals within each
group will be explored to evaluate the variability in disciplinary patterns. This
examination seeks to determine whether disciplinary incidents became more consistent or
varied following the implementation of the Block Schedule, providing insight into the
effectiveness of the scheduling change on student behavior.
Validity
This study will employ an approach centered on content and criterion validity to
assess the impact of the schedule change on student performance. The study will focus on
statistical examinations and comparisons of numerical data pertaining to student marking
period and year-end grades, as well as the results of the Pennsylvania System School

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52

Assessments. By utilizing quantitative measures and standardized assessments, the study
aims to provide a comprehensive understanding of student performance, thereby ensuring
validity. Valid data ensures that conclusions drawn from analyses are accurate and
reliable, leading to informed decision-making and effective educational practices.
The student grade data will be collected from before and after the implementation
of the Block Schedule. The collected data will undergo statistical analysis, such as mean
comparison and t-tests, to discern changes in average grades. The mean grades for each
marking period and year-end for both before and after the implementation will be
compared using a paired t-test to assess whether there exists a significant difference in
average grades after the schedule change. Additionally, a grade distribution analysis will
be used to compare the frequency of each letter grade both before and after the Block
Schedule’s implementation. Descriptive statistics will be used to summarize the
distribution of grades within each period.
The sample will encompass all students. It should be noted that in the years
preceding the schedule change, every student was instructed by the same teacher.
However, after the schedule change, only half of the students continued with this teacher,
while a new teacher was hired to accommodate the remaining students. By including all
students in the sample, regardless of whether they experienced a change in teachers, we
enhance the validity of our findings. This approach allows for a thorough examination of
the effects of schedule change on student performance across the entire student
population. Ultimately, valid data ensures that the conclusions drawn from our analyses
are accurate and reliable, leading to informed decision-making and effective educational
practices.

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53

This study will enhance the validity of its findings by utilizing triangulation
alongside content and criterion validity. Triangulation, a method involving the use of
multiple quantitative methods and data sources, will be employed to bolster the overall
reliability of the study's results. Methodological triangulation will specifically involve the
application of various statistical techniques to analyze the quantitative data collected.
To organize the data effectively, it will be divided into groups representing the
pre- Block Schedule (Years 1-3) and post- Block Schedule (Years 4-7). For each group,
mean grades will be calculated, and a grade distribution analysis will be conducted to
assess changes in the number of failing or high-achieving grades. Descriptive and
inferential statistics, such as t-tests, will be utilized to compare mean grades and identify
significant differences.
Furthermore, the study will examine Mathematics PSSA data to analyze changes
in the number of students scoring at different performance levels. Similar to the grade
data, this PSSA data will also be divided into pre-block and post- Block Schedule groups.
Descriptive and inferential statistics will be applied to compare performance levels over
the years.
Through the implementation of triangulation, integrating different quantitative
methods and data sources, a comprehensive understanding of the impact of the schedule
change on student performance and behavior is sought. This approach guarantees a
thorough examination of the topic.
Fiscal Implications
The research plan outlined in the chapter presents several financial considerations.
However, the actual cost of conducting this research study turned out to be minimal. This

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54

was primarily due to the researcher's decision to handle all necessary tasks internally,
thereby eliminating the need for outsourcing or hiring external services. Furthermore, no
new software licenses were required as the researcher made use of existing tools and
technologies for data collection and analysis. By maximizing the available resources, the
researcher effectively minimized costs ensuring the execution of the research plan while
keeping unnecessary expenses to a minimum.
Summary
The implementation of a modified Block Schedule at Palmerton Area Junior High
School (PJHS) aims to address concerns regarding student performance in mathematics.
This comparative research study seeks to evaluate the impact of the modified Block
Schedule on student achievement, discipline, and attendance over a seven-year period.
The decision to transition from a Traditional Schedule to a modified Block Schedule is
influenced by the success observed at Palmerton Area Senior High School, where the 4x4
Block Schedule has led to improved student performance on standardized assessments.
The purpose of this study is to compare student performance data from the last
seven years, focusing on the transition from a Traditional Schedule to a modified Block
Schedule at PJHS.
These four key Capstone Research Questions guide this investigation:
1. How does the Block Schedule affect student marking period and year-end grades?
2. How does the Block Schedule affect the number of students receiving Advanced or
Proficient on the Mathematics PSSA?
3. How does the Block Schedule affect students' mathematics grades compared to
courses still taught using a Traditional Schedule?

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55

4. How does the Block Schedule affect student discipline and attendance?
The research plan involves collecting and analyzing data from various sources,
including student performance records, standardized test scores, attendance records, and
discipline referrals. Quantitative methods are employed to analyze the data, including
descriptive statistics, and inferential statistics such as mean comparison, t-tests.
The validity of the study is ensured through content, criterion, and methodological
triangulation. By employing multiple quantitative methods and data sources, the study
provides a comprehensive analysis of the impact of the modified Block Schedule on
student outcomes.

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56

CHAPTER IV
Data Analysis and Results
The objective of this Doctoral Capstone project was to evaluate the impact of the
schedule change at Palmerton Area Junior High School (PJHS) on student performance
data. The study analyzed student data over seven years: three years before the
implementation and four years after. By comparing data from the three years prior to the
schedule change with the four years following its implementation, this study aims to
provide a thorough understanding of the academic and behavioral impact of the modified
Block Schedule. The results presented in this chapter will offer valuable insights into the
effectiveness of the schedule change, guiding future educational practices and program
decisions at PJHS.
The study is guided by four key Capstone Research Questions:
1. How does the Block Schedule affect student marking period and year-end grades?
2. How does the Block Schedule affect the number of students receiving Advanced or
Proficient on the Mathematics PSSA?
3. How does the Block Schedule affect students' mathematics grades compared to
courses still taught using a Traditional Schedule?
4. How does the Block Schedule affect student discipline and attendance?
To address these questions, the study employs quantitative methods, using statistical
analyses to uncover patterns and trends within a comprehensive dataset spanning seven
years. The dataset includes student marking period grades, year-end grades, PSSA
results, attendance records, and discipline referrals, all obtained from the Palmerton Area
School District's student information system (SIS) and the DRC Insight website.

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Data Analysis
Capstone Research Question 1
The raw mathematics data collected to address research question number one was
gathered from the Palmerton Area School District’s PowerSchool student information
system (SIS). Access to the reports requires administrative access to the Palmerton Area
School District’s student information system and membership in a user group that has
access to the PCSB Custom Reports. To access the correct student data, administrative
access to the Palmerton Area Junior High School building within the system is also
needed.
Once the filters were corrected and the teacher was selected, the raw data was
exported as a CSV file. Before beginning the data analysis, all identifiable student
information was removed. Additionally, any data from courses not included in this
research, taught by the teacher, was also removed.
After the student data was removed, the remaining data was sorted by marking
period. Once organized by grading period, the data was sorted by numeric grade in
ascending order. Then, the number of A’s, B’s, C’s, D’s, and F’s were calculated and
placed in a corresponding table, as seen in Table 15.

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Table 15
Raw Mathematics Data
YEAR: 2015-16
Subject: Math
Grade

Q1

Q2

Q3

Q4

Y1

A (90-100)

22

18

38

25

22

B (80-89)

66

35

33

49

49

C (70-79)

39

48

35

24

44

D (60-69)

10

30

23

23

19

F (0-59)

2

8

8

17

4

Once the data was collected and organized, it was categorized into two groups:
Group A (Pre-Block Schedule), covering the three years prior to the block
implementation (Years 1-3), and Group B (Post-Block Schedule), covering the years
following the implementation (Years 4-7). From there, the means of the marking period
and year-end grades were calculated and compared to gain an overview of the central
tendency of grades before and after the schedule change. The bar graph seen in Figure 1
compares the mean grades for each marking period as well as year-end.

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59

Figure 1
Mean comparison of marking period and year-end grades

Mean comparison of marking period and
year-end grades
Group A vs Group B

100

82.89 83.04

80

78.67 82.81

82.36 83.59

81.45

81.03

86.36

63.74

60
40
20
0

Q1

Q2

Q3
Group A

Q4

Y1

Group B

As can be seen in Figure 1, Group B outperforms Group A in four out of five
grading periods. In Quarter 1, Group A had a mean score of 82.89, while Group B had a
slightly higher mean score of 83.04. In Quarter 2, Group B again outperformed Group A,
with scores of 82.81 and 78.67, respectively. In Quarter 3, Group B again had a higher
mean score of 83.59 compared to Group A’s 82.36. However, in Quarter 4, Group A
performed better with a mean score of 81.03, whereas Group B’s mean score dropped to
63.74. It is worth noting that the COVID-19 closure occurred in Quarter 4 of 2019, and as
a result, all students were awarded a passing grade, which for this research is equal to a
60. The COVID-19 pandemic caused widespread school closures during the 2019-2020
school year, disrupting education and prompting a rapid shift to remote learning.
This had a significant impact on the 2019 Quarter 4 mean. Despite the drop in
Quarter 4, Group B's yearly average was higher at 86.36 compared to Group A's 81.45.

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60

Overall, the data indicates a trend of improvement in student performance from
Group A to Group B. The drop in Q4 scores for Group B highlights the impact of the
COVID-19 closure. Group B outperformed Group A in three out of the four quarters and
in the yearly average. The data suggests that the modified Block Schedule results in
higher mean grades compared to the Traditional Schedule, except for a the drop in Q4.
The overall yearly average also seems to support the effectiveness of the modified Block
Schedule in improving academic performance.
Additionally, a grade distribution analysis was used to examine the distribution of
letter grades (e.g., A, B, C, D, F) for both Group A (Pre-Block Schedule) and Group B
(Post-Block Schedule). The analysis of Q1 grades from 2015-16 to 2021-22, as shown in
Figure 2, indicates that Group A (2015-16 to 2017-18) exhibited a gradual increase in the
number of A grades earned, starting from 22 in 2015-16 and rising to 42 in 2017-18. This
trend continues in Group B (2018-19 to 2021-22), although with greater variability,
peaking at 44 in 2021-22.

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Figure 2
Grade Distribution Q1

GRADE DISTRIBUTION Q1
B

C

D

F

2015-16

2016-17

57
44

34

25

31

2017-18

2018-19

2020-21

2019-20

0

1

6

7

14
2
1

2
4

8

10
2

2

10

21

22

29

31

37

42

42

38

39

45

50

56

58

62

66

74

A

2021-22

The analysis of Q1 grades over the years reveals several key differences and
trends. Overall, there is a slight increase in the average number of A grades from Group
A (34) to Group B (36.5). The average number of students earning B grades also
increased, with Group A averaging 57.3 and Group B averaging 62.75. Furthermore,
Group B saw a significant decrease in the average number of C grades, dropping to 29.75
from Group A’s 37.7. Group B’s D grades also saw a slight decrease, down to 7.25 from
7.3. Lastly, Group B saw an increase in the number of failing grades, with an average of
3.25 compared to Group A’s 1.67.
These trends indicate an overall improvement in student performance in Group B,
with more students achieving higher grades and fewer students receiving middle-range
grades. However, the increase in failing grades in Group B suggests that there may still
be challenges to address in supporting lower-performing students.

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62

The results of the Q2 Grade Distribution analysis are similar to those seen for Q1,
there is a notable increase in the average number of A grades from Group A (25.3) to
Group B (41). The average number of students earning B grades also increased slightly,
with Group A averaging 49 and Group B averaging 53.75. Furthermore, Group B saw a
significant decrease in the average number of C grades, dropping to 32.5 from Group A’s
36. Furthermore, Group B's D grades also saw a slight decrease, down to 9.75 from 19.
Group B also experienced a significant decrease in the number of failing grades, dropping
to an average of 4 from Group A’s 8.67. Figure 3 illustrates the Q2 grade distribution.
Figure 3
Grade Distribution Q2

GRADE DISTRIBUTION Q2
B

C

D

F

2016-17

11

24

2018-19

1
2020-21

2019-20

1

5

11
12
2017-18

12

20
5

2
2015-16

56
40

46
35

28

32

33
7

8

13

18

22

27

30

35

36

45

44
48

55

51

48

56

61

A

2021-22

These trends also indicate an overall improvement in student performance for
Group B, with more students achieving higher grades and fewer students receiving
middle-range and failing grades. The improvement is particularly noticeable in the
reduction of D and F grades, reflecting better academic outcomes in Group B compared
to Group A. The improvement trends in student performance are consistent across both

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63

Q1 and Q2, with more students achieving higher grades and fewer students receiving
lower grades in Group B compared to Group A.
The results of the Q3 grade distribution are consistent with those seen in Q1 and
Q2, showing overall improvement in student performance from Group A to Group B. As
seen in Figure 4, there is a slight increase in the average number of A grades from Group
A (44.3) to Group B (45.25). The average number of students earning B grades also
increased, with Group A averaging 40.3 and Group B averaging 51.75. The average
number of C grades remained relatively stable, with Group A averaging 31.7 and Group
B averaging 30. However, Group B saw a significant decrease in the average number of
D grades, dropping to 7.25 from Group A’s 15. Lastly, Group B experienced a slight
decrease in the average number of F grades, with 4.25 compared to Group A’s 5.33.
Figure 4
Grade Distribution Q3

GRADE DISTRIBUTION Q3
B

C

D

F

2016-17

42
34

38
38

46

54

2017-18

2018-19

2020-21

2019-20

4
2

3
1

12
11

10
3

8
2

6

8

14

21

23

27

29

39

46

50
39

45
49
31

38
33
35
2015-16

61

62

A

2021-22

These trends indicate an overall improvement in student performance, with more
students achieving higher grades and fewer students receiving lower grades in Group B

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64

compared to Group A. The reduction in D and F grades in Group B also suggests
improved academic outcomes for students who previously struggled.
Lastly, the results of the Q4 grade distribution analysis are consistent with those
of the previous three marking periods. As seen in Figure 5, there is an increase in the
average number of A grades from Group A (36.3) to Group B (54, excluding 2019-20).
The average number of students earning B grades also increased, with Group A averaging
47 and Group B averaging 52.6. Furthermore, Group B saw a significant decrease in the
average number of C grades, dropping to 19.66 from Group A’s 30. Group B’s D grades
also saw a substantial decrease, down to 8.6 from Group A’s 16.7. Lastly, Group B
experienced a decrease in the number of failing grades, with an average of 4 compared to
Group A’s 6.
Figure 5
Grade Distribution Q4

GRADE DISTRIBUTION Q4
B

C

D

F

55
53

45

48

46
45

2015-16

2016-17

2017-18

21

20

2018-19

2019-20

1

6

10
7
0
0
0
0
0

0

1

4

7

10

18

17

20

29

37

38

47

49
24
23

25

59

60

A

2020-21

2021-22

These trends also indicate an overall improvement in student performance, with
more students achieving higher grades and fewer students receiving lower grades in

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65

Group B compared to Group A. The reduction in D and F grades in Group B suggests
improved academic performance for students. The absence of grades in 2019-20 (Q4) are
a result of the COVIC-19 closure.
Across all quarters, there is improvement in student performance from Group A to
Group B, with more students achieving higher grades (A and B) and fewer students
receiving lower grades (C, D, and F). This consistency indicates that the implementation
of a Block Schedule had a positive impact on student performance with regards to
marking period and year-end grades.
In addition to the mean comparison and the grade distribution analysis, a t-test
was performed to test the following hypothesis:
1. Null Hypothesis (H0): There is no significant difference in the mean marking period
grades before and after the schedule change.
2. Alternative Hypothesis (H1): There is a significant difference in the mean marking
period grades before and after the schedule change.
To perform the t-test, the marking period means were calculated and sorted into two
groups: Group A, representing the years on the Traditional Schedule, and Group B,
representing the years on the modified Block Schedule. The differences in mean grades
between Group A and Group B were calculated, and the mean of these differences was
determined. The standard deviation and standard error of the mean difference were also
calculated, and an unpaired t-test was then used to analyze the data and determine if there
was a statistically significant difference. Table 16 represents the data used to perform the
t-test.

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66

Table 16
Marking Period T-test Data
Group
Mean
SD
SEM
N

Group A
81.2793
2.8025
0.7236
15

Group B
79.9060
18.9831
4.2448
20

The results of the t-test produced a P value of 0.3369, indicating that the
difference between the two groups is not statistically significant. This suggests that the
observed difference in mean grades between Group A and Group B is likely due to
random chance rather than the schedule change. The confidence interval for the
difference in means ranges from -5.0226 to 1.7693. The calculated t-statistic is 0.9745,
with 33 degrees of freedom, and the standard error of the difference is 1.669. These
values were used to determine the statistical significance and confidence interval.
Overall, the data does not provide sufficient evidence to conclude that there is a
significant difference in mean grades between the traditional and modified Block
Schedules.
Capstone Research Question 2
To address Capstone Research Question 2, “How did the Block Schedule affect
the number of students receiving Advanced or Proficient on the Mathematics PSSA?”
raw data files were downloaded from the DRC Insight website for each test
administration included in the study. Once downloaded, all identifiable student data was
removed, and the data was sorted by performance levels. Then, the number of students
who scored at each performance level was calculated and placed in the appropriate table
according to the test was administered, as seen in Table 17.

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Table 17
PSSA Raw Data
Year: 2015
Subject: PSSA Mathematics
Performance Level

# of Students

% of Students

Advanced

6

3.92%

Proficient

21

13.73%

Basic

52

33.99%

Below Basic

74

48.37%

No Score

0

0%

Once the data was collected and organized, it was categorized into two groups:
Group A (Pre-Block Schedule), covering the three years prior to the block
implementation (Years 1-3), and Group B (Post-Block Schedule), covering the years
following the implementation (Years 4-7). The mean number of students scoring at each
performance level was then calculated for both groups to provide an overview of the
central tendency of student performance on the PSSA before and after the schedule
change.
Figure 6 compares the mean number of students who scored at each performance
level for Group A and Group B, revealing several key insights about the impact of the
Block Scheduling on student performance.
The data suggests that the Block Schedule implementation has led to an
improvement in student performance at the Advanced level, with Group B showing a
higher average number of students scoring Advanced at 10.3 compared to Group A's 7.6.
Both groups performed similarly at the Proficient level, with Group B having a slightly
higher average number of students at 31.3 compared to Group A's 30.6.

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68

At the Basic level, Group B had a slightly lower average number of students, with
42 compared to Group A's 43.3. Additionally, the number of students performing at the
Below Basic level decreased in Group B to 51 from Group A's 56.3.
However, the average number of students with no scores increased significantly in Group
B to 13.3 from Group A's 1.6. This increase is likely a result of the COVID-19 pandemic.
Overall, the implementation of the Block Schedule has resulted in improvements
at the Advanced level and a reduction in the Below Basic level. However, the significant
increase in the number of students with no scores after implementation is a concerning
trend that requires further investigation.
Figure 6
Comparison of the mean number of students by performance level

Comparison of the mean number of students
by performance level
56.3

60
50

43.3

40

30.6

30
20
10
0

7.6

51

42

31.3
13.3

10.3

ADVANCED

1.6
PRO

BAS
Group A

BEL

NO SCORE

Group B

To further investigate the impact of the schedule change on the PSSA results, the
average percent of students scoring at each performance level was calculated to provide a

EVALUATING THE IMPACT OF BLOCK SCHEDULING

69

clearer analysis of student performance and a more accurate assessment of the schedule
change's effectiveness.
Figure 7, found below, compares the mean percentage of students scoring at each
performance level for Group A (Pre-Block Schedule) and Group B (Post-Block
Schedule).
An analysis of the mean percentage data offers a clearer picture of the distribution
of performance levels relative to the group size. The results for students scoring
Advanced and Proficient were consistent with the findings suggested by the mean
number of students data.
In contrast, at the Basic level, Group B has a slightly lower average number of
students (28.4%) compared to Group A (31.1%). This indicates that the difference in the
percentage of students at this performance level is minimal. However, this slight variation
might be attributed to overall class size differences rather than a significant change in
student performance, suggesting that Group A's marginally higher percentage could
reflect a larger overall class size.
For the Below Basic level, Group A has a higher mean number of students (56.3)
compared to Group B (51). This is also reflected in the percentages, where Group A has a
higher proportion (40.17%) compared to Group B (34.67%). This suggests that the Block
Schedule implementation has helped reduce the number of students performing at the
lowest level.
A notable concern is the No Score category, where Group B has a much higher
mean number of students (13.3) compared to Group A (1.6), with percentages showing a
similar trend (Group B: 13.15%, Group A: 1.39%). However, this is likely a result of

EVALUATING THE IMPACT OF BLOCK SCHEDULING

70

COVID-19. The data for both groups is fairly consistent with the exception of 2021, the
year immediately preceding the COVID-19 closure, where 31 tests were not scored,
significantly impacting the average.
Overall, the mean percent data provides a clearer picture of the distribution of
performance levels relative to group size, highlighting that the Block Schedule
implementation appears to have positively impacted student performance at the
Advanced level and reduced the percentage of students scoring at the Below Basic level.
Figure 7
Mean Percent of Students Scoring at each Performance Level

Comparison of the Mean Percent of Students
Scoring at each Performance Level
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%

40.17%

22.02%
21.12%

34.67%

31.12%
28.43%

13.15%
5.50%

7.02%

ADVANCED

1.39%
PRO

BAS
Group A

BEL

NO SCORE

Group B

A performance level distribution analysis was conducted to examine the
distribution of student scores across different performance levels from 2016 to 2022,
specifically comparing the distribution before the schedule change with results after. The
comparison of Group A (Pre-Block Schedule: 2016-2018) and Group B (Post-Block
Schedule: 2019-2022) reveals several notable trends.

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71

As seen in Figure 8, Group B demonstrated an improvement at the Advanced
level, with higher numbers of students scoring in this category post-implementation.
However, while there was an initial increase in Proficient level scores in 2019,
subsequent years showed a decline, indicating variability in performance. The Basic level
scores in both groups showed a trend towards stabilization, with Group B maintaining
numbers similar to the lower end of Group A. The Below Basic level in Group B initially
decreased but saw an increasing trend in the later years, highlighting a potential area of
concern. Additionally, the No Score category saw a sharp increase in 2021 for Group B
likely due to the COVID-19 pandemic.
Figure 8
Performance Level Distribution

Performance Level Distribution
80

67

70
60

47

50
40
20
10
0

52

50
42

7

2
2016

47 46

41

31

30

30

61
50
42

40

31

8

2
2017
ADV

27
13

8
2018
PRO

BAS

31
20
10

8

1

40

0
2019
BEL

0
2021

2022

NO SCORE

Overall, while the Block Schedule implementation led to some positive outcomes,
particularly at the Advanced level, there are areas that may require targeted interventions
to ensure consistent improvement across all performance levels.

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72

A t-test analysis was performed to determine if there was a statistically significant
difference in the mean percentage of students scoring Advanced and Proficient between
Group A (Pre-Block Schedule) and Group B (Post-Block Schedule) to test the following
hypotheses:
1. Null Hypothesis (H0): There is no significant difference in the mean percentage of
students scoring Advanced or Proficient on the PSSA before and after the schedule
change.
2. Alternative Hypothesis (H1): There is a significant difference in the mean percentage
of students scoring Advanced or Proficient on the PSSA before and after the schedule
change.
Table 18 below highlights the data used to perform the t-test.
Table 18
PSSA T-test Data
Group
Mean
SD
SEM
N

Group A
27.5267
1.9845
1.1458
3

Group B
28.1367
10.7535
6.2085
3

The P value obtained from the analysis is 0.9277. This P value indicates that the
difference in the mean percentages is not statistically significant, as it is well above the
predetermined significance threshold of 0.05. The confidence interval for the mean
difference between Group A and Group B is from -18.1387 to 16.9187, suggesting that
the true difference in means could be anywhere within this range.
Additionally intermediate values used in the calculations include a t-value of
0.0966, with 4 degrees of freedom, and a standard error of the difference of 6.313. Group

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73

A had a mean score of 27.5267 with a standard deviation (SD) of 1.9845 and a standard
error of the mean (SEM) of 1.1458. Group B had a mean score of 28.1367 with a much
higher standard deviation of 10.7535 and a SEM of 6.2085, also based on 3 observations.
In conclusion, the statistical analysis supports the null hypothesis (H0), indicating
that there is no significant difference in the mean percentage of students scoring
Advanced or Proficient on the PSSA before and after the schedule change. The
confidence interval and high P value highlight variability within the data, further
supporting the conclusion that the schedule change did not have a statistically significant
impact on student performance at these levels.
Capstone Research Question 3
The raw science and social studies data collected to address research question
number three was gathered from the Palmerton Area School District’s student
information system, (SIS) utilizing the same steps previously applied to collect the data to
respond to Capstone Research Question #1
After the student data was removed, the remaining data was organized by grading
period, and sorted by numeric grade in ascending order. Then, the number of A’s, B’s,
C’s, D’s, and F’s was calculated and placed in a corresponding table, as seen in Table 19.
Table 19
Raw Academic Data
YEAR: 2015-16
Subject: Science
Grade

Q1

Q2

Q3

Q4

Y1

A (90-100)

32

31

31

33

31

B (80-89)

58

43

43

41

40

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74

C (70-79)

39

51

48

39

53

D (60-69)

15

13

16

23

17

F (0-59)

1

7

5

7

2

As was done with mathematics, once the data was collected and organized, it was
categorized into two groups: Group A (Pre-Block Schedule), covering the three years
prior to the block implementation (Years 1-3), and Group B (Post-Block Schedule),
covering the years following the implementation (Years 4-7). From there, the means of
the marking period and year-end grades were calculated and compared to gain an
overview of the central tendency of grades before and after the schedule change.
The bar graph in Figure 9, titled "Mean Comparison Group A vs. Group B Science," compares the average science grades of both groups across each marking
period and the year-end. Several trends are highlighted in this figure. Throughout the four
quarters, Group B consistently outperformed Group A, with the exception of the third
quarter. In the first quarter, Group B led with a mean score of 85.72 compared to Group
A's 83.65. This trend continued into the second quarter, where Group B again scored
higher (83.74) than Group A (82.27). The third quarter saw a slight shift, with Group A
achieving a higher score (82.03) than Group B (81.48). However, in the fourth quarter,
Group B demonstrated a considerable lead, averaging 85.56 while Group A’s mean score
was 82.94.

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75

Figure 9
Mean Comparison Group A vs Group B - Science

Mean Comparison
Group A vs Group B - Science
87

85.72

86
85
84

85.56
83.74

83.65

83

82.27

82

82.94
82.03

84.75
82.87

81.48

81
80
79

Q1

Q2

Q3
Group A

Q4

Y1

Group B

Overall, the yearly average (Y1) underscores Group B's stronger performance,
with an average score of 84.75 compared to Group A's 82.87. These trends highlight that
while Group A's performance remained relatively stable across the quarters, Group B
consistently scored higher, particularly in the first and fourth quarters.
Additionally, a grade distribution analysis was completed for both Science and
Social Studies to assess the impact of the Block Schedule on student’s grades in subjects
still taught using the Traditional Schedule.
The distribution of Q1 Science grades, as seen in Figure 10, comparing Group A
(2016-2018) with Group B (2019-2022) highlights several trends. Group B shows a
significant increase in the number of students earning A grades, peaking at 96 in 2019,
compared to Group A's steady rise to 60 in 2018. The number of students earning B
grades remains fairly consistent in both groups, with Group B averaging slightly higher.

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76

Notably, Group B has fewer C grades, averaging 18.5 compared to Group A's 31,
indicating fewer middle-range performers.
The number of students earning D grades are low in both groups, with Group B
averaging slightly less. However, Group B has a higher average of F grades (5) compared
to Group A's minimal (0.33).
Overall, Group B exhibits improved performance with more A grades and fewer
C grades, though the increase in failing grades suggests areas needing attention. This
underscores the importance of continuing efforts to support all students, particularly those
who are underperforming.
Figure 10
Grade Distribution Q1 – Science

GRADE DISTRIBUTION Q1 - SCIENCE
B

C

D

F

2018

2019

19
14
8

8
9
5

7
1

0

7
5

16

17

25

31

39

2017

3
0

0

19

38

15
1
2016

52

60
53

49
45

58
39

32

58
62

75

96

A

2020

2021

2022

As shown in Figure 11, titled “Grade Distribution Q2 – Science,” the key trends
in Q2 align with those observed in Q1, demonstrating similar patterns of improvement for
both Group A (2016-2018) and Group B (2019-2022).

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77

Group B shows a significant increase in the number of students earning A grades,
peaking at 72 in 2022 compared to 53 in 2018 for Group A. Additionally, the number of
students earning B grades remained consistently high in both groups, with Group B
averaging slightly lower at 40.75 compared to Group A’s 48.33.
The number of students earning C grades have decreased in Group B, averaging
13.75 compared to Group A’s 37. D grades are also slightly lower in Group B, averaging
9 compared to Group A’s 13. However, Group B has a slight increase in the number of
students earning F grades, averaging 5 compared to Group A’s 4.
Overall, Group B exhibits improved performance with more A grades and fewer
C grades, though the slight increase in F grades suggests areas needing attention. These
trends highlight an overall improvement in student performance.
Figure 11
Grade Distribution Q2 - Science

GRADE DISTRIBUTION Q2 - SCIENCE
B

C

D

F

20
17
4
4

9

12

12
4

4
3

4
0

5

7

13

19

22

29

31

34

50

47
50

46

47
30

31

38
38

43

47

51

53

65

72

A

2016

2017

2018

2019

2020

2021

2022

EVALUATING THE IMPACT OF BLOCK SCHEDULING

78

As seen in Figure 12, the analysis of Q3 Science grades for Group A (2016-2018)
versus Group B (2019-2022) uncovers several trends consistent with the results of Q1
and Q2.
Group B again shows a significant increase in the number of students earning A
grades, peaking at 68 in 2019 and averaging 55.25, compared to Group A's average of 41.
B grades remain high and consistent in both groups, with Group B averaging 42.75 and
Group A averaging 46.33.
There is a notable decrease in the number of students earning C grades for Group
B, averaging 20.5 compared to Group A’s 35.67. The number of students earning D
grades is also lower in Group B, averaging 8.5 compared to Group A’s 18.33. However,
Group B again shows an increase in the number of students earning F grades, averaging
6.75 compared to Group A’s 4.
These trends are consistent with those observed in Q1 and Q2, where Group B
consistently demonstrates higher performance, with more students earning A grades and
fewer earning C and D grades. Overall, the findings highlight an improvement in student
performance in Group B.

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79

Figure 12
Grade Distribution Q3 – Science

GRADE DISTRIBUTION Q3 - SCIENCE
B

C

D

F

63

45

47

50

2016

2018

2019

27

31

14
14

11

6

11

2

4
3

3

13
2017

11

19

23
0

4

5

16

26

31

37
31

36

43

46

48

55

59

60

68

A

2020

2021

2022

The analysis of Q4 Science grades, as seen in Figure 13, comparing Group A
(2016-2018) with Group B (2019-2022) demonstrate trends consistent with the
proceeding grading periods. Group B shows a significant increase in the number of
students earning A grades, peaking at 96 in 2022, compared to Group A's peak of 65 in
2018. The number of students earning B grades remains fairly consistent in both groups,
with Group B averaging lower at 29 compared to Group A's 44.33. additionally, Group B
has fewer C grades, averaging 13.33 compared to Group A's 30, indicating fewer students
earning C’s.
The number of students earning D grades is also lower in Group B, averaging 6
compared to Group A’s 18.67. However, Group B shows a slight increase in the number
of students earning F grades, averaging 5.67 compared to Group A's 5.33.

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80

These trends are consistent with those observed in Q1, Q2, and Q3, where Group
B consistently demonstrates higher performance with more A grades and fewer C and D
grades. However, the increase in the number of students earning F grades across all
quarters indicates a need for targeted support for struggling students. Overall, the findings
suggest an improvement in student performance in Group B.
Figure 13
Grade Distribution Q4 - Science

GRADE DISTRIBUTION Q4 - SCIENCE
B

C

D

F

2016

2017

2018

0
0
2019

2020

8

18

31
22
3

8
7

7
7

0

7

14

22

34

38

44
14

19
2

7

23

33

41
39

48
42
37

65

86

96

A

2021

2022

To assess if there is a significant difference in mean science grades between
Group A (Pre-Block Schedule) and Group B (Post-Block Schedule), a t-test was also
conducted to assess the following hypothesis.
1. Null Hypothesis (H0): There is no significant difference in the mean marking period
grades before and after the schedule change.
2. Alternative Hypothesis (H1): There is a significant difference in the mean marking
period grades before and after the schedule change.

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81

To perform the t-test, the marking period means were calculated and sorted into two
groups: Group A (2016-2018) and Group B (2019-2022). The differences in mean grades
between Group A and Group B were calculated, and the mean of these differences was
determined. The standard deviation and standard error of the mean differences were also
calculated, and an unpaired t-test was then used to analyze the data to determine if there
was a statistically significant difference. Table 20 represents the data used to perform the
t-test.
Table 20
Marking Period T-test Science Data
Group
Mean
SD
SEM
N

Group A
82.7540
3.0600
0.7901
15

Group B
83.1558
6.1252
1.4052
19

The results of the t-test produced a P value of 0.805, indicating that the difference
between the two groups is not statistically significant. This suggests that the observed
difference in mean grades between Group A and Group B is likely due to random chance
rather than the schedule change.
The calculated t-statistic is -0.249, with 32 degrees of freedom. These values were
used to determine the statistical significance.
Given the p-value of 0.805, which is much greater than the predetermined
significance level of 0.05, we fail to reject the null hypothesis. This means there is not
enough evidence to conclude that there is a significant difference in mean grades between
the traditional and modified Block Schedules.

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In addition to science, social studies grades were also analyzed to see if the
implementation of the Block Schedule had an effect on student performance in subjects
still taught using the Traditional Schedule.
The bar graph titled "Mean Comparison Group A vs Group B - Social Studies,"
shown in Figure 14 below, compares the mean social studies grades for each marking
period and the year-end. The graph reveals several noteworthy trends.
In the first quarter (Q1), Group A led with a mean score of 89.69, while Group B
scored 88.04. This trend continued into the second quarter (Q2), where Group A again
performed better, with a mean score of 87.44 compared to Group B's 86.4. In the third
quarter (Q3), Group B slightly outperformed Group A with a mean score of 85.9
compared to Group A's 85.62.
In the fourth quarter (Q4), Group B outperformed Group A with a mean score of
87.74, while Group A scored 87.45. The yearly average (Y1) further underscores Group
B's stronger overall performance, with a mean score of 87.95 compared to Group A's
87.74.
Overall, these trends highlight that while Group A generally performed better in
the first two quarters, Group B showed a slightly better performance in the third and
fourth quarters as well as in the overall yearly average.

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Figure 14
Mean Comparison Group A vs Group B - Social Studies

90

Mean Comparison
Group A vs Group B - Social Studies

89.69

89

88.04

88
87

87.45

87.44
86.4

86

87.74

87.74 87.95

85.62 85.9

85
84
83

Q1

Q2

Q3
Group A

Q4

Y1

Group B

The data suggests that the Block Scheduling of mathematics may have had a
positive impact on social studies grades, as evidenced by Group B's improved
performance in the latter part of the year and the higher yearly average compared to
Group A.
A grade distribution analysis of Social Studies was also conducted to compare
Group A (2016-2018) with Group B (2019-2022) across the four quarters (Q1-Q4) to
evaluate the impact of the schedule changes on student performance in courses still
taught using the Traditional Schedule.
Figure 15, seen below, illustrates the Social Studies grade distribution for both
groups during Q1. As shown in the bar graph, Group A (2016-2018) had a higher average
number of students earning A and B grades, with averages of 80.67 and 49.00,

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respectively, indicating more consistent performance compared to Group B (2019-2022).
Group B had an average of 74.50 students earning A grades and 36.25 earning B grades.
However, Group B had fewer students earning C grades, with an average of
12.00, but averaged more students earning D and F grades, with 4.50 and 2.50
respectively, suggesting an increase in the number of lower-performing students.
Figure 15
Grade Distribution Q1 - Social Studies

GRADE DISTRIBUTION Q1 - SOCIAL
STUDIES
B

C

D

F

66

2016

2017

2018

2019

2020

2021

4
3

14

40
8
4
4

3

8

20

23

32
3
2
0

4
1
0

16
1
0

2
0

15

30

41

53

56

61

72

73

82

97

109

A

2022

In Figure 16, titled “Grade Distribution Q2 - Social Studies,” seen below, Group
A outperformed Group B in the number of students earning A grades, averaging 72.33
compared to 71.25. However, Group B had fewer students earning B grades (27.5 vs.
44.33) and higher averages for D and F grades, with 7.50 and 5.25 respectively, again
indicating an increase in lower-performing students.

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Figure 16
Grade Distribution Q2 - Social Studies

GRADE DISTRIBUTION Q2 - SOCIAL
STUDIES
B

C

D

F

2018

2019

2020

2021

6
8

12

26

30
16
14
12
7

6
4
2

2
0

0
2017

8
4

28

30
6
5

12

15
2
2016

26

41

51

38

38

52

68

81

89

90

106

A

2022

For Q3, Figure 17, Group B had an average number of students earning A grades
(59.5), which is higher than Group A (53.67). However, Group B had a lower average
number of students earning B grades (37.75 vs. 53.67) and fewer earning C grades (15 vs.
26.67). Group B also had higher D and F grades, at 5.75 and 2.75, respectively. Overall,
while Group B outperformed Group A in the top-grade category, they also had more
lower-performing students.

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Figure 17
Grade Distribution Q3 - Social Studies

GRADE DISTRIBUTION Q3 - SOCIAL
STUDIES
B

C

D

F

2019

2020

2

2
1

5

10

9
9

16

23

30
2018

2
3

8
2017

2
1

5
1

4

8

11

20

27
2016

30

48

43

42

40

47

54

57

64

66

69

73

74

A

2021

2022

Lastly, in Q4, Figure 18, Group A (2016-2018) had an average of 73.67 students
earning A grades, while Group B (2019-2022) had an average of 64.67 students earning
A grades. The B grade averages were 43.33 for Group A and 37.00 for Group B. Group
A had more C grades on average (15.33) compared to Group B (10.00). However, Group
A had more students earning D grades, with an average of 7, compared to 4 for Group B.
The average number of F grades differed, with Group A having 1.67 and Group B having
3.33, indicating a higher incidence of failing grades in Group B.

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Figure 18
Grade Distribution Q4 - Social Studies

GRADE DISTRIBUTION Q4 - SOCIAL
STUDIES
B

C

D

F

2016

2017

2019

44

2021

6
1

19
2
4
5

0
0
2020

12

39

2018

2
4

16
2
2

3
1

2

7

13

16

26

35

40

44

48

55

77

78

86

91

A

2022

Overall, the data suggests that implementing the Block Schedule for mathematics
had mixed effects on student performance in Social Studies. While there was an increase
in top performers, there was also a rise in the number of lower-performing students. This
indicates the need for further research.
As was done with both mathematics and science data, the same process was used
to conduct a t-test and test the null hypothesis for Social Studies grades. The hypotheses
tested were as follows:
1. Null Hypothesis (H0): There is no significant difference in the mean marking period
grades before and after the schedule change.
2. Alternative Hypothesis (H1): There is a significant difference in the mean marking
period grades before and after the schedule change.

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To perform the t-test, the marking period means were calculated and sorted into
two groups: Group A (2016-2018) and Group B (2019-2022). The differences in mean
grades between Group A and Group B were calculated, and the mean of these differences
was determined. The standard deviation and standard error of the mean difference were
also calculated. An unpaired t-test was then used to analyze the data and determine if
there was a statistically significant difference. Table 21 represents the data used to
perform the t-test.
Table 21
Marking Period T-test Social Studies Data
Group
Mean
SD
SEM
N

Group A
87.5593
3.4058
0.8794
15

Group B
85.7111
6.7394
1.5461
19

The results of the t-test produced a p-value of 0.3408, indicating that the
difference between the two groups is not statistically significant by conventional criteria.
This suggests that the observed difference in mean grades between Group A and Group B
is likely due to random chance rather than the schedule change.
The calculated t-statistic is 0.9670, with 32 degrees of freedom, and the standard
error of the difference is 1.911. The mean difference between Group A and Group B is
1.8483, with a 95% confidence interval ranging from -2.0450 to 5.7416.
Given the p-value of 0.3408, which is much greater than the predetermined
significance level of 0.05, we fail to reject the null hypothesis. This means there is not

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enough evidence to conclude that there is a significant difference in mean grades between
the traditional and modified Block Schedules.
The analysis of science and social studies grades from the Palmerton Area School
District before and after the implementation of the Block Schedule revealed that Group B
(2019-2022) generally outperformed Group A (2016-2018) in terms of higher average
grades, particularly in science.
However, the results of the t-tests for both subjects indicated that these
differences were not statistically significant, with p-values much greater than the
significance level of 0.05. This suggests that the observed improvements in mean grades
are likely due to random chance rather than the schedule change.
While Group B showed positive trends in performance, the increase in lowerperforming students in some areas underscores the need for targeted support. Overall, the
findings do not provide sufficient evidence to conclude that the Block Schedule had a
significant impact on student performance, highlighting the necessity for further research
and intervention strategies to support all students.
Capstone Research Question 4
To answer Research Question 4, seven years of student discipline and attendance
data was collected to investigate the impact, if any, Block Scheduling mathematics had
on these metrics. As with all other data, student attendance and discipline data were
collected and categorized into two groups: Group A (Pre-Block Schedule, Years 1-3) and
Group B (Post-Block Schedule, Years 4-7).
The graph in Figure 19 illustrates the average number of absences per quarter for
both groups. It shows that Group A consistently had higher absences across all quarters

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compared to Group B. In Q1, Group A had an average of 505 absences, while Group B
had 470. The most significant difference is observed in Q2, where Group A's absences
average 712.33 compared to Group B's 538.5. This trend continues in Q3 and Q4, with
Group A having averages of 620.33 and 666.27 absences respectively, while Group B
had 510 and 537.5.
These patterns suggest that the Block Schedule may have contributed to reduced
absenteeism, highlighting the need for further investigation to understand and address
these underlying causes.
Figure 19
Average Number of Absences Group A vs Group B

Average Number of Absences
Group A vs Group B
800

712.33

700
600
500

505

470

538.5

666.27

620.33
510

537.5

400
300
200
100
0

Q1

Q2
Group A

Q3

Q4

Group B

The standard deviation of daily attendance within each group was also calculated
to provide a better understanding of the consistency of attendance rates before and after
the Block Schedule implementation. For Group A (2016-2018), the standard deviation is
77.05, indicating greater variability in attendance. Conversely, Group B (2019-2022) has

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a standard deviation of 27.86, reflecting lower variability. This suggests that the Block
Schedule implementation may have contributed to more consistent attendance rates.
Therefore, the Block Schedule may have positively impacted student attendance.
Additionally, the annual attendance rates were calculated to identify the patterns,
central tendencies, and variability of student attendance before and after the
implementation of the Block Schedule.
The annual attendance rates were calculated by multiplying the student enrollment
by the total number of days in the school year to determine the total number of student
days. Then, the total number of absences was subtracted to find the total number of days
present. Finally, the days present were divided by the total number of student days and
multiplied by 100 to obtain the percentage.
Figure 20 illustrates the average annual attendance rates from 2015 to 2022,
showing changes before and after the implementation of the Block Schedule. Group A
represents the pre- Block Schedule years (2015-2018) with attendance rates ranging from
94.4% to 95.62% and an average of 95.13%. Group B represents the post- Block
Schedule years (2019-2022) with rates between 95.2% and 96.9% and an average of
96.14%. This indicates an improvement in attendance rates post-implementation, with an
increase of 1.01%.
Group A exhibited less variability, with a range of 1.22%, whereas Group B
showed more variability with a range of 1.7%. Despite this increased variability, the
overall trend indicates higher attendance rates during the post- Block Schedule period.
The higher average attendance rates and increased variability in Group B suggest that the

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Block Schedule may have positively impacted student attendance, warranting further
investigation into the specific factors influencing these changes.
Figure 20
Annual Attendance Rate

Annual Attendance Rate
97.5

96.9

97

96.67

96.5
96

95.37

95.5
95
94.5

96.14

95.78

95.62

95.2

95.13

94.4

94
93.5
93

2015

2017

2018 Group A 2019
Average

2020

2021

2022 Group B
Average

Student discipline data was also collected and analyzed to investigate the impact,
if any, the Block Schedule had on student discipline incident rates. Figure 21 below
shows average annual rates. The discipline data rates included are based on the data
recorded in the district SIS as a PA State Reportable Incident type coded as a behavior
code.
Based on the discipline data, Group B, exhibited higher discipline rates in Q1, Q2,
and Q3 compared to Group A. Specifically, Group B's discipline rates were 5.5 in Q1,
7.75 in Q2, and 6.25 in Q3, compared to Group A's rates of 3.66, 5, and 5 respectively.
However, in Q4, Group B showed a decrease in discipline rates to 4.25, while Group A
had a rate of 5.66.

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This data indicates that the Block Schedule had a mixed impact: although it led to
a reduction in incidents in Q4, the overall increase in disciplinary actions in the first three
quarters suggests a potential negative effect on discipline. The yearly averages further
support this interpretation. Group B's average discipline rate was 23.75, higher than
Group A's average of 19.33. Thus, the data suggests that the Block Schedule may have
had an overall negative impact on discipline for Group B, despite the improvement seen
in the final quarter.
Figure 21
Average Discipline Rates Group A vs Group B

Average Discipline Rates
Group A vs Group B
9
8
7
6
5
4
3
2
1
0

7.75
5.5

6.25
5

5

5.66
4.25

3.66

Q1

Q2
Group A

Q3

Q4

Group B

Figure 22 displays the annual incident count from 2016 to 2022, including the
averages for Group A (2016-2018) and Group B (2019-2022). The counts are: 11
incidents in 2016, 24 in 2017, 23 in 2018, 30 in 2019, 30 in 2020, 3 in 2021, and 32 in
2022. Group A averaged 19.33 incidents, while Group B averaged 23.75 incidents.

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The standard deviation within each group was also calculated to provide a better
understanding of the consistency of discipline rates before and after the Block Schedule
implementation. For Group A (2016-2018), the standard deviation is 5.9, indicating lower
variability in discipline rates. Conversely, Group B (2019-2022) has a standard deviation
of 12, reflecting greater variability. This suggests that the Block Schedule
implementation may have contributed to less consistent discipline rates. Therefore, the
Block Schedule may have negatively impacted student discipline.
The most notable fluctuation is the sharp drop to 3 incidents in 2021, followed by
a spike to 32 in 2022. These changes highlight the need to investigate specific events or
reporting changes affecting these years.
Overall, this data suggests that the impact of the Block Schedule on incident
counts is mixed and warrants further investigation.
Figure 22
Annual Incident Counts

Annual Incident Counts
35

30

30
24

25

23.75

23
19.33

20
15

32

30

11

10
3

5
0

2016

2017

2018

Group A
Average

2019

2020

2021

2022

Group B
Average

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The findings indicate that Block Scheduling may have contributed to reduced
absenteeism and more consistent attendance rates, as evidenced by lower average
absences and decreased variability in attendance post-implementation. However, the
impact on student discipline is more complicated, with an increase in discipline incidents
in three out of four quarters following the adoption of the Block Schedule. The mixed
results in discipline data suggest that while Block Scheduling might positively influence
attendance, its effect on student behavior needs further examination. Overall, these
findings underscore the importance of continued investigation to better understand Block
Scheduling 's impact on various student outcomes.
Summary
The primary objective of this Capstone project was to evaluate the impact of the
schedule change at Palmerton Area Junior High School (PJHS) on student performance
data. The study analyzed seven years of data, encompassing three years prior to the
schedule implementation and four years following, to evaluate the impact of the modified
Block Schedule. The research was guided by four key questions:
1. How does the Block Schedule affect student marking period and year-end grades?
2. How does the Block Schedule affect the number of students receiving Advanced or
Proficient on the Mathematics PSSA?
3. How does the Block Schedule affect students' mathematics grades compared to
courses still taught using a Traditional Schedule?
4. How does the Block Schedule affect student discipline and attendance?
Employing quantitative methods, the study analyzed data from student marking
period grades, year-end grades, PSSA results, attendance records, and discipline referrals

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sourced from the Palmerton Area School District's student information system (SIS) and
the DRC Insight website. The results indicate that the modified Block Schedule led to
improved academic performance. Group B (Post-Block Schedule) outperformed Group A
(Pre-Block Schedule) in three out of four quarters and in the yearly average. Group B had
higher mean grades in most marking periods, despite a significant drop in Quarter 4 of
2019 due to the COVID-19 closure. The grade distribution analysis showed an increase in
A and B grades and a decrease in C and D grades in Group B, though there was also an
increase in failing grades, indicating a need for targeted support for lower-performing
students. However, a t-test comparing mean grades before and after the schedule change
suggests that the improvements might be due to random chance, as the differences were
not statistically significant.
In terms of PSSA Mathematics performance, the results again indicate that the
modified Block Schedule led to improved academic performance. The analysis revealed
an increase in the number of students scoring at the Advanced level in Group B, while
performance at the Proficient level remained similar between the two groups. There was
also a lower mean number of students scoring Basic in Group B and a reduction in
students scoring Below Basic. However, a t-test again indicated no statistically
significant difference in the mean percentage of students scoring Advanced or Proficient
on the PSSA before and after the schedule change.
The study also analyzed science and social studies grades, subjects still taught
using the Traditional Schedule, to assess any indirect effects of the Block Schedule.
Again, Group B generally outperformed Group A in science grades, with consistent
improvements across most grading periods except for a slight drop in Quarter 3. In social

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studies, however, Group B showed mixed results, with higher A grades but also an
increase in lower-performing students (D and F grades). T-tests for both subjects
indicated that the differences in mean grades were not statistically significant.
Regarding discipline and attendance, the study found that Group B had a
reduction in absences and more consistent attendance rates, suggesting a positive impact
of the Block Schedule on student attendance. However, there was an overall increase in
discipline incidents in three out of four quarters for Group B, indicating a potential
negative impact on student behavior. These mixed results highlight the need for further
research to fully understand the implications of the Block Schedule on student discipline
and to identify strategies to mitigate any negative effects.
In conclusion, the modified Block Schedule appears to have positively influenced
academic performance and attendance at PJHS, while its impact on discipline is more
complex and requires further investigation. These findings underscore the necessity for
continued research and targeted interventions to support all students.

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CHAPTER V
Conclusions and Recommendations
This chapter presents the conclusions and recommendations from evaluating the
impact of the Block Schedule implementation on student performance at Palmerton Area
Junior High School (PJHS). The goal of this Doctoral Capstone project was to assess how
the schedule change affected students' academic and nonacademic performance over
seven years, including three years before and four years after implementation. By
comparing data from before and after the schedule change, this study aims to provide
insights into the effectiveness of the schedule and offer recommendations for future
decisions at PJHS.
The research was guided by four key questions:
1. How does the Block Schedule affect student marking period and year-end grades?
2. How does the Block Schedule affect the number of students receiving Advanced or
Proficient on the Mathematics PSSA?
3. How does the Block Schedule affect students' mathematics grades compared to
courses still taught using a Traditional Schedule?
4. How does the Block Schedule affect student discipline and attendance?
To address these questions, the study employed quantitative methods, analyzing a
collection of data that included student grades, PSSA results, attendance records, and
discipline referrals. This data was sourced from the Palmerton Area School District's
student information system (SIS) and the DRC Insight website. The subsequent analysis
provided an examination of the academic and nonacademic impact of the schedule
change, offering insight into its effectiveness.

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The conclusions drawn from this study underline key trends observed in the data,
providing a basis for informed recommendations. The following sections will explore the
specific conclusions for each research question, followed by recommendations to support
ongoing improvements at PJHS.
Conclusions
Capstone Research Question 1
The implementation of the Block Schedule appears to have positively impacted
student performance at the PJHS. The data analysis reveals that Group B, representing
the post- Block Schedule period, outperformed Group A, the pre- Block Schedule period,
in three out of four quarters and in the overall yearly average. Despite the negative impact
of the COVID-19 pandemic on Quarter 4, Group B's overall yearly average was higher
than Group A's, suggesting that the Block Schedule contributed to better student
performance.
Additionally, the grade distribution analysis for Quarters 1 and 2 shows
significant increases in the number of students earning A and B grades and decreases in
the number of students earning C, D, and F grades for Group B. Similar trends were
observed in Quarters 3 and 4, indicating more students achieving higher grades and fewer
receiving lower grades post- Block Schedule. These consistent trends across different
quarters support the conclusion that Block Scheduling positively affected student
performance. However, the results of the t-test indicate that the differences in mean
grades between Group A and Group B were not statistically significant, suggesting that
the improvements might be due to random variation rather than the intervention itself.

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Capstone Research Question 2
The implementation of the Block Schedule has shown varying effects on student
performance on the Mathematics PSSA. Data indicates a notable improvement at the
Advanced level, with an increase in the average number of students scoring Advanced in
Group B (Post-Block Schedule) compared to Group A (Pre-Block Schedule). This
suggests that the Block Schedule has positively impacted higher-performing students.
The Proficient level scores remained relatively consistent, with Group B showing a slight
increase, indicating that student performance at this level was maintained after the
schedule change. Additionally, there was a slight reduction in the number of students
scoring at the Basic level and a significant decrease in those scoring Below Basic in
Group B, highlighting a positive trend for lower-performing students. However, the
number of students with no scores increased significantly in Group B, likely due to the
COVID-19 pandemic, which introduces variability that requires further investigation.
These conclusions are well-supported by the data analysis. The comparison of the
mean number and percentage of students at each performance level before and after the
Block Schedule implementation reveals clear trends. The increase in Advanced scores
and the slight rise in Proficient scores suggest that the Block Schedule has either
maintained or improved performance levels. The reduction in Basic and Below Basic
scores further supports the effectiveness of the intervention in assisting lower-performing
students. The increase in no scores, primarily attributed to the pandemic, aligns with
external disruptions impacting the data.

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Capstone Research Question 3
The analysis of the academic data in science and social studies reveals mixed
outcomes regarding the impact of the Block Schedule on those subjects still taught using
the Traditional Schedule. In science, students in Group B (Post-Block Schedule)
generally outperformed those in Group A (Pre-Block Schedule) across most quarters,
except for the third quarter, with significant improvement in year-end averages. However,
the t-test results indicated that these differences were not statistically significant, with a
p-value of 0.805, suggesting that the improvements are likely due to random chance
rather than the Block Scheduling intervention. Similarly, in social studies, Group B
showed slightly better performance in the latter quarters and year-end averages. Despite
these positive trends, the t-test results for social studies also produced a p-value of
0.3408, indicating no statistically significant difference between the two groups. These
findings imply that while Block Scheduling may have some positive effects, the overall
impact on student performance is not statistically significant.
Capstone Research Question 4
The analysis of attendance data reveals that Block Scheduling has had a positive
impact on student attendance. The data shows that the average number of absences per
quarter was consistently lower for Group B (Post-Block Schedule) compared to Group A
(Pre-Block Schedule). For instance, in Q2, Group A had an average of 712.33 absences,
whereas Group B had 538.5. Additionally, the standard deviation of daily attendance
within each group demonstrated that Group B experienced more consistent attendance
rates, with a standard deviation of 27.86 compared to Group A's 77.05. The annual
attendance rates further validate these findings, with Group B showing an improved

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average attendance rate of 96.14%, an increase from Group A's 95.13%. These results
support the conclusion that Block Scheduling positively influences student attendance, as
evidenced by reduced absences and more consistent attendance patterns.
In contrast, the impact of Block Scheduling on student discipline appears to be
mixed. While there was a notable reduction in disciplinary incidents in Q4 for Group B,
with an average of 4.25 incidents compared to Group A's 5.66, the overall discipline rates
in Q1, Q2, and Q3 were higher for Group B. The annual averages also reflect this trend,
with Group B averaging 23.75 incidents compared to Group A's 19.33. The standard
deviation indicates greater variability in discipline rates post- Block Scheduling, with
Group B having a standard deviation of 12 compared to Group A's 5.9. These results
suggest that while Block Scheduling may reduce disciplinary incidents in the later part of
the school year, it could potentially lead to an increase in incidents during the earlier
quarters.
Limitations
In conducting this study on the impact of implementing a Block Schedule for
mathematics at Palmerton Area Junior High School, several aspects of the research
design and methodology, as well as external factors, influenced the interpretation of
findings.
Firstly, the use of quantitative methods provided a structured approach to
analyzing large datasets of student performance, attendance, and discipline records over a
seven-year period. This methodology enabled statistical analysis, including descriptive
statistics and inferential tests like t-tests, to evaluate changes in student performance
before and after the schedule change. By focusing on numerical trends and statistical

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103

significance, the study objectively quantified the impact of the Block Schedule on both
academic performance and non-academic indicators.
However, the reliance on quantitative data alone also posed limitations. The
emphasis on numerical outcomes through standardized assessments and organizational
records meant that qualitative dimensions, such as the detailed experiences of students
and teachers with the Block Schedule, were not fully captured. This bias towards
numerical data may have concealed potential qualitative insights into how the schedule
change influenced teaching practices, student engagement, or school climate.
External factors also played a role in influencing the interpretation of findings.
After the implementation of the Block Schedule, two teachers taught Mathematics,
introducing variability in teaching practices that could influence student outcomes.
Additionally, the COVID-19 closure occurred during the post-implementation period,
followed by a hybrid schedule in the subsequent year. These significant disruptions likely
impacted student performance and behavior independently of the Block Schedule change.
These external variables, while not directly controlled in the study, needed
acknowledgment as they could affect the observed outcomes.
In summary, while using numbers and statistics helped analyze data trends
effectively, the study's design and outside factors also influenced how the results were
interpreted. Recognizing these research methods and outside influences is essential for
fully understanding how the modified Block Schedule affected student outcomes at
Palmerton Area Junior High School.

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Recommendations for Future Research
Based on the results and conclusions of this study, several areas stand out as
needing additional research to address unanswered questions. Future research should aim
to provide a more comprehensive understanding of the effects of Block Schedule
implementation on student performance.
To better understand the long-term effects of Block Scheduling, future research
should track groups of students over several years. This approach would help identify
trends and patterns that might not be visible in shorter studies. The focus should be on
long-term academic performance in different subjects, graduation rates, and the ongoing
effects on student discipline and attendance.
Along with analyzing numbers, methods like interviews, focus groups, and case
studies should be used to understand the detailed experiences of students, teachers, and
administrators. This approach would provide a deeper understanding of the data and help
explain the reasons behind the observed trends. It would be especially useful for
understanding how students and teachers feel about the Block Schedule, its effect on
classroom interactions, and the challenges and benefits perceived by different
stakeholders.
The success of any scheduling model depends on how prepared and supported the
teachers are. Future studies should evaluate professional development programs that help
teachers adapt to Block Scheduling. This includes looking at teacher training and support
programs, the needs and effectiveness of professional development, and how teachers
plan their instruction.

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105

While this study focused on academic performance, future research should also
look at broader non-academic outcomes of Block Scheduling, such as student well-being,
mental health, and social-emotional development.
The modified Block Schedule at PJHS has shown promising results in improving
academic performance and attendance, with mixed outcomes on student discipline.
Future research should build on these findings by exploring long-term impacts,
qualitative experiences, and effects on specific student populations. Addressing these
areas can help educators make better decisions to improve the effectiveness of Block
Scheduling and support overall student success.
Summary
This Doctoral Capstone project aimed to evaluate the impact of the Block
Schedule implementation on student performance at Palmerton Area Junior High School
(PJHS) by analyzing academic and non-academic data over a seven-year period,
including three years before and four years after the schedule change. The study explored
four key areas, including the impact of Block Scheduling on student grades, Mathematics
PSSA performance, a comparison of mathematics grades to Traditional Schedules, and
the influence on student discipline and attendance.
The findings indicate that the Block Schedule positively impacted student
performance, with improved overall grades and increased numbers of students earning A
and B grades. However, the t-test results suggested that these improvements were not
statistically significant, possibly due to random variation. Performance on the
Mathematics PSSA showed a notable increase in Advanced scores, slight improvement in
Proficient scores, and a decrease in Basic and Below Basic scores, despite a rise in the

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106

number of students with no scores due to the COVID-19 pandemic. In science and social
studies, subjects still taught traditionally, Group B (post- Block Schedule) showed
improved performance, but these results were also not statistically significant. Attendance
improved significantly under the Block Schedule, with fewer absences and more
consistent attendance rates. However, the impact on student discipline was mixed, with
reduced incidents in the later part of the school year but increased incidents in the earlier
quarters.
The study's reliance on quantitative methods provided a structured approach to
data analysis but limited the understanding of qualitative aspects such as student and
teacher experiences with the Block Schedule. External factors like the COVID-19
pandemic and variations in teaching practices also influenced the findings.
In conclusion, the modified Block Schedule at PJHS has shown promising results
in enhancing academic performance and attendance, with mixed effects on discipline.
Future research should focus on long-term impacts, incorporating qualitative methods to
capture detailed experiences, and evaluating teacher training and support programs. By
addressing these areas, educators can make more informed decisions to enhance the
effectiveness of Block Scheduling and support overall student success.

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107

References
Adrian, D. E. (2009). School scheduling models and the achievement of at-risk
students: A causal-comparative study (Publication No. 3354210) [Doctoral
dissertation, University of Phoenix]. ProQuest Dissertations and Theses Global.
Arnold, D. E. (2002). Block schedule and traditional schedule achievement: A
comparison. National Association of Secondary School Principals. NASSP
Bulletin, 86(630), 42–53.
Balsimo, G. (2005). A comparison of traditional scheduling versus block scheduling in
a suburban high school [Unpublished doctoral dissertation]. Saint Mary’s
University.
Berg, L. L., & Others, A. (1970). Individualization of instruction: Flexible scheduling.
Division of Educational Research and Services School of Education University
of Montana.
Butts, R. F., & Cremin, L. (1953). A history of education in American culture. Holt,
Rinehart and Winston.
Byers, T. S. (2011). An investigative study on the effects of block scheduling on
Georgia high school graduation test performance in social studies (Publication
No. 3512673) [Doctoral dissertation, Piedmont College]. ProQuest
Dissertations and Theses Global.
Canady, R. L., & Rettig, M. D. (1995). Block scheduling: A catalyst
for change in high schools (1st ed.). Routledge.

EVALUATING THE IMPACT OF BLOCK SCHEDULING

108

Carnegie Foundation. (2014, August 7). What is the Carnegie unit? Carnegie
Foundation for the Advancement of Teaching.
https://www.carnegiefoundation.org/faqs/carnegie-unit/
Carroll, J. (1989). The Copernican Plan: Restructuring the American
high school. The Phi Delta Kappan, 71(5), 358-365.
Childers, E. (2018). Effects of class scheduling and student
achievement on state testing [Doctoral dissertation, Walden
University]. Walden Dissertations and Doctoral Studies.
https://scholarworks.waldenu.edu/dissertations/5840
Clark, S. G. (2021). The impact of block scheduling on student achievement,
graduation rate, and attendance at the high school level [Doctoral dissertation,
Louisiana Tech University]. Louisiana Tech Digital Commons.
https://digitalcommons.latech.edu/dissertations/896
Comer, E. C. (2012). The 4x4 block schedule: Its impact on student achievement and
school climate (Publication No. 3509851) [Doctoral dissertation, Northern
Arizona University]
Cromwell, S. (1997, October 20). Block scheduling: A solution or a problem?
Education World. https://www.educationworld.com/
a_admin/admin/admin029.shtml
DeMarrais, K. B., & LeCompte, M. D. (1999). The way schools work: A sociological
analysis of education (3rd ed). Longman Publishing Group.

EVALUATING THE IMPACT OF BLOCK SCHEDULING

109

Deuel, L. L. (1999). Block scheduling in large, urban high schools: Effects on
academic achievement, student behavior, and staff perceptions. High
School Journal, 83(1), 14-25.
Ford, Y. J. (2015). A test score comparison between block and traditional
scheduling [Doctoral dissertation, Georgia Southern University]. Georgia
Southern University Digital Commons.
https://digitalcommons.georgiasouthern.edu/etd/1291
Great Schools Partnership. (2013, August 29). Block schedule definition. The Glossary
of Education Reform. https://www.edglossary.org/block-schedule/
Great Schools Partnership. (2014, August 18). High-stakes test. The Glossary of
Education Reform. https://www.edglossary.org/high-stakes-testing/
Gruber, C. D., & Onwuegbuzie, A. J. (2001). Effect of block scheduling on academic
achievement among high school students. The High School Journal, 84(4), 3242.
Hackmann, D. G. (2004). Constructivism and block scheduling: Making the
connection. Phi Delta Kappan, 85(9), 697-702.
https://doi.org/10.1177/003172170408500911
Howard, E. (1998). The trouble with block. The American School Board Journal,
185(1), 35-36.
Johnson, B., & Onwuegbuzie, A. (2004). Mixed methods research: A research
paradigm whose time has come. Educational Researcher, 33(7), 14–26.

EVALUATING THE IMPACT OF BLOCK SCHEDULING

110

Khazzaka, J. (1998). Comparing the merits of a seven-period school day to those of a
four- period school day. The High School Journal, 81(2), 11.
Klein, A. (2015, April 11). No Child Left Behind: An overview. Education
Week. https://www.edweek.org/policy-politics/no-child-left-behind-anoverview/2015/04
Kliebard, H. (2004). The struggle for the American curriculum 1893–1958.
Psychology Press.
Lewis, C. W., Dugan, J. J., Winokur, M. A., & Cobb, R. B. (2005). The effects of block
scheduling on high school academic achievement. NASSP Bulletin, 89(645), 7287. https://doi.org/10.1177/019263650508964506
Matarazzo, T. (1999). "Block scheduling: A comparison of block scheduling and
traditional scheduling and its relationship to student achievement, student
satisfaction and teacher methodology in a junior-senior high school in new
jersey [Doctoral dissertation, Seton Hall University]. Seton Hall University
Dissertations and Theses. https://scholarship.shu.edu/dissertations/311
Maynard, B. R., Vaughn, M. G., Nelson, E. J., Salas-Wright, C. P., Heyne, D. A., &
Kremer, K. P. (2017). Truancy in the United States: Examining temporal trends
and correlates by race, age, and gender. Children and Youth Services Review, 81,
188–196. https://doi.org/10.1016/j.childyouth.2017.08.008

EVALUATING THE IMPACT OF BLOCK SCHEDULING

111

Murray, S. (2008). FlexMod scheduling redux. Principal Leadership, 8(7), 42–46.
National Education Association of the United States. Committee of Ten on Secondary
School Studies. (1894). Report of the committee of ten on secondary school
studies: With the reports of the conferences arranged by the committee.
National Educational Association.
National Center for Education Statistics. (n.d.). Schools and staffing survey (SASS).
https://nces.ed.gov/surveys/sass/tables/state_2004_07.asp
National Education Commission on Time and Learning. (1994). Prisoners of Time:
Report of the National Education Commission on Time and Learning.
(ED489343). ERIC. https://files.eric.ed.gov/fulltext/ED489343.pdf
Pennsylvania Department of Education. (2024). School performance—Palmerton Area
JHS. https://futurereadypa.org/Performance/
199248064053154212079216217182115171204142200213/0052390311282370
43073224054033120001236046199036029207129077147027118153036141045
188120042059009
Rettig, M., & Canady, R. (1999, March 1). The effects of block scheduling. The School
Superintendents Association. https://www.aasa.org/resources/resource/theeffects-of-block-scheduling
Schroth, G. (n.d.). Scheduling. Education State University.
https://education.stateuniversity.com/pages/2385/Scheduling.html

EVALUATING THE IMPACT OF BLOCK SCHEDULING

112

Spence, M. J. (2020). Block versus traditional scheduling in high school: Teacher and
student attitudes (Publication No. 28022943) [Doctoral dissertation,
Lindenwood University]. ProQuest Dissertations and Theses Global.
Unlocking Time. (2018a). Standard periods school schedule.
https://unlockingtime.org/school-schedules/Standard-Periods-Schedule
Unlocking Time. (2018b). Rotating standard periods school schedule.
https://unlockingtime.org/school-schedules/Rotating-Standard-PeriodsSchedule
Unlocking Time. (2018c). A practical guide to common bell schedules.
https://unlockingtime.org/school-schedules
Underwood, A. (2014). A comparative study of the effect of block scheduling and traditional
scheduling on student achievement for the Florida algebra 1 end-of-course

examination [Doctoral dissertation, University of Central Florida]. Central
Florida Electronic Theses and Dissertations.
https://stars.library.ucf.edu/etd/4519
US History Scene. (2024). Education to the masses. US History Scene.
https://ushistoryscene.com/article/rise-of-public-education/
Valencia, A. A. (1969). Flexible-modular scheduling and related instructional
strategies. (ED037809). ERIC. https://eric.ed.gov/?id=ED037809
Veal, W. R., & Schreiber, J. (1999). Effects of block scheduling. Education Policy
Analysis Archives, 7(29). https://doi.org/10.14507/epaa.v7n29.1999

EVALUATING THE IMPACT OF BLOCK SCHEDULING

113

Whisman, A., & Hammer, P. C. (2014). The association between school discipline
and mathematics performance: A case for positive discipline approaches
(ED569903). ERIC. https://eric.ed.gov/?id=ED569903
Williams, C. (2011). The impact of block scheduling on student achievement,
attendance, and discipline at the high school level (ED538287). ERIC.
https://eric.ed.gov/?id=ED528899
Williamson, R. (2010). Scheduling: Seven period day (ED538287). ERIC.
https://eric.ed.gov/?id=ED538287
Yeban, Y. (2024, January 5). Compulsory education laws: Background. FindLaw.
https://www.findlaw.com/education/education-options/compulsory-educationlaws-background.html

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APPENDICES

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Appendix A
IRB Approval

Dear Daniel,

Institutional Review Board
California University of Pennsylvania
Morgan Hall, 310
250 University Avenue
California, PA 15419
instreviewboard@calu.edu
Melissa Sovak, Ph.D.

Please consider this email as official notification that your proposal titled
“Evaluating the Impact of Block Scheduling Mathematics at the Palmerton
Area Junior High School” (Proposal #20-052) has been approved by the
California University of Pennsylvania Institutional Review Board as
submitted.
The effective date of approval is 9/10/21 and the expiration date is 9/9/22.
These dates must appear on the consent form.
Please note that Federal Policy requires that you notify the IRB promptly
regarding any of the following:
(1) Any additions or changes in procedures you might wish for your study
(additions or changes must be approved by the IRB before they are
implemented)
(2) Any events that affect the safety or well-being of subjects
(3) Any modifications of your study or other responses that are
necessitated by any events reported in (2).
(4) To continue your research beyond the approval expiration date of
8/12/22 you must file additional information to be considered for continuing
review. Please contact instreviewboard@calu.edu
Please notify the Board when data collection is complete.
Regards,
Melissa Sovak, PhD.
Chair, Institutional Review Board

EVALUATING THE IMPACT OF BLOCK SCHEDULING
Appendix B
IRB Approval Extension

Re: IRB Review Request

Melissa Sovak
Sat 9/16/2023 7:20 AM

To:Daniel Heaney ;InstReviewBoard


Approved. You have an extension of your original study until 9/15/2024.

116

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Appendix C
Raw Academic Data Collection Form
YEAR: 2016
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2016
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2016
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2017
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2017
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

EVALUATING THE IMPACT OF BLOCK SCHEDULING

YEAR: 2017
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2018
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2018
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2018
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2019
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)

118

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

EVALUATING THE IMPACT OF BLOCK SCHEDULING
YEAR: 2019
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2019
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2020
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2020
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2020
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)

119

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

EVALUATING THE IMPACT OF BLOCK SCHEDULING
YEAR: 2021
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2021
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2021
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2022
Subject
Grade
Math
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)
YEAR: 2022
Subject
Grade
Science
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)

120

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

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Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

EVALUATING THE IMPACT OF BLOCK SCHEDULING
YEAR: 2022
Subject
Grade
S. S.
A (90-100)
B (80-89)
C (70-79)
D (60-69)
F (0-59)

Q1

Q2

Q3

121

Q4

Y1

EVALUATING THE IMPACT OF BLOCK SCHEDULING

122

Appendix D
PSSA Raw Data Collection Form
YEAR: 2016
Subject
PSSA
Mathematics

YEAR: 2017
Subject
PSSA
Mathematics

YEAR: 2018
Subject
PSSA
Mathematics

YEAR: 2019
Subject
PSSA
Mathematics

YEAR: 2020
Subject
PSSA
Mathematics

Performance

# of Students

% of Students

Performance

# of Students

% of Students

Performance

# of Students

% of Students

Performance

# of Students

% of Students

Performance

# of Students

% of Students

EVALUATING THE IMPACT OF BLOCK SCHEDULING
YEAR: 2021
Subject
PSSA
Mathematics

YEAR: 2022
Subject
PSSA
Mathematics

123

Performance

# of Students

% of Students

Performance

# of Students

% of Students

EVALUATING THE IMPACT OF BLOCK SCHEDULING

124

Appendix E
Raw Nonacademic Data Collection Form
YEAR: 2016
Absences
Discipline
YEAR: 2017
Absences
Discipline
YEAR: 2018
Absences
Discipline
YEAR: 2019
Absences
Discipline
YEAR: 2020
Absences
Discipline
YEAR: 2021
Absences
Discipline
YEAR: 2022
Absences
Discipline

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

Q2

Q3

Q4

Y1

Q1

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Q4

Y1

Q1

Q2

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Q4

Y1

Q1

Q2

Q3

Q4

Y1