National Louis University National Louis University
Digital Commons@NLU Digital Commons@NLU
Dissertations
9-2020
The Evaluation of the Jostens Renaissance Incentive Program The Evaluation of the Jostens Renaissance Incentive Program
and the Impact it has on Students at a Southwest Florida High and the Impact it has on Students at a Southwest Florida High
School School
Saneik Buchanan
National Louis University
Follow this and additional works at: https://digitalcommons.nl.edu/diss
Part of the Academic Advising Commons, Counseling Commons, Curriculum and Instruction
Commons, Educational Administration and Supervision Commons, Other Social and Behavioral Sciences
Commons, and the Secondary Education Commons
Recommended Citation Recommended Citation
Buchanan, Saneik, "The Evaluation of the Jostens Renaissance Incentive Program and the Impact it has
on Students at a Southwest Florida High School" (2020).
Dissertations
. 514.
https://digitalcommons.nl.edu/diss/514
This Dissertation - Public Access is brought to you for free and open access by Digital Commons@NLU. It has been
accepted for inclusion in Dissertations by an authorized administrator of Digital Commons@NLU. For more
information, please contact [email protected].
1
THE EVALUATION OF THE JOSTENS RENAISSANCE INCENTIVE PROGRAM AND
THE IMPACT IT HAS ON STUDENTS AT A SOUTHWEST FLORIDA HIGH SCHOOL
by
Saneik Buchanan
A Dissertation Submitted to the Graduate School
of National University Louis in Partial Fulfillment
of the
Requirements for the Degree
of
Doctor of Education
TAMPA, FLORIDA
August 2020
2
Saneik Buchanan
August 2020
Copyrighted © 2020
By
Saneik Buchanan
All rights reserved
3
THE EVALUATION OF THE JOSTENS RENAISSANCE INCENTIVE PROGRAM
AND THE IMPACT IT HAS ON STUDENTS AT A
SOUTHWEST FLORIDA HIGH SCHOOL
by
SANEIK BUCHANAN
Approved:
_________________________________
Dr. Caroline Perjessy
Dissertation Chairperson
____________________________________
Dr. Donna Kirkpatrick Pinson
Dissertation Committee Member
4
ABSTRACT
Saneik Buchanan: The Evaluation of the Jostens Renaissance Incentive Program and the Impact
it has on Students at a Southwest Florida High School
(Under the direction of Dr. Caroline Perjessy, Dissertation Chair)
As state requirements for academic achievement continue to become more rigorous,
school administrators and counselors strive to create positive school environments. Therefore,
administrators and counselors must find creative incentive programs tailored to meet the needs of
respective school settings, such as the Jostens Renaissance incentive program. The purpose of
this study was to examine the efficacy of the Jostens Renaissance education incentive program at
one Title I high school in Southwest Florida by studying the relationship among students’ GPAs,
attendance, and disciplinary issues before participation in the program. The evaluation was a
quantitative, quasi-experimental design that utilized pre-existing data of students at a Florida
high school. Seventy-nine students participated in the Jostens Renaissance incentive program
between 2019 to 2020 in the span of one school semester. Quantitative data showed the
participants’ demographics of race, ethnicity, grade level, and academic information. A
pretest/posttest design showed the impact of the Jostens Renaissance incentive program on
students’ GPAs, attendance, and disciplinary issues. Results showed a significant increase in
GPA and significant decrease in days absent and number of discipline cards occurred before and
after the implementation of the Jostens Renaissance Program. Therefore, the program was
successful at improving students’ goals.
Keywords: attendance, discipline card, GPA, implementation, incentive, school climate
and culture
5
DEDICATION
I dedicate this work to the first person to call me doctor, my dad, before the thought even
came in my mind of pursuing such an honorary degree. I could not have manifested this
dissertation without your words and always believing in me. To my prayer warrior and the
strongest woman I know, my mother, I thank you for your endless love, support, and motivation
never to give up. You both have always been by my side, and I just want to make you proud and
say thank you for being the most loving and supportive parents!
I dedicate this work to my big little sissy and little big brother; being your older sister, I
wanted to set an example for you both. I wanted to be a constant reminder that you can do
anything that you set your mind to, even when it seems impossible to accomplish; with hard
work and perseverance, you can do the impossible if you believe. I love you both.
I dedicate this work to my grandmother, grandfather, aunts, and uncles. Without family, I
am nothing, and you have all been there for me in so many ways, emotionally, physically,
spiritually, and mentally. Thank you all for your continued guidance and support; my family is
my greatest gift!
I dedicate this work to my best friend Eunika; I thank you for believing in my dreams and
your constant motivation to continue this journey, even when I felt like giving up. Thank you for
not taking it personally because I missed so many moments due to writing and conducting
research. Now, I will not miss a minute of spending time and sharing new memories with you
all!
I dedicate this work to my students; you push me every day to be a better woman,
counselor, educator, and mentor, and because of you, I am here today. You all remind me daily
why I chose this career; you inspire me, and I could not have done this without all of you,
6
encouraging me to learn constantly. Thank you for making me feel like the best school
counselor!
I dedicate this accomplishment to each of you!
7
ACKNOWLEDGEMENTS
First, I would like to acknowledge my administration and school counseling team! If it
were not for you, I would not have accomplished this dissertation; you have been instrumental in
helping me throughout this journey. I could not have asked for a more supportive, helpful, and
encouraging counseling team. Jackie and Cyndi, I thank you for being understanding and
supportive supervisors!
To my committee chair, Dr. Perjessy, thank you for taking on this task with me and
guiding me through this journey; your encouraging words pushed me to go harder when I felt
discouraged or unsure, and you always provided words of comfort, all while teaching me and
pushing me to think critically to be better. Dr. Kirkpatrick, thank you for your time, words,
dedication, and support and for always helping when I needed it; even though we have only
shared this year, I thank you for being there. It is very much appreciated.
Thank you to the faculty, specifically Dr. Wesley, Dr. Perjessy, and Dr. Suprina for being
wonderful and exemplary leaders, for broadening my perspective within the counseling field;
because of you, I am a better counselor, educator, and supervisor. I have learned so much from
each of you, and after having been in your classes these last few years, you continue to inspire
me to keep learning and growing.
To my classmates, Lourdes, Michele, and Patrice, and closest friends, Taris, Priscilla,
Mark, and Eunika, who have encouraged me throughout this doctorate journey, I thank you for
being there and loving me. Thank you all for everything you have done to assist me in
accomplishing my goal of earning this Doctoral Degree; I will forever be grateful for your love
and support.
Smile for me; I did it!
8
TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION ................................................................................................. 12
Background of Study ........................................................................................................ 13
Problem Statement ............................................................................................................ 15
Purpose of the Study ......................................................................................................... 16
Research Questions and Hypotheses ................................................................................ 17
Assumptions, Limitations, and Delimitations ................................................................... 17
Definition of Terms........................................................................................................... 18
Summary and Organization of the Study .......................................................................... 20
CHAPTER 2: LITERATURE REVIEW ...................................................................................... 22
Introduction ....................................................................................................................... 22
Theoretical Framework ..................................................................................................... 22
Social Learning Theory and Social Cognitive Theory ......................................... 22
Motivation Theories .............................................................................................. 29
Review of Relevant Literature .......................................................................................... 31
School-Wide Positive Behavioral Interventions and Supports ............................. 31
The Covey Model ................................................................................................. 32
Jostens Renaissance Program ............................................................................... 35
School Culture and School Climate ...................................................................... 38
Teachers’ Perceptions of School Climate ................................................. 40
Students’ Perceptions of School Culture .................................................. 41
Implementation ..................................................................................................... 43
Fidelity .................................................................................................................. 45
Sustainability......................................................................................................... 46
Interdependence of Teachers and Students ........................................................... 48
9
Competing Explanations ....................................................................................... 49
Student Social Relationships..................................................................... 51
Environmental Factors .............................................................................. 52
Brain Plasticity .......................................................................................... 52
Extrinsic or Intrinsic Motivation ............................................................... 53
Motivation Over Intelligence .................................................................... 54
Expectancy-Value Theory ........................................................................ 54
Cultural Bias ......................................................................................................... 55
Summary ........................................................................................................................... 55
CHAPTER 3: METHODOLOGY ................................................................................................ 59
Introduction ....................................................................................................................... 59
Statement of the Problem .................................................................................................. 60
Research Questions and Hypotheses ................................................................................ 61
The Renaissance Logic Model .......................................................................................... 63
Research Methodology ..................................................................................................... 64
Research Design................................................................................................................ 65
Population and Sample Selection...................................................................................... 66
Data Source ....................................................................................................................... 66
Step 1: Scheduling ................................................................................................ 68
Step 2: Duration .................................................................................................... 68
Step 3: Lesson Plan Structure Per Class Period .................................................... 68
I Do ........................................................................................................... 69
We Do ....................................................................................................... 69
You Do it Together ................................................................................... 69
You Do It .................................................................................................. 70
10
Validity ............................................................................................................................. 71
Reliability .......................................................................................................................... 72
Data Collection and Management ..................................................................................... 73
Data Analyses Procedures................................................................................................. 73
Ethical Considerations ...................................................................................................... 74
Limitations and Delimitations ........................................................................................... 75
Summary ........................................................................................................................... 76
CHAPTER 4: RESULTS .............................................................................................................. 77
Introduction ....................................................................................................................... 77
Data Collection ................................................................................................................. 79
Intervention ....................................................................................................................... 79
Results ............................................................................................................................... 81
Summary ........................................................................................................................... 85
CHAPTER 5: CONCLUSIONS ................................................................................................... 87
Introduction and Summary of the Study ........................................................................... 87
Summary of Findings ........................................................................................................ 88
Implications and Conclusion............................................................................................. 92
Recommendations for Future Research ............................................................................ 96
REFERENCES ............................................................................................................................. 99
APPENDIX A: COURSE SYLLABUS ..................................................................................... 117
APPENDIX B: BUSINESS DONATIONS LETTER ................................................................ 120
APPENDIX C: RENAISSANCE CLASS APPLICATION ....................................................... 121
11
LIST OF TABLES
Table 1. The Renaissance Logic Model ........................................................................................ 62
Table 2. Frequencies and Percentages of Participants' Demographic Characteristics (N = 79) ... 82
Table 3. Kolmogorov-Smirnov Normality Test of Study Variables............................................. 83
Table 4. Paired Samples t-Test Between Grade Point Average Scores Before and After the
Implementation ............................................................................................................................. 84
Table 5. Wilcoxon Signed-Rank Test Between Days Absent Before and After the
Implementation ............................................................................................................................. 84
Table 6. Wilcoxon Signed-Rank Test Between Discipline Cards Before and After the
Implementation ............................................................................................................................. 85
12
CHAPTER 1
INTRODUCTION
As state requirements for academic achievement continue to become more rigorous,
school administrators and counselors have strived to create positive school environments.
Therefore, administrators and counselors must find creative incentive programs tailored to meet
the needs of respective school settings. Within this context, leaders of a multitude of U.S.
schools have implemented incentive programs to address reducing academic test scores,
improving student behaviors, and improving attendance rates within schools (Herman et al.,
2016).
Incentive programs that address these issues are important, as the implementation often
fosters improvement in school settings, especially regarding perceived support (Balu & Ehrlich,
2018; Eren, 2019; Herman et al., 2016). When the implementation of school incentive programs
is successful, leaders often motivate and support staff and students by creating learning
environments that facilitate greater learning and attendance while reducing behavioral issues
(Balu & Ehrlich, 2018; Kearney et al., 2019). Implementation is especially important within
secondary education, as reduced school attendance and disciplinary issues become more
prevalent (Herman et al., 2016; Levitt et al., 2016).
With secondary students struggling with romantic and peer relationships, as well as
trying to pursue future endeavors, secondary students may begin to struggle academically
(Bugbee et al., 2019; Wentzel, 2017). As such, academically based incentive programs may be
beneficial for improving academic performances (Jostens Renaissance, 2020). This researcher
examined the efficacy of one such program, the Jostens Renaissance (2020) education program,
on grade point average (GPA) of secondary students in a Title I high school, as well as its
13
effectiveness on improving attendance and mitigating disciplinary issues, associated with
declines in academic success.
Background of Study
Students within secondary education must navigate a variety of pressures while
completing their respective educations. Peer pressure, romantic relationships, and considerations
of future goal achievements can impact academic performances (Bugbee et al., 2019; Wentzel,
2017). These factors especially occur among students in Title I high schools.
Leaders of Title I schools receive supplemental funds from school districts to assist
schools with the highest student concentrations of poverty to meet school educational goals (Taie
& Goldring, 2019). High schools qualify for Title I status when most students are from lower
socioeconomic statuses (SES; Kainz, 2019). With Title I funding, school leaders can educate
disadvantaged students and provide education that otherwise would be difficult for these schools
to provide (Kainz, 2019). As faculty of Title I high schools educate primarily impoverished
students, leaders must work harder than non-Title I schools to provide a school climate that
mitigates issues with attendance and discipline while increasing academic performances
(Bridgeland et al., 2008; Rubenstein & Wodatch, 2000).
Students in Title I schools are more likely to be absent than student counterparts in non-
Title I schools (Ginsburg et al., 2014; Roberts, 2019). According to Ginsberg et al. (2014),
students from Title I schools have significantly lower GPAs and are more apt to have
disciplinary issues. This situation is hypothesized to occur because Title I students face
challenges in getting to school, which may include unreliable transportation, community
violence, and unstable housing (Ginsburg et al., 2014; Taie & Goldring, 2019).
14
In addition to dealing with unreliable access to school settings, students within secondary
education must routinely deal with a variety of pressures. High school students must succeed
academically to ensure proper entry into higher education or the workplace (Brown-Williams et
al., 2019; Deci & Ryan, 2016). Additionally, secondary students must navigate increased social
pressures, including romantic relationships and relationships with peers, while determining
strategies for attainment of future goals (Bursztyn & Jensen, 2015; van Zantvliet et al., 2018).
Leaders of secondary schools must create and maintain school environments to aid
students in mitigating issues that support and encourage student engagement and academic
achievement (Herman et al., 2016). One such strategy is the use of incentive programs (Herman
et al., 2016). However, researchers have debated incentive programs within educational settings,
especially regarding proper implementation (Balu & Ehrlich, 2018; Kaimal & Jordan, 2016).
Balu and Ehrlich (2018) found that the implementation of a recognition and rewards
system was beneficial for students and faculty to produce a positive school climate and culture.
Similarly, Kaimal and Jordan (2016) examined the efficacy of incentive programs on both
student achievement and staff retention. Kaimal and Jordan confirmed that incentive programs
were beneficial to student achievement but only within the short-term. This short-term success
was attributed to students becoming complacent with the reward and punishment system.
Moreover, the incentive programs had no impact on educator retention, reinforcing the mixed
results common among studies of incentive programs in education (Kaimal & Jordan, 2016).
Recently, there has been renewed attention on a type of incentive program known as the
Jostens Renaissance (2020) education program (Buchanan, 2015; English, 2019). Educators
began the Jostens Renaissance (2020) recognition-based incentive program for both students and
teachers in 1988. Faculty can use the Jostens Renaissance education program primarily as an
15
intervention to improve school climate and school culture (Coyne, 2012). There is little
agreement on what those two terms mean. Still, the school climate is generally regarded as about
the organizational or administrative component of educational settings, while school culture
applies to how students function within the school (Rudasill et al., 2018).
There was a significant correlation between faculty perceptions of school climate/school
culture and academic achievement in schools for leaders of schools who chose to use the Jostens
program for over 3 years (Coyne, 2012). Campbell (2016) explored the link between attendance
and graduation rates for a 2-year average both before and after schools began using the Jostens
Renaissance education program. Campbell found no impact on attendance but a statistically
significant rise in graduation rates. However, the impact on attendance went unexplained. Thus,
Campbell suggested the study be replicated at other schools. Similarly, Coyne (2012) also cited
the need for more empirical research into the Jostens program. Therefore, this current researcher
addressed this gap in literature and practice to determine the effectiveness of the Jostens
Renaissance incentive educational program on improving students’ academic performances,
attendance, and behaviors when attending a Title I Southwest Florida high school.
Problem Statement
The problem addressed within the study was the gap in the literature about how faculty
using the Jostens Renaissance education program could impact GPA, disciplinary issues, and
rates of attendance among secondary students in a Title 1 school in Southwestern Florida.
Secondary students within Title I schools must face a variety of obstacles to a greater degree than
do non-Title I students, including unreliable access to education settings, peer-pressure, romantic
relationships, and planning for future endeavors within higher education and the workforce
(Bugbee et al., 2019; van Zantvliet et al., 2018; Wentzel, 2017).
16
The Jostens Renaissance (2020) educational program has been impactful since its early
beginnings in both improving school environments and helping to raise graduation rates
(Campbell, 2016). However, it is unclear how the use of this incentive program impacts GPA,
disciplinary issues, or rates of attendance within Title I secondary schools. Additionally,
Campbell (2016) and Coyne (2012) opined that future research was needed on the efficacy of
Jostens Renaissance (2020) educational programs, especially focused on academic achievements
and reduction of disciplinary issues.
Purpose of the Study
The purpose of this quantitative, quasi-experimental study was to examine the efficacy of
the Jostens Renaissance incentive program at one Title I high school in Southwest Florida. The
researcher used a pretest/posttest design to determine the impact of the Jostens Renaissance
(2020) incentive program on student GPA, attendance, and disciplinary issues. This researcher
focused on the relationship among students’ GPAs, rates of attendance, and any rates of
disciplinary issues before participation in the Jostens Renaissance incentive program, and
whether those variables changed significantly after the implementation of the incentive program
for one semester for those students who participated.
The researcher gained permission from the study setting to access relevant data. Data
regarding GPA, number of disciplinary issues, and attendance were collected from the 2018-
2019 school year to establish pretest data. Posttest data were collected from the 2019-2020
school year and compared to pretest data. Additionally, associated demographic data were
gathered, including gender, race, ethnicity, and grade level. Demographic data were gathered to
ascertain if there existed differences among student groups.
17
Research Questions and Hypotheses
The following research questions were used to guide this study:
RQ1: How does the Jostens Renaissance education program impact the GPAs of students
after the implementation of participating in the program?
H
o1
: There is no increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
H
1
: There is an increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
RQ2: How does the Jostens Renaissance education program impact the student absences
after the implementation of participating in the program?
H
o2
: There is no decrease in student absences after the implementation of participating in
the Jostens Renaissance education program.
H
2
: There is a decrease in student absences after the implementation of participating in
the Jostens Renaissance education program.
RQ3: How does the Jostens Renaissance education program impact the discipline cards
of students after the implementation of participating in the program?
H
o3
: There is no decrease in the number of discipline cards after the implementation of
participating in the Jostens Renaissance education program.
H
3
: There is a decrease in the discipline cards after the implementation of participating in
the Jostens Renaissance education program.
Assumptions, Limitations, and Delimitations
There were a few assumptions necessary for the completion of this study. The first
assumption was that there was a correlation between incentive programs and GPAs. Another
18
assumption was that the students would like to improve their GPAs or felt like their GPAs
needed improvement. Finally, an assumption existed that participants were truthful during the
completion of this study, and faculty of the school setting remained unbiased, consistent, and
accurate in recording electronic data.
Several limitations existed within this present study. First, all data collected were from
one specific high school within Southwest Florida. Therefore, the results of the study would only
be generalizable to the population of students within the respective academic institution. Next,
there were varying ways of implementing the Jostens Renaissance (2020) education program;
thus, the results might not apply to other institutions implementing this incentive program. A
third limitation was that the data collected only reflected one semester in the school year, instead
of a full calendar school year of two full semesters. As such, this study did not allow for a
longitudinal study of the efficacy of the Jostens Renaissance education program.
The following delimitations were adopted for the current study. First, the data collection
was limited to one Title I secondary school in Southwest Florida. Students from other schools
were not included in the study because there might have been differences in how the program
was introduced and implemented. This study was also delimited by including data for one
semester of the school year.
Definition of Terms
Attendance. For this research study, attendance refers to the number of days a student
reports or does not report to school in a given school year.
Discipline card. Discipline cards are given to students who commit infractions of school
policy. Discipline cards are routinely given for disruption, dress-code violation, drug-related
concerns, insubordination, and other disciplinary actions noted in the school’s code of conduct.
19
Fidelity. For this research study, fidelity refers to having the systems in place to support
a program's core features (e.g., data collection, funding, regulations, and teams). Fidelity is
important because people behave differently among different social contexts (Horner & Sugai,
2015).
Grade point average (GPA). For this research study, GPA refers to the grade point
averages of secondary students’ cumulative grades for a semester of eight classes (Santoso et al.,
2018).
Implementation. For this research study, implementation refers to faculty putting in
place a program or intervention intended to benefit students (Coyne, 2012). Multiple strategies
for implementation exist. The social-emotional characteristics of a school’s students provide one
example of the factors that administrators need to consider (Bradshaw et al., 2015).
Jostens Renaissance program. Jostens Renaissance (2020) program is a proprietary
incentive program begun by educators in 1988 as an intervention to improve school attendance
and graduation rates by providing rewards and recognitions for positive behaviors. Campbell
(2016) stated, “The program is customizable to each individual school and supported by Jostens
representatives. Most schools recognize and reward students for good grades, improved grades,
and good attendance records” (p. 20).
School-wide positive behavioral interventions and supports (SWPBIS). For this
study, SWPBIS refers to programs that appealed to school district leaders who faced mandated
changes without any increase in funds, increased testing and assessment, and the threat of
sanctions if the improvement is not achieved within an allotted time (Coyne, 2012). Faculty who
implement SWPBIS are usually examples of applying behavioral theory and rely on operational
definitions and interventions derived from theory (Horner & Sugai, 2015).
20
School climate and culture. For this research, climate and culture refer to “the affective
and cognitive perceptions regarding social interactions, relationships, values, and beliefs held by
students, teachers, administrators, and staff within a school” (Rudasill et al., 2018, p. 38).
Summary and Organization of the Study
Secondary students routinely struggle with navigating romantic and peer relationships, as
well as designing plans about entering higher education or the workforce (Bursztyn & Jensen,
2015; van Zantvliet et al., 2018). With all these pressures, secondary students may begin to
struggle academically (Bugbee et al., 2019; Wentzel, 2017). One possible strategy to increase
academic performances of secondary students involves faculty using incentive programs, such as
the Jostens Renaissance (2020) education program (Herman et al., 2016; Jostens, 2019).
However, the efficacy of this program within secondary education to increase students’ GPAs
remains understudied.
This researcher examined the efficacy of the Jostens Renaissance (2020) education
program on raising the GPA among secondary students, using a quasi-experimental
pretest/posttest design. Additionally, this researcher determined the efficacy of this incentive
program on decreasing disciplinary problems and increasing attendance, which were factors
associated with academic achievement.
Chapter 1 introduced the study. Within this chapter, the background of the study was
presented, as was the problem statement and purpose of this study. The first chapter also
presented information on the research questions, assumptions and limitations, and definitions of
key terms.
Chapter 2 contains a comprehensive review of the literature. Within the second chapter,
information is included on key themes, including the history of incentive programs, social
21
learning theories, and the Jostens Renaissance (2020) incentive program. Additionally, the
Jostens Renaissance incentive program and its relationship to increasing academic achievement,
mitigating disciplinary issues, and improving attendance is discussed.
Chapter 3 contains the methodology needed to complete the study. Topics within the
third chapter include the research design, research question, population, sample, data collection,
analysis procedures. Additionally, the ethical considerations about improving the GPA and
decreasing absences and discipline cards among high school students at a Southwest Florida
High School are presented.
Chapter 4 presents the raw data produced by data analysis. The fourth chapter contains
descriptive statistics of the sample, as well as pretest and posttest scores. Additionally, a short
description or explanation of the findings is provided for context.
Chapter 5 includes the discussion of the study results. The fifth chapter provides
conclusions and implications for future practices and researchers on student incentive programs.
Finally, recommendations for future researchers are provided.
22
CHAPTER 2
LITERATURE REVIEW
Introduction
Political pressure to improve schools and get more value for the tax dollar is omnipresent
and constant. Educators created the Jostens Renaissance (2020) educational program in 1988 as
one of the first efforts at providing school districts with a structured program of behavioral
interventions. Faculty of the program act on school climate and culture to improve attendance
and graduation rates, increase academic achievement, and promote school spirit. Federal law in
1997 opened funding to a variety of SWPBIS, and those programs multiplied. Buchanan (2015)
reported faculty of more than 3,000 school districts used the Jostens program in 2015, while
Horner and Sugai (2015) wrote that more than 23,000 SWPBIS programs were in place that
same year.
However, a meta-analysis of the motivation theory in schools showed that almost any
intervention could be termed moderately successful (Lazowski & Hulleman, 2016). Lazowski
and Hulleman (2016) found descriptive differences in effect sizes across many variables, but the
only significant difference was in experimental design. A critical need exists to determine if
faculty of these programs provide students benefits, as well as to determine why and how to
improve such programs.
Theoretical Framework
Social Learning Theory and Social Cognitive Theory
Social learning can effectively change behaviors or understandings through the social
interactions among actors within specific systems; consequently, social learning theory was
chosen as the theoretical framework for this study. School students are still growing but do not
23
live in a vacuum. The relationships within their schools, as with any environment, are complex.
Therefore, one should examine how the social learning theory developed and evolved to explore
its ability to answer this study’s research questions.
Psychologists Lewin (1943) and Tolman (1932) challenged attempts to explain actions
through stimulus and response. Tolman was a behaviorist but argued that actions had to be
examined in context. Tolman’s immanent determinants, initiating causes, behavioral
adjustments, and capacities all addressed operations within an environment (Pepper, 1934).
Lewin (1943) was a cognitive theorist who also argued for a contextual approach through his
field theory, where an individual interacted with the environment to produce a behavior. Per
Lewin (1943), “Field theory is probably best characterized as a method: namely, a method of
analyzing causal relations and of building scientific constructs” (p. 292). Any change in behavior
is dependent on the field at that time. Contemporaries then combined the works to produce a
Lewin-Tolman model of learning where knowledge and need were the key terms (White, 1943).
In 1941, Miller and Dollard produced social learning and imitation theory. They argued
that observational learning depended on a response based on the individual’s perceptions of both
an action and its reinforcement. Imitation can become a habit without any gain in knowledge,
and the authors compared that to operant conditioning without a role for cognition (Juwel &
Ahsan, 2019). Rotter (1954) then deviated from behaviorists in 1947 by developing the social
learning theory. Rotter suggested that the experiences of individuals might not only influence the
behaviors of others but also allowed for external forces and stimuli that provided a role for
cognition (Juwel & Ahsan, 2019). Cognition meant that social learning was dependent on an
individual’s expectations and incentives. Expectations were based on the perceived connections
24
within the observer’s environment, the envisioned results of contemplated action, and the
observer’s capacity to act.
However, incentives can come from a locus of control (Rotter, 1954). Cognition meant
that a person could control their behavior or hold a belief in their behavior across situations.
Individuals can use an internal locus of control to see themselves as reinforcers of any action.
Individuals can use the external locus to look outward for that reinforcement. Behaviors in social
learning then depend on four factors: behavioral potential, expectations, the perceived value of
reinforcement, and the psychological situation. Social contexts and environments work alongside
an individual’s psychological situation (Rotter, 1954).
Bandura and Walters (1963) tried to improve and popularize the social learning theory.
The researchers synthesized a complete merger of behaviorists and cognitive theorists where
observing, imitating, and taking a model from one’s environment were essential concepts of
learning, though imitation was just one aspect. Learning could still occur without imitation if the
observations were stored mentally for later use (Eyyam et al., 2012; Juwel & Ahsan, 2019).
However, most behaviors were learned by observing others and following a model (Bandura &
Walters, 1963).
Bandura et al.’s (1961) famous “Bobo doll” experiments with children occurred in 1961
and 1963. The Bobo doll was a tall toy with a rounded bottom that returned upright if hit, and
three groups of children watched as a researcher abused the doll. One group that saw the
researcher praised for his actions was more likely to mimic it later. A group that saw the
researcher admonished was more respectful to the Bobo. The experiment not only showed there
was a difference between learning and performance but also that the observed experiences of the
model are an influence (Eyyam et al., 2012). Individuals do not reproduce modeled behavior if
25
the model is punished for it, which Bandura (1986) called “vicarious learning” (Juwel & Ahsan,
2019, p. 20). Essential to vicarious learning is that individuals will model behaviors of those
either like them or superior to them (Bandura, 1986). This type of learning occurs in a school
environment in many ways.
The locus of control was another issue of Rotter’s (1954) social learning theory that
posed problems for Bandura (1977). The locus of control was a concept about the self in a
generalized sense. Bandura (1977) believed the need existed to describe one’s beliefs about their
capabilities related to specific situations; therefore, Bandura introduced self-efficacy in 1977
(Rosenstock et al., 1988). According to Bandura (1977),
Convictions that outcomes are determined by one’s own actions can have any number of
effects on self-efficacy and behavior. People who regard outcomes as personally
determined but who lack the requisite skills would experience low self-efficacy and view
activities with a sense of futility. (p. 204)
Self-efficacy also grew out of Bandura’s (1977, 1986) development of reciprocal
determinism, an attempt to explain behaviors through the interrelationships among individuals,
environments, and behaviors. Bandura (1977, 1986) wrote that previous efforts to explain human
behaviors had been unidirectional, where a limited set of situational factors would influence
actions. Unidirectional analysis of interaction has one factor acting on the other to combine and
produce a result. Bandura (1977, 198) used both approaches to treat behavior as a dependent
factor; however, the environment is not the sole actor because “people create and activate
environments.” (Bandura, 1978, p. 344). People, then, do not simply respond to external stimuli.
For example, “Cognitive factors partly determine which external events will be observed, how
they will be perceived, whether they have any lasting effects, what valence and efficacy they
26
have, and how the information they convey will be organized for future use” (Bandura, 1978, p.
345). Any act has determinants produced by the self. Bandura (1978) stated, “Personal and
environmental factors do not function as independent determinants; rather, they determine each
other” (p. 345). Experience from one behavior then affects thought, expectations, and efficacy to
influence all future behaviors.
People activate their environments through their physical and social attributes. For
example, “The differential social treatment affects recipients’ self-conceptions and actions in
ways that either maintain or alter their environmental biases” (Bandura, 1978, p. 346). The
relative influence of the environment, cognition, and behavior will vary with each individual and
circumstance. One cannot determine which of the inter-related triad is the ultimate cause of a
behavior, as a single observation can be a stimulus, response, or environmental reinforcement.
People usually use cognition to assess the progression of events continually while behaving.
Bandura (1978) maintained that the predictive power of any theory of behavior could only be
increased if cognition was considered. Humans derive conceptions about themselves and the
environment through four processes: past experiences, vicarious experiences, judgments of
others, and logical verifications. These processes may not seem to differ much from the four
factors that Rotter (1954) cited for behavior (e.g., behavioral potential, expectations, the
perceived value of reinforcement, and the psychological situation) aside from the apparent
absence of an incentive.
Bandura (1978) addressed incentives differently than Rotter (1954). Rotter expressed that
individuals would think about the consequences of their actions in a situation; therefore, their
actions were determined based on their beliefs. Although Bandura (1978) expressed humans’
actions were based on what they observed from other individuals, the triad meant that self-
27
generated events “not only operate as reciprocal determinants of behavior but they play a role in
the perception and formation of the environmental influences themselves” (p. 348). Bandura
(1978) wrote that a global self-image could not show explanations of large disparities in an
individual’s behaviors. Bandura (1978) defined a self-system as not only a psychic agent who
controls behaviors but also the “cognitive structures that provide reference mechanisms and to a
set of subfunctions for the perception, evaluation, and regulation of behavior” (p. 348).
The mechanisms that provide self-regulation include self-observation, a judgmental
process, and self-response. Bandura (1978) wrote, “Depending on value orientations and the
functional significance of given activities, people attend selectively to certain aspects of their
behavior and ignore variations on non-relevant dimensions” (p. 349). Individuals use self-
observation to gain some data, mostly related to performance dimensions, but not any basis for
self-response. Self-response is assigned to the judgmental process, which has important
subfunctions to evaluate behavior. Personal standards subfunction includes both modeling and
reinforcement sources. A referential performance subfunction involves personal experience,
while another subfunction rates the valuation of the activity. Performance attribution then
considers whether the behavior has resulted from an internal or external locus (Bandura, 1978).
Bandura (1978) did not ignore incentives, but the reciprocal determinism of the triad
(environment, cognition, and behavior) required that incentives be less explicit than in Rotter’s
(1954) model.
The incentives and motivations considered in the judgmental process then lead to self-
response—another area where individuals will vary. Bandura (1978) stated, “Much human
activity is regulated through self-evaluated consequences in the form of self-satisfaction, self-
pride, self-dissatisfaction, and self-criticism” (p. 350). The precise self-response will depend on
28
how the individual views the determinants of their behaviors. People will think more highly of
self-directed behaviors than performances based on external factors. Performance that has little
value to the individual will produce little or no self-response (Bandura, 1978).
Behaviorists continue to argue with Bandura’s (1978) concept of self-regulation and
assert that self-control requires a subset of responses waiting to be activated externally. Bandura
(1978) admitted that once established, the individual’s self-regulation system might act
automatically. The result would be much like the cybernetic control of machines. However,
“Social learning theory regards self-generated influences not as autonomous regulators of
behavior but as contributory influences in a reciprocally interacting system” (Bandura, 1978, p.
352), which represented Bandura’s (1978) triad of environment, cognition, and behavior. The
self-regulation mechanisms are also not trained, but such mechanisms do require standards to
evaluate performance. Bandura (1978) wrote, “Behavioral standards are established by precept,
evaluative consequences accompanying different performances, and exposure to the self-
evaluative standards modelled by others” (p. 353). Generic standards are processed until
individuals adopt their own. Bandura (1978) defined reciprocal determinism as “individuals are
neither powerless objects controlled by environmental forces nor entirely free agents who can do
whatever they choose” (p. 357).
Reciprocal determinism and Bandura’s (1978) earlier concept of self-efficacy were more
fully integrated within his 1986 text, Social Foundations of Thought and Action, when he
renamed his theory the social cognition theory. Reciprocal determinism was later renamed to
triadic reciprocal causation (Bandura, 1989). Bandura (2001) later put more effort into
exploring human agency and the role of freedom, but the 1978 paper was seminal for
29
understanding the interchange of the triad. However, similarities still exist between the social
cognition theory and behaviorist theories.
Experience, reinforcement, and feedback play important roles in both (Eyyam et al.,
2012). Behaviorists like Skinner (1989) did not deny cognition but believed behaviors could be
fully explained by reinforcements alone (Rosenstock et al., 1988). Cognition introduces the roles
of expectations and value. Behavior then becomes a matter of the subjective value of both a
result and the odds that action will achieve a goal or behavior. People who perceive value in a
changed behavior have incentive and will try to enact change if their present course endangers
that value. Those people believe themselves capable of the change (Rosenstock et al., 1988).
Motivation Theories
Saracho (2019) used Lewin’s (1943) and Tolman’s (1932) work on social learning to
study the value that individuals give to goals. The motivation and incentive theories promoted
since Maslow published his hierarchy of needs in 1943 are also largely a response to the claims
of behaviorists that actions are a response to stimuli. McDougall (1923) published a list of 18
human instincts that purported to conduct behavior, but drives and needs soon replaced instinct
as a scientific focus. However, Maslow (1943) differentiated between deficit needs that must be
met and developmental needs that permitted advancement later. Individuals do not need to meet
lower-level needs completely before fulfilling others (Maslow, 1943). Allport (1955) and White
(1943) introduced personality and value scales to expand the possibilities for incentive, and the
number of motivational theories has grown (Saracho, 2019).
Beckmann and Heckhausen (2018) wrote, “Striving to realize affect-laden goals is a
central facet of motivation. Incentive theories of motivation assume that behavior is goal-
oriented” (p. 163). The ability to regulate one’s behavior is not a reaction to instinct or stimuli.
30
There is no need for a drive to influence action because the desired goal is attracting the person
instead. Incentive theorists also maintain that individuals learn expectations about relationships
rather than simple connections between a stimulus and response. Cognition means motivation is
then a function of expectancy and value.
Many modern theories of motivation rely on the expectancy-value relationship, and
critics argue the approach is more economic rationalization than psychological. Wright (2016)
attempted to reintroduce the discussion of incentive and motivation. Wright argued that motives
were reasons for actions, with varying strengths ranging from active to latent. Efforts toward a
goal can correspond to the strength of the motive but not necessarily; thus, desire and other
emotions play vital roles.
Ryan and Deci (2000, 2017) proposed the self-determination theory as a way of bridging
expectancy-value models, personalities, and emotions based on studying intrinsic and extrinsic
motivation. The authors identified three basic psychological needs that would drive autonomous
motivation: the needs for competence, autonomy, and relatedness. A person’s needs operate
much the way Maslow’s (1943) did in his later work, where it does not matter for individual
wellbeing if some of the needs go partially unfilled. Neto (2015) explored self-actualization
needs related to academic motivation and found those needs could also be applied to the self-
determination theory. However, typical adolescents display a marked decline in intrinsic
academic motivations over time (Gnambs & Hanfstingl, 2016). The three essential needs of
autonomy, competence, and relatedness should apply to teenagers as well, so the theory would
explain that decline in motivation may be a result of a school’s failure to meet those needs. These
examples show how the varied theories of motivation and incentive might not only hold value
for this topic but might also lack predictive ability.
31
Review of Relevant Literature
School-Wide Positive Behavioral Interventions and Supports
The Jostens Renaissance (2020) education program is one example of a SWPBIS, and
most incentive programs are nonproprietary. Buchanan (2015) reported that faculty of more than
3,000 U.S. schools used the Jostens program in 2015, but there were more than 23,000 SWPBIS
programs in place nationwide that same year (Kincaid & Horner, 2017). Political pressure in the
1990s to increase school standards and accountability led to a focus on behavioral interventions.
Leaders of the Individuals with Disabilities Education Act of 1997 enabled behavioral
intervention programs to be expanded to include all students, and a school-wide emphasis
emerged with three tiers (Simonsen & Sugai, 2019). SWPBIS programs appealed to school
district leaders who faced mandated changes without any increase in funds, increased testing and
assessments, and the threat of sanctions if improvement was not achieved within an allotted time
(Coyne, 2012).
Faculty who use SWPBIS usually apply behavioral theory and rely on operational
definitions and interventions derived from theory (Horner & Sugai, 2015). Leaders of the
programs draw on a multitiered prevention approaches borrowed from public health. Tier I
programs have a school-wide emphasis, Tier II is targeted to those “at-risk” who need more
structure, and Tier III addresses the needs of students who benefit from individual treatment
plans. Leaders of the interventions focus on behavior management, which includes safety issues
(e.g., bullying); classroom management; social skills training; academic achievement or
accommodation; early screening; and attendance/dropout prevention. Faculty of Tier I programs
focus on a small set of expectations about student behaviors to influence school culture/school
climates, which are followed by reinforcements once students meet those expectations,
32
facilitated by the collection and analysis of data. These expectations must be logical and
integrated with all other aspects of the school environment because all students will receive the
intervention.
However, school district leaders face a problem that almost any intervention can be seen
as moderately successful, depending on how the study is designed (Lazowski & Hulleman,
2016). A meta-analysis of programs based on the motivation theory showed descriptive
differences in effect sizes across many variables, but the only significant difference was in
experimental design. Lazowski and Hulleman (2016) found that randomized designs produced
smaller effect sizes than a quasi-experimental approach. A difficulty for school districts was that
both the interventions and theories in question were tested using both approaches. Horner and
Sugai (2015) had the U.S. Department of Education funding for their study on SWPBIS. The
researchers questioned why so much emphasis was placed on these programs over the last 20
years, “while other examples of behavior analysis with impressive results have been ignored”
(Horner & Sugai, 2015, p. 83). However, Horner and Sugai (2015) did not identify those
examples.
The Covey Model
Covey (2020) developed a model based on the powers of a paradigm shift for a person. A
paradigm shift involves a fundamental change in approaches or underlying assumptions, which
people can do to better their lives. Therefore, the current researcher of this proposal believed that
the Covey model could be applied to enhancing students’ abilities to self-learn through
motivating them to enhance the success of the Renaissance course. Covey discussed seven habits
that could lead a person to a paradigm shift. These habits include the following:
1. Be proactive.
33
2. Begin with the end in mind.
3. Put first things first.
4. Think win-win.
5. Seek first to understand, then to be understood.
6. Synergize.
7. Sharpen the saw. (Covey, 2020, p. 20)
Covey (2020) applied these habits to power paradigms of effectiveness based on
personal, interpersonal, and organizational skills. The researcher wanted people to gain self-
awareness to succeed in life. Specifically, the seventh habit, which involves a person sharpening
the saw, refers to a person honing his or her greatest asset, which Covey defined as a person’s
self. The author encouraged people to persevere despite obstacles and to be willing to grow as
individuals to succeed in life.
Covey (2020) discussed a person’s need for motivation and education to grow into
success. The current researcher of this proposal found these paradigms applicable to the
leadership Renaissance class, as learned during a conference that the researcher attended. In the
leadership Renaissance class, the goal is for learners to educate themselves through self-
motivation. They must achieve this goal to learn to self-study and enhance their education levels.
Covey supported the notion that self-learning can enhance a person’s experience of education. A
person who self-learns can experience the information fully and retain it better, according to
Covey. Therefore, the current researcher of this proposal believed enhancing students’ abilities to
self-learn through motivating students would enhance the success of the course.
The Renaissance program involves motivating the students and educating them to be
better through incentives, as suggested by Covey (2020). A person or teacher can use the
34
paradigm of motivation to encourage students to lead their learning, which is what leaders of the
Renaissance class desire for students’ success. With a bit of encouragement with incentives,
students can achieve success due to their self-confidence building. The current researcher of this
proposal believes that the Covey model ties into the principles of the Renaissance program
because teachers should instill values. Students should have a sense of proactivity, belief in the
goals of a project, a willingness to find the beginning of the objective, a desire to understand the
subject, an ability to synergize the goals to achieve a successful outcome, and finally to grow
beyond when they first entered the classroom to achieve a more educated, self-aware version of
themselves as students (see Covey, 2020).
1. Be proactive.
2. Begin with the end in mind.
3. Put first things first.
4. Think win-win.
5. Seek first to understand, then to be understood.
6. Synergize.
7. Sharpen the saw. (Covey, 2020, p. 20)
Students could use “Habit 1: Be Proactive” (Covey, 2020, p. 20) to begin their journey in
the school program as proactive participants in the class. Then, students could use “Habit 2:
Begin With the End in Mind” (Covey, 2020, p. 20) to picture their future and adjust that future
based on what they saw. For instance, students could see that their attendance and grades, as well
as their self-learning abilities, improved if they applied the principles of Covey. Students could
continue to “Habit 3: Put First Things First” (Covey, 2020, p. 20) to become more organized in
their learning. Once through the seven habits, students can see how they have progressed in the
35
course, and they will have evolved in their learning abilities. They will have learned self-
motivating behaviors and become adept self-learners.
Moreover, the researcher attended a conference where the speakers expressed that data
from students’ attendance and grades showed student improvement in the course. Therefore, the
Renaissance course was designed by keeping in mind the goal of enhancing students’ values.
Students should exit the course with self-learning skills based on motivational incentives, with an
expanded education that can further their success in life, as based on the Covey (2020) model.
Jostens Renaissance Program
Jostens Renaissance (2020) education is a proprietary incentive program begun by
educators in 1988 as an intervention to improve school climate and school culture (Coyne, 2012).
There is little agreement on what those two terms mean, but school climate is generally regarded
as about the organizational side, while school culture applies to how students function within it
(Rudasill et al., 2018). Leaders of the Jostens Renaissance (2020) program claimed that the
program “contributes to increased attendance and graduation rates, improved academic
performance and behavior, higher teacher retention rates, and a boost in overall school spirit”
(para. 2). Jostens Renaissance (2020) sold a variety of products and access to resources for
school leaders to tailor their programs to local needs, and all concerned the program’s formula:
respect, recognize, reward, and reinforce.
Jostens Renaissance (2020) is well known for selling school rings and graduation
products for high schools and colleges. Their main competitor, Herff Jones, has its proprietary
incentive program (Coyne, 2012). The Jostens program is used more in the southern and
southeastern United States and has attracted more attention in academic literature. Faculty of
more than 3,000 U.S. schools used the Renaissance program in 2015 (Buchanan, 2015). The only
36
way a teacher or administrator can gain detailed information about either program is through a
sales representative (Coyne, 2012). However, the proprietary nature of the Jostens Renaissance
(2020) does not mean it is without value.
Coyne (2012) wrote that Jostens worked on a “pride factor model” (p. 6) that depended
on school culture, student motivation, performance, and recognition. These factors differ from
the four elements of the Jostens formula published on its website, where faculty of individual
schools must determine the elements of the Jostens Renaissance (2020) formula that will work.
Leaders of educational systems have historically used rewards and punishments to aid in
behavioral control. However, punishments can disrupt the development of relationships between
teachers and students. Rewards may have the opposite effect.
School leaders use data on individual student grades, attendance, and disciplinary records
for each grading period to allocate rewards based on the criteria decided by each school’s
Renaissance committee. Buchanan (2015) reported on the first year of implementing the Jostens
program in a Florida high school and noted that about half the 1,800 students received a
Renaissance card that entitled students to discounts from local merchants or admission to events
after the first quarter. The color-coded cards represented different GPAs and provided varying
levels of reward. Students ineligible for the reward cards often expressed curiosity and were
provided guidelines, which were published in the school newsletter. The quarterly nature of the
program gave students ample chances to be rewarded.
All the students at the school that Buchanan (2015) examined received free or reduced-
price lunches and represented a wide range of ethnicities or races, while qualitative data
indicated uniform effects on student perception. Acknowledgment of achievement led students to
feel someone cared, which had spillover effects on leadership, teamwork, and school spirit.
37
Coyne (2012) reported, “Most students who are unmotivated perceive that they are not connected
to the school or to people in the school” (p. 43). Those students would often protect their feelings
of value and self-worth. Such recognition that is personal, purposeful, distinctive, and related to
task-completion enhanced a student’s intrinsic motivation. The change in motivation also
influenced teachers, who resisted putting maximum effort into raising students’ interests if
students seemed unmotivated (Coyne, 2012). The fear of being unable to reach a student
degrades teacher self-efficacy. Teachers place a high value on effort, even if the student is
unsuccessful, and teachers will assign reasons when the effort is lacking. Teacher performances
and strategies are then related to student grades and performances.
Coyne (2012) found there was a significant correlation between faculty perceptions of
school climate/school culture and academic achievement in schools with the Jostens program for
more than three years. The qualitative phase of Coyne’s study showed extensive faculty support
for the program and a perception that school culture was vital to achievement. Campbell (2016)
explored the link between attendance and graduation rates for a 2-year average both before and
after school faculty began using the Renaissance program. Campbell found no impact on
attendance but a statistically significant rise in graduation rates. However, the lack of a link went
unexplained, and Campbell suggested the study be replicated at other schools. Coyne (2012) also
cited the need for more empirical research into the Jostens program.
Sustainability of the Renaissance program is also an under-researched area (Coyne,
2012). Implementation requires the creation of a leadership team, buy-in, support from faculty,
and collaboration. Implementation is dependent on funding, marketing, training, and modeling or
demonstrations. Buchanan (2015) added time as a requirement because the leadership team had
to conceive new ideas that created or sustained both faculty and student interests. The need to
38
plan and secure rewards and activities added to time constraints, as did the need to maintain
funding.
School Culture and School Climate
Faculty of the Jostens Renaissance (2020) program and other SWPBIS efforts all seek to
influence school culture and school climate in Tier I. However, “little conceptual consensus”
(Rudasill et al., 2018, p. 35) exists on what these terms mean, which provides inconsistent
guidance for research. Rudasill et al. (2018) suggested a new definition derived from the
ecological systems theory of Bronfenbrenner (1989). School climate should include “the
affective and cognitive perceptions regarding social interactions, relationships, values, and
beliefs held by students, teachers, administrators, and staff within a school” (Rudasill et al.,
2018, p. 38). However, other researchers have made a clear distinction between the
organizational culture that faculty and administrators operate within the school climate and the
environment of the students regarding school culture (Jones & Shindler, 2016). A further
complication stems from the nature of “evidence-based community interventions” (Beehler &
Trickett, 2017, p. 455), which include SWPBIS programs in these environments. Political
pressures drove the search for evidence-based solutions, but “the values and assumptions
underlying the evidence-based movement have much in common with those the field initially
challenged” (Beehler & Trickett, 2017, p. 455).
Other researchers have combined school climate and culture to suggest the approach will
create better conditions for learning and teaching to take place effectively (Dernowska, 2017).
Relationships are essential, and both teacher/student and peer relationships matter. California
goes a step further by wedding social-emotional learning to climate/culture on the basis that
officials can then better understand the student and school performances (Hough et al., 2017).
39
The state argued that the approach was valid and allowed decision-makers to differentiate
between schools, relate academic to non-academic measures, and display areas needed for
improvement. Leaders conflating the terms also show areas of student achievement that continue
unmeasured by traditional means. The debate over school culture and climate are reminiscent of
a famous opinion by U.S. Supreme Court Justice Potter Stewart (Jacobellis v. Ohio, 1964) where
researchers cannot define a term, but know it when they see it.
Most state departments of education do not track either school climate or school culture
(T. Smith & Shouppe, 2018). However, Georgia now has an annual requirement for school
leaders to meet listed criteria on the school climate as related to professional development. T.
Smith and Shouppe (2018) examined whether a link existed between those annual climate ratings
of schools and the academic achievement of their students. The team used a path analysis model
and factorial multivariate analysis of variance for both Title I and non-Title I schools. The
researchers found a positive relation between climate ratings and student achievement as
measured on standardized competency tests.
Ramelow et al. (2015) wrote that measures of climate or culture published between 2003
and 2013 suffered from “insufficient validity testing” (p. 731). The authors also argued that
theory to justify the measures developed was usually missing. A study of California schools
using several measures of school climate showed climate as the greatest predictor of the faculty’s
ability to drive academic achievement (Jones & Shindler, 2016). However, data did not show
causality in any relationship. Shindler et al. (2016) then replicated the study in five states, with
similar results. Shindler et al. (2016) stated, “It appears that the use of practices that promote a
‘psychology of success’ lead to greater achievement and higher quality climate, and those that
promote a ‘psychology of failure’ lead to underperformance” (p. 9). These problems of validity
40
and causation need to be kept in mind because all behavioral interventionists seek to influence
school climate or school culture.
Teachers’ Perceptions of School Climate
The perception by teachers of school climate does affect their sense of self-efficacy and
job satisfaction (Aldridge & Fraser, 2016). Aldridge and Fraser (2016) used structural equation
modeling to find that teacher self-efficacy and job satisfaction were not only both related to
climate factors, but that there was also a positive relationship between self-efficacy and job
satisfaction alone. Malinen and Savolainen (2016) used a structural equation model to show
perceived school climate had a positive effect on teacher job satisfaction, partially mediated by
self-efficacy. Self-efficacy in behavior management also affected both job satisfaction and
burnout. However, perceptions of collective efficacy lacked these effects. Other methodologies
produced different results, as Ozen (2018) used phenomenology and thematic analysis to suggest
that a positive school climate produced an open relationship between faculty and administrators
that would encourage self-efficacy and a sense of responsibility. Teachers were previously
deterred from using new techniques or ideas that often curtailed the effort they expended.
Collective efficacy seemed to have a role.
Houchens et al. (2017) compared schools with and without SWPBIS programs. However,
research was mixed on whether SWPBIS programs improved academics, SWPBIS programs
improved teacher perception of overall organizational health, which might affect the teaching.
Houchens et al. also analyzed varied levels of SWPBIS implementation. Teachers in SWPBIS
schools reported improved climate led to a better understanding of behavioral expectations
between faculty and students, plus more trust and respect. There were no significant differences
in achievement levels between SWBIS and non-SWPBIS schools. Still, the achievement was
41
greater at schools where faculty implemented their programs with high fidelity rather than lower
fidelity. This finding may be due to a possible link between school climate and teacher self-
efficacy.
The issue becomes more complicated when the role of a school’s principal is considered.
Dutta and Sahney (2016) found that the principal’s leadership style had no direct effect on either
teacher job satisfaction or student academic achievement. The authors considered two
predominant leadership styles used in schools, transformational leadership, which focuses on a
shared vision to enforce change in school climate and an instructional focus on changes to a
curriculum. Much has been written on the value of transformational leadership in promoting
teacher job satisfaction. However, Dutta and Sahney found both styles had small indirect effects,
and the comparatively greater impact of instructional leadership on academic achievement led
many principals to favor its use. A contrasting study on principals used linear regression models
and international data from the Organization for Economic Co-operation and Development to
explore teacher perceptions of transformational and instructional leadership styles. Bellibas and
Liu (2018) found that transformational leadership had positive effects on staff’s mutual respect,
but no bearing on behavioral matters, such as delinquency and school violence. School size and
SES were greater predictors of school safety, which could also affect teacher perceptions of
climate. All these studies have shown potential research gaps related to school climate and
teaching.
Students’ Perceptions of School Culture
School culture is a social construct, and students may not fit within that culture neatly.
Perceptions of school culture are entirely subjective, “and personal characteristics influence
individual outcomes and behaviors” (Kutsyuruba et al., 2015, p. 103). Faculty of behavioral
42
interventions, such as the Jostens Renaissance (2020) program and SWPBIS, seek to alter the
actions of small communities, but those communities must first be understood. Aldridge et al.
(2016) found that student wellbeing had a greater role in their academic achievements than
school culture. Six measures of school culture were examined using structural equation
modeling: providing teacher support, connecting peers, connecting schools, affirming diversity,
creating rule clarity, and reporting and seeking help. However, Aldridge et al. found the six
variables had a weak effect on wellbeing mostly moderated through a student’s ethnic and moral
identity, resilience, and life satisfaction. Kwong and Davis (2015) used a longitudinal study of
16,258 students at 1,954 U.S. schools to show a stark contrast. They found that student
perception of school climate was highly predictive of academic achievement measured by
standardized tests in reading and mathematics. However, one complication was that the authors
found that high levels of institutional surveillance or monitoring of behaviors diminished any
positive effects from the perceptions of school culture. A reduction in surveillance mitigated SES
inequalities within the schools.
Salle et al. (2015) discussed the complexity of school culture and proposed a cultural-
ecological model of school climate. The authors argued that prior researchers did not grasp all
the variables that went into a student’s perception of school culture. School culture is important
because it affects psychological, social, and academic results, but the definitions of culture are
too narrow. The result is that Salle et al.’s cultural-ecological model of school climate captures
individual, family, school, and community variables. The authors also argued for culture-specific
interventions, which their model would be able to indicate. More on student interactions is
provided in the coming sections. Still, the literature on school culture has shown the complexity
43
of the student environment and the inherent difficulty of trying to influence students through
behavioral interventions.
Implementation
A premise of the behavioral theory is to focus on the smallest functional unit that can
create an intended outcome, which represents a benefit in SWPBIS programs because school
district leaders can separate a strategy from the individual features of any contemplated program
(Horner & Sugai, 2015). Multiple strategies and interventions need to be assessed for any single
need because of the local environment. The social-emotional characteristics of a school’s
students provide one example of the factors that administrators need to consider (Bradshaw et al.,
2015). Bradshaw et al. (2015) examined the socio-emotional character of an ethnically and
racially diverse school, where 49% of the students received free or reduced-price meals. Four
classes were identified: high risk (6.6%), at-risk (23.3%), normative (36.5%), and socially-
emotionally skilled (33.6%). Interventions had the greatest effects on high-risk and at-risk
students.
However, Horner and Sugai (2015) found that programs and the core features were often
combined in practice when school leaders urged adoption of the program without confirming its
implementation. Administrators believe an emphasis on the core features of a program without
regard to the practices needed to alter student behaviors would allow them to alter courses later
with new strategies to suit local needs. The specific expectations taught to students and the
manner of teaching those students should be left to the individual school. This approach can be
problematic, as the following quote shows:
First, implementation of PBIS in schools requires a district or regional implementation
team. Students are the unit of impact, schools are the unit of intervention, but districts are
44
the unit of implementation. Teams are the mechanism for comprehensive and sustained
implementation. The district leadership team is more than advisory or informative they
actively manage and guide the implementation process. (Horner &Sugai, 2015, p. 83)
Demonstration projects should be conducted first to build the capacity for scalability
(Horner & Sugai, 2015). Horner et al. (2019) found that a key element in the success of the
district-wide implementation was how leaders leveraged the demonstration projects. Scaling
upward requires collecting and managing data while planning and managing resources. Scaling
is important because school district leaders need to leverage scarce funding and other resources
to ensure the use of interdisciplinary teams during implementation and when maintaining
program fidelity (Muller, 2002). The implementation of SWPBIS programs is also more difficult
within high schools because of the training and coaching involved—another consideration for
administrators (Freeman et al., 2016). Nese et al. (2019) studied 708 schools across five states to
explore the time required for adequate implementation of Tier I SWPBIS programs. Programs in
elementary schools were the quickest to establish, then middle schools. Implementation in Title I
schools lagged those that were non-Title I, and suburban schools were easier to implement than
those in cities. The longitudinal study had a 5-year timeframe.
Horner et al. (2019) examined seven states that had established SWPBIS programs in at
least 500 schools. They identified significant capacities in administrative leadership and funding,
as well as the development of local training and coaching expertise, behavioral expertise, and
local evaluation capacity. Horner et al. found that success in implementing these programs in a
minimum of 100 schools led to the political and financial support needed to expand efforts.
45
Fidelity
SWPBIS programs are not implemented simply by putting teachers through training or
buying an instructional product, such as Jostens Renaissance (2020), because the process must be
measured through fidelity assessment (Horner & Sugai, 2015). A key to fidelity includes having
the systems in place to support a program’s core features (e.g., data collection, funding,
regulations, and teams). Horner and Sugai (2015) stated these features indicated people behaved,
not organizations, and they behaved differently within social contexts. Otherwise, there is no
guarantee that any program will work as intended. Intervention practices and the practices to
change an organization are separate, and data collection at the school-wide and individual levels
is essential. Horner and Sugai found that 11,524 schools of the more than 23,000 with SWPBIS
programs measured \ implementation fidelity in 2014, and 81% met Tier I criteria. That finding
left more than half of the schools, about 13,600, with the fidelity as either unknown or below
Tier I levels. The Benchmarks of Quality survey is a validated measure of SWPBIS fidelity
(Childs et al., 2016). Childs et al. (2016) conducted a longitudinal study and found a significant
decreasing trend between disciplinary incidents and SWPBIS but only in schools with the
highest Benchmarks of Quality scores.
Several different fidelity surveys can be used to evaluate SWPBIS programs, but those
surveys are not interchangeable, which causes problems for administrators in decision-making
(Mercer et al., 2017). Because researchers use validated fidelity surveys to define measures
differently or use different measures entirely, problems occur for evaluating the interventions.
Mercer et al. (2017) examined five validated fidelity surveys. There was moderate divergence
across four, but the School-Wide Evaluation Tool was an exception in “failing” more schools.
Another survey, the Positive Behavioral Interventions and Supports Self-Assessment Survey,
46
showed the survey was sensitive to differences in schools with higher levels of implementation.
Freeman et al. (2016) considered a large sample of high schools across 37 states, and a variety of
these fidelity surveys were used. Freeman et al. explored the effects of SWPBIS on academic
achievement, attendance, and behavioral outcomes. Their study indicated positive links occurred
between SWPBIS and both behavior and attendance, provided the programs were implemented
with fidelity.
Sustainability
The hurdles to the sustainability of these SWPBIS efforts largely coincide with those
described for the Jostens Renaissance (2020) educational program in an earlier section.
Pinkelman et al. (2015) conducted a qualitative study of the faculty and administration of 860
schools that had either implemented or had prepared to implement SWPBIS programs. The
thematic analysis produced 13 themes that pertained to enablers and barriers related to
sustainability. The most important enablers included staff buy-in, the administration’s support,
and consistency. The most cited barriers to sustainability included staff buy-in, resources, time,
and money. Andreou et al. (2015) took a different approach to explore sustainability by
considering 227 critical incidents to SWPBIS over several years within one school district.
Content analysis showed 13 categories of incidents related to sustainability, and the most critical
included the following:
Continuous Teaching, Positive Reinforcement, SWPBIS Team Effectiveness, Staff
Ownership, School Administrator Involvement, Adaptation, Community of Practice, Use
of Data, Involving New Personnel, Access to External Expertise, Maintaining Priority,
Staff Turnover, and Conflict of Personal Beliefs/Mistaken Beliefs. (Andreou et al., 2015,
p. 157)
47
These incidents were also related to implementation and fidelity.
The link between fidelity and sustainability was further illustrated by a study of whether
school characteristics and the speed of program implementation could predict if SWPBIS would
be abandoned (McIntosh et al., 2016). McIntosh et al. (2016) explored elementary, middle, and
high schools at 1, 3, and 5 years after implementation. They found the greatest variance in
fidelity was at the state level, but the speed of implementation was a significant predictor of
SWPBIS sustainability. High school leaders were the most likely to abandon their programs.
Another longitudinal study of 915 schools across three states had a similar goal but a different
approach. Nese et al. (2016) watched these schools for 5 years after teachers received their initial
training in SWPBIS; however, 7% of school leaders abandoned the effort during implementation.
The authors found that location was the strongest predictor of abandonment, with schools located
in cities most likely to scrap the programs. Nese et al. expressed that implementation was also
the most difficult in city schools, leading to issues of fidelity. Nese et al. cited the need for
additional training and coaching as a hurdle to sustainability.
McIntosh et al. (2018) found a more explicit link between fidelity and sustainability, not
just an observational one, in a 3-year study. The researchers explored 860 schools in 14 states.
Implementation fidelity and greater team use of data in decision-making in the first year were the
strongest predictors that programs would still be in place by the third year. McIntosh et al.
defined having a “critical mass” of schools within a district adopting SWPBIS as a factor. These
studies indicated that leaders of a sustainable SWPBIS program should get the implementation
and fidelity right in the beginning, which led Kincaid and Horner (2017) to recommend using
“implementation science” (p. 20), a systems approach.
48
Interdependence of Teachers and Students
Teachers and students are influenced by two different systems (i.e., the organizational
climate and school culture), and the intersection is important. Weiner and Higgins (2017)
examined this issue using a longitudinal sample of approximately 130,000 students and 9,000
teachers. Trust and respect between teachers and students were some of the questions that they
explored in this intersection. Weiner and Higgins (2017) stated, “We find that when the teachers
report a strong collaborative culture, believe they have adequate materials, and feel physically
safe, students report a stronger and more positive learning culture” (p. 21). Prior researchers
considered the two populations separately and ignored the intersection, which means prevailing
views on behavioral interventions may need review.
Combined Effects of School-Wide Positive Behavioral Interventions and Supports
Programs, School Culture, and Climate
Leaders of interventions within SWPBIS programs should focus on behavioral
management, which includes safety issues (e.g., bullying); classroom management; social skills
training; academic achievement or accommodation; early screening; and attendance/dropout
prevention (Horner & Sugai, 2015). Results of the programs’ efficacy are mixed. Gage et al.
(2017) stated, “Most experimental research has found little to no evidence that SWPBIS has a
distal effect on school-level achievement” (p. 158). However, Gage et al. noted a consensus in
the academic community that improved behaviors allowed greater achievement for all students.
Gage et al. analyzed 10 years of academic achievement data for all Florida schools to estimate
the average SWPBIS treatment effects. There are approximately 2,000 schools in Florida, and an
average of 235 implemented SWPBIS each year with fidelity. Gage et al. found that the number
of students performing at or above grade-level benchmarks at SWPBIS schools compared to
49
those without the programs was statistically significant, but the effect sizes were small. The
authors argued that few evidence-based academic interventions had large effects when evaluated
experimentally.
The potential link between improved disciplinary action and achievement fits with data
gathered by Pas et al. (2019). The researchers found that SWPBIS consistently had greater
effects on student suspensions than on academic achievement or other measures. They also found
a correlation between lower suspensions rates and higher mathematics and reading scores, but
effect sizes for all outcomes other than suspensions were low or medium. Higher implementation
fidelity of SWPBIS programs led to lower suspensions in a study of 153 Ohio schools, but no
significant trend was found for academic achievement (Noltemeyer et al., 2019).
Faculty of SWPBIS programs seem to have an influence, but it appears limited to
disciplinary issues at the Tier I level. Kotok et al. (2016) used structural equation modeling to
find that disciplinary actions and school attachment had a greater impact on dropout rates than
the individual characteristics of the student. Many studies may be part of the problem as a meta-
analysis of SWPBIS literature showed most were single descriptive studies. Noltemeyer et al.
(2019) stated, “Of the cases that performed statistical analyses, the majority reported
unanimously positive or predominately positive findings, and these findings were notably more
positive for behavioral outcomes than for academic outcomes” (p. 7).
Competing Explanations
There are several competing explanations for why SWPBIS has little effect on academic
achievement, and some are competing models. These explanations are based on theories of
learning, motivation, and incentive, thereby making the explanations important to consider, and
many can be integrated within a Tier I SWPBIS structure. Researchers and leaders can use the
50
authoritative school climate model to maintain that schools with high structure and student
support have greater levels of student engagement (e.g., class participation and student
preparation), which translates into higher academic achievement (Konold et al., 2018). Based on
this model, researchers’ arguments of school culture can go in a different direction; for example,
Konold et al. (2018) tested the model with a sample of 60,441 students across 298 high schools.
Konold et al. did not dismiss the role of school culture and noted that the U.S. Department of
Education awarded more than $70 million in school climate transformation grants to 138
recipients in 38 states in 2014. However, “school climate theory has been severely limited by a
failure to construct conceptual models that identify mechanisms by which specific features of
school climate are associated with student outcomes such as academic achievement” (Konold et
al., 2018, p. 1). Further research on student engagement remained needed because Konold et al.’s
(2018) study showed it directly led to achievement gains.
Alhosani et al. (2017) used a meta-analysis to determine that the parents also played a
vital role, as mentioned in the discussion of the cultural-ecological model of school climate in
this current dissertation. The authors found that both school leadership and school culture played
a role in academic achievement, but parents mediated their achievements. Nisar and Mahmood
(2017) argued that parental SES and study habits were the greatest determinants of academic
achievement. Because SES could not be immediately changed, school leaders should focus on
the institution's culture and promoting study habits. A social identity perspective was used in
another study because it was assumed school culture and social belonging were important
variables and had been rarely studied in tandem (Reynolds et al., 2017). However, the three
variables found most important to student achievement were parental education, SES, and school
identification. Berkowitz et al. (2017) sought to eliminate the effects of SES in their meta-
51
analysis of papers dating to 2000 but found “scientific evidence establishing directional links and
mechanisms between SES, school climate, and academic performance is inconclusive” (p. 425).
Berkowitz et al. (2017) added, “Most studies do not provide a basis for deducing a directional
influence and causal relations” (p. 425).
Student Social Relationships
The size and diversity of a school were the greatest determinants of whether school
culture would have any bearing on an individual student (C. Smith, 2016). There also had been
little research on how the school environment affects student relationships. Still, network
analysis showed student perceptions of school culture were dependent on the social context they
occupy within that environment. Student relationships mattered because Wentzel et al. (2016)
argued that the direct effects of emotional support differed with the source. Support from parents
and teachers was found linked to academic achievement, but peers predicted social behaviors.
Researchers have no idea how positive peer relationships may help with academic and
social functioning, but it does (Wentzel & Ramani, 2016). Positive peer relationships provide
emotional well-being, enhance beliefs about the self, and encourage prosocial forms of
behavioral and social interactions. A need exists to find ways to promote positive peer
relationships, but it still will not answer the question of how faculty can aid academic
functioning. A longitudinal study showed academic performance and truancy were best
explained by peer selection (Rambaran et al., 2017). Friends influenced one another to increase
or decrease performance and attendance. Another longitudinal network analysis showed that
dissimilarity was a factor in prosocial relations, as little interaction existed between high and low
popularity students (Choukas-Bradley et al., 2015; Van Rijsewijk et al., 2016). Student
alignment was highly popular when peers were held in an experiment using public and private
52
Internet “chat rooms.” The same behaviors were found whether the students were in public or
private mode, which signaled their norms were internalized (Choukas-Bradley et al., 2015). The
locus of control is an important factor in these peer relationships. Although researchers are
unsure about how it operates in minority students, an internal locus of control has greater weight
for Whites in peer relationships (Kang et al., 2015). Moreover, culture seems a factor of
influence.
Environmental Factors
The proposed systems view of school climate (Rudasill et al., 2018) was discussed earlier
in this chapter, but Maxwell (2016) used structural equation modeling to explore school culture
and attendance as mediators for the physical environment and achievement. Maxwell’s model
included building conditions and social climate. The findings indicate that “academic
achievement is linked to building conditions mediated by the social climate and student
attendance” (Maxwell, 2016, p. 206). Less attention has been given to school culture in suburban
districts, but Sulak (2016) found that the location of the school, student behavior, and
surrounding crime might influence suburban academic achievement levels. Identity-based
motivation theory can also be used in these situations to assess why minorities do not perform
well despite their aspirations (Oyserman & Lewis, 2017). Macrolevel social stratification shapes
identity-based motivation and determines the behaviors that seem congruent with important
identities.
Brain Plasticity
Traditional interventions employed by SWPBIS work well for children but not
adolescents, who have a greater desire to be respected and given status (Yeager et al., 2018). The
faculty of such interventions can be effective. Nonfinancial rewards are most effective on
53
younger students, and the motivating effects of the incentive disappear if delivery is delayed,
which can lead to the under-investment of students’ efforts (Levitt et al., 2016). Some
researchers have used a neuroeconomic approach to show how decision processes change during
adolescence (Hartley & Somerville, 2015). Decision processes, such as reward reactivity,
uncertainty tolerance, delay discounting, and experiential assessments of value and risk, operate
differently from adolescence to adulthood. Much of these differences are due to anatomical
changes to the prefrontal cortical, striatal, and salience processing systems of the brain.
However, decision making in adolescents can be optimized toward specific goals.
Extrinsic or Intrinsic Motivation
Researchers of intrinsic motivation to learn have positively linked academic achievement,
indirectly, to classroom engagement (Froiland & Worrell, 2016). Researchers have shown
engagement as a factor earlier in the authoritative culture model. Froiland and Worrell (2016)
used structural equation modeling and both parent education and prior GPA as controls. Their
model worked for African American and Latinx students, as well as Whites, which led the
authors to recommend that school leaders should foster intrinsic motivations and learning goals.
A longitudinal study based on self-determination theory and social cognitive theory showed
whether the development of self-efficacy could explain the growth in motivation (Kyndt et al.,
2019). The results showed that growth in “autonomous motivation” actually predicted growth in
self-efficacy. However, Gnambs and Hanfstingl (2016) wrote, “Adolescents typically exhibit a
marked decline in intrinsic academic motivation throughout their school careers” (p. 1691). Self-
determination theorists can suggest that adolescents need three things: autonomy, competence,
and relatedness. Any failure to meet these needs can lead to declines in intrinsic motivation. The
54
authors tested their theory using a longitudinal study to show where differences in need
satisfaction would predict drops in motivation.
Motivation Over Intelligence
Motivation has stronger effects on achievement than intelligence, as measured by a
student’s final grade, while intelligence has a greater role in standardized tests (Köller et al.,
2019). For example, “The belief that intelligence is malleable has important consequences for
achievement and motivation” (De Castella & Byrne, 2015, p. 245). High school students who
believed intelligence was fixed had lower goals, more helplessness, and worse grades. The belief
also predicted self-handicapping, truancy, and disengagement. Renaud-Dubé et al. (2015)
argued, “An incremental theory of intelligence is associated with greater school persistence
intentions, as well as being motivated in an intrinsic manner” (p. 255). School leaders should
promote a student’s incremental intelligence belief and intrinsic motivations.
Expectancy-Value Theory
Achievement motivation is often studied using the expectancy-value theory, and the
student’s beliefs for specific subjects in this context are precursors of achievement-related
behaviors. Still, little research has been made into the task values that students assign (Guo,
2016). The validity of expectancy-value theory was found using a 10-point scale on adolescents
and confirmatory factor analysis on a three-factor scale (Kosovich et al., 2015). The scales can
identify which interventions will work best in each student and whether the interventions have
worked. Expectancy-value models are valuable because researchers can inherently account for
two important factors: the student’s expectancy of being successful in a task and the value of
engaging in that task (Barron & Hulleman, 2015). The cost of engaging is an important
55
motivator for the student, but the cost has gotten little attention because of questions over how to
quantify it.
Cultural Bias
Motivation researchers have focused on European American, middle class, and educated
people (Kumar et al., 2018). That research showed four keys to motivation: meaningfulness,
competency, autonomy, and relatedness. Kumar et al. (2018) stated, “Contemporary theories of
achievement motivation are primarily social-cognitive in nature” (p. 96); therefore, cultural
differences should be appreciated, valued, and understood. The authors argued for a learning
climate to support autonomy and mastery because simple interventions could have large effects
on outcomes, especially for first-generation students or people of color. However, those
interventions are not currently part of Tier I SWPBIS. A danger exists, though, that a culturally
responsive and relevant education can go too far the other way and ignore the existing body of
knowledge. Intersections do exist and should be leveraged.
The focus on a Western cultural setting in the research also means that “studies of student
goals toward academic and social achievement have been independent of studies of the results
themselves” (Liem, 2016, p. 37). The implications of the research for school functioning are real,
but the need exists to consider the meaning of goals and coordinate those goals through a cultural
lens. Student goals have additive effects on outcomes in line with Wentzel and Ramani’s (2016)
hierarchical model of goals. Mastery and performance goals are more adaptive than those for
avoidance, but social development goals have positive effects.
Summary
Jostens Renaissance (2020) is a structured incentive program begun by educators in 1988
as an intervention to improve school climate and school culture (Coyne, 2012). There is little
56
agreement on what those two terms mean, but school climate is generally regarded as about the
organizational side, while school culture applies to how students function within it (Rudasill et
al., 2018). Jostens Renaissance (2020) claimed that the program “contributes to increased
attendance and graduation rates, improved academic performance and behavior, higher teacher
retention rates, and a boost in overall school spirit” (para. 3). Buchanan (2015) reported that
more than 3,000 U.S. schools used the Jostens program in 2015, but there were more than 23,000
SWPBIS programs in place nationwide that same year (Kincaid & Horner, 2017).
Political pressure in the 1990s to increase school standards and accountability led to a
focus on behavioral interventions. The Individuals with Disabilities Education Act of 1997
enabled behavioral intervention programs to be expanded to include all students, and a school-
wide emphasis emerged with three tiers (Simonsen & Sugai, 2019). School district leaders found
SWPBIS appealing when facing mandated changes without any increase in funds, increased
testing and assessment, and the threat of sanctions if improvement did not occur within an
allotted time (Coyne, 2012).
SWPBIS are usually examples of applied behavioral theory and rely on operational
definitions and interventions derived from theory (Horner & Sugai, 2015). The Jostens
Renaissance (2020) program fits the Tier I, school-wide criterion. Faculty of the interventions
focus on behavioral management, which includes safety issues (e.g., bullying); classroom
management; social skills training; academic achievement or accommodation; early screening;
and attendance/dropout prevention. Faculty of Tier I programs focus on a small set of
expectations regarding student behaviors to influence school culture/school climate, which are
followed by reinforcements once students meet those expectations, as facilitated by the
collection and analysis of data. Expectations must be logical and integrated with all other aspects
57
of the school environment because all students will receive the intervention. However, a problem
for school districts is that almost any intervention can be seen as moderately successful,
depending on how the study is designed (Lazowski & Hulleman, 2016).
The implementation of these programs with fidelity is a challenge for most school
districts, and multiple studies have shown that whatever results produced are highly dependent
on fidelity. Several instruments are available to measure fidelity, but those instruments are not
interchangeable. Measures or weights vary, posing another quandary for administrators because
ensuring fidelity is a key to SWPBIS sustainability. Whether the programs work is often hard to
interpret. Political pressures drove the search for evidence-based solutions, but “the values and
assumptions underlying the evidence-based movement have much in common with those the
field initially challenged” (Beehler & Trickett, 2017, p. 455).
Most interventions were based on the social cognition theory, but most effects were
found in one’s ability to improve disciplinary issues, with little or no proven impact on academic
achievement. However, a consensus exists that better actions lead to better teaching. Covey
(2020) also modeled the ability for one to achieve success based on seven habits of success,
which applied to the leadership Renaissance class. Students could grow their self-learning skills
through motivational incentives, thereby increasing their chances of success or and growth as
people with skills to reach heightened levels of achievement. The potential link between
improved order and achievement fits with data gathered by Pas et al. (2019), who found
SWPBIS consistently had greater effects on student suspensions than on academic achievement
or other measures.
Competing explanations exist for the limited effectiveness of the Jostens Renaissance
(2020) and other SWPBIS programs, also largely based on social cognitive theory. These
58
programs include the need to promote student engagement, SES, the role of parents, the role of
peers, community environment, adolescent brain plasticity, the role of intrinsic motivation, and
cultural bias. Most of these competing explanations can provide simple interventions that can be
integrated within SWPBIS and are worth exploring. Some of these explanations are discussed
further in the next chapter on methodology.
59
CHAPTER 3
METHODOLOGY
Introduction
Educators created the Jostens Renaissance (2020) educational program in 1988 as one of
the first efforts at providing school districts with a structured program of behavioral
interventions. Creators of the program claim to influence school climate and school culture
positively by improving attendance and graduation rates, increasing academic achievement, and
promoting school spirit. For instance, other researchers have fused school climate and culture to
suggest the approach created better conditions for learning and teaching to occur effectively at
their schools (Dernowska, 2017). Researchers found that relationships were essential, and
teacher, student, and peer relationships mattered when creating a safe environment for students
to learn. California goes a step further by wedding social-emotional learning to climate/culture
on the basis that school officials saw an increase in students’ discipline and overall school
performances (Hough et al., 2017).
The purpose of this quantitative, quasi-experimental study was to examine the efficacy of
the Jostens Renaissance incentive program at one Title I high school in Southwest Florida.
Discipline cards represent the number of times a student is reprimanded for any behavioral issue
during school hours. Data from high school students who participated in the Jostens Renaissance
educational program were gathered from their school records before and after their participation
in the program to determine whether their GPAs increased. At the same time, absences and
discipline cards decreased after the implementation of the program.
This chapter provides a detailed description of the research methodology and research
design chosen for the study. This chapter also provides a detailed description of the population
60
and samples selected for the study. Data sources, validity, and reliability of data are discussed.
The chapter further provides a discussion of the step-by-step procedures of data collection and
data analyses considered for the study. Finally, this chapter provides a discussion of the ethical
considerations, limitations, and delimitations, as well as the summary of the methodology
considered in the study.
Statement of the Problem
It is unclear from existing studies whether the Jostens Renaissance program is effective in
improving the GPA and decreasing absences and discipline cards among high school students
(Campbell, 2016; English, 2019). Part of the rationale for conducting this program evaluation
study was to determine the program’s effectiveness. Jostens Renaissance (2020) educational
program is a proprietary incentive program developed by educators in 1988 as an intervention to
improve school climate and school culture (Coyne, 2012). There is little agreement on what
those two terms mean, but school climate is generally regarded as about the organizational side,
while school culture applies to how students function within it (Rudasill et al., 2018).
School climate is generally regarded as the effect of the school on stakeholders, such as
students, teachers, and administrators, as well as how they perform their functions (e.g., learning
and teaching; Rudasill et al., 2018). School culture refers to how the stakeholders work together
to form values and beliefs (Rudasill et al., 2018). Jostens Renaissance (2020) stated that the
program “contributes to increased attendance and graduation rates, improved academic
performance and behavior, higher teacher retention rates, and a boost in overall school spirit”
(para. 2). Buchanan (2015) reported that more than 3,000 U.S. schools used the Jostens program
in 2015, but there were more than 23,000 to SWPBIS programs in place nationwide that same
year (Kincaid & Horner, 2017). Political pressure in the 1990s to increase school standards and
61
accountability led to increased discussion and focused on behavioral interventions. However, a
problem for school districts is that almost any intervention can be seen as moderately successful,
depending on how the study is designed (Lazowski & Hulleman, 2016).
The implementation of these programs with fidelity is a challenge for most school
districts, and multiple studies have shown that whatever results produced are highly dependent
on fidelity. Several instruments are available to measure fidelity, but those instruments are not
interchangeable. In this study, fidelity is defined as how much the guidelines for implementing
the Renaissance program are followed during actual implementation. Measuring fidelity is
another issue for administrators because ensuring fidelity is key to SWPBIS sustainability.
Whether the programs work is often hard to interpret. Therefore, this researcher addressed the
gap in literature and practice by determining the effectiveness of the Jostens Renaissance (2020)
educational program in improving academic performances, attendance, and behaviors of
students.
Research Questions and Hypotheses
The following research questions and hypotheses were used to guide this study:
RQ1: How does the Jostens Renaissance education program impact the GPAs of students
after the implementation of participating in the program?
H
o1
: There is no increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
H
1
: There is an increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
RQ2: How does the Jostens Renaissance education program impact the student absences
after the implementation of participating in the program?
62
H
o2
: There is no decrease in student absences after the implementation of participating in
the Jostens Renaissance education program.
H
2
: There is a decrease in student absences after the implementation of participating in
the Jostens Renaissance education program.
RQ3: How does the Jostens Renaissance education program impact the discipline cards
of students after the implementation of participating in the program?
H
o3
: There is no decrease in the number of discipline cards after the implementation of
participating in the Jostens Renaissance education program.
H
3
: There is a decrease in the discipline cards after the implementation of participating in
the Jostens Renaissance education program.
This researcher of the evaluation of the Jostens Renaissance (2020) program used the
logic model as a design to approach the assessment of students’ GPA, attendance, and discipline
(Table 1).
Table 1
The Renaissance Logic Model
Inputs
Activities
Outputs
Outcomes
Impact
• Implement
Renaissance
Program
•Train Staff for
Leadership Class
(Curriculum)
•Time
•Funding
•Administrator
Support
•Leadership Class
•Student Rewards
•Rallies
•Prize
Giveaways,
Rewards Cards)
•Participants(students)
•Teacher
•Program Coordinator
•Administrator
(About 80 Or More
Students Attend The
Renaissance Leadership
Class)
•Students Are
Encouraged
•Motivated to
Improve In
School.
•At Least 95% Of
The Students Have
Improved GPA,
Attendance And
Discipline
•Increase GPA
• Lower
Attendance or
Disciplinary
Concerns
•All Students
Will Be
Successful In
School
63
The Renaissance Logic Model
The logic model is a graphic showing the inputs, activities, outputs, and short- and long-
term goals of a program (Fitzpatrick et al., 2011). The logic model is a visualization of how the
input of activities (the Renaissance leadership class, student reward rallies, prizes and giveaways,
etc.) and the output (student participation, teacher/program coordinator, and school
administrator) resulted in the outcome desired of the program and its impact. The input was
processed through the activities to have an output. The output influenced the outcome of the
program. Using a logic model, the results from the study showed detailed and vital information
to a school administrator to make informed decisions on whether the input of the Jostens
Renaissance (2020) program was evident to positive changes due to the outcomes of students’
GPAs, attendance, and discipline.
Student data were collected from actual records of the school for school years 2018-2019
and 2019-2020, which constituted the school years before and after the implementation of the
Jostens Renaissance (2020) education program, respectively. The data for student records were
in a computerized program used by the high school, titled FOCUS (2020). FOCUS is a web-
based platform used by school district leaders to import and export secure data related to
students, educators, and school employees. The FOCUS software data were exported to a
Microsoft Excel spreadsheet containing student records for each year.
Data collected in the study included demographic characteristics, such as gender, race,
ethnicity, and grade level, GPAs, days absent, and the number of discipline cards for the
semester. The GPA was the overall average of a student's scores in all subjects or courses for the
semester. The days absent was the number of days a student had been absent for the semester.
The discipline card variable was the number of behavioral disciplinary violations committed by a
64
student for the semester. A pretest/posttest study was conducted to compare the before and after
implementation data of participants. The researcher used the pretest/posttest design to determine
whether there was a difference in the outcome of GPAs, days absent, and disciplinary cards
received by a student over a specific period. More details are discussed about the results for this
study in Chapter 4.
Research Methodology
A quantitative approach was appropriate for a study on the effectiveness of the input of
the Jostens Renaissance (2020) education program because the variables of interest were
expressed in numerical values (Sherapis et al., 2017). All the output variables considered in the
stud,y such as the GPA, days absent, and disciplinary cards received, were numerical. The focus
of the study was to determine whether there was a difference in the output of GPA, days absent,
and discipline cards received by the student before and after the implementation of the input of
the Jostens Renaissance (2020) educational program. Therefore, a quantitative, as opposed to a
qualitative, study was chosen.
Generalizations of the findings can be enhanced using a pretest/posttest design, which
focuses on a comparison of datasets for two data collection periods. Moreover, the design allows
the collection of data from a large sample size (Sherapis et al., 2017). Additionally, the collection
of student data, as expressed in numbers, allows the variables of interest to be easily quantified
for statistical analysis (Sherapis et al., 2017). The differences or levels of changes can be
determined using quantitative methods, as well as inferences and statistical relationships between
variables and other covariates that can be observed (Sherapis et al., 2017). A quantitative
methodology was selected because the researcher wanted to confirm existing theories, and the
65
proposed study was deductive (see Atieno, 2009). Qualitative researchers require observational
descriptions and in-depth interviews (Atieno, 2009).
Researchers often use qualitative methodologies to develop hypotheses and theories,
whereas researchers using quantitative methods begin with theories and hypotheses that they test
or examine. The qualitative approach is inappropriate when testing a theory or relationships
between variables (Merriam & Tisdell, 2015). Therefore, quantitative methods were more
appropriate for the current study because the purpose was to collect data from a large sample of
participants to test and possibly extend existing theories about the effectiveness of the
intervention in improving academics, attendance, and behaviors of students.
Research Design
A quantitative, quasi-experimental, pretest/posttest design was used to determine the
effectiveness of the input of the Jostens Renaissance (2020) program in improving students’
GPAs and decreasing absences and discipline cards among high school students in Southwest
Florida. A quasi-experimental design showed whether the outcome of the dependent variables of
GPA, days absent, and discipline cards had changed as effects of the input of the Jostens
Renaissance educational program (see Sherapis et al., 2017). A true experimental design could
not be implemented because students could not be asked to participate in the intervention
programs within an academic institution (see Sherapis et al., 2017). Therefore, a random
assignment of participants to controlled and experimental groups was impossible.
The correlation was inappropriate because a cause and effect conclusion could not be
made by employing this design, given that a correlation implied a relationship (Schweizer &
Furley, 2015), which was not sought in this examination. A quantitative quasi-experimental
study using a pretest/posttest design was the most appropriate to investigate the effect of an
66
intervention program on the academic successes, attendance, and behaviors of high school
students. The assignment of participants to control (unstructured teaching approach) and
treatment (structured teaching approach) groups was impossible because the data collection
occurred in an academic environment. Therefore, pretest and posttest data were collected to
compare whether there was an increased outcome in academic achievement, as measured using
increased GPA, decreased days absent, and decreased discipline cards received by the students.
Population and Sample Selection
The target population for the study included high school students within an academic
institution that implemented the Jostens Renaissance (2020) education program in School Year
2019-2020. The sample included data from male and female students in Grades 9 through 12.
Secondary data, such as students’ ethnicities, races, and grade levels, were obtained because the
focus of the research was to collect and gather student data. Therefore, actual school records
were accessed with the written permission of the school administrator. The school had a total of
79 students from Grades 9 to 12 who participated in the sample size.
A power analysis was conducted to determine the minimum sample size necessary for the
study. The power analysis involved several factors that included the effect size, significance
level, and type of analysis. Considering a medium effect size of .5, a significance level of .05, a
power of 80%, and a paired samples t-test, a sample of at least 34 participants were necessary for
the study.
Data Source
Students who enrolled in a leadership development course were asked to participate in
the Renaissance program through classes that were 80 minutes per session, following a block
scheduling system. The faculty trained specifically in implementing the Renaissance educational
67
program handled the classes. The Jostens Renaissance (2020) program was created to help
educators provide opportunities for students to practice proper discipline and improve class
attendance. The Renaissance program was a product of a leadership development project focused
on boosting the school’s culture with the excitement, appreciation, and uplifting energy of
awards and accolades to inspire underachieving students and overworked staff.
A conference on the Renaissance program was attended in July 2019 to craft the program
for the school. The program was implemented in School Year 2019 to 2020, wherein the
Renaissance class was involved in brainstorming and planning events to motivate both students
and staff. The goal of the program was to teach students the philosophy and strategies of the
Renaissance while learning leadership abilities. The objective of the course was to apply
leadership skills in students’ activities within the campus. The course was divided into eight
sections. Each section addressed an important element in developing Renaissance and was
combined with one of the 7 Habits of Highly Effective Teens (see Covey, 2020). Each class
teacher must invest in a classroom set of the 7 Habits of Highly Effective Teens by Covey (2020)
because the content of each session was built based on the book. The seven habits include the
following:
1. Be proactive.
2. Begin with the end in mind.
3. Put first things first.
4. Think win-win.
5. Seek first to understand, then to be understood.
6. Synergize.
7. Sharpen the saw. (Covey, 2020, p. 20)
68
Student participants were encouraged to read Covey (2020). The Renaissance program
used a rewards system to encourage students to practice proper behaviors and attendance, which
educators hoped would later positively impact grades, as well. The classes involved the use of
computers, as well as pen and paper for activities. The Renaissance leadership class was
conducted based on steps, as discussed in the following subsections.
Step 1: Scheduling
The high school ran on a block scheduling format where students were in class for 80
minutes per session on an alternating schedule where they would see the same teacher and or
Renaissance coordinator for 2 days or 3 days in a given week. Therefore, students completed a 5-
day cycle of a lesson in 2 weeks. Thus, lesson plans were created on a 5-day plan to be executed
by the end of 2 calendar weeks.
Step 2: Duration
The class ran on a block schedule. This course ran (two) quarters, which equaled a
semester. Therefore, the entire course lasted approximately 18 weeks. The Covey (2020) model
had seven habits; each habit had 5 days of lessons, with four habits per quarter, and all seven
habits were covered in one semester.
Step 3: Lesson Plan Structure Per Class Period
Bell ringers started the class. Bell ringers refer to short, concise assignments that are
available to the student upon entering the classroom. These short assignments consisted of either
a short-written response or multiple choice questions. The purpose of the bell ringers was to
engage students and help them refocus on the learning that would happen in the Renaissance
class versus their prior classes. Faculty used bell ringers to teach students to become organized
quickly and be ready to learn as soon as they entered the class. The bell ringer in the Renaissance
69
leadership class served several purposes, such as retrieval of background knowledge of new
information, a reflection of learning from previous class lessons, and synthesis of information
learned thus far. A teacher (I do), student (You do), teacher and student (We do), and or
independent learning center occurred each class.
I Do
After bell ringers were conducted, the coordinator or teacher of the class presented new
information to students, in the case of the leadership class—this section of the lesson introduced
one of the seven habits of the Covey (2020) model. The teacher would incorporate readings,
short films, guest speakers, and more as part of the deliverance of new information (teacher-
centered).
We Do
In this section of the lesson, the teacher-led the lesson but encouraged the students to
engage in active learning as opposed to just receiving information. Students were active
participants throughout the lesson. In the case of the leadership class, in this section of the
lesson, students and teachers engaged in meaningful class discussions regarding the habit that
they learned. This section of the lesson gave way for students to access their prior knowledge
about the habit while sharing anecdotal stories with their peers. They applied the information
taught to their everyday lives (teacher-student centered).
You Do it Together
In this section of the lesson, students worked collaboratively on various tasks where
accountability was required of all group members. These activities ranged from team building
activities to project/problem-based projects. This section of the lesson taught students,
70
independently of the habit being discussed, the need to work well with others toward a common
goal (student-centered).
You Do It
In this section of the lesson, students worked independently on any given task. Students
were asked to incorporate what they had been working on with their teacher, and then with their
peers while personalizing it to themselves and their everyday lives. In this section of the lesson,
the activities ranged from individual reflection pieces to individually driven project management.
This section also included the assessment piece of the program. These assessments required the
students to self-reflect on their interpretations of the habits and how well they believed that they
had exhibited the skills reflected in the habit. This piece was perhaps the most important aspect
of the leadership class because the students could give and receive constructive feedback on their
behaviors independent of the influences of others (independent learning). Appendix A contains a
syllabus for the Renaissance course in leadership.
The Jostens Renaissance (2020) education program involved awarding students with a
Renaissance card if they received a GPA of 2.00 or higher, which was the required GPA for high
school graduation; they had no more than three unexcused absences, and they had no disciplinary
referrals for the semester. Students would earn color-coded Renaissance cards based on the
GPAs that they received after completing a semester at the school. Once they earned the
Renaissance card, students could attend reward pep-rallies and receive prizes and giveaways
based on the gold, silver, and blue honor Renaissance cards that they received for their academic,
disciplinary, and attendance achievements. Students received a reward or prize if they did not get
a discipline card, which meant they were not reprimanded for any behavioral issues during the
duration of the course. The school had partnered with restaurants and merchants where students
71
could use free vouchers and coupons to receive discounts and free items (see Appendix B for
business letter text). Students were encouraged to plan events to demonstrate the leadership skills
presented in class. Students' records were reviewed and analyzed to determine whether their
grades, attendance, and discipline records qualified for a Renaissance card. Students got a chance
to earn a card each semester. Thus, the Renaissance program requirements started over after each
semester.
Permission was sought from the school administrator to obtain a copy of students' data
containing information on their demographic characteristics, GPAs, days absent, and discipline
cards before and after the implementation of the Jostens Renaissance (2020) educational
program. The Jostens Renaissance educational program was implemented for one semester. The
GPAs, days absent, and the number of discipline card records of students for a semester were
gathered as pretest data. These pretest data were used as data before implementation. After, data
for another semester were gathered as posttest data. These posttest data were used as data after
the implementation. Pretest and posttest data were compared to determine whether there had
been an improvement in GPAs, days absent, and the number of discipline cards of students
selected for the study. The data gathered in the study were based on actual student records from
the identified high school in the study. However, only de-identified data were obtained. These
data did not contain names, student numbers, and any information that could identify the
individual student.
Validity
Research validity refers to the truthfulness of the inferences made in a study. This study
was considered quasi-experimental research, so external validity should be the primary focus.
However, internal validity must also be maintained to determine pretest and posttest differences
72
(see Christensen et al., 2014). Statistical conclusion validity centered on a person’s ability to
infer that when participants were exposed to the intervention of the Renaissance program,
corresponding variation occurred in the dependent variables of students’ GPAs, attendance, and
discipline cards. Statistical significance signified the probability that the difference observed was
not due to chance alone (see Christensen et al., 2014). A significance level of .05 was used for all
analyses to ensure the trustworthiness of the findings. Moreover, actual records of students were
used as the data source to ensure that data were collected using the same procedures and given
the same measuring instruments, such as exams, attendance records, and disciplinary records.
Data were collected promptly (from August 2019 to January 2020) to ensure the relevance of
findings.
Reliability
The researcher used data from the actual records of students. Therefore, reliability was
ensured because data were collected using a standard measure used in the school. The outcome
of GPAs was measured as the average student scores on all classes and activities within a
semester. The days absent was gathered from the attendance records of each student to determine
the outcome. Attendance was taken daily. Students who were absent during the day were marked
absent in the records. At the end of the semester, the sum of the days absent was recorded. Also,
discipline cards were given to students who had behavioral issues at the school for the semester
that the study was conducted. The behavioral issues were based on the guidelines in the student
handbook. The reliability of the measures was acceptable, given that actual data were used for
the study.
73
Data Collection and Management
Before collecting data, approval from the National Louis University Institutional Review
Board (IRB) was sought. During the IRB approval process, permission from the school
administrator of the high school was obtained. After IRB approval was granted and the IRB
application number was received (NLU IRB #ER00771), a formal letter of permission was sent
to the academic institution to seek approval on gathering data before and after the
implementation of the Jostens Renaissance (2020) educational program. The input of students’
data included demographic characteristics, such as gender, grade level, race and ethnicity, GPA,
number of days absent for the semester, and the number of discipline cards received for the
semester. Pretest and posttest data were collected to allow for comparison. These data were
obtained electronically (to avoid a human error that could result from transferring data). All data
collected were de-identified to ensure anonymity and confidentiality of students.
Data Analyses Procedures
The goal of this study was to determine whether the input of the Jostens Renaissance
(2020) at a Florida high school displayed a statistically significant difference in the outcome of
(a) GPA, (b) attendance, and (c) discipline. The data collected in the study were prepared for
analysis using Statistical Package for the Social Sciences (SPSS; Version 24.0). The
demographic characteristics of participants were presented using frequencies and percentages. In
contrast, study variables, such as GPA scores, number of days absent, and number of discipline
cards, were presented using measures of central tendencies (e.g., mean, standard deviation, and
range values). Pretest and posttest data were compared using paired samples t-test to determine if
the input of the Jostens Renaissance program showed any differences in the outcomes of
students’ GPAs, attendance, and discipline.
74
The dependent variables included the pretest and posttest data for the outcome of GPAs
for the first research question. The dependent variables included the pretest and posttest data
outcome for the number of days absent for the second research question. The dependent
variables included the pretest and posttest data outcome for the number of discipline cards
received during the quarter for the third research question. A significance level of .05 was used
for all analyses.
Ethical Considerations
The study was guided by the IRB process of the National Louis University, along with
the following ethical principles based on the American Counseling Association Code of Ethics
(2014). These ethical principles were used as guidelines for research to reduce all potential harm
and risks to human participants. The rights of participants were respected throughout the study.
Participation in the study remained completely voluntary. Voluntary participation was ensured
by seeking permission from the school administrator to provide de-identified student data for the
study. The school administrator randomly selected a list of students included in the study. All
student data were de-identified, thus ensuring the anonymity and confidentiality of the students.
The confidential nature of the collected data meant participants should not be concerned
over any negative outcomes if data were breached. Overall, participation in the study was
optional, all data were collected with consent from the academic institution administrator, and no
new data were collected in the study. All data collected were based on records from the school
before and after implementation of the Jostens Renaissance (2020) education program.
No possible risk of privacy or confidentiality issues during the data-collection process
was expected. Only this researcher had access to the data collected. Data collected will be stored
for 3 years after the study has been published before being destroyed. Data were encrypted,
75
transmitted, secured, and stored in a password-protected computer. No hard copies were used in
the study.
Limitations and Delimitations
Several limitations existed in the present study. The first limitation was that the findings
of this study were limited to the data collected from the specific high school considered in the
study. Therefore, the results of the study would only generalize to the population of students
within the academic institution. There might be varying ways of implementing the program;
thus, the results might not apply to other institutions implementing the Jostens Renaissance
(2020) educational program.
A second limitation was that the study was limited to the data collected in the school. No
additional data were collected in the study. The data measurements were already established
within the school. Therefore, the interpretation of results was based on the outcome measures of
academic performance, attendance, and discipline cards of the students in the study.
A third limitation was that the data collected only showed one semester in the school
year, instead of a full calendar school year. A full school year would have included two full
semesters. Therefore, the study was only specific to one semester instead of multiple semesters
of data to measure the outcome of the GPA, attendance, and discipline among the students in the
program over time.
The following delimitations were adopted for the current study. First, the data collection
was limited to the specific school considered in the study. Students from other schools were not
included because there might have been differences in how the program was introduced and
implemented. Another delimitation was that the research design might show statements about
differences, but causal comparisons were limited because the study was not a true experiment.
76
Summary
The purpose of this quantitative, quasi-experimental study was to examine the efficacy of
the Jostens Renaissance incentive program at one Title I high school in Southwest Florida. A
sample of at least 79 high school students was employed in the study. Data from actual school
records were obtained to gather data for the pretest included GPAs, the number of days absent,
and the number of discipline cards. Data for the posttest included GPA, number of days absent,
and number of discipline cards. Gathered data were analyzed using SPSS to obtain statistical
data, which included paired samples t-tests to address the research questions. A significance
level of .05 was used for all analyses in this study.
77
CHAPTER 4
RESULTS
Introduction
The purpose of this quantitative, quasi-experimental study was to examine the efficacy of
the Jostens Renaissance incentive program at one Title I high school in Southwest Florida. The
research questions addressed whether the Jostens Renaissance (2020) program influenced
students’ GPAs, number of absences, and the number of discipline cards. The target population
for this study included high school students who participated in the Jostens Renaissance
program.
Participants comprised students in Grades 9 to 12 at a Southwest Florida High school
who had completed participation in the Jostens Renaissance (2020) program. Participants signed
up for a course on leadership development and participated in the Jostens Renaissance program
during the course. Data from school reports were gathered for this study. After gathering the
reports for the 79 students who participated in the study, data were collected a semester before
and a semester after the implementation of participating in the Jostens Renaissance program to
determine if there were changes in students’ GPAs, the number of absences, and the number of
discipline cards. Discipline cards represented the number of times a student was reprimanded for
any behavioral issue during school hours. Data from high school students who participated in the
Jostens Renaissance educational program were gathered from their school records before and
after their participation in the program to show whether their GPAs increased. At the same time,
absences and discipline cards decreased after the implementation of the program.
The following research questions and hypotheses were used to guide this study:
78
RQ1: How does the Jostens Renaissance education program impact the GPAs of students
after the implementation of participating in the program?
H
o1
: There is no increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
H
1
: There is an increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
RQ2: How does the Jostens Renaissance education program impact the student absences
after the implementation of participating in the program?
H
o2
: There is no decrease in student absences after the implementation of participating in
the Jostens Renaissance education program.
H
2
: There is a decrease in student absences after the implementation of participating in
the Jostens Renaissance education program.
RQ3: How does the Jostens Renaissance education program impact the discipline cards
of students after the implementation of participating in the program?
H
o3
: There is no decrease in the number of discipline cards after the implementation of
participating in the Jostens Renaissance education program.
H
3
: There is a decrease in the discipline cards after the implementation of participating in
the Jostens Renaissance education program.
This chapter provides a discussion of the data collection conducted for the study. This
chapter also describes the samples gathered in the study. The results of the statistical analyses
conducted to address the research questions in the study are discussed. This chapter ends with a
summary of the key findings of the study.
79
Data Collection
Permission was sought from the school administrator to obtain a copy of students' data
containing information on their demographic characteristics, GPAs, days absent, and discipline
cards before and after the implementation of the Jostens Renaissance (2020) educational program
(Appendix B). The Jostens Renaissance educational program was implemented for one semester.
The data for student records were in a computerized program used by the high school, titled
FOCUS (2020). FOCUS is a web-based platform that leaders of school districts can use to
import and export secure data related to students, educators, and school employees. The
researcher used the FOCUS software to export data to a Microsoft Excel spreadsheet containing
student records for each year. All data collected were placed in a Microsoft Excel spreadsheet
and then transferred to SPSS Version 24.0 for analyses.
Students’ GPAs, days absent, and the number of discipline card records for a semester
were gathered as pretest data. The pretest data were used as data from before program
implementation. After, data for another semester were gathered as posttest data. The posttest data
were used as data after completion of the program implementation. Pretest and posttest data were
compared to determine whether there was a positive or negative difference in GPAs, days absent,
and the number of discipline cards of students. The data gathered in the study were based on
actual records from high school. However, only de-identified data were obtained. The data did
not contain names, student numbers, or any information that could identify any study
participants.
Intervention
Students who signed up for a leadership development course were asked to participate
voluntarily in the Renaissance program through classes that were 80 minutes per session,
80
following a block scheduling system. Students did not receive any incentive from participating in
the program. Faculty trained specifically in implementing the Renaissance education program
taught the classes. Students who wanted to participate in the program received an application that
they completed participating in the Jostens Renaissance (2020) program voluntarily (Appendix
C). The Jostens Renaissance program was started to practice proper discipline and encourage
attending classes. Faculty of the Renaissance program used a rewards system to encourage
students to practice proper behavior and attendance, which should impact grades later. The
classes involved students using computers, as well as pen and paper for activities. The content of
each session was built based on Covey (2020). The seven habits include the following:
1. Be proactive.
2. Begin with the end in mind.
3. Put first things first.
4. Think win-win.
5. Seek first to understand, then to be understood.
6. Synergize.
7. Sharpen the saw. (Covey, 2020, p. 20)
Faculty of the Jostens Renaissance (2020) educational program awarded students with a
renaissance card if they got a GPA of 2.00 or higher, which was the required GPA for high
school graduation; had no more than three unexcused absences; and had no disciplinary referrals
for the semester. The renaissance cards were color-coded GPA cards with prizes and giveaways
for students who reached the gold, silver, and blue honor cards academically. Students also
received a reward or prize if they did not get a discipline card, which meant they were not
reprimanded for any behavioral issues during the duration of the class. Leaders of the school had
81
partnered with restaurants and merchants where students could avail discounts and free items.
The faculty encouraged students to plan events to demonstrate the leadership skills presented in
class. Faculty assessed students' records to determine whether their grades, attendance, and
discipline records qualified for a renaissance card. Students had a chance to earn a card each
semester. Thus, the requirements started over after each semester.
Results
Data were inputted and cleaned in SPSS Version 24.0. Samples with missing values were
excluded from the analyses. A total of 79 participants were included in the implementation of the
Jostens Renaissance (2020) program. Participant data were collected before and after the
implementation of the program.
Table 2 presents the demographic characteristics of the participants. Among the 79
participants, 45 participants identified as female (57.0%), while 34 participants identified as male
(43.0%). Regarding race, 54 participants identified as Black (68.4%), 23 participants identified
as White (29.1%), and two participants identified as biracial (2.5%). Regarding ethnicity, 36
participants identified as African American (45.6%), 17 participants identified as Haitian
(21.5%), 16 participants identified as Hispanic (20.3%), and seven participants identified as
Caucasian (8.9%). The grade levels of the participants ranged from Grades 10 to 12. A total 41
participants were in Grade 12 (51.9%), 24 participants were in Grade 11 (30.4%), and 14
participants were in Grade 10 (17.7%).
82
Table 2
Frequencies and Percentages of Participants' Demographic Characteristics (N = 79)
Category
Frequency
Percent
Gender
Female
45
57.0
Male
34
43.0
Total
79
100.0
Race
Biracial
2
2.5
Black
54
68.4
White
23
29.1
Total
79
100.0
Ethnicity
African American
36
45.6
Caucasian
7
8.9
Haitian
17
21.5
Hispanic
16
20.3
Jamaican
1
1.3
Native American
2
2.5
Total
79
100.0
Grade level
10
14
17.7
11
24
30.4
12
41
51.9
Total
79
100.0
Before conducting the hypothesis tests, Kolmogorov-Smirnov tests were conducted to
determine whether the dependent variables followed the normal distribution. These data were
analyzed using SPSS Version 24.0. If the data followed the normal distribution, a paired samples
t-test was conducted. If the data did not follow the normal distribution, a Wilcoxon Signed-Rank
test was conducted.
Table 3 shows the descriptive statistics and results of the Kolmogorov-Smirnov tests.
After inputting the data in SPSS Version 24.0, the results showed an increase in GPA was
observed from before (M = 3.56, SD = .76) to after (M = 3.92, SD = .83). A decrease in days
absent was observed from before (M = 9.86, SD = 8.91) to after (M = 5.99, SD = 7.19). A
decrease in the number of discipline cards was also observed from before (M = 1.29, SD = 1.84)
to after (M = .18, SD = .42). The normality test showed that variables of days absent and
discipline cards were abnormally distributed, while the GPA variables were normally distributed.
Therefore, a parametric test was appropriate for testing the differences between the GPA from
83
before and after the Jostens Renaissance (2020) program, while nonparametric tests were
appropriate for testing the differences between days absent and discipline cards from before and
after the Jostens Renaissance program. A paired-samples t-test was used to test the differences in
GPAs, while Wilcoxon signed-rank tests were used to test the differences in days absent and
discipline cards.
Table 3
Kolmogorov-Smirnov Normality Test of Study Variables
Category
GPA:
Before
Days absent:
Before
Discipline
card: Before
GPA:
After
Days
absent:
After
Discipline
card:
After
N
79
79
79
79
79
79
Normal parameters
a,b
M
3.56
9.86
1.29
3.92
5.99
.18
SD
.76
8.91
1.84
.83
7.19
.42
Most extreme
differences
Absolute
.142
.177
.353
.056
.203
.500
Positive
.142
.177
.353
.056
.191
.500
Negative
-.103
-.135
-.242
-.045
-.203
-.335
Kolmogorov-Smirnov Z
1.258
1.576
3.141
.501
1.801
4.446
Asymp. sig. (2-tailed)
.084
.014
0.000
.963
.003
0.000
a. Test distribution is Normal.
b. Calculated from data.
A paired-samples t-test was conducted to test the first null hypothesis that stated that
there was no increase in GPAs of high school students before and after the implementation of the
Jostens Renaissance (2020) educational program. Table 4 shows the result of the analysis. The
results showed that there was a significant difference between the before and after GPA mean
scores (t = -12.621, p-value < .05). The negative mean difference indicated that the before scores
were significantly lower than the after scores (M difference = -.36, SD = .25). Therefore, the
results showed that there was a significant increase in GPA scores after implementing the Jostens
Renaissance program. There was enough evidence to reject the first null hypothesis that stated
that there was no increase in GPAs of high school students after the implementation of
participating in the Jostens Renaissance education program.
84
Table 4
Paired Samples t-Test Between Grade Point Average Scores Before and After the
Implementation
Paired differences
T
df
Sig. (2-
tailed)
M
SD
SE
95% Confidence
Interval of the
Difference
Lower
Upper
GPA difference
(before-after)
-.36127
.25442
.02862
-.41825
-.30428
-12.621
78
.000
A Wilcoxon signed-rank test was conducted to test the second null hypothesis that stated
that there was no decrease in days absent of high school students before and after the
implementation of the Jostens Renaissance (2020) educational program. Table 5 shows the result
of the analysis. The results showed that there was a significant difference between the before and
after days, absent mean rank scores (Z = -4.396, p-value < .05). The positive mean rank
difference indicated that the before scores were significantly higher than the after scores.
Therefore, the results showed that there was a significant decrease in days absent mean rank
scores after implementing the Jostens Renaissance program. There was enough evidence to reject
the second null hypothesis that stated that there was no decrease in student absences after the
implementation of participating in the Jostens Renaissance educational program.
Table 5
Wilcoxon Signed-Rank Test Between Days Absent Before and After the Implementation
N
M rank
Sum of ranks
Z
p-value
Days absent
(before - after)
Negative ranks
58
a
35.25
2044.50
-4.396
0.000
Positive ranks
13
b
39.35
511.50
Ties
8
c
Total
79
a. Days Absent - After < Days Absent – Before
b. Days Absent - After > Days Absent – Before
c. Days Absent - After = Days Absent – Before
85
A Wilcoxon signed-rank test was conducted to test the third null hypothesis that stated
that there was no decrease in discipline cards of high school students before and after the
implementation of the Jostens Renaissance (2020) educational program. Table 6 shows the
results of the analysis. The results showed that there was a significant difference between the
before and after discipline cards mean rank scores (Z = -4.858, p-value < .05). The positive mean
rank difference indicated that the before scores were significantly higher than the after scores.
Therefore, the results showed that there was a significant decrease in discipline cards mean rank
scores after implementing the Jostens Renaissance program. There was enough evidence to reject
the third null hypothesis that there was no decrease in the number of discipline cards after the
implementation of participating in the Jostens Renaissance educational program.
Table 6
Wilcoxon Signed-Rank Test Between Discipline Cards Before and After the Implementation
N
M rank
Sum of
ranks
Z
p-value
Discipline card
(before - after)
Negative ranks
31
d
19.68
610.00
-4.858
0.000
Positive ranks
4
e
5.00
20.00
Ties
44
f
Total
79
d. Discipline Card - After < Discipline Card – Before
e. Discipline Card - After > Discipline Card – Before
f. Discipline Card - After = Discipline Card – Before
Summary
The purpose of this quantitative, quasi-experimental study was to examine the efficacy of
the Jostens Renaissance incentive program at one Title I high school in Southwest Florida. A
total of 79 participants were included in the implementation of the Jostens Renaissance (2020)
program. A paired-samples t-test was conducted to compare the before and after GPA scores.
Wilcoxon signed-rank tests were conducted to compare the before and after days absent and
discipline cards. The results showed that there is a significant increase in GPA and significant
86
decreases in days absent and the number of discipline cards before and after the implementation
of the Jostens Renaissance program. Therefore, there was enough evidence to reject the null
hypotheses posed in the study.
87
CHAPTER 5
CONCLUSIONS
Introduction and Summary of the Study
Leaders of a multitude of U.S. schools have implemented incentive programs to address
reducing academic test scores, improving student behaviors, and improving attendance within
schools (Herman et al., 2016). As state requirements for academic achievement continue to
become more rigorous, school administrators and counselors strive to create positive school
environments through programs, such as the Jostens Renaissance (2020) program. Faculty use
the Jostens Renaissance educational program as an intervention to improve school climate and
school culture (Coyne, 2012). However, the efficacy of the Jostens Renaissance (2020) program
remained understudied, especially in secondary education. Therefore, the current researcher
explored understanding the program and whether its implementation in schools correlated with
positive academic outcomes for students. Therefore, the purpose of this quantitative, quasi-
experimental study was to examine the efficacy of the Jostens Renaissance incentive program at
one Title I high school in Southwest Florida.
This researcher examined the efficacy of the Jostens Renaissance (2020) educational
program on raising the GPA among secondary students by using a quasi-experimental pretest-
posttest design. Data from students’ GPAs, number of disciplinary issues, and attendance were
collected from the 2018-2019 school year to establish pretest data for all 79 students before the
students were in the Renaissance program. Posttest data were collected from the 2019-2020
school year for the 79 students after the implementation of the Renaissance program and
compared to the pretest data. Additionally, associated demographic data were gathered, including
88
students’ genders, races, ethnicities, and grade levels. Demographic data were gathered to
ascertain if differences existed among student groups.
Once the GPA, attendance, and discipline records were collected, the researcher
implemented all data in a Microsoft Excel spreadsheet. The records before the implementation of
the Renaissance program for a semester and after the implementation of the program for a
semester were then calculated to determine the averages between the two sets of semesters.
Using SPSS, a paired samples t-test was used to address the first and third research questions.
Additionally, a Wilcoxon signed-rank test was used to address the second research question.
This chapter is the final chapter of the study. This chapter contains a summary of the
study findings. Then, the researcher discusses results and conclusions within the context of
existing literature to provide context for study findings. Finally, this chapter concludes with a
discussion of empirical, theoretical, and practical implications for future practices and
recommendations for future researchers on student incentive programs.
Summary of Findings
The researchers of this program evaluation identified whether students’ GPAs,
attendance, and discipline were affected after the implementation of the Jostens Renaissance
(2020) program at a Florida high school. Data from high school students who participated in the
Jostens Renaissance education program were gathered from their school records before and after
their participation to determine whether their GPAs increased and whether the students’ absences
and discipline cards decreased after the implementation of the program.
A total of 79 participants were included in this study. The sample contained varied
participant demographics. Most participants (57%) identified as female, while the other 43%
identified as male. When the races of participants were examined, the overwhelming majority
89
(68.4%) of participants identified as Black, while approximately 29% of participants identified as
White, and 2.5% of participants identified as biracial.
In addition to the sample being varied in genders and races, sample participants varied in
grade levels. When the researcher compiled descriptive statistics, all participants came from
Grades 10, 11, or 12. Most participants, approximately 52%, were enrolled within the 12th grade,
with the next largest cohort representing the 11th grade—the smallest group of participants,
approximately 18%, composed students from Grade 10.
Once students’ data statistics were compiled, the researcher examined the pretest/posttest
data gathered from the FOCUS web-based platform regarding GPA, the incidence of disciplinary
issues, and the number of student absences. The following research questions guided this study:
RQ1: How does the Jostens Renaissance education program impact the GPAs of students
after the implementation of participating in the program?
RQ2: How does the Jostens Renaissance education program impact the student absences
after the implementation of participating in the program?
RQ3: How does the Jostens Renaissance education program impact the discipline cards
of students after the implementation of participating in the program?
The first research question was concerned with how the Jostens Renaissance (2020)
education program impacted the GPAs of students after participating in the implementation of
the program. Therefore, a paired samples t-test was used to test the difference in GPAs before
and after the implementation of the program. Once the t-test was completed, data indicated a
statistically significant increase in students’ GPAs after participation in the Jostens Renaissance
education program. Thus, the null hypothesis was rejected.
90
Correlation between enrollment in incentive programs and higher academic achievement
is reinforced by previous research by Gage et al. (2017) and Konold et al. (2018). Gage et al.
(2017) found academic achievement was higher among students in schools where leaders
implemented incentive programs than students in educational settings where leaders did not
employ such a program. Pas et al. (2019) also found that there might be a possible link between
academic achievement and decreased disciplinary issues, which was a focus of the Jostens
Renaissance educational program. As students were placed in environments where discipline is
less likely an issue, academic achievement increased (Pas et al., 2019). Additionally, Konold et
al. (2018) found that incentive programs led to increased student participation in classroom
activities, thereby indirectly improving academic achievements among students enrolled in
academic incentive programs. Thus, findings from this study indicated that participation in the
Jostens Renaissance (2020) educational program was congruent with earlier findings.
The second research question focused on how participation in the Jostens Renaissance
(2020) educational program impacted the number of absences for students. The researcher
completed a Wilcoxon signed-rank test to determine if students’ absences improved after the
implementation of the program. The results indicated there was a significant improvement in
students' absences after participating in the Jostens Renaissance educational program. As
students were less likely to be absent after participation in the Jostens Renaissance education
program, the null hypothesis was rejected.
According to Jostens Renaissance (2020), one of the main tenets of the program is to
increase attendance while decreasing student absence and truancy. Jostens Renaissance (2020)
hypothesized that when students were absent less, that academic achievement would increase.
91
Thus, results aligned with the purpose of the Jostens Renaissance (2020). Additionally, the
results of this study were bolstered by previous research completed by Coyne (2012).
Coyne (2012) found that participation in incentive programs could create school
environments where students were engaged and vested in their success. Findings of Coyne
(2012) were like those of De Castella and Byrne (2015), which showed that when students were
truant or otherwise disengaged within the classroom, academic achievements were reduced, and
disciplinary issues increased. When students were more engaged, students were less likely to be
absent or truant from class, increasing the overall rates of attendance (Coyne, 2012).
The final research question focused on how the Jostens Renaissance (2020) educational
program impacted the discipline rates of students after participating in the implementation of the
program. A t-test was completed to answer this research question. The results indicated that there
was a significant difference in discipline rates for students before and after participation in the
Jostens Renaissance educational program. Specifically, discipline rates declined after completion
of the program, meaning that the Jostens Renaissance education program was useful in
mitigating disciplinary issues within educational settings. Thus, as with the first two research
questions, the null hypothesis was rejected.
Coyne (2012) and Pas et al. (2019) indicated that participation in incentive programs
could mitigate disciplinary issues within educational settings. With rewards given to students
who do not get disciplinary infractions, Jostens Renaissance (2020) educational programs and
other incentive programs often motivate students to behave more often and reduce incidences of
acting out (Pas et al., 2019). Further, incentive programs, such as the Jostens Renaissance (2020)
educational program, can improve student engagement, which can reduce disciplinary issues
within the classroom (Beehler & Trickett, 2017).
92
Overall, the results of this study showed that there was a significant increase in GPAs,
significant decreases in days absent, and decreased number of discipline cards before and after
participating in the implementation of the Jostens Renaissance (2020) program. Therefore, there
was sufficient evidence to reject all the null hypotheses posed in the study. The findings of this
research indicated that faculty using the Jostens Renaissance program were successful in
motivating students to improve their GPAs, improve attendance, and lessen discipline
infractions. These findings have a variety of implications for current practice and future
researchers.
Implications and Conclusion
The results of this study have a variety of empirical, theoretical, and practical
implications. First, the results of this study indicated that empirically, the Jostens Renaissance
(2020) education program could be added to the knowledge taught to educators and
administrators charged with increasing academic performance while decreasing disciplinary
issues and student absences.
This study also has implications for theory. Both the social learning theory and social
cognitive theory, as popularized by Bandura and Walters (1963) and Lewin (1943), focus on how
individuals learn through modeling and socially normative behavior. However, these theories are
rarely applied to specific learning incentive programs. Thus, theoretical implications include the
use of social learning theory and social cognitive theory to give context to the efficacy of the
Jostens Renaissance (2020) educational program on reducing disciplinary issues, improving
attendance, and improving academic performances among students in secondary education.
Finally, there exist practical implications for the results of this study. As it pertained to
this research, the goal was to determine whether the Jostens Renaissance (2020) education
93
program would positively impact students’ GPAs, attendance, and discipline at a Title I school.
The goal was to improve a school’s overall climate and culture. The results of this research
confirmed that the Jostens Renaissance (2020) education program had a significant impact on all
three facets: increasing students’ educational achievement, decreasing absences, and mitigating
disciplinary issues. Students’ GPAs, attendance, and discipline were significantly improved.
Thus, this researcher found that faculty could use the Jostens Renaissance education program to
address the goals that creators of incentive programs aimed to achieve.
Teachers, administrators, and school counselors can use the results of this research on the
implementation of the Jostens Renaissance (2020) education program to understand objective
information on the program and its efficacy on increasing students’ academic achievements and
attendance rates while decreasing disciplinary issues. With this information, administrators can
gain knowledge to both aid students and remain competitive for federal monies, especially those
schools under Title I.
Balu and Ehrlich (2018) found that the implementation of a recognition and rewards
system was beneficial for students and faculty to produce a positive school climate and culture.
Similarly, Kaimal and Jordan (2016) examined the efficacy of incentive programs on both
student achievement and staff retention. Kaimal and Jordan (2016) confirmed that incentive
programs were beneficial to student achievements but only within the short-term. Educators and
counselors can use the findings of the current to gain the information needed to make informed
decisions about incentive programs for student improvement in schools.
Although school administrators could use the results of this study to make informed
decisions about the implementation of the Jostens Renaissance (2020) education program,
94
several limitations existed in the present study. The first set of limitations concerned the
generalizability of study results.
First, a limitation existed concerning the absence of representation from ninth grade
students among study participants. Within the sample, no freshman students were used, thereby
reducing the generalizability of results. Without representation for first-year students, the
efficacy of the Jostens Renaissance (2020) education program on younger secondary students
remained unknown. Additionally, results could not be generalized to ninth-grade students.
The findings of this study were limited to the data collected from a specific high school
considered in the study, as data were only gathered from one school. As only one school was
used for data, results could not be generalized to other schools. Moreover, the school sampled
was a Title I, a secondary school in Southwest Florida, meaning that schools outside of Title I
and secondary education were not considered. Further, the researcher did not consider schools
outside the area of Southwest Florida. Thus, results might not be generalizable to schools of
other designations and localities or outside of secondary education.
In addition to limitations regarding the generalizability of results, this study was limited
by the use of only secondary data. A third limitation was that only secondary data were used
within this study; meaning, no primary data were used for data analysis. Because the
perspectives of educators and students were not used as part of data collection, nor were reasons
for absences, factors that impacted GPA or factors that impacted disciplinary issues resulted in a
limitation of this study. As primary data were not gathered and reasons for GPA, absences, or
disciplinary issues were not explored, the improvement in student absences, discipline, or GPAs
might not be solely related to participation in the Jostens Renaissance (2020) education program
for some participants.
95
Another limitation was the implementation of the program. There were various ways of
implementing the program. Because the administration can choose how to implement the Jostens
Renaissance (2020) education program, the results may not apply to other institutions
implementing the Jostens Renaissance (2020) education program in ways that vary from the
school of interest within this study.
The degree to which the Jostens Renaissance (2020) education program is implemented
or enforced can also impact efficacy in reducing absences or disciplinary issues or improving
student GPA. As such, schools that differ in the degree of implementation or enforcement may
show differing results. Further, as faculty of the Jostens Renaissance education program use
community services and goods to reward good grades and behaviors, schools that differ in the
types of rewards may have reduced efficacy with the Jostens Renaissance education program in
both improving academic achievements and reducing absences or disciplinary issues for
respective students.
A final limitation was that the school administrator had the ability and discretion to
identify student data included in the study. Thus, the administration might have knowingly or
subconsciously biased the results of this study by choosing student data that showed an increase
in student achievement, a decrease in school absences, or mitigated disciplinary issues.
Additionally, school administrators might have inadvertently biased results by knowing the
identities of the student participants. If school administrators knew the identities of the students,
they might have been able to choose students who had shown marked improvements using the
Jostens Renaissance (2020) education program. However, this researcher encouraged
administrators to use a random sample of participants to achieve valid results for the study and
mitigate biases within the study results.
96
Although there were some limitations within this study, the results of this study were
significant. Using a quantitative methodology, the researcher determined that the Jostens
Renaissance (2020) education program had a positive impact on the students at a Southwest
Florida Title I high school. When the Jostens Renaissance education program was implemented,
students who participated were more likely to experience increased GPAs, reduced absences, and
decreased disciplinary issues. Based on these findings, the Jostens Renaissance education
program should be recommended for administrators of schools or school districts that wish to
accomplish these goals in the future. In this way, faculty can use the Jostens Renaissance
education program to both benefit students and allow Title I secondary schools to remain
competitive for federal grant monies associated with increased academic success, increased
attendance, and reduced disciplinary issues.
Recommendations for Future Research
Although findings of this current study were statistically significant, indicating that the
Jostens Renaissance (2020) education program was beneficial in improving student GPA,
improving student attendance, and reducing disciplinary issues, this study was subject to
limitations. Future researchers can address such limitations. Therefore, several recommendations
are made for future study:
1. The first recommendation is that future researchers can use a similar methodological
approach to this study but use more data from students to create a longitudinal study.
In this way, future researchers can describe GPA, attendance, and disciplinary issues
under the Jostens Renaissance (2020) education program based on a duration longer
than one semester. A longitudinal study approach can better show the effects of the
Jostens Renaissance education program in secondary education.
97
2. The second recommendation for future research is the completion of a similar study
utilizing a qualitative or mixed-methods approach. By including qualitative data from
administrators, educators, or students enrolled within the Jostens Renaissance (2020)
education program, future researchers can determine a more comprehensive
understanding of the efficacy of the program. Additionally, the student participants
may be better able to explain the usefulness or drawbacks of the Jostens Renaissance
education program.
3. Future researchers can include other schools in the same school district that use the
Renaissance program in the same manner to show differences and similarities among
students. Thus, by using schools with the same degree and enforcement of
implementation, as well as implementation, in the same manner, future researchers
can determine a more comprehensive understanding of the impact of this program on
students.
4. Future researchers may want to complete a similar study where they can compare the
Jostens Renaissance (2020) education program among students of both non-Title I
schools and Title I schools. As the demographics, SES, and reliance on federal
monies differs between non-Title I schools and Title I counterparts, such future
researchers can explain the efficacy of the Jostens Renaissance education program
among schools of differing SES.
5. Finally, future researchers can examine the impact of the Jostens Renaissance (2020)
education program among students of different grade levels, races, and genders. By
including these demographics as modifying factors, future researchers can gain a
better understanding of the Jostens Renaissance education program for different
98
races, genders, and ages. Faculty can use the results of this type of study to tailor the
program to student groups better, perhaps improving the efficacy of the program for
students.
99
REFERENCES
Aldridge, J. M., & Fraser, B. J. (2016). Teachers’ views of their school climate and its
relationship with teacher self-efficacy and job satisfaction. Learning Environments
Research, 19(2), 291–307. https://doi.org/10.1007/s10984-015-9198-x
Aldridge, J. M., Fraser, B. J., Fozdar, F., Ala’i, K., Earnest, J., & Afari, E. (2016). Students’
perceptions of school climate as determinants of wellbeing, resilience and identity.
Improving Schools, 19(1), 5–26. https://doi.org/10.1177%2F1365480215612616
Alhosani, A. A., Singh, S. K., & Al Nahyan, M. T. (2017). Role of school leadership and climate
in student achievement: The mediating role of parental involvement. International
Journal of Educational Management, 31(6), 843–851. https://doi.org/10.1108/IJEM-05-
2016-0113
Allport, G. W. (1955). Becoming: Basic considerations for a psychology of personality (Vol.
20). Yale University Press.
American Counseling Association. (2014). Code of ethics. Author.
Andreou, T. E., McIntosh, K., Ross, S. W., & Kahn, J. D. (2015). Critical incidents in sustaining
school-wide positive behavioral interventions and supports. The Journal of Special
Education, 49(3), 157–167. https://doi.org/10.1177%2F0022466914554298.
Atieno, O. P. (2009). An analysis of the strengths and limitation of qualitative and quantitative
research paradigms. Problems of Education in the 20th Century, 13, 13-18.
https://scientiasocialis.lt/pec/files/pdf/Atieno_Vol.13.pdf
100
Balu, R., & Ehrlich, S. B. (2018). Making sense out of incentives: A framework for considering
the design, use, and implementation of incentives to improve attendance. Journal of
Education for Students Placed at Risk (JESPAR), 23(1-2), 93–106.
https://doi.org/10.1080/10824669.2018.1438898
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.
Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
Bandura, A. (1978). Reflections on self-efficacy. Advances in Behaviour Research and Therapy,
1(4), 237–269. https://doi.org/10.1016/0146-6402(78)90012-7
Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of
Social and Clinical Psychology, 4(3), 359–373. https://doi.org/10.1521/jscp.1986.4.3.359
Bandura, A. (1989). Regulation of cognitive processes through perceived self-efficacy.
Developmental Psychology, 25(5), 729–735. https://doi.org/10.1037/0012-1649.25.5.729
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of
Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1
Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of
aggressive models. The Journal of Abnormal and Social Psychology, 63(3), 575–582.
https://doi.org/10.1037/h0045925
Bandura, A., & Walters, R. H. (1963). Social learning and personality development. Rinehart
and Winston.
Barron, K. E., & Hulleman, C. S. (2015). Expectancy-value-cost model of motivation. In J. S.
Eccles & K. Salmelo-Aro (Eds.), International encyclopedia of social and behavioral
sciences (2nd ed., pp. 261–271). Elsevier.
101
Beckmann, J., & Heckhausen, H. (2018). Motivation as a function of expectancy and incentive.
In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (3rd ed., pp. 163–220).
Springer.
Beehler, S., & Trickett, E. J. (2017). Community psychology misdirected? The case of evidence-
based interventions. In M. A. Bond, I. Serrano-García, C. B. Keys, & M. Shinn (Eds.),
APA handbook of community psychology: Theoretical foundations, core concepts, and
emerging challenges (pp. 455–468). https://doi.org/10.1037/14953-023
Bellibas, M. S., & Liu, Y. (2018). The effects of principals’ perceived instructional and
distributed leadership practices on their perceptions of school climate. International
Journal of Leadership in Education, 21(2), 226–244.
https://doi.org/10.1080/13603124.2016.1147608
Berkowitz, R., Moore, H., Astor, R. A., & Benbenishty, R. (2017). A research synthesis of the
associations between socioeconomic background, inequality, school climate, and
academic achievement. Review of Educational Research, 87(2), 425–469.
https://doi.org/10.3102%2F0034654316669821
Bridgeland, J. M., DiIulio, J. J., Streeter, R. T., & Mason, J. R. (2008). One dream, two realities:
Perspectives of parents on America's high schools. Civic Enterprises.
Bracht, K. D. (2010). Causes of nonattendance and its effect on student achievement at the high
school level (Publication No. 3433096) [Doctoral dissertation, Lindenwood University,].
ProQuest Dissertations & Theses Global.
102
Bradshaw, C. P., Waasdorp, T. E., & Leaf, P. J. (2015). Examining variation in the impact of
school-wide positive behavioral interventions and supports: Findings from a randomized
controlled effectiveness trial. Journal of Educational Psychology, 107(2), 546–557.
https://doi.org/10.1037/a0037630
Bronfenbrenner, U. (1989, April). The developing ecology of human development: Paradigm lost
or paradigm regained [Conference session]. Society for Research in Child Development,
Kansas City, MO, United States. https://www.srcd.org/
Brown-Williams, Z., Rhoades, G., Smith, M., & Thompson, D. (2019). Aspiring to higher
education? Choice, complexity and confidence in secondary students’ decision-making.
Educationalfutures, 10(1), 31–58. https://wlv.openrepository.com/handle/2436/622788
Buchanan, S. (2015). Building on student achievement through incentive programs (ED565495).
ERIC. https://eric.ed.gov/?id=ED565495
Bugbee, B. A., Beck, K. H., Fryer, C. S., & Arria, A. M. (2019). Substance use, academic
performance, and academic engagement among high school seniors. Journal of School
Health, 89(2), 145–156. https://doi.org/10.1111/josh.12723
Bursztyn, L., & Jensen, R. (2015). How does peer pressure affect educational investments? The
Quarterly Journal of Economics, 130(3), 1329–1367. https://doi.org/10.1093/qje/qjv021
Campbell, P. C. (2016). An evaluation of the Jostens Renaissance program on award-winning
high schools' attendance rates and graduation rates (Publication No. 10158677)
[Doctoral dissertation, Tennessee State University]. ProQuest Dissertations Publishing.
Carney-Ray, M. C. (2013). The impact of the Jostens Renaissance program on overall
achievement in a New Jersey middle school (Publication No. 3573283) [Doctoral
dissertation, Widener University]. ProQuest Dissertations Publishing.
103
Childs, K. E., Kincaid, D., George, H. P., & Gage, N. A. (2016). The relationship between
school-wide implementation of positive behavior intervention and supports and student
discipline outcomes. Journal of Positive Behavior Interventions, 18(2), 89–99.
https://doi.org/10.1177%2F1098300715590398
Choukas-Bradley, S., Giletta, M., Cohen, G. L., & Prinstein, M. J. (2015). Peer influence, peer
status, and prosocial behavior: An experimental investigation of peer socialization of
adolescents’ intentions to volunteer. Journal of Youth and Adolescence, 44(12), 2197–
2210. https://doi.org/10.1007/s10964-015-0373-2
Christensen, L. B., Johnson, R. B., & Turner, L. A. (2014). Research methods, design, and
analysis (12th ed.). Pearson
Cohen, J. (2006). Social, emotional, ethical, and academic education: Creating a climate for
learning, participation in democracy and well-being. Harvard Educational Review, 76(2),
201–237. https://doi.org/10.17763/haer.76.2.j44854x1524644vn
Covey, S. R. (2020). The 7 habits of highly effective people. Simon & Schuster.
Coyne, A.Y. (2012). The relationship between perceptions of school climate and student
achievement in schools that use Jostens Renaissance programs (Publication No.
3534808) [Doctoral dissertation, University of Southern Mississippi]. ProQuest
Dissertations Publishing.
De Castella, K., & Byrne, D. (2015). My intelligence may be more malleable than yours: The
revised implicit theories of intelligence (self-theory) scale is a better predictor of
achievement, motivation, and student disengagement. European Journal of Psychology of
Education, 30(3), 245–267. https://doi.org/10.1007/s10212-015-0244-y
104
Dernowska, U. (2017). Teacher and student perceptions of school climate. Some conclusions
from school culture and climate research. Journal of Modern Science, 32(1), 63–82.
https://www.ceeol.com/search/article-detail?id=530806
Dutta, V., & Sahney, S. (2016). School leadership and its impact on student achievement: The
mediating role of school climate and teacher job satisfaction. International Journal of
Educational Management, 30(6), 941–958. https://doi.org/10.1108/IJEM-12-2014-0170
English, G. (2019). The change process and implementation of high school Jostens Renaissance
program: A multiple case study [Doctoral dissertation, East Tennessee State University].
Electronic Theses and Dissertations.
http://dc.etsu.edu/cgi/viewcontent.cgi?article=4988&context=etd
Eren, O. (2019). Teacher incentives and student achievement: Evidence from an advancement
program. Journal of Policy Analysis and Management, 38(4), 867–890.
https://doi.org/10.1002/pam.22146
Eyyam, R., Doğruer, N., & Meneviş, İ. (2012). Social learning theory. In A. Akademir, K. Zeki,
& A. A. Oznur (Eds.), Teaching and learning theories, models and approaches (pp. 47–
59). Çözüm Publishing.
FOCUS. (2020, July). Learn more. https://focusschoolsoftware.come/technology/
Froiland, J. M., & Worrell, F. C. (2016). Intrinsic motivation, learning goals, engagement, and
achievement in a diverse high school. Psychology in the Schools, 53(3), 321–336.
https://doi.org/10.1002/pits.21901
105
Gage, N. A., Leite, W., Childs, K., & Kincaid, D. (2017). Average treatment effect of school-
wide positive behavioral interventions and supports on school-level academic
achievement in Florida. Journal of Positive Behavior Interventions, 19(3), 158–167.
https://doi.org/10.1177/1098300717693556
Ginsburg, A., Jordan, P., & Chang, H. (2014). Absences add up: How school attendance
influences school success. http://www.attendanceworks.org/research/absences-add/
Gnambs, T., & Hanfstingl, B. (2016). The decline of academic motivation during adolescence:
An accelerated longitudinal cohort analysis on the effect of psychological need
satisfaction. Educational Psychology, 36(9), 1691–1705.
https://doi.org/10.1080/01443410.2015.1113236
Gottfried, M. A. (2010). Evaluating the relationship between student attendance and achievement
in urban elementary and middle schools: An instrumental variables approach. American
Educational Research Journal, 47(2), 434-465.
https://doi.org/10.3102/0002831209350494
Guo, J. (2016). The relations among academic motivation, self-concept, aspirations and choices:
Integrating expectancy-value and academic self-concept theory [Master’s thesis,
Australian Catholic University]. https://doi.org/10.4226/66/5a9cc030b0ba5
Hartley, C. A., & Somerville, L. H. (2015). The neuroscience of adolescent decision-making.
Current Opinion in Behavioral Sciences, 5, 108–115.
https://doi.org/10.1016/j.cobeha.2015.09.004
Herman, R., Gates, S. M., Chavez-Herreias, E. R., & Harris, M. (2016). School leadership
interventions under the Every Student Succeeds Act. RAND Corporation.
106
Horner, R. H., & Sugai, G. (2015). School-wide PBIS: An example of applied behavior analysis
implemented at a scale of social importance. Behavior Analysis in Practice, 8(1), 80–85.
https://doi.org/10.1007/s40617-015-0045-4
Horner, R. H., Ward, C. S., Fixsen, D. L., Sugai, G., McIntosh, K., Putnam, R., & Little, H. D.
(2019). Resource leveraging to achieve large-scale implementation of effective
educational practices. Journal of Positive Behavior Interventions, 21(2), 67–76.
https://doi.org/10.1177/1098300718783754
Houchens, G. W., Zhang, J., Davis, K., Niu, C., Chon, K. H., & Miller, S. (2017). The impact of
positive behavior interventions and supports on teachers’ perceptions of teaching
conditions and student achievement. Journal of Positive Behavior Interventions, 19(3),
168–179. https://doi.org/10.1177/1098300717696938
Hough, H., Kalogrides, D., & Loeb, S. (2017). Using surveys of students' social-emotional
learning and school climate for accountability and continuous improvement [Policy Brief
17-1]. Policy Analysis for California Education.
Individuals With Disability Education Act Amendments of 1997. (1997).
https://www.congress.gov/105/plaws/publ17/PLAW-105publ17.pdf
Jacobellis v. Ohio. (1964). 378 US 184, 84 S. Ct, 1676, 12.
Jones, A., & Shindler, J. (2016). Exploring the school climate--student achievement connection:
making sense of why the first precedes the second. Educational leadership and
administration. Teaching and Program Development, 27, 35–51. http://www.capea.org
Jostens Renaissance. (2020, July). Learn more. https://www.jostensrenaissance.com/learn-more/
107
Juwel, R., & Ahsan, M. A. (2019). Social learning and governance. In A. Farazmand (Ed.),
Global encyclopedia of public administration, public policy, and governance.
https://doi.org/10.1007/978-3-319-31816-5_1977-1
Kaimal, G., & Jordan, W. J. (2016). Do incentive-based programs improve teacher quality and
student achievement? An analysis of implementation in 12 urban charter schools.
Teachers College Record, 118(7).
https://www.tcrecord.org/Content.asp?ContentId=20450
Kainz, K. (2019). Early academic gaps and Title I programming in high poverty, high minority
schools. Early Childhood Research Quarterly, 47, 159–168.
https://doi.org/10.1016/j.ecresq.2018.08.012
Kang, H. S., Chang, K. E., Chen, C., & Greenberger, E. (2015). Locus of control and peer
relationships among Caucasian, Hispanic, Asian, and African American adolescents.
Journal of Youth and Adolescence, 44(1), 184–194. https://doi.org/10.1007/s10964-013-
0083-6
Kearney, C., Gonzálvez, C., Graczyk, P., & Fornander, M. (2019). Reconciling contemporary
approaches to school attendance and school absenteeism: Toward promotion and nimble
response, global policy review and implementation, and future adaptability (Part 2).
Frontiers in Psychology, 10, Article 2605. https://doi.org/10.3389/fpsyg.2019.02605
Kincaid, D., & Horner, R. (2017). Changing systems to scale up an evidence-based educational
intervention. Evidence-Based Communication Assessment and Intervention, 11(3–4), 99–
113. https://doi.org/10.1080/17489539.2017.1376383
108
Köller, O., Meyer, J., Saß, S., & Baumert, J. (2019). New analyses of an old topic. Effects of
intelligence and motivation on academic achievement. Journal for Educational Research
Online, 11(1), 166–189.
https://pure.mpg.de/rest/items/item_3037604/component/file_3047444/content
Konold, T., Cornell, D., Jia, Y., & Malone, M. (2018). School climate, student engagement, and
academic achievement: A latent variable, multilevel multi-informant examination. AERA
Open, 4(4), 2332858418815661. https://doi.org/10.1177/2332858418815661
Kosovich, J. J., Hulleman, C. S., Barron, K. E., & Getty, S. (2015). A practical measure of
student motivation: Establishing validity evidence for the expectancy-value-cost scale in
middle school. The Journal of Early Adolescence, 35(5–6), 790–816.
https://doi.org/10.1177/0272431614556890
Kotok, S., Ikoma, S., & Bodovski, K. (2016). School climate and dropping out of school in the
era of accountability. American Journal of Education, 122(4), 569–599.
https://doi.org/10.1086/687275
Kumar, R., Zusho, A., & Bondie, R. (2018). Weaving cultural relevance and achievement
motivation into inclusive classroom cultures. Educational Psychologist, 53(2), 78–96.
https://doi.org/10.1080/00461520.2018.1432361
Kutsyuruba, B., Klinger, D. A., & Hussain, A. (2015). Relationships among school climate,
school safety, and student achievement and well‐being: A review of the literature. Review
of Education, 3(2), 103–135. https://doi.org/10.1002/rev3.3043
Kwong, D., & Davis, J. R. (2015). School climate for academic success: A multilevel analysis of
school climate and student outcomes. Journal of Research in Education, 25(2), 68–81.
http://www.eeraorganization.org
109
Kyndt, E., Donche, V., Coertjens, L., Van Daal, T., Gijbels, D., & Van Petegem, P. (2019). Does
self-efficacy contribute to the development of students’ motivation across the transition
from secondary to higher education? European Journal of Psychology of Education,
34(2), 457–478. https://doi.org/10.1007/s10212-018-0389-6
Lazowski, R. A., & Hulleman, C. S. (2016). Motivation interventions in education: A meta-
analytic review. Review of Educational Research, 86(2), 602–640.
https://doi.org/10.3102%2F0034654315617832
Levitt, S. D., List, J. A., Neckermann, S., & Sadoff, S. (2016). The behavioralist goes to school:
Leveraging behavioral economics to improve educational performance. American
Economic Journal: Economic Policy, 8(4), 183–219.
https://doi.org/10.1257/pol.20130358
Lewin, K. (1943). Defining the “field at a given time.” Psychological Review, 50(3), 292–310.
https://doi.org/10.1037/h0062738
Liem, G. A. D. (2016). Academic and social achievement goals: Their additive, interactive, and
specialized effects on school functioning. British Journal of Educational Psychology,
86(1), 37–56. https://doi.org/10.1111/bjep.12085
Malinen, O. P., & Savolainen, H. (2016). The effect of perceived school climate and teacher
efficacy in behavior management on job satisfaction and burnout: A longitudinal study.
Teaching and Teacher Education, 60, 144–152.
https://doi.org/10.1016/j.tate.2016.08.012
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396.
https://doi.org/10.1037/h0054346
110
Maxwell, L. E. (2016). School building condition, social climate, student attendance and
academic achievement: A mediation model. Journal of Environmental Psychology, 46,
206–216. https://doi.org/10.1016/j.jenvp.2016.04.009
McDougall, W. (1923). Outline of psychology. Scribner.
McIntosh, K., Mercer, S. H., Nese, R. N., Strickland-Cohen, M. K., & Hoselton, R. (2016).
Predictors of sustained implementation of school-wide positive behavioral interventions
and supports. Journal of Positive Behavior Interventions, 18(4), 209–218.
https://doi.org/10.1177%2F1098300715599737
McIntosh, K., Mercer, S. H., Nese, R. N., Strickland-Cohen, M. K., Kittelman, A., Hoselton, R.,
& Horner, R. H. (2018). Factors predicting sustained implementation of a universal
behavior support framework. Educational Researcher, 47(5), 307–316.
https://doi.org/10.3102%2F0013189X18776975
Mercer, S. H., McIntosh, K., & Hoselton, R. (2017). Comparability of fidelity measures for
assessing Tier 1 school-wide positive behavioral interventions and supports. Journal of
Positive Behavior Interventions, 19(4), 195–204.
https://doi.org/10.1177%2F1098300717693384
Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and
implementation (4th ed.). Wiley and Sons.
Miller, N. E., & Dollard, J. (1941). Social learning and imitation. Yale University Press.
Muller, E. (2002). Statewide behavior initiatives--literature review and survey results
(ED472089). ERIC. https://files.eric.ed.gov/fulltext/ED472089.pdf
111
Nese, R. N., McIntosh, K., Nese, J., Hoselton, R., Bloom, J., Johnson, N., Richter, M., Phillips,
D., & Ghemraoui, A. (2016). Predicting abandonment of school-wide positive behavioral
interventions and supports. Behavioral Disorders, 42(1), 261–270.
https://doi.org/10.17988%2FBD-15-95.1
Nese, R. N., Nese, J. F., McIntosh, K., Mercer, S. H., & Kittelman, A. (2019). Predicting latency
of reaching adequate implementation of Tier I schoolwide positive behavioral
interventions and supports. Journal of Positive Behavior Interventions, 21(2), 106–116.
https://doi.org/10.1177%2F1098300718783755
Neto, M. (2015). Educational motivation meets Maslow: Self-actualization as contextual driver.
Journal of Student Engagement: Education Matters, 5(1), 18–27.
https://ro.uow.edu.au/jseem/vol5/iss1/4/
Nisar, N., & Mahmood, K. (2017). Determinants of students' academic achievement at secondary
school level. Bulletin of Education and Research, 39(1), 145–158.
http://pu.edu.pk/home/journal/32
Noltemeyer, A., Palmer, K., James, A. G., & Petrasek, M. (2019). Disciplinary and achievement
outcomes associated with school-wide positive behavioral interventions and supports
implementation level. School Psychology Review, 48(1), 81–87.
https://doi.org/10.17105/SPR-2017-0131.V48-1
Noltemeyer, A., Palmer, K., James, A. G., & Wiechman, S. (2019). School-wide positive
behavioral interventions and supports (SWPBIS): A synthesis of existing research.
International Journal of School & Educational Psychology, 7(4), 253–262.
https://doi.org/10.1080/21683603.2018.1425169
112
Oyserman, D., & Lewis, N. A., Jr. (2017). Seeing the destination AND the path: Using identity‐
based motivation to understand and reduce racial disparities in academic achievement.
Social Issues and Policy Review, 11(1), 159–194. https://doi.org/10.1111/sipr.12030
Ozen, H. (2018). A qualitative study of school climate according to teachers' perceptions.
Eurasian Journal of Educational Research, 74, 81–97.
https://doi.org/10.14689/ejer.2018.74.5
Pas, E. T., Ryoo, J. H., Musci, R. J., & Bradshaw, C. P. (2019). A state-wide quasi-experimental
effectiveness study of the scale-up of school-wide positive behavioral interventions and
supports. Journal of School Psychology, 73, 41–55.
https://doi.org/10.1016/j.jsp.2019.03.001
Pepper, S. C. (1934). The conceptual framework of Tolman's purposive behaviorism.
Psychological Review, 41(2), 108–133. https://doi.org/10.1037/h0075220
Pinkelman, S. E., McIntosh, K., Rasplica, C. K., Berg, T., & Strickland-Cohen, M. K. (2015).
Perceived enablers and barriers related to sustainability of school-wide positive
behavioral interventions and supports. Behavioral Disorders, 40(3), 171–183.
https://doi.org/10.179882F0198-7429-40.3.171
Rambaran, J. A., Hopmeyer, A., Schwartz, D., Steglich, C., Badaly, D., & Veenstra, R. (2017).
Academic functioning and peer influences: A short‐term longitudinal study of network–
behavior dynamics in middle adolescence. Child Development, 88(2), 523–543.
https://doi.org/10.1111/cdev.12611
Ramelow, D., Currie, D., & Felder-Puig, R. (2015). The assessment of school climate: Review
and appraisal of published student-report measures. Journal of Psychoeducational
Assessment, 33(8), 731–743. https://doi.org/10.11772F0734282915584852
113
Renaud-Dubé, A., Guay, F., Talbot, D., Taylor, G., & Koestner, R. (2015). The relations
between implicit intelligence beliefs, autonomous academic motivation, and school
persistence intentions: A mediation model. Social Psychology of Education, 18(2), 255–
272. https://doi.org/10.1007/s11218-014-9288-0
Reynolds, K. J., Lee, E., Turner, I., Bromhead, D., & Subasic, E. (2017). How does school
climate impact academic achievement? An examination of social identity processes.
School Psychology International, 38(1), 78–97.
https://doi.org/10.11772F0143034316682295
Roberts, J. (2019). Increasing student attendance in a Title I school.
https://aquila.usm.edu/highereddoctoralprojects/5/
Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health
belief model. Health Education Quarterly, 15(2), 175–183.
https://doi.org/10.1177%2F109019818801500203
Rotter, J. B. (1954). Social learning and clinical psychology. https://doi.org/10.1037/10788-000
Rubenstein, M. C., & Wodatch, J. K. (2000). Stepping up to the challenge: Case studies of
educational improvement in Title I secondary schools.
https://www.mdrc.org/sites/default/files/full_515.pdf
Rudasill, K. M., Snyder, K. E., Levinson, H., & Adelson, J. L. (2018). Systems view of school
climate: A theoretical framework for research. Educational Psychology Review, 30(1),
35–60. https://doi.org/10.1007/s10648-017-9401-y
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic
motivation, social development, and well-being. American Psychologist, 55(1), 68–78.
https://doi.org/10.1037/0003-066X.55.1.68
114
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in
motivation, development, and wellness. Guilford.
Salle, T. P. L., Meyers, J., Varjas, K., & Roach, A. (2015). A cultural-ecological model of school
climate. International Journal of School & Educational Psychology, 3(3), 157–166.
https://doi.org/10.1080/21683603.2015.1047550
Santoso, A. B., Simatupang, E. C. M., & Sofyandi, R. H. (2018). Factors affecting the
improvement of students’ Grade Point Average (GPA). In Increasing management
relevance and competitiveness (pp. 261-264). CRC Press.
Saracho, O. N. (2019). Motivation theories, theorists, and theoretical conceptions. In O. N.
Saracho (Ed.), Contemporary perspectives on research in motivation in early childhood
education (pp. 21–46). Information Age.
Sherapis, C. J., Young, J. S., & Daniels, M. H. (2017). Counselling research: Quantitative,
qualitative, and mixed methods (2nd ed.). Pearson.
Shindler, J., Jones, A., Williams, A. D., Taylor, C., & Cardenas, H. (2016). The school climate-
student achievement connection: If we want achievement gains, we need to begin by
improving the climate. Journal of School Administration Research and Development,
1(1), 9–16. http://www.jsard.org/
Simonsen, B., & Sugai, G. (2019). School-wide positive behavioral interventions and supports:
A systems-level application of behavioral principles. In S. G. Little & A. Akin-Little
(Eds.), Applying psychology in the schools book series. Behavioral interventions in
schools: Evidence-based positive strategies (pp. 35–60).
https://doi.org/10.1037/0000126-003
115
Skinner, B. F. (1989). The origins of cognitive thought. American Psychologist, 44(1), 13–18.
https://doi.org/10.1037/0003-066X.44.1.13
Smith, C. (2016). The relationship between adolescent social network structure and perceptions
of school climate [Doctoral dissertation, University of Washington]. Research Works
Archive.
https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/36572/Smith_was
hington_0250E_15649.pdf?sequence=1
Smith, T., & Shouppe, G. (2018). Is there a relationship between schools' climate ratings and
student performance data? National Teacher Education Journal, 11(1), 15–21.
https://ntejournal.com/
Sulak, T. N. (2016). School climate and academic achievement in suburban schools. Education
and Urban Society, 48(7), 672–684. https://doi.org/10.1177%2F0013124514541465
Taie, S., & Goldring, R. (2019). Characteristics of public and private elementary and secondary
schools in the United States: Results from the 2017-18 National Teacher and Principal
Survey (NCES 2019-140). National Center for Education Statistics.
Tolman, E. C. (1932). Lewin's concept of vectors. The Journal of General Psychology, 7(1), 3–
15. https://doi.org/10.1080/00221309.1932.9918444
Van Rijsewijk, L., Dijkstra, J. K., Pattiselanno, K., Steglich, C., & Veenstra, R. (2016). Who
helps whom? Investigating the development of adolescent prosocial relationships.
Developmental Psychology, 52(6), 894–908. https://doi.org/10.1037/dev0000106
Van Zantvliet, P. I., Ivanova, K., & Verbakel, E. (2018). Adolescents’ involvement in romantic
relationships and problem behavior: The moderating effect of peer norms. Youth &
Society, 52(4), 574–591. https://doi.org/10.1177%2F0044118X17753643
116
Watson, N. (2001). Promising practices: What does it really take to make a difference?
Education Canada, 40(4), 4–6. https://www.edcan.ca/articles/promising-practices-what-
does-it-really-take-to-make-a-difference/
Weiner, J. M., & Higgins, M. C. (2017). Where the two shall meet: Exploring the relationship
between teacher professional culture and student learning culture. Journal of Educational
Change, 18(1), 21–48. https://doi.org/10.1007/s10833-016-9292-6
Wentzel, K. R. (2017). Peer relationships, motivation, and academic performance at school.
https://psycnet.apa.org/record/2017-17591-031
Wentzel, K. R., & Ramani, G. B. (2016). Peer influence on students’ motivation, academic
achievement, and social behavior. In K. R. Wentzel & G. B. Ramani (Eds.), Handbook of
social influences in school contexts (pp. 23–40). Routledge.
Wentzel, K. R., Russell, S., & Baker, S. (2016). Emotional support and expectations from
parents, teachers, and peers predict adolescent competence at school. Journal of
Educational Psychology, 108(2), 242–255. https://doi.org/10.1037/edu0000049
White, R. K. (1943). The case for the Tolman-Lewin interpretation of learning. Psychological
Review, 50(2), 157–186. https://doi.org/10.1037/h0057002
Wright, R. A. (2016). Motivation theory essentials: Understanding motives and their conversion
into effortful goal pursuit. Motivation and Emotion, 40(1), 16–21.
https://doi.org/10.1007/s11031-015-9536-4
Yeager, D. S., Dahl, R. E., & Dweck, C. S. (2018). Why interventions to influence adolescent
behavior often fail but could succeed. Perspectives on Psychological Science, 13(1), 101–
122. https://doi.org/10.11772F1745691617722620
117
APPENDIX A
COURSE SYLLABUS
XXXXXXX High School
Renaissance Course Syllabus Ms. XXXXXX
Room XXXX
Student Name ________________________________________________ Student ID#
__________________ Period _____
WELCOME to Room 2109 !
I. Course Objectives:
1) To develop your Renaissance program led by the Renaissance class.
2) To apply leadership skills in management roles within the Student Renaissance Organization.
3) To have a working knowledge of the 7 Habits of Highly Effective Teens, Sean Covey.
4) To integrate the principles of leadership, character and effective life skills into the mission of
your Renaissance program.
5) To educate the student body, the faculty and the community in the essential elements of
Renaissance.
6) To consistently communicate the value of academic achievement, continuous improvement
and exemplary character to the student body through media, visual arts, music, publications etc.
7) To organize two academic pep-rallies (ceremonies) per year recognizing students, staff and
faculty for excellence and improvement.
8) To coordinate additional ceremonies at the end of 18 week periods to honor specific groups.
9) To organize the Awards Ceremonies at the end of the year.
10) To contact and establish community support for academic cards, incentives, shirts,
celebrations etc.
11) To volunteer in the community in return for Renaissance support. (5-10 hours)
II. Course Goals
The goal of the Renaissance Leadership class is to teach students the philosophy and the
strategies of Renaissance. Students will study leadership, character and organizational skills to
enhance their efforts in developing Renaissance on their campus.
III. Classroom Policy and Expectations:
• Always try your best
• Respect yourself, and others.
• Take time to clean up your mess
• It is imperative you listen when the teacher talks
• Store projects and materials in proper locations
• Tolerance for negative behavior is zero
• I understand that NO electronics (cell phones, cameras, iPods, iPad) unless given
permission in the classroom.
IV. Resources
118
1. The 7 Habits of Highly Effective Teens, Sean Covey
2. 7 Habits of Highly Effective Teens Journal, Sean Covey
3. “New Heights”, Renaissance magazine, produced by Jostens
V. What to bring to class:
A journal to record exercises, ideas and notes is necessary.( composition book)
Publications where leaders are featured-newspapers, magazines, journals
VI. Grades:
Grading may be based on the following criteria:
1) Credit will be given for participation in the implementation of Renaissance events.
2) Credit will be given for participation on the Student Renaissance Committee (after school
club), attendance at meetings and committee involvement.
3) Grades for the term will include scores on tests covering the content of the 7 Habits of Highly
Effective Teens; journal entries; a presentation of one of the ‘7 Habits’ to the class.
4) Participation as Committee Chairs and/or Members will be graded.
5) Extra Credit will go to those completing 10 hours of community service.
6) A mid-term and final exam will be given
VI. Home/ School Communication: I encourage open home/school communication.
Contact XXXXX at XXXXXXXXXX or by phone XXX-XXX-XXXX ext. XXX. To keep on
top of students’ assignments, grades and attendance please download the FREE FOCUS APP to
your cell phone for easy access to this information. Grades and attendance will be updated and
posted weekly. The Website is: http://focus.xxxxxxxxxx.net
VII. Supplementary Information:
• Tardies – the door locks when the bell rings. If you are tardy to class 3 times a referral
will be written.
• Sleeping in Class – DO NOT sleep in class. If you are too tired to stay awake in class,
you will be sent to the clinic and given an infraction.
• Dress Code – Adhere to the dress code policy of Lehigh Senior High School. If a student
has a dress code violation, they will be sent direction to ISS.
• Besides journal assignments, there rarely is homework. Students should use class time
and finish projects in class. Occasionally, however, they may need to bring work home to
finish.
Please stay in touch if you have any questions or concerns. Please fill in the following
information* so I can
communicate with you directly. Your signature indicates that you understand and agree with
these expectations.
Student Name (printed) ____________________________
Student Signature _____________________________
Parent/Guardian Name (printed) ____________________
119
Parent Signature_______________________________
Phones: Parent Day ____________ Parent Cell _____________ Home ______________
Student ______________
E-mail address: Parent ______________________________ Student
_____________________________________
I am interested in volunteering or chaperoning on field trips. ____ Yes ___ No
______________________________________________________________________________
______________________________________________________
*Please sign and turn in.
120
APPENDIX B
BUSINESS DONATIONS LETTER
August 9, 2019
Name of Recipient
Street Address
City, State, Zip
Dear [Name of Recipient]:
My name is XXXXXX. Some of you may know me through my position as a School Counselor
at XXXXXXX High School (“XXXX”). This year I have implemented the Renaissance program
here at LSHS with the support of our Principal XXXXXXXX.
At XXXX our mission is to increase achievement and improve teaching. Our commitment is to
make sure XXXX is a place where students strive to reach their highest academic potential as
scholars and where school spirit is contagious.
This is where your support and contributions come into play. The Renaissance program is an
incentive-based program at XXXX, designed to change the climate and culture of the school by
recognizing students for their hard work and dedication in academics, discipline and attendance.
The goal of the program is to encourage students to strive for academic and overall excellence
and reward them for achieving it.
Please help support our program by providing us with free vouchers and coupons to your
business that can be redeemed at your establishment(s) when presented by our students.
Thank you in advance for your support.
Sincerely,
XXXXXX
Renaissance Coordinator
121
APPENDIX C
RENAISSANCE CLASS APPLICATION
Renaissance Class Application
Due: August 10
th
, 2019
Name: Grade: Current GPA: Absences this year:
Please an answer the following questions completely and honestly:
1. Please write down some ideas or specifics you would like to work on if in the class:
2. List 5 qualities about yourself that would be an asset in leadership class
3. Please list one thing about yourself that you need to work on and why you consider it a
problem.
4. Create a catchy slogan to promote each of the following aspects of being a Lehigh
Scholar:
a. Spirit
b. Pride
c. Academic Achievement
d. Citizenship
e. Leadership
5. Please choose one of the words from the list and write a paragraph explaining how the
word relates to you and your leadership style/personality.
Helpful Integrity Spirited Welcoming Responsibility Courage
Honesty Compassion Performance Work Ethic Positive Involved
Character Moxie Prestige Commitment Perseverance Moral
Initiative Confidence
By signing this paper, I understand that leadership is a time commitment. It will require
me too often work outside of school day, and the ability to do so will have an effect on
my success in the class.
Student Signature:
