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Evaluation study: building teacher efficacy in K8 computer science integration

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EVALUATION STUDY: BUILDING TEACHER EFFICACY IN K8
COMPUTER SCIENCE INTEGRATION

by
Julienne Lee

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

August 2020

Copyright 2020 Julienne Lee

ii
Dedication
First and foremost, I dedicate this dissertation to my Heavenly Father, who gives me
love, joy, and strength beyond what I deserve, to Jesus Christ my savior, and the Holy Spirit,
who guides me and leads me to the light. To my husband, Stephen, I love you and owe you
beyond words. Thank you for your love, support, encouragement and cups of coffee that kept me
going. Thank you, Mom, for believing in me. To my four children, you give me reason to work
hard, laugh, love life, and fight on.

iii
Acknowledgements
My journey through the USC doctoral program was quite a wild ride! I’m so fortunate to
have met fellow Trojans who have encouraged me and guided me through the process. First, a
big thank you to my Chair, Dr. Emmy Min, for your quick responses, your encouragement, and
your professional and gentle approach in providing feedback. Dr. Canny, your input was
priceless. To my dissertation committee, Dr. DeMark and Dr. Freking, thank you for your
support and guiding me toward the right direction.
I would like to express my deepest appreciation to Cohort 11, who was a part of the
journey. We bonded through our online classes and the chat box. The text threads kept me going.
I wish you the best on your future endeavors. You are all amazing!
To all my OCL professors, you made this journey worth it. Thank you, USC for this
amazing adventure!

iv
TABLE OF CONTENTS
Dedication ....................................................................................................................................... ii

Acknowledgements ........................................................................................................................ iii

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

Abstract ........................................................................................................................................ viii

Chapter One: Overview of the Study ...............................................................................................1
Needs for Computer Science Implementation .....................................................................2
Related Literature .................................................................................................................3
Organizational Context and Mission ...................................................................................4
Organizational Performance Goals ......................................................................................6
Importance of the Evaluation ...............................................................................................6
Description of Stakeholder Groups ......................................................................................7
Stakeholder Group for the Study .........................................................................................7
Stakeholder Groups’ Performance Goals .............................................................................8
Methodological Framework .................................................................................................9
Definitions ..........................................................................................................................10
Organization of the Project ................................................................................................11

Chapter Two: Review of the Literature .........................................................................................12
Decline in Student Interest in Computer Science Education .............................................12
Gender Disparities .................................................................................................13
Early Influences .................................................................................................................15
Building Teacher Efficacy in Computer Science Education .............................................17
Clark and Estes (2008) Conceptual Framework in Gap Analysis .....................................18
Knowledge, Motivation and Organizational Influences ....................................................19
Knowledge Influence .............................................................................................19
Motivation ..............................................................................................................23
Organizational Culture ...........................................................................................27
Interactive Conceptual Framework ....................................................................................31
Summary ............................................................................................................................34

Chapter Three: Methodology .........................................................................................................35
Research Questions ............................................................................................................35
Participating Stakeholders .................................................................................................36
Interview and Focus Group Sampling Criteria and Rationale ...........................................36
Interview and/or Focus Group Recruitment Strategy and Rationale .................................36
Data Collection and Instrumentation .................................................................................37
Interviews ...............................................................................................................38
Focus Groups .........................................................................................................38

v
Documents .............................................................................................................39
Data Analysis .....................................................................................................................39
Step 1: Organize and Prepare the Data ..................................................................40
Step 2: Read the Data .............................................................................................40
Step 3: Code the Data ............................................................................................40
Step 4: Generate Description and Themes .............................................................41
Step 5: Representing Description and Themes ......................................................41
Credibility and Trustworthiness .........................................................................................41
Ethics ..................................................................................................................................42
Limitations and Delimitations ............................................................................................43

Chapter Four: Results and Findings ...............................................................................................45
Participating Stakeholders .................................................................................................45
Conceptual Framework ......................................................................................................46
Findings .............................................................................................................................47
Research Question One ......................................................................................................47
Factual and Procedural Knowledge .......................................................................48
Research Question Two .....................................................................................................53
Building Teacher Efficacy .....................................................................................54
Research Question Three ...................................................................................................60
Cultural Norms .......................................................................................................61
Synthesis ............................................................................................................................67
Knowledge .............................................................................................................68
Motivation ..............................................................................................................69
Organizational Culture ...........................................................................................70

Chapter Five: Recommendations ...................................................................................................72
Recommendations for Practice to Address KMO Influences ............................................72
Knowledge Recommendations ..............................................................................73
Motivational Recommendations ............................................................................75
Organizational Recommendations .........................................................................77
Strengths and Weaknesses of the Approach ......................................................................80
Limitations and Delimitations ............................................................................................80
Future Research .................................................................................................................83
Conclusion .........................................................................................................................84

References ......................................................................................................................................86

Appendices .....................................................................................................................................94
Appendix A Protocol for Face-to-Face Interview .........................................................................94
Appendix B Interview Protocol for Focus Group ..........................................................................98
Appendix C Document Analysis Protocol ...................................................................................101

vi
List of Tables
Table 1 Organizational Mission, Global Goal, and Stakeholder Goals ...........................................9
Table 2 Knowledge Influence, Knowledge Types, and Knowledge Assessment ..........................22
Table 3 Motivational Influence and Motivation Influence Assessment ........................................26
Table 4 Organizational Influence and Organizational Assessment ...............................................30
Table 5 Demographic Information of Participants ........................................................................46
Table 6 Demographic Information of Focus Group Participants ...................................................46
Table 7 Knowledge Recommendations: Summary of Knowledge Influences and
Recommendations ..........................................................................................................................74
Table 8 Motivation Recommendations: Summary of Motivational Influences and
Recommendations ..........................................................................................................................76
Table 9 Organizational Recommendations: Summary of Organizational Influences and
Recommendations ..........................................................................................................................78

vii
List of Figures
Figure 1: Presentation of Conceptual Framework .........................................................................32

viii
Abstract
This study evaluates the effectiveness of coding implementation in the classroom by examining
instructional practices that promote teacher efficacy. This study closely examined teachers’ role
in building the program’s structure and content, delivering coding instruction, and measuring
students’ proficiency with integrating coding skills to content areas. An evaluation of the
Computer Science Pathways Program was conducted through a qualitative study. Based on the
study’s purpose to seek knowledge, motivational and organizational influences as well as their
impact on teacher efficacy, the research was conducted by gathering data through individual
teacher interviews, focus groups, and document analysis. The research questions explored how
the organization was meeting its goal, how knowledge and motivational factors influence
teachers’ ability to learn and provide quality coding instruction, and how organizational
processes support the goal. In this study, the stakeholder population of focus was third through
eighth grade teachers who participate in the Computer Science Pathways Program at a K8 public
school. The study focused on teachers who are committed to the program using the TechSmart
Coding Curriculum and received coding training through professional development. This study
used inter-method mixing of data collection through interviews, a focus group, and document
analysis to gather data on stakeholder participation towards the organizational goal. The
researcher’s role was to organize the collection of data, analyze the data, establish themes and
interpret the meaning based on the research questions and conceptual framework. Based on
Creswell and Creswell’s (2018) five-step process for data analysis, this study followed each step
to interpret the data. A full report with the study’s analysis and recommendations was provided
to the site that may serve as next steps. A published dissertation may also assist other school sites
with computer science integration.
1
Chapter One: Overview of the Study
In today’s globally competitive market, there is a need to provide students with the skills
necessary to be successful in careers related to computer science. This dissertation addressed the
problem of the public educational system in the United States not adequately preparing students
for the computer science field’s competitive job market. The evidence highlights that there are
2.06 million jobs related to computer science with an annual growth of 4.78%, yet there were
only 41,793 computer science degrees awarded in 2017 (Data USA, 2017). This is an important
problem to address because half a million new jobs projected in the STEM fields are related to
computer science, and career opportunities in STEM will lack qualified candidates to fill
positions (Fayer et al., 2017). The underlying reason for this disconnect in the job market
between the computer science labor force supply and demand must be examined at various levels
of our current educational system.
Trends in the job market indicate the need for computer science education in K12 schools
so students can develop skills to be ready for related careers (Google Inc. & Gallup Inc., 2016).
Yet, 39 states out of 51 (including Puerto Rico) do not have an adopted policy to provide
computer science in high school, and only five states offer any computer science in K12
education (NCES, 2012). A Gallup poll on trends in the state of computer science in the U.S.
reveals that the two-thirds of teachers, principals and superintendents do not agree that computer
science instruction is a priority at their schools (Google Inc. & Gallup Inc., 2016). Additionally,
the poll reveals that more computer science opportunities were offered at the high school level
than kindergarten through

eighth grade, with 27% of high school principals responding that the
school offered at least three to five different types of computer science courses compared to 4%
in elementary school (Gallup, 2016).

2
Computer science instruction in the elementary grades is crucial in preparing students for
a competitive job market (Howard, 2018). Early childhood experiences allow students to be
better prepared for specialized courses in high school, which supports the need for a pathway
across grade levels (California Department of Education [CDE], 2018; Howard, 2018).
Additionally, studies show early childhood participation can impact students’ interest and
motivation in the computer science field (Berg et al., 2018; Michell et al., 2018; Shashaani,
1994; Vekiri, 2013). Early childhood experiences in computer science can influence a child’s
attitude towards computer science as a college major or career choice.
Needs for Computer Science Implementation
In September 2014, Assembly Bill 1539 passed into law, which amended section 60605.4
to the California Education Code to require the development computer science content standards
on or before July 31, 2019 (CDE, 2018). In 2016, President Obama released Computer Science
for All, an initiative to help fund computer science instruction in schools and to address
challenges with race and gender disparities. In November 2016, the Computer Science
Framework for K-12 was released, followed by a draft of the California Computer Science
Strategic Implementation Plan Draft in September 2018. However, based on report findings from
Google, a shortage of qualified computer science teachers contributes to the lack of computer
science being taught in schools (Google Inc. & Gallup Inc., 2016). There is an increased need for
more trained and qualified teachers to provide computer science instruction in public education
(Howard, 2018). Findings from a qualitative study conducted in five states show that teachers
face challenges with their professional needs in learning computer science (Howard, 2018).
Administrators at K12 public schools find difficulty in preparing qualified teachers to
teach computer science, particularly in elementary education (Howard, 2018). Based on a Gallup

3
poll (2016), 74% of Superintendents and 63% of K12 principals who do not have computer
science instruction at their schools and districts stated that a lack of qualified teachers is the main
factor in not offering computer science to students.
To keep up with new and quickly advancing technology, teachers need relevant
professional development to support their learning (Menekse, 2015). Teacher efficacy plays an
important role in implementing a quality program (Bender et al., 2016). Increased content
knowledge and skills in computer science will help educators build confidence and motivation
that have an impact on active choice, persistence, and mental effort (Clark & Estes, 2008). This
study further examined the development of pedagogical knowledge, motivational factors, and
beliefs that affect teachers’ learning and ability to successfully deliver computer science
instruction.
Related Literature
In elementary education, the teachers’ knowledge and ability to teach computer science
are key factors in students’ learning computer science (Howard, 2018; Manches & Plowman,
2017; Moreno-León et al., 2016). A quasi-experimental study examined the relationship between
the teachers’ programming ability after a 4-week course and the outcome of the students’
performance. The study found that successfully executed programming lessons increased the
academic performance of sixth graders at two different school sites (Moreno-León n et al.,
2016). Additionally, elementary teachers were motivated to develop skills, evaluate tools and
create their own lessons when they are provided with opportunities for professional learning in
the area of computer education (Manches & Plowman, 2017). Manches and Plowman (2017)
state that, when educators feel confident about their knowledge in what they are teaching, there
is a positive impact on student learning. Howard (2018) agrees that elementary teachers need

4
ongoing support through professional development and rich conversations regarding computer
science, focusing on pedagogy and practice. Based on a qualitative study using interviews with
K–5 teachers who provide computer science instruction in elementary grades, continual teacher
support and training are imperative in sustaining practices in the area of computer science
integration (Howard, 2018). Opportunities for professional growth through computer science
professional development have an impact on teachers’ ability to provide quality instruction that
impacts student achievement (Howard, 2018; Manches & Plowman, 2017; Moreno-León, 2016).
Organizational Context and Mission
CS Academy K8 School is a public school located in an urban city in California that
serves 924 students in kindergarten through eighth grade. Based on the School Accountability
Report Card, CS Academy K8 School is composed of a diverse student population: 84% Asian,
6% Caucasian, 5% Hispanic, and 1% African American. English language learners represent
about 25% of the student population. CS Academy K8 School offers a wide range of educational
programs, including a Computer Science Pathways Program, supported by 30 classroom teachers
and 30 support staff members. CS Academy K8 School’s vision statement was created by the
student, teacher and parent community. The vision exemplifies the school’s purpose in
developing students to become leaders, forward thinkers, and strategists who become problem
solvers, impacting their community and the world. CS Academy K8 School’s mission is for their
students to empower each other to use 21st century skills, such as to create, communicate,
collaborate and think critically, in an environment that is rich with technology.
CS Academy K8 School was built in 2004 based on a vision centered on science and
technology. Over 16 years, cumulative practices helped build upon a vision that continues to
thrive through the school’s Computer Science Pathways Program, with purposeful and

5
personalized learning in each classroom. First and second grade students use one-to-one iPads,
and third through eighth grade students have one-to-one laptops that serve as vehicles for
optimizing learning. The program offers progressive coding instruction and application of coding
skills tied to curricular content, with a focus on relevant career pathways such as robotics, web
development, and app development. Coding skills are taught during the school day to all students
and build upon skills learned from kindergarten through eighth grade, addressing coding
concepts from block coding to language-based coding. The program highlights the greater
purpose of utilizing tools and technology skills to discover pathways towards passion,
accelerated academics, and/or future careers. The state of California awarded the program with
the 2016 Gold Ribbon recognition for its use of cutting-edge tools and the design thinking
process. The school was also recognized with the 2018 California Distinguished School Award
for its model program and innovative practices. Most recently, the school received the 2019
National Blue Ribbon recognition for its exemplary program.
Unique to the district is a department dedicated to informational technology, innovation
and instructional support. This department supports the Computer Science Pathways Program by
offering relevant professional development led by teachers on special assignment (TOSAs).
TOSAs help teachers design lessons by attending planning sessions and supporting classroom
instruction in the areas of coding, robotics, and STEM instruction. The site has an IT specialist
who is on call for technology needs and recommends resources and tools that best meet the
vision of the program. The district values technology integration and emphasizes its importance
through its local control accountability plan (LCAP). LCAP Goal 2 conveys the district’s
commitment to ensuring quality resources and tools, offering teachers professional development,

6
and supporting student proficiency in informational literacy. Based on the LCAP, funding is
allocated for technology, which includes equipment, professional development, and personnel.
Organizational Performance Goals
By the end of the 2019–2020 school year, all third through eighth grade students at CS
Academy K8 School will apply coding skills to a focus in computer science, such as robotics,
circuitry, drones, app development, or website creation. Students use a coding platform called
TechSmart, which monitors students’ progress in developing and applying coding skills. With
the help from the district, this goal was created during the 2017–2018 term by staff, parents, and
students as a part of a 3-year computer science sustainability plan in response to the state’s
initiative on expanding computer science instruction in K12 education. The program’s goal is
measured by the students’ ability to apply coding to robotics, drones, artificial intelligence, and
app development at a showcasing event, such as Robot Nation, Inventions Fair, Congressional
App Challenge, Take Flight Drone Challenge, and the Vex Robotics Competition.
Importance of the Evaluation
An evaluation of the organization’s performance is important in maintaining progress
towards the organizational goal to have all third through eighth grade students apply their
learning of coding towards a computer science pathway. Data from the evaluation will help
identify areas of strengths and challenges that allow stakeholders to plan next steps towards
implementation. An evaluation of the program will help teachers reflect on best practices that
enhance coding lessons, which will help build teacher efficacy and increase student engagement.
Additionally, the program evaluation will offer recommendations for school sites to build
programs and practices around computer science integration, increasing the number of schools
and qualified teachers to fulfill the state’s initiative Computer Science for All.

7
Description of Stakeholder Groups
Three stakeholder groups play a role in the success and outcome of the Computer Science
Pathways Program. The staff, parents, and students work together to support the program in
different capacities. The instructional staff provides coding instruction and reflects on teaching
practices and support systems that help them teach computer science in the classroom. Support
staff meets regularly to discuss the program’s systematic needs, such as information technology
support. Annually, as a part of a parent meeting, feedback is collected from parents and teachers
to conduct a needs assessment and to review necessary changes to the program. Parent groups
help fundraise to provide monetary support for program sustainability. Students have an
opportunity to express their thoughts and ideas of their most meaningful computer science
experience by drawing a picture of their most memorable computer science lesson. The
stakeholder groups collaboratively review the goal based on the school’s vision, monitor the
progress and evaluate the effectiveness of the program based on teacher, parent and student
feedback during site council meetings held five times a school year.
Stakeholder Group for the Study
Teachers’ commitment to implementing coding instruction is needed at the start of the
school year. By December of 2019, third through eighth grade teachers will provide all students
with weekly coding instruction during the instructional day, evidenced by student work
performance through TechSmart. This stakeholder goal was established in collaboration with the
teaching staff, based on the school’s vision and school district technology initiatives. To help
support this goal, teachers are provided one-to-one training on coding through three trainings and
given on-call support by the TechSmart staff throughout the school year. Evidence of weekly
instruction includes lesson plans, progress monitoring data from the TechSmart platform, and

8
classroom activities that require students to apply coding skills. Teachers meet twice a week for
planning through professional learning communities (PLCs). Teachers work collaboratively to
discuss and analyze data, identify student needs, and plan and create lessons through shared best
practices. PLCs are well established at CS Academy K8, and teachers utilize this time to plan
technology integration for the Computer Science Pathways Program. With the help of relevant
resources, training and systematic planning time, teachers commit to delivering quality lessons
and creating meaningful learning experiences that allow students to practice coding skills.
This study closely examined the teachers’ role of building the program’s structure and
content, delivering coding instruction, and measuring students’ proficiency with integrating
coding skills to content areas as well applying coding to a computer science pathway.
Additionally, this study examined teacher support through relevant professional development,
which is essential in improving effective instructional practices and increasing student
motivation (Howard, 2018).
Stakeholder Groups’ Performance Goals
Table 1 addresses the organizational mission, global goal, and stakeholder goals. This
table identifies tangible goals and the timeline for each stakeholder. The stakeholder goals
support the organization mission and vision as well as the organization’s performance goal.

9
Table 1
Organizational Mission, Global Goal, and Stakeholder Goals
Organizational Mission and Vision
At CS Academy K8 School, students empower each other to apply 21st century skills to
create, communicate, collaborate and think critically using technology. CS Academy K8
students will become the strategists and leaders who innovate solutions to impact their
community and the world.

Organizational Performance Goal

By the end of the 2019–2020 school year, all third through eighth grade students at CS
Academy K8 will apply coding skills to a computer science pathway, which includes a
showcase opportunity at the end of the school year. The program’s goal will be measured by
students’ ability to apply coding to a pathway such as robotics, circuitry, drones, app
development, and website creation through multiple measures including portfolio and projects.

Stakeholder 1
Teachers
Stakeholder 2
Parent Community
Stakeholder 3
Students
By December 2019, third

through eighth grade teachers
will provide all students with
weekly coding instruction
during the instructional day,
evidenced by student work
performance through
TechSmart, a coding program.

By March 2020, 70% of the
parent community will
attend an informational
meeting and/or classroom
tour held three times a
school year, evidenced by
meeting minutes and sign-
ins.

By May 2020, 100% of
students in grades third
through eighth grade will
showcase a culminating
project based on coding skills
applied to a computer science
pathway at a community
event or competition.

Methodological Framework
The purpose of this research is to evaluate the effectiveness of coding implementation in
the classroom by examining instructional practices that promote teacher efficacy. Clark and
Estes (2008) present a conceptual framework that helps identify knowledge, motivation, and
organizational (KMO) influences that affect stakeholder performance as well as the
organizational goal. Gaps were identified between the performance level and the goal by
examining KMO influences using Mayer’s (2011) cognitive processes on meaningful learning
and active processing and motivational theories on self-efficacy and goal orientation.

10
Additionally, this paper addressed organizational influences that impacted teacher efficacy
through resources, professional development, and workplace culture. The study’s outcome offers
recommendations for organizational practice in the areas of KMO resources to achieve the
organizational goal.
The following questions address the KMO influences that affected the program and
helped identify needs to reach the organizational goal.
1. To what extent is the school meeting its goal to offer computer science instruction to all
third through eighth grade students?
2. What increases knowledge and motivation to help build teacher efficacy in the area of
computer science instruction?
3. In what ways does the organizational culture support teacher efficacy in the area of
computer science integration?
An evaluation of the Computer Science Pathways Program was conducted through a
qualitative study. Based on the study’s purpose to seek KMO influences and their impact on
teacher efficacy, the research was conducted by gathering data through individual teacher
interviews, focus groups and document analysis. The researcher triangulated the data based on
the varied data collection methods. The research questions explored how the organization was
meeting its goal, how knowledge and motivational factors influence teachers’ ability to learn and
provide quality coding instruction, and how organizational processes supported the goal.
Definitions
● Partnership for 21st Century Skills (P21) is 21st century readiness, involving learning and
innovation skills, or the 4Cs: create, collaborate, communicate, critically think
(Partnership for 21st Century Skills, 2008)

11
● Professional learning community (PLC) are meetings that allow a group of educators to
collaborate on best teaching practices, share their knowledge and lessons, discuss and
analyze student data, plan lessons, and create assessments to increase student
achievement.
● School site council is a decision-making group of parents, classified staff, certificated
staff, administration and students.
● SMART Goals stand for specific, measurable, action-oriented, realistic, and timely goals.
Organization of the Project
This study is composed of five chapters. Chapter One contains the problem of practice,
the importance of the topic, the purpose of the study, the background on the organization, and
information on the stakeholder group for the study. Chapter Two provides a review of relevant
literature on declining student interest in computer science, early influences, and building teacher
efficacy through professional development and goal setting. Chapter Three includes the research
methodology, including the research questions, sample and population, instruments and methods
for data collection. It also addresses the validity and reliability of the study. Chapter Four reveals
the findings and results from the study, which were analyzed based on the research questions and
conceptual framework. Chapter Five discloses the recommendations and concludes the study
with an evaluation plan.

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Chapter Two: Review of the Literature
The following literature review identifies current issues that may have an impact on
computer science education in public schools and analyze possible gaps in the implementation of
computer science instruction in K8 classrooms. This review addresses the decline in student-
based interest in pursuing computer science as a career choice, especially among females. The
literature review also highlights the need for a progressive building of skills in computer science,
beginning in the early grades. Lastly, teacher support is examined through professional
development and the influence it has on computer science integration in the classroom. The
literature review is followed by a gap analysis, based on Clark and Estes’ (2008) gap analysis
conceptual framework, that examines the KMO influences on building teacher efficacy in
computer science education.
Decline in Student Interest in Computer Science Education
While the need for qualified STEM candidates continues to grow, there is a decline in
student-based interest in pursuing computer science as a career option (Biggers et al., 2008;
Carter, 2006; Yardi & Bruckman, 2007). Yardi and Bruckman (2007) state that students do not
see a relevant connection with computer science and their future. Through qualitative research
conducted by interviewing participants ranging from teenagers to graduate students, findings
revealed that students perceive computer science to lack purpose in real-world applications
(Yardi & Bruckman, 2007). Carter (2006) agrees that many students who show an aptitude for
computer science do not choose computer science as a college major. A qualitative study with
828 students showed that a majority (80%) of them did not know what computer science entailed
as a college major (Carter, 2006). Biggers et al. (2008) argued that the dropout rate of computer
science college majors is high due to differences in content-area knowledge and a lack of

13
understanding of how computer science’s real-world applications. Based on a qualitative study
with computer science majors at Georgia Tech, 60% of the students left the major by their first
year due to content area rigor and perceptions of relevancy (Biggers et al., 2008). A decline in
interest leads to a shortage of qualified candidates pursuing computer science as a possible career
due to a lack of understanding on how computer science is applied in the real world.
Gender Disparities
Gender disparities affect females’ interests in computer science as a major in college or
as a career choice (Beyer, 2014; Cheryan et al., 2011; Cheryan et al., 2013; Cheryan et al., 2011;
Master et al., 2015). Cheryan et al. (2011) add that women who had interactions with
stereotypical role models had immediate and lasting negative perceptions of computer science. A
quantitative study with 100 female participants with non-computer science majors indicated that
women’s perspectives were altered negatively by short interactions with stereotypical role moles
due to a lack of connection (Cheryan et al., 2011). Similarly, Cheryan et al. (2011) state that
women who interacted with stereotypical males, when compared with women who interacted
with non-stereotypical role models, did not believe they would be successful in computer
science. Based on a quantitative study with 85 women in non-computer science fields, women
did not connect with stereotypical role models whom they viewed as dissimilar and their
interactions damaged women’s views of computer science (Cheryan et al., 2011). In addition to
the stereotype of those who are successful in the computer science field, there may be a
disconnect between what women seek in their career and what they perceive is available as a
career in computer science.
Beyer (2014) suggests that gender stereotypes portrayed in computer science positions
deter women from pursuing a computer science major. Stereotypes include intrinsic concerns,

14
such as a lack of social interaction, which women value in their working environment. A
quantitative study, involving 1,319 first-year college students in a public liberal arts university in
the United States, found that women were more concerned about seeking careers that provide
social interaction, helping others, and family-oriented careers (Beyer, 2014). Negative
stereotypes of computer science majors lacked these qualities, and women lost interest in seeking
computer science as a career choice (Beyer, 2014). Cheryan et al. (2013) highlight the role media
plays in representing computer scientists as stereotypical male, which deters women’s interest in
computer science. Based on a quantitative study with 318 students from Stanford University,
stereotypical media, which depicted computer scientists as highly intelligent and socially
awkward, prevented women from entering the computer science field (Cheryan et al., 2013).
However, Master et al. (2016) argue that increasing the sense of belonging for women in the
computer science field can significantly reduce gender disparities. Based on a study conducted
with two high schools, a sense of belonging is one of the lead indicators in computer science
interest levels based on stereotypical and non-stereotypical environments (Master et al., 2016).
Evidence from these studies highlights the impact gender stereotypes have on women,
discouraging them from further pursuit of computer science (Beyer, 2014; Cheryan et al., 2013;
Cheryan et al., 2011; Master et al., 2015). Beyond addressing stereotypical role models in
computer science, stereotypical learning environments need to be addressed to improve the
continued interest in computer science by females.
Both non-stereotypical and stereotypical environments influence attitudes toward
computer science (Cheryan et al., 2009; Cheryan et al., 2011; Frieze & Quesenberry, 2013).
Cheryan et al. (2011) claim that non-stereotypical learning environments increase interest in
computer science. Based on two experiments to change learning environments to non-

15
stereotypical virtual learning spaces, gender disparities decreased and interest levels for
computer science increased for females (Cheryan et al., 2011). Cheryan et al. (2009) agree that
environments that promote masculinity discouraged women from feeling a sense of belonging
when participating in computer science. A quantitative study conducted with 42 undergraduate
students revealed that women were not interested in joining an environment that promoted
masculine stereotypes and did not feel a sense of ambient belonging, especially in the field of
computer science (Cheryan et al., 2009). However, Frieze and Quesenberry (2013) argue that, in
a non-stereotypical environment, women can be successful in computer science and help create a
culture that promotes computing. Based on a case study conducted by Carnegie Mellon
University in 2011, qualitative data from interviews and surveys of college students enrolled in
computer science found that71% of females felt that the computer science environment was a
positive match (Frieze & Quesenberry, 2013). All of these cited authors agree that a non-
stereotypical learning environment can positively influence how women perceive computer
science (Cheryan et al., 2009; Cheryan et al., 2011; Frieze & Quesenberry, 2013). Gender
equitable practices at an early age impact female students’ perceptions of pursuing computer
science.
Early Influences
Early childhood influences can have an impact on student-based interest in the computer
science field. (Berg et al., 2018; Michell et al., 2018; Shashaani, 1994; Vekiri, 2013). Shashaani
(1994) states that early childhood experiences in computer science influence future attitudes
towards computer science as a college major or career choice. A qualitative study with 1,730
secondary school students revealed that there is a high correlation between exposure/experience
to computer science and positive attitudes and interests (Shashaani, 1994). Michell et al. (2018)

16
agree that early childhood experience at home can affect student interests in computer science.
Quantitative analysis of data collected through teacher surveys in Australia revealed that one
barrier to girls pursuing computer science is a lack of support in the home and discouragement
from parents (Michell et al., 2018). Berg et al. (2018) concur that many young girls have
negative perceptions of computer scientists based on stereotypes and childhood experiences. A
qualitative study with 96 children indicates that girls between ages 13 and 17 believe that
becoming a computer scientist means being alone and hinders a female from having a family in
the future (Berg et al., 2018). Vekiri (2013), however, argues that girls relate to classroom
instruction that values the relevancy of computer science in application to life and social
situation. Based on a qualitative study conducted with 326 adolescents in middle school, findings
showed that pedagogical practices used in the classroom affect girls’ motivation to pursue
computer science (Vekiri, 2013). The evidence from the studies indicates that early childhood
experiences can have an impact on future interest levels in computer science (Berg et al., 2018;
Michell et al., 2018; Shashaani, 1994; Vekiri, 2013). Exposure to computer science at early
grades needs to be part of a progressive multi-year plan with clear long-term goals that could
help students to transition to successful entry into the field of computer science as a college
major as well as a career.
Progressive skills in computer science, beginning in early childhood, could help support
students’ understanding of computer science. (Barr & Stephenson, 2011; Sengupta et al., 2013;
Webb et al., 2017). Webb et al. (2017) state there is a growing consensus that a range of skills
should be addressed beginning in primary grades. Based on studies from five countries
worldwide, qualitative data show that students benefit from developing an understanding of the
discipline, skills, and thinking strategies in elementary schools (Webb et al., 2017). Sengupta et

17
al. (2013) agree that problem-solving skills are essential in preparing students for STEM and that
progressive plans, focusing on early intervention with computer science skills, better prepare
students for career readiness in the computer science field (Sengupta et al., 2013).
Barr and Stephenson (2011) concur that long-term goals are essential in building
progressive computer science skills, beginning at an early age. A multi-phased project aimed at
building computational thinking displayed the work of 26 school site leaders that developed a
shared vision across grade level spans, starting in primary grades (Barr & Stephenson, 2011).
The study concluded that schools can better prepare students for the computer science field by
developing a progressive plan to build skills across grade levels (Barr & Stephenson, 2011).
Progressive plans, focusing on early support with computer science skills, better prepare students
for career readiness in the computer science field. Therefore, professional development for
teachers that address instructional practices and skills aligned with computer science standards,
is essential in building teacher capacity.
Building Teacher Efficacy in Computer Science Education
Teacher support through professional development is a key factor influencing computer
science integration in the classroom (Çetin, 2017; Menekse, 2015; Sun & Strobel, 2014). Çetin
(2017) claims professional development can affect teachers’ views in a positive manner
regarding computer science integration in the classroom. As a result of a professional
development study conducted in Turkey, findings indicate that the majority of teacher
perspectives (59%) on computer science integration were changed positively due to professional
development (Çetin, 2017). Menekse (2015) also agrees that a lack of quality professional
development can lead to a shortage of K12 computer science teachers. Based on the findings of
82 studies published in professional journals, a majority of the teacher training sessions on

18
computer science did not measure up to quality professional development standards nor cover
effective practices to improve teacher capacity (Menekse, 2015). Sun and Strobel (2014) found
equipping teachers with subject matter knowledge impacts student learning. Based on a study
conducted with three sets of qualitative data, professional development in the area of engineering
improved teachers’ content-area knowledge as well as strategies for engineering integration (Sun
& Strobel, 2014). Professional development for educators, to support content-area knowledge as
well as strategies for integration, have an impact on student learning in computer science-related
fields, and teacher capacity has an impact on enhancing students’ mindsets towards computer
science.
Teachers’ confidence in their knowledge of computer science as a content subject affects
the quality of lessons that are delivered in the classroom (Çetin, 2017; Howard, 2018; Menekse,
2015; Sun & Strobel, 2014). Based on a qualitative case study that used purposeful sampling,
five teachers were interviewed to examine their beliefs on computer science programs and
preparation (Howard, 2018). The study concluded that teachers were more motivated to teach
computer science when they shared universal beliefs and were provided sustainable support
systems such as ongoing professional development (Howard, 2018). Teachers rely on
professional development to increase their subject matter content in computer science, which
improves their confidence about the subject and heightens new insight into computer science
integration. As with any subject, a well-prepared and confident computer science teacher can
deliver a positive experience for students through meaningful lessons.
Clark and Estes (2008) Conceptual Framework in Gap Analysis
Clark and Estes (2008) present a conceptual framework that helps identify KMO
influences that affect stakeholder performance as well as the organizational goal. Gaps are

19
identified between the performance level and the goal by examining KMO influences.
Additionally, the study uses Mayer’s (2011) cognitive processes on meaningful learning and
active processing and motivational theories to examine self-efficacy and goal orientation. This
study also analyzes organizational influences that affect teacher efficacy through resources,
professional development, and workplace culture.
Using the KMO gap analysis, the needs to meet the performance goal were analyzed,
addressing the factors that influence teacher efficacy. First addressed was the mastery of content
knowledge and skills to provide weekly coding lessons in the classroom. Then, motivational
factors are examined when building teacher efficacy and establishing goals. Lastly,
organizational needs to promote teacher efficacy, such as processes, resources, and professional
development support, were further studied to help teachers and the school achieve the
organizational goal.
Knowledge, Motivation and Organizational Influences
The following section examines relevant literature, focusing on the impact knowledge,
motivation, and the organization have on teacher efficacy to achieve the stakeholder goal. By
December of 2019, third through eighth grade teachers will provide all students with weekly
coding instruction during the instructional day, evidenced by student work performance through
TechSmart, a coding program. Teachers’ content knowledge in coding, as well as their
motivation to learn coding, are essential in the success of this program.
Knowledge Influence
Based on a revision of Bloom’s Taxonomy, there are six areas of cognitive domain.
Krathwohl (2002) categorizes knowledge into “taxonomies” or structures of the cognitive
process (remember, understand, apply, analyze, evaluate and create). The categories range from

20
simple cognition to more complex thinking that represents factual knowledge, conceptual
knowledge, procedural knowledge, and metacognitive knowledge. The use of higher-order
thinking skills helps the learner interact with knowledge, which leads to a more meaningful
learning experience. Different dimensions of learning indicate which cognitive skills are required
to complete the objective. Rueda (2011) also suggests that, for learning to be integrated into
higher levels of application, learners need to know the why and when in addition to what and
how.
Teachers need factual and procedural knowledge through training opportunities that serve
as the basis for further development. Teachers need training on principles and programming
structures used in coding, basics of coding from block coding to language-based coding,
including definitions, inquiry and algorithms and strategies on coding integration and
applications. Teachers can build efficiency by learning content knowledge, provided through
quality professional development opportunities and training. The coding training from
TechSmart begins by teaching educators the basics of coding, from block coding concepts to
language-based coding. Teachers learn factual knowledge on how to code using block coding
and language-based coding and then learn the principles and structures used in coding. The
training becomes progressively more rigorous and moves from factual lessons based on
definitions to integration of coding concepts in the classroom. Specific skills are addressed, such
as inquiry, algorithms, and techniques used when applying coding to a program. Lastly, teachers
learn to apply their knowledge of coding to perform tasks or create products using code such as
applications and websites.
Transfer of learning. Learning content knowledge alone does not ensure that the
learning has transferred to long-term memory or is applied to higher levels of thinking (Schraw

21
& McCrudden, 2006). Based on information processing theory, the learner needs to take what
was learned and interact with new knowledge to construct meaning to retain the information,
which requires high order thinking skills (Schraw, 2006). Progression from fact retention to
application is needed to make learning permanent. According to Schraw and McCrudden (2006),
information and learning become permanent in the working and long-term memory when
information is inferred, accessible, and organized. The learner must engage and interact with the
learning, such as prior knowledge, to create meaningful experiences that can be retained in
memory. Active processing occurs when the learner uses cognitive processes in a meaningful
context (Mayer, 2011).
At CS Academy K8 School, TechSmart training sessions serve as opportunities for
teachers to experience new knowledge to build experience integrating the knowledge to practice
in the classroom. Training sessions are held twice a year and allow teachers to learn new
concepts, implement lessons in the classroom and attend training again mid-year to develop new
lessons based on prior experience. Third through eighth grade teachers are required to attend
coding training to learn the rules and technical elements to block and language-based coding. For
teachers to apply factual and procedural knowledge of coding to classroom practice, teachers are
provided ongoing opportunities to practice coding through the TechSmart online platform.
Teachers prepare lessons collectively with their colleagues and practice coding on robots,
circuitry, computer applications, artificial intelligence programs, drones, and websites. Teachers
benefit from interacting with new knowledge to better understand the subject matter and to
implement the knowledge to practice in the classroom (Koehler & Mishra, 2005; Niess, 2005).

22
Table 2 shows the knowledge influences that address the organizational and stakeholder
goals. The table also identifies the knowledge type and assessments needed to support the
knowledge influence.
Table 2
Knowledge Influence, Knowledge Types, and Knowledge Assessment
Organizational Mission and Vision
At CS Academy K8 School students empower each other to create, communicate, collaborate and
think critically in a technology-rich environment. Students will become the forward thinkers,
strategists, and leaders who transform their future and innovate solutions for a better world.
Organizational Global Goal
By the end of the 2019–2020 school year, third through eighth grade students at CS Academy K8
School will apply coding skills to a computer science pathway. The program’s goal will be
measured by students’ ability to apply coding to robotics, circuitry, drones, app development, and
website creation. Additionally, all students are given the opportunity to join a competitive team
and/or showcase their coding ability at a culminating event.
Stakeholder Goal
By December 2019, third

through eighth grade teachers will provide all students with weekly
coding instruction during the instructional day, evidenced by student work performance through
TechSmart, a coding program.
Knowledge Influence

Knowledge Type
(i.e., declarative
(factual or
conceptual),
procedural, or
metacognitive)
Knowledge Influence Assessment
Transfer of Learning
Teachers need knowledge in
coding language.
Declarative Factual,
Conceptual
Teachers were asked to attend 3 full
day trainings on coding led by
TechSmart and apply their content
knowledge in practice activities,
evidenced by training agenda and
TechSmart training activities.
Transfer of Learning
Teachers need to know how
coding skills build progressively.
Procedural Teachers were asked to create a
scope and sequence to teach and
build coding concepts.
Transfer of Learning
Teachers need to know how to
integrate higher order thinking
skills in cross-curricular lessons
that embed coding skills.
Procedural Teachers were asked to observe a
coding lesson integrating
computational thinking and were
provided a planning day to create
lessons

23
Motivation
Many factors influence how knowledge from training transfers to skills in the workplace
through self-efficacy and goal setting. Grossman and Salas (2011) indicate that, the higher the
self-efficacy, the higher the confidence level in successfully completing the task. High
motivation is a strong indicator of teachers’ interest in building efficacy in areas they are not
initially familiar with, such as coding. Learners must believe that they can learn and that their
efforts will make an impact on the outcome for the transfer of knowledge from a training session
to occur (Grossman & Salas, 2011). Rueda (2011) also suggests that higher levels of interest
impact intrinsic motivation, leading to a stronger outcome.
Setting goals helps clarify the expectation as well as provides the motivation to complete
the task by validating the purpose of the goal. Goal content, based on social cognitive theory,
focuses on goal characteristics by defining how tangible the goal is to achieve (Bandura, 2001).
Rueda (2011) states that people give up on goals when goals do not specify how to achieve them
or if they are too challenging. Goal orientation is centered on the reasons to achieve the goal,
whether for mastery or performance. Mastery goal orientation looks to achieve the goal by
learning the content, while performance goal orientation focuses on demonstrating the
knowledge (Rueda, 2011). Mayer (2011) concurs that learners put forth more effort to achieve
the goal when the goal includes building proficiency based on a mastery goal.
Building teachers’ self-efficacy. Building teachers’ self-efficacy in coding begins with
building confidence with one’s ability to learn and apply coding skills. Teachers are more
motivated to build individual efficacy when they receive validation of their beliefs and
capabilities, such as thorough feedback from administrators based on goals. Timely feedback
allows teachers to reflect on instructional practices, to make adjustments as needed, and build

24
confidence as they work towards goals. According to Clark and Estes (2008), commitment to
goals increases when one’s confidence to complete the goal increases. Additionally, Clark and
Estes (2008) state that the team’s confidence also helps build team collective efficacy and the
team’s ability to reach their goals. Individuals and teams with high efficacy are more persistent,
positive, and confident and exert more effort towards meeting goals (Rueda, 2011). Educators’
ability to teach coding in the classroom is influenced by their levels of confidence and
commitment to achieve goals.
Based on Bandura’s (1977) self-efficacy theory, teachers’ perception of how well they
are versed in technology affects how they utilize technology in the classroom. Fanni et al. (2013)
state that self-efficacy can change teachers’ perception of technology and its use in the
classroom. A qualitative study with primary school teachers confirmed that, by increasing
teachers’ knowledge in computer science, teacher efficacy also increased (Fanni, et al., 2013).
Positive attitudes and perceived self-efficacy with technology affect the implementation of
computer education in the classroom (Celik & Yesilyurt, 2013). A study conducted by Celik and
Yesilyurt (2013) revealed that computer anxiety and attitudes towards technology have an impact
on teacher efficacy for new teacher candidates. Marcoulides (2005) also found a significant
correlation between computer anxiety and computer efficacy in a school setting. Confidence in
one’s ability in technology and having a positive perception toward its use help teachers build
efficacy to implement technology in the classroom.
Goal orientation. Based on goal orientation theory, specific mastery goals help motivate
teachers by providing direction towards an outcome (Pintrich, 2003). Mastery goals help foster a
better understanding of performance expectations and desired outcomes. Clark and Estes (2008)
state that every member of the team should thoroughly know their performance goal and how

25
they will achieve the goals. Goals should be clearly outlined to include levels of mastery and a
focused approach on how the goal will be achieved. SMART goals help educators meet mastery
goals by providing attainable objectives that are task-oriented (Doran, 1981). SMART goals can
help the educator achieve tasks by providing clarity to what needs to be completed, how it will
be measured, and when it will be completed. According to Reeves and Fuller (2018), SMART
goals allow organizations to prioritize what is important, break down complex steps, identify
needed resources, and measure progress.
Setting goals based on evidence helps both students and teachers connect their goals to
the learning. O’Neill (2000) states that goals that are set based on student learning help teachers
to “see, learn from, and communicate their results” (p. 1). SMART goals allow educators to
measure how effective their instructional practices are in connection to student learning.
Strategies should be based on evidence and progress measured to confirm effectiveness (Jung,
2007). Additionally, data-centered goals provide information on how successful school-wide
programs are and how great an impact there was on student learning.
Table 3 shows the motivational influences that address the organizational and stakeholder
goals. The table also identifies the assumed motivational influences and assessments needed to
support the motivational influence.

26
Table 3
Motivational Influence and Motivation Influence Assessment
Organizational Mission and Vision
At CS Academy K8 School, students empower each other to create, communicate, collaborate
and think critically in a technology-rich environment. Students will become the forward
thinkers, strategists, and leaders who transform their future and innovate solutions for a better
world.
Organizational Global Goal
By the end of the 2019–2020 school year, third through eighth grade students at CS Academy
K8 School will apply coding skills to a computer science pathway. The program’s goal will
be measured by students’ ability to apply coding to robotics, circuitry, drones, app
development, and website creation. Additionally, all students are given the opportunity to join
a competitive team and/or showcase their coding ability at a culminating event.
Stakeholder Goal
By December 2019, third

through eighth grade teachers will provide all students with weekly
coding instruction during the instructional day, evidenced by student work performance
through TechSmart, a coding program.
Assumed Motivation Influence Motivational Influence Assessment

Building Teachers’ Self-Efficacy - Instructors
need to believe they are capable of learning and
teaching coding to their students.
Interview on Self-Efficacy:
“What is your experience with learning a
new concept such as coding?”
“Tell me about your experience with
computer science instruction.”
Goal Orientation - Instructors create mastery
goals for themselves and teams.
A staff interview will measure the success
of goal implementation. An opportunity
for reflection was provided.

Does your school have computer science
goals, in one year, five year?
How involved are you with the process of
creating the vision?
How much do the goals come into
meetings with your colleagues?

27
Organizational Culture
Culture can be defined in a work environment through goals, values, and beliefs (Clark &
Estes, 2008). Organizational culture affects how well employees work collaboratively to meet
organizational goals. The culture of the organization is formed based on shared experiences and
develops an identity based on core values over time (Schein, 2017). According to Schein (2017),
there are three levels of culture analysis: artifacts, espoused beliefs and values, and basic
underlying assumptions. These levels represent visible and tangible evidence of observable
cultural structures as well as abstract beliefs that underlie cultural norms. Observable artifacts
include behavior of the group that affects the climate of the organization (Schein, 2017).
Espoused beliefs and values of an organization are the values and goals that have been validated
and accepted by the group (Schein, 2017). Groups find comfort with similar underlying
assumptions of an organization and those assumptions provide a “sense of identity” (Schein,
2017, p. 23). Through shared experiences, groups within organizations form relationships, build
trust and collectively share practices in safe learning environments.
Organizations can define and build culture based on cultural settings and cultural models
(Gallimore & Goldenberg, 2001). Cultural settings include tangible tasks, such as providing
feedback, that affect the working environment. Cultural models include organizational practices
that share ideas and understandings of how the organization operates. (Gallimore & Goldenberg,
2001). Both cultural settings and cultural models have an impact on an organization’s climate
and working relationships.
Building trust through feedback. Schein’s (2017) three levels of cultural analysis
identify three distinct levels of culture that feature visible artifacts, values and ideals, and covert
assumptions of organizational culture. Providing and receiving feedback can also be a part of an

28
organization’s ideology as well as a tangible artifact that is a part of an organizational process
(Schein, 2017). As a part of the organization’s cultural setting, effective communication through
feedback can affect employee motivation and the overall climate of the organization (Roussin &
Zimmerman, 2014; Stone & Heen, 2014). Feedback allows educators to reflect on teaching
practices based on information received through methods such as a formal evaluation, classroom
walkthrough, or in conversation. Roussin and Zimmerman (2014) state that how an individual
responds to feedback indicates how much trust exists in the school culture. Schools that
exhibited trusting relationships used feedback to improve instructional practices (Roussin &
Zimmerman, 2014). Schools that lack a trusting and collaborative working environment may
perceive feedback as negative. A lack of feedback may also have an impact on a teacher’s
professional growth. Based on a qualitative study conducted by the Center for American
Progress, teachers indicate that their evaluation program did not lead to changes in their teaching
pedagogy due to a lack of feedback (Donaldson, 2012).
Feedback plays an important role in the development of an educator’s teaching practices,
especially in a shared learning environment (Roussin & Zimmerman, 2014; Runhaar et al.,
2010). Clark and Estes (2008) confirm that feedback is an important component for team-based
organizations and can have an impact on motivation. Feedback in a shared learning environment
evokes reflection and engagement. According to Runhaar et al. (2010), teachers are more
inclined to reflect and ask for feedback when interacting with their colleagues. Based on a
quantitative study with 90% of participants being teachers, self-efficacy grew stronger through
reflection and feedback (Runhaar et al., 2010). The study also found that teachers were more
willing to participate in reflection and ask for feedback when goals were created to improve
practices.

29
Collective accountability. Based on Schein’s (2017) three levels of cultural analysis,
group learning is validated socially through collective artifacts, beliefs, and values. Included in
an organization’s cultural model, collaboration builds collective capacity through shared
teaching practices that affect and improve student performance. According to Fullan and
Hargreaves (2015), the social capital strategy builds individual strengths by building collective
capacity in a push-pull model of peer accountability. This model of collaboration influences the
overall culture and morale of the organization and supports a professional culture of shared
responsibility. In organizations that reflect a collaborative climate, all members of the staff
celebrate the success and achievement of all students (Fullan & Hargreaves, 2015).
Collective accountability through goal setting builds a culture of shared responsibility in
the working environment. Bolman and Deal (2017) offer four frames to examine organizational
leadership and reframe organizations. Based on the structural frame, goal setting is an effective
practice that guides the organization in the right direction (Bolman & Deal, 2017). Bolman and
Deal (2017) also state that goal setting enhances employee commitment through open
communication, according to the human resource frame. According to Fullan and Hargreaves
(2015), goals can be created collectively in a shared learning environment and work effectively
in educational systems to build collective responsibility.
Table 4 shows the organizational influences that address the organizational and
stakeholder goals. The table also identifies the assessments needed to support the organizational
influence.

30
Table 4
Organizational Influence and Organizational Assessment
Organizational Mission and Vision
At CS Academy K8 School, students empower each other to create, communicate, collaborate and
think critically in a technology-rich environment. Students will become the forward thinkers,
strategists, and leaders who transform their future and innovate solutions for a better world.
Organizational Global Goal
By the end of the 2019–2020 school year, third through eighth grade students at CS Academy K8
School will apply coding skills to a computer science pathway. The program’s goal will be measured
by students’ ability to apply coding to robotics, circuitry, drones, app development, and website
creation. Additionally, all students are given the opportunity to join a competitive team and/or
showcase their coding ability at a culminating event.
Stakeholder Goal
By December 2019, third

through eighth grade teachers will provide all students will weekly coding
instruction during the instructional day, evidenced by student work performance through TechSmart, a
coding program.
Assumed Organizational Influences Organization Influence Assessment
Cultural Setting Influence: Feedback
The organization needs to provide teachers with
feedback from administration and fellow
colleagues during PLCs around instructional
pedagogy and practice.
Interview questions/responses showed how
meaningful and impactful feedback is to
performance when provided collectively.

What does feedback look like for you as a
teacher?
Is the feedback formal and/or informal?
What does the feedback look like?
Do you receive feedback specifically in the area of
computer science instruction?
What administration’s role in the area of feedback
in computer science instruction?
What would you hope in getting feedback from
computer science instruction?
Cultural Model: Collective Accountability
The culture of the organization needs to be based
on collective accountability.
Interview questions/responses for a teacher focus
group indicated how much impact collective
responsibility has building organizational trust.

How do you work with your colleagues?
Tell me about a time you were given an
opportunity to discuss computer science
instruction?
Describe a recent interaction with a colleague or
administrator discussing computer science
integration.
How, if at all, do you work with your team to set
goals?

31
Based on a conceptual framework presented by Clark and Estes (2008), this section
examined the need for content knowledge and skills that teachers will use to provide weekly
coding lessons that are integrated into the curriculum. Technical and procedural knowledge of
coding skills transfer to integration of skills based on the user’s mindset, according to Niess
(2005). Motivational factors were considered when building self-efficacy and establishing goals
in computer science instruction. Motivational influences were examined through Mayer’s (2011)
cognitive processes on meaningful learning and active processing and motivational theories on
self-efficacy and goal orientation. Support from the organization plays an important role in
promoting teacher efficacy and meeting the organizational goal through support in professional
development, processes, and resources. Timely feedback and collective accountability promote a
cohesive culture, which builds trust and systems for effective communication.
Interactive Conceptual Framework
A conceptual framework, or theoretical framework, supports the study’s ideas, values,
and beliefs, which helps validate and justify tentative theories, methods and goals of the research
(Maxwell, 2013). Research questions, or problems, are included in the conceptual framework to
serve and guide the research process. According to Maxwell (2013), the conceptual framework is
based on experiential knowledge, existing theory and research, and exploratory research.
However, qualitative research constructs or changes existing theory based on an inductive
process by interpreting data and findings (Merriam & Tisdell, 2016).
Based on literature from self-efficacy theory, goal orientation theory, and transfer of
learning theory, KMO influencers are significant in meeting the organizational goal. The KMO
framework examines influencers in the organization as well as the gaps that impact performance
(Clark & Estes, 2008). Through professional development, teachers build self-efficacy in the

32
area of computer science instruction. Motivation increases as teachers build on content
knowledge and apply learned coding skills, creating relevant lessons through a goal-setting
process. Collective accountability and effective communication help to build trust in a shared
learning environment.
Figure 1
Presentation of Conceptual Framework

The figure above presents how KMO influences overlap to have an impact on the
organizational goal. The black circle represents the organization: CS Academy K8 School.

33
Within the organization is the green circle, representing the stakeholder. The three blue circles in
the center of the stakeholder’s green circle demonstrate the interrelated influences that play an
important role in the stakeholder’s goal. The Venn diagram illustrates that the influences are not
experienced in isolation and are to be considered connected as the stakeholder works to achieve
the organizational goal.
To reach the stakeholder goal, teachers must build their efficacy in learning content
knowledge through training and quality professional development. Coding instruction requires
knowledge in both factual and procedural areas to effectively teach coding as a content area to
students in the classroom. Rueda (2011) suggests the importance of building efficacy to deliver
quality instruction. However, the knowledge learned through training sessions and professional
development must be transferred to long-term memory and processed in higher levels of
cognition. According to Mayer (2011), one must use a higher level of thinking process to create
meaningful context and actively participate in learning. Teachers must interact with the factual
and procedural knowledge to create meaningful experiences and better understand the
application of coding skills.
Active learning helps teachers build confidence in the content area and motivates them to
further their learning. Authors agree that learners have higher confidence and interest levels as
they build their knowledge and efficacy (Grossman & Salas, 2011; Rueda, 2011). Additionally,
setting goals allows teachers to measure progress and outcomes as well as connect student
learning to specific, measurable, attainable, realistic and timely goals (Reeves & Fuller, 2018).
As educators measure the effectiveness of their instructional practices in coding, feedback from
colleagues and administration will help teachers reflect and build on their teaching practices.

34
Collective accountability through goal setting will both help build team efficacy and
create a learning environment that promotes trusting and positive relationships (Clark & Estes,
2008; Rueda, 2011). Additionally, feedback is used to improve teaching practices and strengthen
instructional models in schools that demonstrate trust and strong relationships (Roussin &
Zimmerman, 2014). Educators will find it beneficial to create collective goals in learning and
teaching coding as well as to provide feedback to build on both procedural knowledge and
effective teaching practices. Both feedback and goal setting, when conducted in a shared learning
environment, help build collective responsibility and ownership in meeting the organizational
goal.
Summary
The purpose of this study was to examine the impact knowledge, motivational and
organizational factors have on teachers’ development and progress in computer science
integration in a K8 public school setting using Clark and Estes (2008) KMO conceptual
framework. The literature review presented potential gaps in education that adversely affect
students’ interests to pursue computer science as a major and/or potential career, focusing on
gender disparities due to stereotypical role models and environments. The literature confirms that
early intervention has a positive impact on young students’ motivation to learn computer science
and stresses the importance of building progressive skills starting in the elementary grades.
Lastly, the review of literature suggests that professional development is a key factor in building
teacher efficacy, promoting computer science integration in the classroom. This evaluation study
examined the professional development, instructional practices and opportunities for collective
goal setting at a school site that support the organizational goal. Chapter Three will present the
methodological approach and how the study was conducted.

35
Chapter Three: Methodology
This dissertation presents questions that prompted investigation on how the organization
was meeting its goal through an inquiry research process in a natural setting. Additional
questions explored how knowledge and motivational factors influence teacher efficacy and how
organizational processes support the goal. Qualitative methodology focused on examining
participants’ experiences and how they construct meaning, which allowed the researcher to
generate themes based on participant responses (Creswell & Creswell, 2018; McEwan &
McEwan, 2003; Merriam & Tisdell, 2016). Based on the purpose to examine the impact of KMO
influences on teacher efficacy, the research was conducted using qualitative methods to gather
data through interviews, a focus group, and document analysis. This chapter provides the
research approach and methodology used to investigate the best practices around computer
science integration at a K-8 school through qualitative measures.
Research Questions
The following questions address the KMO influences that impacted the program and
helped identify needs to reach the organizational goal.
1. To what extent is the school meeting its goal to offer computer science instruction to all
third through eighth grade students?
2. What increases knowledge and motivation to help build teacher efficacy in the area of
computer science instruction?
3. In what ways does the organizational culture support teacher efficacy in the area of
computer science integration?

36
Participating Stakeholders
In this study, the stakeholder population of focus was third through eighth grade teachers
who participated in the Computer Science Pathways Program. The study focused on teachers
who were committed to the program using the TechSmart Coding Curriculum and who received
coding training through professional development. The study also focused on the level of
participation, teacher expertise in computer science, and the success of integrating coding skills
into the curriculum. This study aimed to acquire experiential information from teachers who are
new to the teaching profession as well as teachers who are experienced with computer science
instruction. Teachers were chosen as the stakeholder population for this study to measure the
impact on student achievement based on teacher efficacy. This study used inter-method mixing
of data collection through interviews, a focus group and document analysis to gather data on
stakeholder participation towards the organizational goal.
Interview and Focus Group Sampling Criteria and Rationale
Criterion 1. Face-to-face interviews with teachers were conducted to collect beliefs,
thoughts and values around computer science and on acquiring factual and procedural knowledge
in coding instruction.
Criterion 2. Face-to-face interviews with eight teachers were conducted to collect
thoughts on motivation towards building efficacy through goal setting and feedback.
Criterion 3. A focus group of three educators, representing one grade level, was
conducted to collect feelings about collective accountability through feedback and goal setting.
Interview and/or Focus Group Recruitment Strategy and Rationale
Purposeful sampling was used to select stakeholders relevant to the study (Johnson &
Christensen, 2015). Criteria for eligibility were created for teachers who were implementing

37
coding in the curriculum during the instructional day. The study had eight participants,
representing both elementary and junior high grades. As a subset to those interviewed, three
participants were a part of a focus group and asked open-ended questions on their interest in the
topic, their motivation to increase self-efficacy, and their experience on collective responsibility.
The discussion served to better understand teachers’ role in computer science instruction,
identify potential challenges, and generate new creative ideas (Johnson & Christensen, 2015).
The study had three participants in the focus group. Johnson and Christensen (2015) state that
small groups allow the researcher to gain an in-depth understanding of the study, which adds
perspective to the study.
Data Collection and Instrumentation
Evidence was gathered from multiple sources to reach a substantive theory that addressed
the process of how teachers learn new content and pedagogy. A grounded theory methodology
supports a comparative model, where analysis of data reveals patterns based on relationships
(Merriam & Tisdell, 2016). Interviews, including focus groups, were led using open-ended
questions that prompted further thought on how goals were reached through effective measures.
Documents, such lesson plans and TechSmart artifacts, were collected as an additional source of
information on how data drives grade-level discussions during opportunities for teachers to
collaborate. According to McEwan and McEwan (2003), the evidence from documents can
confirm the accuracy of the data collected through observations or interviews and also fill in
missing parts. Inductive analysis of the varied data collected allowed the researcher to establish
themes, patterns and categories that support the purpose of the study (Creswell & Creswell,
2018; Merriam & Tisdell, 2016).

38
Interviews
Qualitative interviews allowed the researcher to collect first-hand information on
participants’ perspectives, feelings and experiences, which are not observable (Merriam &
Tisdell, 2016; Patton, 2002). For this study, eight teachers who teach coding at the site were
interviewed. During these semi-structured interviews, the interview guide utilized provided
topics that helped to focus of the conversation within a limited time frame while allowing for the
development of answers within the discussion through the use of probes (Merriam & Tisdell,
2016; Patton, 2002).
Participants were contacted via email to request time for a face-to-face interview. The
email disclosed the study’s purpose and logistics, such as the location, schedule, and time
allocation. During the interview, the participant received the interview protocol, which outlined
the request for permission to record, the overview of the open-ended interview questions,
confidentiality, and voluntary participation. The interview questions were focused on
participants’ thoughts and experience with learning coding through professional development,
with computer science integration, with goal setting individual and collectively, and with
feedback. The interview protocol is attached as Appendix A.
Focus Groups
A focus group interview was used in this study, grouping participants together to promote
conversations on collective efficacy around computer science integration. Focus groups allow
participants to further conversations through interactions that may not be realized in one-on-one
conversations (Bogdan & Biklen, 2007; Merriam & Tisdell, 2016). The focus group served as an
opportunity to share ideas, thoughts and feelings around instructional practices and support
collaboration with instructional planning. A notable limitation of focus groups is the participants’

39
unwillingness to share based on embarrassment to voice their thoughts publicly (Bogdan &
Biklen, 2007). A method to overcome this limitation is to provide an opportunity for the
participants to write their answers instead of sharing them aloud to the group. Protocols for the
focus groups match the processes used for face-to-face interviews and are attached as Appendix
B.
Documents
Personal documents are a data source in qualitative research that can be used to support
themes from interviews by revealing participants’ perspectives and beliefs (Merriam & Tisdell,
2016). Participants for this study were asked to voluntarily submit coding lesson plans, reflective
notes, and/or PLC minutes. This request was disclosed in an email when the interview invitation
was sent to all participants and outlined in the protocols for the individual interviews (Appendix
A) and the focus group interviews (Appendix B). Personal documents like PLC notes, lesson
plans, and samples of student activities reveal the impact of professional development, progress
toward goals, and efforts in collective responsibility. A document analysis protocol was created
based on the research questions to examine how the personal documents support the themes
presented in this section. The document analysis protocol has been attached as Appendix C.
Data Analysis
Data analysis is the process of creating an explanation of what the data means in
relevance to the study (Merriam & Tisdell, 2016). The researcher’s role is to organize the
collection of data, analyze the data, establish themes and interpret the meaning based on the
research questions and conceptual framework. Based on Creswell and Creswell’s (2018) five-
step process for data analysis, this study followed each step to interpret the data collected. The
steps are outlined in the following section.

40
Step 1: Organize and Prepare the Data
The purpose of Step 1 is to prepare the data by organizing the information for analysis.
Visual documents were scanned. Data were sorted and organized based on the different
categories depending on the different collection methods. Data were examined based on
responses from face-to-face interviews, focus groups and documents collected. Data were also
organized based on themes generated from the responses.
Step 2: Read the Data
The researcher read through the transcripts to better understand the responses in reference
to the research questions and the conceptual framework. The researcher examined the underlying
meaning of the data and wrote notes on the margins of the transcripts to record general thoughts.
Notes also recorded the tone of the responses and relevance to the topic.
Step 3: Code the Data
The researcher noted emerging themes by highlighting and labeling the transcript based
on categories. The researcher followed Tesch’s (1990) eight-step coding process to help guide
the coding process. First, the ideas were noted as the transcripts were read carefully. Underlying
meanings were noted on the margins. After the transcripts were read, the researcher created a list
of all the topics and organized the responses based on the topics. Topics were abbreviated as
specific codes. The researcher continued to code terms based on significance for continual
coding of categories. The categories were then combined based on similarities and relationships.
The codes were finalized and alphabetized.

41
Step 4: Generate Description and Themes
Based on the codes, descriptions were created to represent themes. The themes and
categories were color-coded based on relevance and significance of the response. The researcher
acknowledged the different layers of the themes in preparation for a narrative (Creswell &
Creswell, 2018).
Step 5: Representing Description and Themes
The descriptions and themes were written in narrative format and present the findings
from the data. The narrative includes an overview of themes based on chronology of events,
represented by multiple perspectives. Subthemes and interconnected themes are also conveyed in
the findings (Creswell & Creswell, 2018).
Credibility and Trustworthiness
As the researcher plays a vital role in collecting data, reflexivity is an important part of
the research process (Creswell & Creswell, 2018; Merriam & Tisdell, 2016). A researcher holds
natural bias through perspectives based on experiences. According to Creswell and Creswell
(2018), reflexivity allows the researcher to reflect on his/her role and how personal bias can
affect how the data is collected and analyzed. A researcher can also have bias as a result of
professional relationships with the participants. To maintain the integrity of the study and uphold
credibility and trustworthiness, the researcher offset the bias through intentional practices such as
critical self-reflection, reflexivity, and member checking.
My background as an educator at this site may have an impact on how teachers respond
to the interviews as well as how I interpret the data. Teachers, as well as the school site, were
purposefully selected to study a 16-year journey developing the Computer Science Pathways
Program. I have experience implementing the computer science program as a teacher as well as

42
serving as a supervisor at the same site. I believe that my relationships with the participants
allowed for honest conversations from a mutual understanding of the school’s mission and
vision. Nevertheless, I disclosed my role as a researcher to all participants to clarify my role as
an interviewer, not an educator, to ensure that the participants did not feel pressured to
participate. I also clarified that the interview had no impact on participants’ performance
evaluations and/or promotions. As a part of the degree requirement, I was cleared by the
Collaborative Institutional Training Initiative (CITI) to conduct research at the site as a certified
researcher. An approval letter from the institutional review board (IRB) with full disclosure of
the data collection process, as well as my previous involvement with the site, was available to the
participants.
Measures were taken to validate both the data and the procedures used for collection.
Triangulation of data, using multiple sources, was implemented to strengthen the validity of the
study (Creswell & Creswell, 2018). This study incorporated interviews, focus groups and
analysis of documents as data to support the themes. Additionally, member checking allowed for
the participants to confirm the accuracy of the findings (Creswell & Creswell, 2018). Transcripts
of the interviews were provided to the participants for review and confirmation of accuracy.
Hence, the accuracy and credibility of the findings were ensured through the use of multiple
validity procedures and full disclosure of my personal experiences through the reflexivity
process.
Ethics
In qualitative data collection, ethical concerns may arise based on the researcher’s
relationships with the participants (Merriam & Tisdell, 2016). It is a researcher’s responsibility
to ensure that ethical principles are maintained when human participants are involved in the

43
study. An informed consent form at the start of the study disclosed the voluntary nature of the
study, confidentiality of the information shared, and the participants’ ability to withdraw from
the study at any time (Glesne, 2011). For this study, informed consent forms disclosed privacy,
confidentiality of the information, voluntary participation, and the right to withdraw without
penalty. During the interview, permission to audio record the interview was obtained verbally.
The participants were allowed to request the recorder be turned off at any time during the
interview. Audio recordings were stored on a password-protected hard drive. Copies of the
transcripts were provided to all participants, and a timeline of the process was disclosed. Full
disclosure of the recorded conversation ensured that the responses were not altered in any way.
Additionally, the study was submitted to the University of Southern California IRB, and
rules and guidelines to protect the participants’ rights and safety were followed, with no harm to
participants. Participants were not compensated for their time; however, a small token of
appreciation was provided in the form of a monetary gift card.
Limitations and Delimitations
Some limitations to this study that were outside of the researcher’s control were as
follows:
● The study involved qualitative research at only one school site, therefore limiting the
scope of the results.
● The study was subject to the bias of the researcher based on her previous relationships
with the participants.
● The study was subject to truthfulness of the responses based on a prior relationship with
the researcher.

44
● The study was conducted over a brief period and only captured information during the
school’s implementation timeline.
Some delimitations to this study that were within the researcher’s control were as follows:
● The participants in the study were purposely selected.
● The study was conducted at one school site.
● The researcher determined the interview dates and timeline.
● The researcher chose the methods of data collection and instruments.

45
Chapter Four: Results and Findings
This chapter includes the results and findings focused on KMO influences and is based
on the following questions that guided this study:
1. To what extent is the school meeting its goal to offer computer science instruction to all
third through eighth grade students?
2. What increases knowledge and motivation to help build teacher efficacy in the area of
computer science instruction?
3. In what ways does the organizational culture support teacher efficacy in the area of
computer science integration?
Qualitative data were collected using face-to-face interviews, a focus group interview, and
document analysis. The interview protocols included questions designed to examine teachers’
experience with learning factual and procedural knowledge in coding, factors that motivated
teachers to further their learning and apply what they learned to instruct students, and
organizational support that promoted shared responsibility and collective practices.
Participating Stakeholders
The participating stakeholders for the study were teachers who implemented coding
instruction in the classroom from the start of the school year through the TechSmart coding
platform. For the purpose of the study, third grade through eighth grade teachers, who were
trained by TechSmart, were asked to voluntarily be a part of a face-to-face interview. Eight out
of 12 teachers responded to the interview invitation sent via email along with a digital copy of
the interview protocols. Additionally, one grade level of three teachers agreed to participate in a
focus group interview. Participants had varied teaching experience ranging from five years to
over 30 years. One teacher was brand new to the school site, three teachers had worked for less

46
than three years at the school site, two teachers had worked for over 10-years at the school site,
and two teachers had been at the school site since its inception, 16 years prior to this study. None
of them had any prior coding knowledge or experience teaching coding.
Table 5
Demographic Information of Participants
ID Grade Level Teaching Experience Years at the Site
T3.1 Third Grade 20 years 16 years
T3.2 Third Grade 5 years 4 years
T4.1 Fourth Grade 31 years 16 years
T4.2 Fourth Grade 15 years 15 years
T4.3 Fourth Grade 5 years 1 year
T5 Fifth Grade 19 years 14 years
T6 Sixth Grade 16

years 2 years
T7.8 Seventh/Eighth Grade 7 years 2 years

Data from a focus group interview were examined to capture grade-level thoughts on
collective responsibility at the site. The focus group consisted of three fourth grade teachers who
represented different levels of coding expertise, teaching experience, and years at the site.
Table 6
Demographic Information of Focus Group Participants
ID Grade Level Teaching Experience Years at the Site
T4.1 Fourth Grade 31 years 16 years
T4.2 Fourth Grade 15 years 15 years
T4.3 Fourth Grade 5 years 1 year

Conceptual Framework
According to Maxwell (2013), a conceptual framework identifies the key elements of the
study as well as the relationship between the factors and serves to validate theories presented in
the research. The interactive conceptual framework, introduced in Chapter Two, which supports
the knowledge, motivational, and organizational (KMO) influences on the organization and

47
stakeholders’ goals is used in this study. The KMO framework identifies gaps that have an
influence on performance (Clark & Estes, 2008).
Figure 1 describes how KMO influences overlap to impact the stakeholder goal and the
organizational goal. The center blue circles of the figure identify the interrelated influences that
affect the stakeholder’s goal. This diagram highlights that experiences are blended rather than
practiced independently. These experiential influences need to be perceived as integrated in
order for teachers to build self-efficacy to reach their goal. Having a theoretical perspective helps
guide the researcher through the inductive process of gathering data, forming categories, themes,
and generalizations (Creswell & Creswell, 2018).
Findings
The research findings are represented by themes that illustrate the interrelationship
between the conceptual factors (Creswell & Creswell, 2018). The following results include
knowledge influences based on factual and procedural knowledge, motivational influences based
on building self-efficacy, and organizational influences that helped support the school’s vision
through collective responsibility. Based on interview responses, focus group responses, and
document analysis, themes were generated pertaining to KMO influencers, which are presented
in this section organized by the research question.
Research Question One
To what extent did the school meet its goal to offer computer science instruction to all
third through eighth grade students? Based on data findings, teachers at CS Academy met the
stakeholder goal to provide students with weekly coding instruction during the instructional day,
evidenced by lesson plans submitted for document analysis and teacher testimony on student
performance. All third grade through eighth grade teachers interviewed were trained by

48
TechSmart through a 5-day training session held at the beginning of the year, and a three-day
training during the middle of the year. Teachers were trained either in Skylark, which addressed
block coding, or in Python, a language-based coding program. During the training, teachers
received opportunities to actively participate and interact with new knowledge by practicing
algorithms and techniques on coding skill integration. As teachers actively participated in hands-
on practice and interacted with coding lessons, their efficacy in building and extending their
coding lessons grew, evidenced by data from face-to-face interviews as well as documents of
lesson plans demonstrating integration across subjects. Twice a week, all eight teachers provided
coding lessons to their students that included factual and procedural coding instruction along
with opportunities to apply coding skills. At the time of the interviews, teachers were working
toward providing opportunities for students to apply their coding skills to areas highlighted in the
organizational goal, such as robotics, drones, app development, and website creation.
Factual and Procedural Knowledge
Active learning through professional development. Active learning allows teachers to
build confidence in their knowledge and helps motivate them to further learning (Rueda, 2011).
Teachers found the TechSmart training valuable due to its hands-on approach that allowed them
to practice factual knowledge and better understand procedural knowledge when applying coding
skills. Six out of eight participants expressed their appreciation for hands-on professional
development that allowed active participation to help with their learning. T4.1 shared,
Well, the support and training from TechSmart is amazing because they really walk you
through and have you practice the lessons. So, you actually have them all saved in the
student portal. You can go in as a student and go through the lessons. You’ll have hints
on the side and the hints will go from showing you step-by-step with the blocks. So, that

49
helps guide, you can figure out where you are, where your students are. I can open up
lessons and my kids that are advanced, they can just go and start doing the lessons and
turn them in.
T6 stated that the “struggle” to learn coding provided her the hands-on opportunity to learn it:
We went through the training as students, and that’s how I learned to code. I appreciate
that because I had to struggle through it myself and that’s the best way for me to learn, is
to have to struggle through it. I think trying the material on my own without the solution
key would be the best way; the hands-on training.
TechSmart training allowed teachers to gain factual and procedural knowledge of coding as well
as navigating through the user-interface platform. Consequently, teachers gained confidence as
well as the ability to anticipate and prepare interventions to guide students who may encounter
challenges while learning to code. Thus, it provided the professional development necessary for
teachers to provide effective computer science content to students.
Based on information processing theory, higher-order thinking skills are used when
interacting with new content knowledge (Schraw, 2006). All teachers indicated that they
participated in training that required active learning through the use of higher-order thinking
skills. During the TechSmart training, teachers were able to elevate their understanding of coding
beyond just factual and procedural knowledge by using higher-order thinking skills during
coding practice at the training. For instance, T4.1 stated, “More I learned about coding, I
realized how much computational thinking that’s involved in it.” When teachers were asked
about their experiences with the TechSmart training, T5 stated,
I love the problem solving and the trial and error that went along with the training, so I
think coding skills apply to everyday work in that sense, being able to troubleshoot a

50
problem and try something and if it doesn’t work, try something else and eventually you
will come to a solution. I think it’s great.
TechSmart was effective in training teachers due to its emphasis on mastery of both factual and
procedural knowledge of coding through an active learning approach. As teachers actively
engaged in learning coding and transferred their skills to application during the training sessions,
they became better prepared to guide students through learning and practicing coding and
encouraged students to use the critical thinking skills they developed to program, create, and
apply coding to various subjects. TechSmart was effective in training teachers due to its
emphasis on mastery of both factual and procedural knowledge of coding through an active
learning approach.
Transfer of learning and application of coding skills. Teachers gain a better
understanding of the subject matter by interacting with new knowledge to better implement
practices in the classroom (Koehler & Mishra, 2005; Niess, 2005). As teachers learned coding
through a hands-on approach, teachers began to develop lessons that modeled their learning and
provided students with a learning environment to use higher-order thinking skills, apply coding
skills integrated with other subjects, and apply coding to a computer science pathway, such as
robotics. Results indicate that teachers participated in active learning opportunities and
transferred their learning to apply coding skills outside the TechSmart platform. All eight
teachers interviewed mentioned a transfer of learning and practices from the training to the
classroom. In many instances, teachers revealed specific lessons, strategies, and content they
practiced in the training transfer to the classroom to their students. T4.3 emphasized,
I’m a very hands-on learner and a very visual learner. I like to get up and move around. I
like to try it out myself. I like to talk to other people about it, which helps when I was in

51
the training because I got to converse with the other teachers. I think a lot of my students
are like that, too. They like to talk to each other in the classroom whenever we do coding.
I let them sit next to whoever they want and they get to do coding with them. It seems to
be effective.
Content knowledge by itself does not guarantee that learning has transferred to long-term
memory, as the learner needs to interact with knowledge to construct meaning (Schraw &
McCrudden, 2006). Students were provided opportunities to interact with new knowledge
through open-ended assignments that required a transfer of learning from factual coding to
applied coding. T6 explained a student activity that was created outside of the TechSmart
platform, which included an open-ended challenge using higher-order thinking, such as problem-
solving and computational skills. T6 said,
I just made up my own assignment. Students had to actually think, “How would I create
code that would know what year goes with which Zodiac sign?” It was amazing because
some of the kids came up with different ways to do it. Some of them divided to figure it
out, some of them subtracted to figure it out. So, the kids were able to really do it, and it
was pretty impressive.
Students were able to interact with learned coding knowledge to figure out the answer, thus
allowing students to apply what they learned.
Based on the information learned through the training, teachers began to integrate coding
content and skills into other curricular areas. All teachers interviewed used coding beyond the
factual and procedural knowledge learned through the training to other content areas or real-
world applications. As teachers applied coding content and skills to other curricular areas, they
extended coding lessons outside of the TechSmart platform and taught coding beyond just twice

52
a week, as noted in their schedule. The focus group referred to integrated science, math and
coding lessons as well as projects incorporating engineering standards and coding to create
inventions and app development. Four teachers submitted lesson plans and student activities that
reflect cross-curricular integration with coding. According to a student activity document
submitted by T6, students were given a coding math activity based on numeric patterns using the
Chinese Zodiac. T6 stated that coding teaches computational thinking and logical reasoning that
are valuable skills for math. T5 indicated the need to go beyond coding using the TechSmart
platform, by applying skills to other content areas such as math:
So, I feel like we should be able to apply it. We need to now take this out of TechSmart,
which is where we are learning it and take it out into something that is in the real world
and then have a real application that is either curriculum-driven or solving real-world
problems. I like to focus on the math involved in coding. There is a lot of math that
applies to fifth grade with the coordinate grid and geometry, like distances for things and
angle trajectories. So, I try to bring in math wherever I can and it makes it a bit more
relevant for the kids.
According to a student activity document submitted by T3.1, students were given an activity to
find the perimeter of a jog-a-thon track based on calculating wheel rotations of a Lego
Mindstorm EV3 robot that was programmed using block coding. Coding skills were used as part
of the process in programming the robots, while the learning objective was math calculations to
find the perimeter. The focus group highlighted their experience: “So, students are building
websites, they are designing applications. They’re creating GIFs and flying drones and coding
with more purpose. So, it’s coding with a purposeful outcome.”

53
Teachers not only learned factual and procedural knowledge through professional
development, but they also benefitted from a hands-on and collaborative approach, which
promoted active learning to take place during training. Mayer (2011) states that, when a learner
uses cognitive processes in a meaningful context, an active process of learning occurs. Data from
interview responses and document analysis suggest that teachers exceeded expectations and the
stakeholder goals by creating lessons that incorporated higher-order thinking skills, integrated
their coding lessons with cross-curricular areas beyond the TechSmart platform, and applied
coding skills to real-world applications. Teachers and students alike demonstrated that a transfer
of learning occurs from factual and procedural knowledge to applied learning through
opportunities to interact with new knowledge, use higher-order thinking skills and apply coding
skills to real-world application.
Research Question Two
What increases knowledge and motivation to help build teacher efficacy in the area of
computer science instruction? As teachers built their knowledge of factual and procedural coding
content and skills through professional learning, they felt more confident in developing lessons
that incorporated active learning opportunities and application of coding skills. Additionally,
teachers felt more confident in creating learning experiences for their students that incorporated
higher-order thinking skills. Confidence also came from knowledge of progressive coding skills
and a trial-and-error learning environment. Teachers also noted that observing student
interactions and engagement during coding instruction and subsequent activities that
incorporated active learning, problem solving, and applying coding skills served as motivating
factors for them to develop more engaging coding lessons. Teachers voiced suggestions for
future training to support them in elevated approaches to teaching coding, such as open-ended

54
assignments in TechSmart and professional development around application of coding to
relevant career fields. Additionally, all interviewed teachers communicated the need for
computer science education in elementary and junior high school, as it better equips students
with skills for their future careers. This understanding also served as motivation for teachers to
pursue learning computer science and delivering quality coding instruction.
Building Teacher Efficacy
Confidence and motivation. As learners build their efficacy through knowledge, it
increases higher confidence levels, which builds motivation for further learning (Grossman &
Salas, 2011). All teachers who participated in the in-person interviews indicated that their
knowledge in coding grew through professional development offered by TechSmart. CS
Academy’s vision includes grade-level focus areas in coding based on a progressive build of
coding skills from block coding to language-based coding. The vertical coding structure
motivated T5 to create comprehensive lessons that had multi-year goals. T5, who taught fourth
grade in year one and moved to fifth grade in year two of this study, shared her experience on
teaching block coding and transferring her knowledge to language-based coding:
I started with TechSmart Skylark, which is drag and drop block coding. I started it when I
taught fourth grade, so I had experience with that and then, when I moved up to fifth
grade, I got trained in Python, which is the same coding concepts and skills. It’s now
language-based coding, which you type in all the commands. It was helpful to come from
the drag and drop block coding, and thinking about coding in a scaffolded way of
leveling up. So, I got to see the progression that the kids have gone through, that they
started with block coding, so they understand what-if-then does. So, I think the
progression is smart and helpful, not only for them, but as a teacher as well.

55
T5 stated that having vertical knowledge of block coding to language-based coding skills helped
her understand her students’ experience with the transition of two coding modalities and led her
to design lessons to assist students in transitioning from one coding platform to another. Gaining
the knowledge of vertical coding structure across grade levels increased teachers’ insight and
confidence and had a positive impact on their motivation to build comprehensive lessons that
incorporate a vertical build of coding skills.
Teachers became more confident with teaching coding through a trial-and-error
experience and provided similar opportunities to their students. One of the interviewees, T4.1,
shared that being able to explore and try new things allowed her to embrace the “messy” coding
and break through the initial apprehension with coding. T4.1 stated,
Coding is messy. It was very messy, but it was okay because I believed that I wanted to
be a role model for the kids. Okay, I’m new to this. I’m not going to know it all. Let’s
figure it out together. I didn’t want my attitude to limit my students, and, if I was
intimidated by it, I was going to prevent them from trying things. That was the last thing I
wanted to do. I need to be able to let go of some things in order for them to have the
exposure and experience for things. I’m not an expert, and I can’t be. And it’s okay to not
be. So, the ones that figure things out faster than I can, I let them do it.
T4.1 modeled a trial and error approach and attitude to her students that allowed students to
explore coding in a risk-free environment. A trial-and-error learning environment encouraged
exploration, inquiry-based learning, and problem solving.
Grossman and Salas (2011) suggest that, the higher the self-efficacy, the higher the
confidence to complete the task successfully. As teachers’ confidence grew based on coding
knowledge over time as well as through their experience with trial-and-error learning, they felt

56
more confident in themselves as learners. Rueda (2011) suggests that learners with greater
confidence in their abilities exhibit more motivation and perseverance when interacting with an
activity.
Student engagement and teacher motivation. As students displayed excitement to
learn, teachers were more inspired to continue offering coding instruction and active learning
experiences. All teachers who participated in the interviews mentioned student engagement in
the forms of excitement and active participation. All teachers shared that one of the key factors
in their increased motivation to continue learning and teaching coding was their students’
positive responses during coding lessons. T3.2 spoke to students’ excitement to find solutions to
problems.
I think the excitement that the kids have and show when they’re coding and they’re
figuring things out... and they finally get it right. They’re just like, “Yes!” and you hear
them cheering. They work so hard to solve problems and it’s like the tiniest little thing,
but it makes a huge difference. And just seeing that celebration of “we did it, we
accomplished it.” It makes me want to keep teaching it.
T3.1 shared how her students’ excitement motivated her to continue with coding lessons that
were integrated with other subjects, and how integration of coding into other subjects elicited
excitement from her students, which further motivated her:
You realize when you see the excitement in the kids and how much fun they have with it,
that makes you want to do it the next day. I also realized that I could merge the subjects
together with the coding and then the kids would get excited about it. I was then more
sold by the concept.

57
Teachers also indicated that student engagement was evident when students were actively
engaged with learning through applied coding skills to a computer science pathway, such as
robotics and app design. T7.8, a middle school teacher, stated how student engagement with
applied coding affected her personally:
It’s been amazing to see students not just learn coding, but for them to apply it to other
areas of learning, other subjects…their creativity, and to watch them take what they learn
and make it come alive. Whether it’s building an app or coding a program they want to
build, it’s amazing. I think it’s motivating for them because they learn something they put
into practice and they see their program run. It’s so instantly gratifying. It’s challenging
also, but they feel very achieved and successful through their coding. One of my students
said something that I think will always stay with me, and it made my year. She said, “I’m
learning through this class not just to think outside the box, but that there is no box.” That
fact that coding is helping students explore this whole new world that they didn’t have
access to before, I think it’s been really cool to see.
According to Howard (2018), student motivation in computer science instruction
influenced teachers’ desire and enthusiasm to teach integrated computer science. Student
engagement, excitement, and positive responses to coding instruction and activities motivated
teachers to continue to build their efficacy around computer science instruction. Student
engagement propelled teachers to develop relevant instruction and student activities that
demonstrated an application of coding skills. Consequently, stimulating tasks for students lead to
higher levels of students’ intrinsic motivation to do well and be more involved with learning
(Rueda, 2011).

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Relevance and motivation. An understanding of the need for coding in the future
workforce motivated teachers to build their efficacy to learn and teach coding to their students.
All teachers interviewed mentioned the school’s goals and responsibility to teach coding based
on their personal thoughts on how coding will impact their students’ future. Seven out of eight
teachers believed that computer science knowledge will aid their students in their future careers
and/or college. T7.8 stated, “The more I teach coding, the more I realized how important it is for
schools to target coding and computer science for students because of the job field, especially in
the future, how so much of it’s going to rely on computer science.” T6 emphasized,
I feel like big data is everywhere now, so math and coding are going hand in hand
because, with so much data, it’s almost impossible to do everything by hand, so,
eventually, kids and people that go into that profession are going to have to know how to
code, and make equations, and sort through big data.
Teachers believed that students possessing coding skills and conceptual knowledge of coding
relevance as it applies to careers will benefit their students’ future opportunities in the workforce.
Consequently, the teachers became motivated stakeholders in taking collective responsibility for
the school’s vision of developing skills as problem solvers, leaders, and forward thinkers who
will transform their future through innovative solutions that impact their community and the
world.
Computer science skills that begin in early education can help support the understanding
of progressive skills in computer science (Barr & Stephenson, 2011). Early intervention of
coding skills affects students’ interest in the computer science field (Berg et al., 2018). Two
teachers mentioned that early exposure and intervention will benefit students in their future. T3.1
stated,

59
I’m realizing more and more how important coding is. I realize every time I’m on the
phone with Tech Help, it is from another country. And so, it became very important to
me to expose kids early because it gives them a leg up or an option to find a career in the
future.
Similarly, T4.1 indicated,
There is a need. I think it’s shocking to hear about the imbalance of genders in coding
and ethnicities. Americans don't have their own coders, and they are hiring people to
come in. So, I’m thinking, there are classes available in high school, but when you’re
finding out kids aren’t really sticking to it, and it’s too hard by the time they get to
college even. We need to start exposing them sooner because I’m watching the kids do
it...and they are doing it. They’re finding it engaging and fun.
Eight teachers interviewed unanimously suggested that schools have a responsibility to teach
coding in the elementary years and how relevant coding was based on global needs. A teacher
indicated that coding is a part of today’s technology that students need to be familiar with. T4.1
stated,
When you realize that everything I’m using on a daily basis is coded, and it’s only going
to get more, our kids need to be able to speak that language. It’s like another language
and there is a need to be multilingual, not just verbal language, but codable language. I
just think it’s such a need, and, if we start them earlier, it becomes non-threatening. It
becomes like math and reading and everything else that we are doing. It just becomes a
part of who they are.
There is a need for learners to understand the why, when, what and how for higher levels of
application and learning (Rueda, 2011). By examining the needs of the changing environment,

60
teachers were motivated to provide coding instruction to prepare their students for career
readiness.
At CS Academy, motivational factors impacted teachers’ efficacy in learning and
teaching coding. Teachers committed to ongoing professional learning and built confidence in
their coding knowledge. Teachers were motivated by their students who responded positively to
coding instruction, and they expanded opportunities for students to apply coding in areas of
interest. Students’ excitement for opportunities to problem solve and apply coding in cross-
curricular activities positively affected teacher motivation to pursue further professional growth
in computer science instruction. Additionally, school responsibility to teach coding includes
early intervention, computer science instruction that embeds comprehensive coding skills, and
opportunities to apply coding skills to relevant subject areas. At CS Academy, teachers were
motivated to teach coding based on relevant connections to global needs.
Research Question Three
In what ways does the organizational culture support teacher efficacy in the area of
computer science integration? Shared experiences based on core values help to develop an
identity and form a culture within the organization (Schein, 2017). The organizational culture of
a school influences how well teachers and staff work collaboratively to establish norms and meet
goals. CS Academy was built in 2004 with a technology vision that included a 1:1 laptop
program for students in third grade through eighth grade. A cultural norm was established that all
teachers would pursue professional growth in the areas of technology integration. At CS
Academy, half of the teachers interviewed, who worked at the site over 10 years, attested to
expectations that were focused on technology, established since the school’s inception in 2004.
In 2017, the school began a computer science pathways program that focused on progressive

61
coding skills from kindergarten through eighth grade. All teachers in the study mentioned the
school’s vision along with the expectations and norms to learn coding and provide coding
instruction. All teachers expressed the expectation for shared responsibility for the computer
science vision, which includes a progression of coding skills. Data revealed that all teachers
interviewed understood the school expectations that coding training and coding instruction were
mandatory. Included in the document analysis, the third grade team submitted a weekly schedule
that revealed time commitment to coding during the instructional day. All eight teachers
indicated that they received informal and formal feedback from TechSmart trainers, students,
teachers, and administration in various forms, which helped teachers reflect on effective teaching
practices. However, gaps were identified in the area of specific feedback in computer science
based on individual goals. Collective responsibility was understood through shared goals based
on the school’s vision. Teachers felt accountable to the goals, understanding that the skills and
concepts were progressive and that skills were built year after year. Half of the teachers
interviewed indicated the desire to have more vertical communication to better understand the
skills and concepts of the grade levels below and above them.
Cultural Norms
Feedback to build efficacy. Effective communication based on feedback can enhance
the climate of an organization and affect an employee’s motivation (Roussin & Zimmerman,
2014; Stone & Heen, 2014). Feedback guides reflection and can lead to improved instructional
practices as well as professional growth. Feedback allows teachers to reflect on their
instructional practices and help set future goals. Feedback can come in different forms that
include informal and formal processes. All teachers interviewed spoke to feedback through
varied methods such as classroom walkthroughs, informal feedback from parents and students,

62
and formal evaluations. However, five out of eight teachers indicated that they would like more
feedback, specifically around computer science instruction.
A form of feedback on coding instruction was provided by TechSmart trainers. Four out
of eight teachers indicated that TechSmart offered suggestions on lesson design and delivery of
instruction through immediate chat support through their online platform, email responses with
quick turn-around time, and trainers who provided their direct contact information. Four out of
eight teachers specifically mentioned the benefits to an immediate support system in helping
provide quality instruction to their students. T4.3, a fourth grade teacher mentioned receiving
feedback from TechSmart at the training. T4.2, also a fourth grade teacher, stated,
My biggest pet peeve about other trainings is [that] it’s a one-day show, and no one ever
asks you how it’s going. There is no one to help you along the way. We have instant chat
on our system with TechSmart, so, if there’s a problem, somebody is there right away.
It’s really nice to just know if I called them and said I’m having trouble with a lesson,
they would walk me through it.
TechSmart offered teachers input on lesson design, instruction, and scope and sequence of the
lessons through various modes of communication. The communication was immediate, allowing
instant changes to instruction. Additionally, TechSmart provided check-ins throughout the year,
asking teachers if they need support or assistance. Teachers felt the immediate response had an
impact on their learning, reflecting, and making relevant changes to instruction.
Six out of eight teachers interviewed indicated that they received some form of feedback
this school year from students, parents and administration in the area of computer science
instruction. T3.1 stated that her feedback from administration this year was positive and
encouraging because administration knew that coding is not easy to implement. T3.2, also a third

63
grade teacher mentioned technology tours, where administration would bring the parent
community to observe integrated computer science lessons. Feedback was provided to teachers
from tour visitors and administration through a note system. T5 mentioned that students gave her
positive feedback and showed their excitement for coding when they saw it on the daily agenda.
However, the study revealed a gap on how feedback is provided based on individual goals.
Teachers stated that there was no feedback based on specific goals, such as specific, measurable,
attainable, relevant, and time-bound (SMART) goals, created around an individual’s professional
growth in computer science. Five out of eight teachers indicated that they would like more
feedback in pedagogy and practice in the area of computer science. Additionally, middle school
teachers, who teach computer science as a single-subject content, felt they did not have support
individually or in teams regarding goal setting, feedback, and shared accountability. T6 shared
her feelings of isolation in teaching middle school students:
I feel kind of isolated because I’m the only one teaching it at this grade level. There is a
middle school technology teacher who teaches coding in another grade, and there is a
different middle school teacher teaching AP Computer Science, but neither of them
knows what I’m doing. So, I feel we can talk about coding a bit, but not to the point
where I feel like I have a colleague that we can bounce ideas off of. So, I feel a little
isolated.
Feedback not only allows educators to reflect on their teaching practices to build efficacy and
professional growth, but also establishes trust within the organization. Individual feedback to
improve instructional practices promotes trusting relationships (Roussin & Zimmerman, 2014).
A lack of feedback and collaboration may harm the trust built between teachers and their grade-
level teams as well as with administration. At CS Academy, teachers value input and feedback

64
on practices regarding computer science instruction. Teachers can benefit from establishing
specific individual goals that allow reflection on instructional practices on professional growth.
Collective goals and shared responsibility. Collective goals in a shared learning
environment help build collective responsibility in an educational system (Fullan & Hargreaves,
2015). The computer science program at CS Academy has grade-level goals that build a
progressive coding experience based on an elevation of skills from third grade through eighth
grade. During the interview, all teachers spoke to the collective goals that make up the school’s
computer science program. The focus group spoke to collective goals regarding the school’s
vision. Six out of eight teachers who participated in the interviews spoke to their grade-level
goals. In the interviews, the question was asked, “How much do goals come into your meetings
with your colleagues?” A teacher in the focus group replied, “We know we’re expected to teach
coding and we have goals on how far we get in the year and different things we teach within
coding.” Teachers had an understanding that students learn coding skills and concepts
progressively at each grade level. Therefore, each year, teachers rely on the previous year’s
knowledge to build and continue the coding pathway for students.
Teachers referred to a shared responsibility and accountability of the efforts and plans to
teach grade-level specific coding content and skills. Shared responsibility included ongoing
grade-level conversations for teachers in grades three through five during PLCs to discuss
instructional best practices, share ideas, and create collaborative lesson plans. The focus group
indicated that conversations around coding instruction were fluid during the day, and the team
would often check in with each other to discuss and share lesson plans. T3.2 stated,
If we come up with a new lesson plan, then we share it with each other. Like T3.1 shared
here EV3 lesson, and I did something with geometry so I can share with her. We come up

65
with these different lessons or ideas and share them at PLCs or as we’re walking to and
from anywhere. They communicated the site’s vision for progressive coding skills across
the grade levels.
A progressive build of coding skills through grade-level goals helped establish a shared
responsibility for the school’s vision and organizational goal. T4.2 said, “Every grade level has a
focus, and the idea is that it gets more in depth and moves more from learning coding to the
process of coding and to different types of application.” Additionally, the grade-level team
members supported each other and held each other accountable for coding instruction. Shared
responsibility included fellow grade-level teacher support. For example, a teacher went on
maternity leave and needed assistance from her grade-level team to teach coding instead of the
substitute. T3.1 stated,
I think it really helps when your team that you work with is willing to try new things and
bounce back ideas back and forth. I think we work well with our team all the time and
talk about what we can do. We are talking about it during our PLC time, and, sometimes,
we talk about it after school. Right now, one of my team members is on maternity leave
so my other team member is teaching both coding classes, one for the sub.
A new teacher to the site also felt supported by her teammates. T4.3 said,
When you are learning a new skill, it helps to be with your planning team, with your
grade-level team and coming up with ideas and supporting each other. I can literally bug
my colleagues whenever I want with a question, and they’ll be there to help me. We were
talking about preparing a binder with all the solution[s] and all the codes to help me out.
We check in with each other during PLCs to see, where are you at?

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However, there was inconsistency with collaborative practices identified in middle
school, grades six through eight. The PLC schedule differed in grades three through five, which
was considered elementary, and grades six through eight. Elementary grade-level teams met
twice a week for 50 minutes to have PLCs, and the middle school team had one hour and 30
minutes once every month. Two middle school teachers indicated a need for continual vertical
articulation of computer science skills across the grade level to address students’ skills as they
build their coding knowledge. The two middle school teachers stated that sixth grade through
eighth grade require a different support system than the elementary grades, and there are
currently no opportunities to collaborate with other middle school teachers to plan vertically.
They also mentioned that discussing collective goals is difficult in middle school since only one
teacher teaches coding in each middle school grade level. T6, a middle school teacher, stated,
There are no formal goals that have been set that I’m aware of and no formal meetings
because we are isolated being [in] middle school and all [grade level and teacher]
teaching a different type of coding. So, I think maybe that’s something that should be
looked at, is whether there should be some articulation as far as what should be taught
year to year.
Half of the teachers interviewed communicated a desire to extend vertical conservations with
other grade-level teachers to discuss cross-grade-level supports and continuity of lessons based
on progressive skills.
Collaborative practices are correlated with holistic school reform, which extends beyond
individual teachers (Gallimore & Goldenberg, 2001; Darling-Hammond et al., 2017). In the
elementary grades at CS Academy, conversations about computer science instruction during
PLCs were ubiquitous among teachers. Teachers discussed grade-level goals, developed lessons

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together, discussed best practices, shared lesson plans and supported each other in the area of
computer science instruction. Collaborative practices allowed the teams to build effective
computer science lessons that met grade-level goals around coding skills, which collectively
helped the organization meet its goals to provide all third through eighth grade students with
opportunities to apply coding skills based on progressive levels. According to Runhaar et al.
(2010), the importance of time teachers need to develop individual goals and learn from other
educators to build self-efficacy. Middle school teachers did not receive the same opportunities
and support for collaborative discussion due to PLC structures and scheduling. Middle school
teachers can be better supported with professional growth around computer science instruction
by offering flexible times to meet, providing support with facilitated goal setting, and
establishing collaborative norms. A cultural norm at CS Academy was the expectation for all
teachers to integrate technology into the curriculum, based on the school’s mission statement and
vision for the Computer Science Pathways Program. Consistent collaborative practices across all
grade levels will better support this expectation and allow for universal conversations on moving
forward with the school’s vision.
Synthesis
This chapter includes the findings from qualitative interviews that were conducted to
identify KMO factors that have an impact on building teacher efficacy in computer science
instruction. Responses from one-on-one interviews and focus group interviews allowed the
researcher to analyze data to establish patterns and themes to support the study (Creswell &
Creswell, 2018; Merriam & Tisdell, 2016). Triangulation of data from multiple sources was used
to examine themes around the study’s research questions. Personal documents were examined to
support teachers’ references to lesson plans, student activities, and schedules. The findings show

68
that teachers are motivated to learn and grow professionally in the area of coding instruction, and
evidence supports that building content knowledge and skills also build confidence to improve
the quality of coding lessons. A focus group interview was conducted to gain insight into
collective accountability regarding established cultural norms and expectations. Gaps in
feedback on individual goals and consistent collaborative practices were identified based on the
KMO interactive conceptual framework.
Knowledge
In early education, the educator’s ability and knowledge to teach computer science
content are key indicators in students’ learning the skills and concepts (Howard, 2018). All
teachers interviewed attended a 5-day training session at the beginning of the year to learn
factual and procedural knowledge of block and language-based coding through the TechSmart
platform. Although teachers did not have any coding background, they participated in coding
training with TechSmart to learn factual and procedural knowledge to provide coding instruction
to their students for two consecutive years. During the TechSmart training, exploratory and trial-
and-error approaches were instrumental in allowing teachers to learn in safe, risk-free
environments. Based on the interview responses, collaborative and hands-on training, with an
immediate support system, was preferred and beneficial in building teacher confidence and
efficacy with computer science instruction. Active learning methods used in professional
development are imperative to the success of the training and help increase student learning
(Darling-Hammond, 2017; Meneske, 2015). Active learning during the training allowed teachers
to learn coding by exploring, experimenting, and extending their learning using higher-order
thinking skills to apply coding to other curricular areas.

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Building efficacy is important in the delivery of quality instruction (Rueda, 2011). As
teachers’ factual and procedural knowledge grew, they were able to transfer their skills to
develop coding content that extended beyond the TechSmart platform. Evidence shows that
teachers created lessons that integrated coding with other curricular subjects and incorporated
opportunities for students to interact with new knowledge, experience hands-on learning, and use
higher-order thinking skills in a risk-free environment. Additionally, teachers offered coding
embedded activities that involved programming apps, robots, and drones, addressed in real-world
applications.
Motivation
Commitment to goals increases when the confidence to complete the goal also increases
(Clark & Estes, 2008). As teachers’ confidence in their coding knowledge and skills grew,
teachers were motivated to offer students similar experiences in the classroom. Positive
perceptions of self-efficacy affect one’s attitude toward computer science education (Celik &
Yesilyurt, 2012). Teachers’ confidence grew from experiencing coding themselves as learners
through a trial-and-error approach as well as better understanding progressive skills that are
required between grade levels. As students displayed excitement over coding, teachers were
inspired to build learning experiences that involved higher-order thinking skills, such as problem
solving and computational thinking, integrated cross-curricular lessons, and activities involving
real-world application. An increase in student engagement motivated teachers to pursue extended
learning around coding and build their own efficacy around computer science skills. Teachers’
perception of how coding skills affect their students’ future served as motivation for teachers to
provide coding lessons to students in elementary grades and to partake in the responsibility for a

70
school-wide vision to progressively build on coding skills. Learners must believe that their
efforts will have an impact on the outcome (Grossman & Salas, 2011).
Organizational Culture
School-wide norms and expectations regarding technology integration have existed for 16
years at CS Academy. However, with the recent installment of the computer science pathways
vision in 2017, teachers re-established norms around expectations with professional
development, collective goals, and shared responsibility for the school’s vision. At CS Academy,
teachers received feedback from students, parents, administration and trainers. However,
feedback based on individual goals have not been provided to the teachers interviewed through
informal or formal methods. According to Clark and Estes (2008), team members should know
their performance goals and how they will achieve them. SMART goals outline priorities, help
identify realistic timelines, and monitor and measure progress (Reeves & Fuller, 2018). Goals
based on evidence allow a learner to connect their goals to the learning (O’Neill, 2000).
Based on the focus group interview with the fourth grade team, elementary grade levels
with three teachers in each grade level had consistent PLC times allocated for conversations
about instructional practices, lesson plans, and reflection. However, middle school teachers, from
sixth grade through eighth grade, found collaboration difficult based on a lack of collective
goals, a lack of unified times to meet and one teacher representing the entire grade level. Middle
school teachers seek opportunities to collaborate with fellow computer science/math teachers and
desire to have conservations about comprehensive coding skills that affect each middle school
grade level. Collaboration in safe environments fosters individual reflective practices, which
promote inquiry, problem solving, and risk taking (Darling-Hammond et al., 2017). For the site
to build trust among teachers and staff school-wide, consistent practices must be established

71
around goal setting, providing feedback, and grade-level collaboration. The team’s confidence
affects collective efficacy and a team’s ability to reach goals (Clark & Estes, 2008).
Barr and Stephenson (2011) state a schoolwide plan based on a progressive build of
computer science skills across grade-levels helps develop a shared vision. Teachers at CS
Academy referenced collective goals based on the school’s vision. Teachers at CS Academy
referenced collective goals based on the school’s vision. All teachers indicated why they believe
they have a responsibility to teach coding in elementary and junior high school and expressed the
need to learn coding at an early age for their students to have better future opportunities in the
job market. Teachers had an understanding of the school’s vision to progressively build coding
skills across the grade levels. Collective goals around progressive skills promoted a culture of
shared responsibility and the need to continue professional learning to deliver relevant computer
science instruction.

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Chapter Five: Recommendations
Chapter Four offers the results and findings from analysis of qualitative data, triangulated
across multiple sources to answer the study’s research questions. The research questions aimed
to identify factors pertaining to KMO influences on teacher efficacy in computer science
instruction. Themes were generated from responses based on in-person interviews, a focus group
interview, and document analysis and supported by hypothesized KMO influences, which were
validated in Chapter Four. Chapter Four also identified gaps in KMO practices and supports the
recommendations for organizational practice presented in Chapter Five. In this last chapter,
recommendations for practice are presented based on KMO influences. Additionally, strengths
and weaknesses of the study’s approach are examined along with the limitations and
delimitations of the study. Concluding thoughts serve to summarize this study’s implications and
provide insights for future study on computer science instruction in early education.
Recommendations for Practice to Address KMO Influences
The research questions from this study aimed to uncover participants’ thoughts and ideas
on learning coding based on their experiences with professional development, application of
skills to enrich their learning, and the transfer of their coding knowledge to lesson design and
delivery of instruction. The study inquired about the impact teachers’ experiences in coding
instruction had on their motivation to deliver robust coding lessons and pursue further
professional development in computer science. The study also assessed organizational practices
that promoted shared accountability towards meeting the organizational goal. Additionally, the
study also focused on analyzing performance gaps using an interactive framework based on
Clark and Estes’ (2008) KMO framework. The following sections highlight implications and

73
recommendations for educational professional practice in computer science education in primary
grades based on KMO influences.
Knowledge Recommendations
Factual and procedural knowledge serve as the basis for enhanced development of
professional learning and growth in the area of computer science instruction. Darling-Hammond
et al. (2017) indicate that professional development for teachers can transform their effectiveness
in instructional pedagogy and practice, thus altering student performance. Based on Krathwohl
(2002) and the four types of knowledge introduced in the Structure of the Knowledge Dimension
of the Revised Taxonomy, factual and procedural knowledge are necessary to understand
foundational elements to interact with knowledge, solve problems, and use skills and techniques
in inquiry. Teachers must engage in the learning through active participation, where cognitive
processes are used in meaningful context. Additionally, Clark and Estes (2008) reinforce the
importance of training and clear performance goals, which affect one’s work performance
through an automation of learned skills to practice.
Table 7 identifies knowledge influences, knowledge types, and themes that align with
recommendations from data collection and analysis.

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Table 7
Knowledge Recommendations: Summary of Knowledge Influences and Recommendations
Knowledge Influence Knowledge Type Theme Recommendation
Teachers need
knowledge in coding
language.

Teachers need to
know how coding
skills build
progressively.

Teachers need to
know how to
integrate higher order
thinking skills in
cross-curricular
lessons that embed
coding skills.
Declarative Factual

Procedural

Procedural
Active learning
through professional
development supports
knowledge building.

Transfer of learning
occurs through cross-
curricular integration.

Transfer of learning
occurs through
application of coding
skills.
Provide on-going
training to learn
coding content and
skills through hands-
on approach.

Provide on-going
training on
progressive skills and
provide time for team
conversations.

Provide on-going
training support on
application of coding
skills using higher
order thinking skills.

Factual and procedural knowledge. Professional development for teachers can help
them build expertise in content knowledge as well as strategies for integration of skills (Sun &
Strobel, 2014). At CS Academy, teachers received coding training focused on factual and
procedural knowledge twice a year from TechSmart. This study validated the claim that teachers
who received training focused on hands-on practices and application of factual and procedural
knowledge to real-life problems offered similar experiences to their students. Evidence showed
that teachers used their coding knowledge and skills to design lessons that incorporated higher-
order thinking skills and cross-curricular integration of subjects. As a result, students were
offered learning experiences and opportunities to apply coding skills to diverse areas of a
Computer Science Pathway, such as robotics, app design, and website creation.

75
On-going professional support is key to building teacher capacity in computer science
and building sustainable implementation of practices (Howard, 2018). This study recommends
that the organization continue to provide teachers with support and encouragement to explore
coding in a risk-free environment. This allows for teachers to, in turn, replicate a risk-free
learning environment that encourages their students to learn from trial and error without the fear
of immediate repercussions at every stage of their learning. Teachers are further motivated to
teach computer science when there are sustainable support systems, such as relevant, on-going
professional development (Howard, 2018). On-going professional development is necessary for
teachers at CS Academy to further develop coding pedagogy and practice to meet the needs of
students with disparate coding experiences. This will equip teachers to develop future lessons
that elevate coding skill instruction and help teachers develop scaffolded coding lessons for their
students targeting diverse needs, especially those new to the school. Moreover, collaborative
conversations will continue to support teachers in developing lesson plans, sharing resources and
encouraging each other with coding instruction. Groups find comfort based on collective shared
beliefs and practices in safe learning spaces (Schein, 2017).
Motivational Recommendations
Active participation with learning during professional development encourages teachers
to further their learning and enhances a learner’s confidence and interest level (Grossman &
Salas, 2011; Rueda, 2011). Building confidence in teachers’ ability to learn and apply coding
skills helps build their self-efficacy in teaching the knowledge and skills to their students. As
teachers build self-efficacy in computer science, they develop a positive perspective, impacting
classroom implementation (Celik & Yesilyurt, 2012). Based on Bandura’s (1977) self-Efficacy
theory, confidence in one’s own ability has a positive effect on an individual’s self-perception as

76
a learner, hence influencing teacher performance. Additionally, the team’s confidence helps
build team efficacy and the ability to reach collective goals (Clark & Estes, 2008). Established
goals allow teams to work together and provide support to each other.
Table 8 identifies motivational influences along with themes found from data collection
and analysis, followed by the recommendations.
Table 8
Motivation Recommendations: Summary of Motivational Influences and Recommendations
Motivational Influence Theme Recommendation
Teachers need to believe they
are capable of learning and
teaching coding to their
students.

Teachers create mastery goals
for themselves and teams.
Confidence increased by
practicing coding skills
through trial and error.

Mastery of grade level goals
led to elevated lesson plans
that increased student
engagement.
Continue to provide a risk-
free environment for teachers
to learn coding through trial
and error and encourage the
development of lessons in a
similar environment for
students.

Provide teachers with cross-
grade level articulation
opportunities to create goals
in transitioning coding skills.
Incorporate the use of
SMART goals for teachers to
create individual goals for
mastery.

Building teacher efficacy. Based on this study, the findings validate the assertion that
teachers’ confidence in teaching coding grew over time due to increased knowledge in factual
and procedural coding. A trial-and-error approach allowed for exploration and development of
skills. Results also support that students’ excitement, in response to applied coding lessons that
promote higher-order-thinking skills, motivated teachers to continue creating open-ended, cross-
curricular assignments and activities.

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Progressive computer science skills across grade levels, starting in early education, better
prepare students for career readiness in the computer science field (Barr & Stephanie, 2011).
Teachers’ understanding of the demands for coding, as well as the demand for coding skills in
the future job market, helped motivate teachers to offer coding to elementary and junior high
students. Teachers were motivated to create relevant lesson plans and provide students with
learning experiences to meet grade-level goals as a part of the school-wide vision to build
progressive coding skills.
This study recommends opportunities for vertical articulation among grade levels that
will initiate conversations regarding the transition of skills from one grade level to another.
Teachers can design coding lessons based on cross-grade level input and help students better
transition from block coding to language-based coding from fourth to fifth grade. Grade level
goals can be re-examined to include inter-grade level transition for smoother continuity of
coding education across grade levels. Additionally, SMART goals help teachers build mastery
through attainable objectives (Doran, 1981). SMART goals will help teachers focus on building
skills specific to their areas of need and continue meaningful professional growth. Specific
SMART goals around computer science instruction will allow teachers to reflect on their
progress in a timely manner.
Organizational Recommendations
Organizational culture plays a role in how employees work together to meet goals. Schein
(2017) suggests that similar shared values and experiences unite groups because they provide a
sense of identity to the organization. Based on the three levels of cultural analysis, feedback is
one form of visible artifact, which can be a part of the process that shapes organizational culture

78
(Schein, 2017). In an educational system, goals can be created collectively and promote
collective responsibility in a shared learning environment (Fullan & Hargreaves, 2015).
Table 9 identifies organizational influences aligned to themes found from data collection
and analysis, followed by the recommendations.
Table 9
Organizational Recommendations: Summary of Organizational Influences and
Recommendations
Organizational Influence Theme Recommendation
Cultural Setting: The
organization needs to provide
teachers with feedback from
administration and fellow
colleagues during
Professional Learning
Communities (PLC) around
instructional pedagogy and
practice.

Cultural Model: The culture
of the organization needs to
be based on collective
accountability.
Feedback builds teacher
efficacy.

Collective goals promote
shared responsibility.
Provide teachers with specific
feedback on computer science
instruction, lesson design and
instructional practice as well
as opportunities to reflect on
the feedback.

Establish consistent PLC
practices school-wide to
discuss computer science
instruction and lesson design.
Provide opportunities for
cross-grade level articulation
to improve continuity of the
program.

Cultural influences. Although data shows that teachers received feedback from
TechSmart trainers, parents, students, and administration, receiving individual feedback based on
goals in computer science was not an established norm at CS Academy. Individual feedback
influences one’s performance as well as motivation to improve (Roussin & Zimmerman, 2014).
As individual teachers shared unique lessons to show the incorporation of computational skills,

79
problem-solving experiences, and cross-curricular integration, individual feedback would
provide an opportunity for teachers to reflect on lesson design, the scope and sequence of
lessons, and instructional practices relevant to their personal goals. Self-reflection opportunities
are critical for teachers who teach students of the 21
st
century (Darling-Hammond et al., 2017). It
is recommended that teachers are provided with time to reflect on feedback and are supported in
modifying individual goals to make relevant changes to improve practice.
Effective communication regarding employee feedback can enhance organizational
culture and promote trusting relationships (Roussin & Zimmerman, 2014; Stone & Heen, 2014).
An improvement in overall school climate can be achieved as CS Academy develops consistent
practices and cultural settings to provide opportunities to set individual goals and collective goals
that include middle school teachers. Middle school teachers indicated a need and desire for
collaborative conversations about computer science instruction, including lesson design. Current
teacher schedules make it difficult for middle school teachers to meet regularly. Allocating time
for middle school articulation will help support consistent collaborative practices, and
consequently improve the overall school climate.
At CS Academy, collective grade-level goals regarding coding skills encouraged a sense
of collective responsibility for the school mission and vision, which seek to implement and
integrate technology into all aspects of student education. CS Academy had established cultural
norms and expectations that included shared goals, which was embedded in their cultural model
of collective responsibility. Findings indicate that teachers felt responsible for the achievement
of their grade-level goals, understanding that their goals were a part of a larger organizational
goal to deliver progressive coding instruction. In contrast to middle school, evidence of frequent
spontaneous collaboration was found in the primary and intermediate grades. PLC practices that

80
allow consistency across grade levels will improve vertical communication and increase
collaboration across all grade levels, including elementary and middle school staff
Strengths and Weaknesses of the Approach
Coding implementation at a K8 school site and its program effectiveness was examined
through the instructional design and practices of teachers who participated in the Computer
Science Pathways Program. This study highlighted teachers’ experiences in building the
program’s structure and content and delivering coding instruction. The study’s purpose was to
assess the KMO influences on teacher self-efficacy for professional growth around coding
integration and instruction. The KMO framework by Clark and Estes (2008) provided a method
to analyze and validate results based on influences regarding the development of teachers’ self-
efficacy in learning and teaching coding to their students. An interactive framework regarding
KMO influences allowed the researcher to identify gaps that affect teachers’ professional
learning as well as the stakeholder and organizational goal. However, the gap analysis showed
limitations in that reasons for the gaps were not disclosed by the participants. In-depth
information regarding the reasons for the gaps would be valuable to the organization, in that they
would provide insight to help identify next steps.
Limitations and Delimitations
As noted in Chapter Three, there were limitations of this study based on several factors
regarding the site, scope of the results, researcher’s bias, and the timeframe in which the study
was conducted. The study involved qualitative research at only one school site; therefore, the
scope of the results is limited. The stakeholder group for this study consisted of teachers at CS
Academy who were trained with TechSmart to learn and deliver coding instruction in block
coding or language-based coding in grades three to eight. Teachers were asked via email to

81
voluntarily participate in an in-person interview. The study relied on voluntary participation;
thus, one limitation noted was grade-level representation was out of the researcher’s control. Of
the eight teachers who responded, six represented elementary grades and two were from middle
school. Due to middle school being represented by single-subject credentialed teachers, two out
of the three middle school computer science teachers participated in the study. However, the
study represented each grade level involved in the computer science program, third through
eighth grade.
A researcher’s role is crucial in collecting data, with reflexivity being a vital part of the
research process (Creswell & Creswell, 2018; Merriam & Tisdell, 2016). A researcher gains
natural bias through experiences. The study was subject to the bias of the researcher based on her
previous relationships with the participants. Additionally, the study was subject to the
truthfulness of the responses based on prior relationships with the researcher. Self-reflection,
reflexivity, and member checking were conducted to offset the researcher’s bias and uphold the
integrity of the study as well as validate its credibility and trustworthiness. In Chapter Three, the
researcher noted her former teaching and administrative positions at the site, including
established relationships that go back 16 years with some of the participants. Prior to the
interview, the researcher disclosed to all participants her position as a CITI-certified researcher
who passed the IRB process for the study. It was communicated to all participants that the
interview would have no bearing on the participant’s performance evaluations and/or
promotions. The researcher analyzed reflective and analytic memos to note any participant
reactions to the interview questions. Reflective notes were also analyzed to identify potential
researcher assumptions based on history at the site. The researcher reframed questions in
subsequent interviews to mitigate assumptions. Based on relationships with the participants,

82
candid conversations proved to produce robust testimonies, wherein participants also disclosed
challenges during the short period of the interview time. Additionally, respondent validation
secures internal validity through member checking, meaning feedback from participants is
solicited based on early inductive findings (Merriam & Tisdell, 2016). All transcripts, along with
coded annotations and assertions, were provided to all participants to confirm accuracy. An
additional opportunity to add comments or clarifications was provided to the participants. All
participants responded to the request and confirmed accuracy of the transcripts, annotations, and
assertions.
This study was conducted at one site and over a brief period of time; therefore, the
limitations of time and location affected the study. The site is unique due its technology vision,
established when the school was built in 2004. Therefore, this study and its findings may not be
suitable for generalizations to other public K-8 schools. Nevertheless, the results of the study
disclose the correlation between KMO influencers and building teacher efficacy in professional
growth as well as instructional practices with coding instruction in the classroom. Findings from
this study can be valuable to other schools as they develop professional practice regarding
computer science instruction. For better results, a mixed-method approach using surveys and
observations can add elements to the study, such as a comparison of perspectives, a larger
sample size, and representation from marginalized groups (Creswell & Creswell, 2018).
Some delimitations to this study that were within the researcher’s control. CS Academy
and its staff were purposefully chosen to examine the computer science program for elementary
and junior high grades. The results highlight the program’s success and areas for improvement
that may serve to assist other school sites in developing practices to support teachers as they
learn coding. Based on the interview dates and timeline, the researcher was able to gather

83
information based on the completion of two-thirds of the school year. The stakeholder goal was
met, however, the site was in the process of meeting the organizational goal. Additionally, the
researcher chose the methods of data collection and instruments. Qualitative methods were
chosen to capture the diverse experiences of teachers who have started a journey of learning new
content and implementing coding during the instructional day.
Future Research
Computer science encompasses a large field and the needs represented in the problem of
practice are greater than this study investigated. This particular study focused on coding
integration, which represents only one aspect of computer science. Further study in other areas of
computer science disciplines, such as computer graphics and computer engineering, may provide
an inclusive perspective of computer science integration. Studies conducted at school sites
similar to CS Academy can be used in comparisons to provide more comprehensive results.
Interviews were conducted in the middle of the school year; therefore, the organizational goal set
for the complete school year cannot be fully measured. Future research that allows for the
collection of data at the beginning, middle and end of a school year will provide a better
representation of a teacher’s journey in establishing goals, monitoring progress, and reflecting on
goals based on results. Future studies, especially mixed-methods research, can identify needs
across all grade levels and will help capture cultural norms to represent all staff at one site.
Additional methods through questionnaires may result in findings that will help identify needs
among underrepresented groups at the site. Further study at CS Academy to identify needs and
support for middle school will benefit the school site by building a stronger culture of trust and
collective responsibility based on specific goals and feedback.

84
Conclusion
Computer Science for All, an initiative released by President Obama in 2016, established
the directive to provide computer science instruction in schools. Shortly following the release of
the initiative, the Computer Science Framework for K-12 grades was introduced to schools. In
2018, the state released the California Computer Science Strategic Implementation Plan;
however, findings show that there is a shortage of qualified teachers to teach computer science
(Howard, 2018). Teacher efficacy in computer science is vital to the quality of a program and
the success of implementation (Bender et al., 2016). The purpose of this study was to evaluate a
site’s effectiveness in coding implementation in the classroom by examining the teachers’ role in
learning coding and instructing coding in a K8 school. The study investigated the KMO factors,
which influenced whether teachers took part in professional learning and built efficacy around
coding instruction.
Clark and Estes’ (2008) KMO framework helped to identify motivating factors for
professional growth and gaps in organizational practices that help support teachers in building
efficacy through specific goals, feedback and collaborative support. The findings were organized
by the study’s research questions, which illustrated teachers’ experiences with active learning,
exploring, and applying coding skills to transfer the experience to their students. Teachers
created learning opportunities for their students that promoted critical thinking, computational
thinking and problem solving in activities that integrated other disciplines. Students were
challenged to use coding skills and computational skills to program robots, create apps to address
global problems, and program drones for a mock search and rescue mission. Results indicate that
teacher motivation increased as students responded positively to coding instruction and as
teachers saw the relevance of applied coding to future opportunities their students may have in

85
the workforce. To fulfill the expectation to implement computer science in K-12 education,
schools must begin by identifying needs to equip teachers with the tools to teach effectively.
Relevant professional development, establishing collaborative support systems, and motivating
teachers to build their efficacy in both factual and procedural knowledge are critical in
establishing a school-wide computer science program. Teacher effectiveness will lead to student
engagement and a positive impact on student achievement for today and for tomorrow.

86
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Appendix A
Protocol for Face-to-Face Interview
I would like to begin by thanking you for your time. I appreciate your willingness to meet
with me to answer some questions regarding your role with computer science implementation at
the site. The purpose of the research is for inquiry as well to further examine teachers’ efficacy
with computer science. This interview should take about one hour. Thank you for bringing a
copy of a coding lesson plan per request (voluntary.).
Before we begin the interview, I would like to provide you with a quick overview on
what we will discuss today. Please feel free at any time to ask me questions regarding today’s
interview. I will be having 1:1 conversation with teachers who implement coding in their
classrooms. I will ask questions regarding your thoughts on computer science/coding
implementation, your experience with computer science/coding implementation, your experience
with professional development on coding, your thoughts on implementation support and thoughts
and involvement with the school’s vision. Do you have any questions about today’s
conversation?
I would like to review the logistics of the interview process. Your participation is
voluntary. Everything that we talk about here today is confidential. No names will be provided to
anyone or any names associated with the conversation, findings and/or results. No data collected
will be shared with district personnel, including other staff members or administration. The data
will not be used or distributed for future studies. Data will be stored electronically on a
password-protected device. Refusal or a change of mind to participate is done without penalty.
You may also discontinue participation of this interview at any time.

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I have a recorder to capture our conversation. This will allow me to have a conversation
freely without worrying about taking notes. If at any time you would like for me to stop the
recording, please let me know, and I will turn off the recorder. May I have your permission to
record our conversation?
(I will begin recording. I will mention the date, time, and interviewee’s position)
Interview Questions
We will begin the interview with questions on your thoughts and experience in education,
specifically around computer science. But before we begin, may I ask how long you have been in
the teaching profession? (Demographic)
1. I would love hear about your thoughts on coding skills as they apply to everyday
work.
a. What are your personal thoughts on coding?
b. How is coding relevant to schools?
c. Do you believe that schools have a responsibility to teach coding?
2. I would love to hear about your experience with Computer Science instruction.
a. Have you had any experience teaching coding?
b. Describe a typical coding lesson with students.
i. How are the coding lessons going?
ii. What is it like delivering new concepts/content?
iii. What drives you to continue teaching coding?
c. How would you describe yourself as a learner?
i. What is your experience with learning a new concept such as coding?

96
3. As mentioned in the overview, we discussed your thoughts and experiences with
coding implementation. Now we will discuss professional development. Think about
the most recent coding training.
a. What are your thoughts about the coding training and its relevancy? Are
trainings mandated?
b. Describe a successful training in the past.
c. What would be an ideal coding training?
4. We are at the mid-point of the interview. I will now ask questions on needed supports
for coding implementation.
a. To move forward with the skills that you’ve learned with computer science,
what supports do you need?
b. What supports have you received?
c. How do you work with your colleagues? Tell me about a time you were given
an opportunity to discuss computer science instruction?
d. Describe a recent interaction with a colleague or administrator discussing
computer science integration.
e. What does feedback look like for you as a teacher?
i. Is the feedback formal and/or informal?
ii. Do you receive feedback specifically in the area of computer science
instruction?
iii. What is administration’s role in the area of feedback in computer
science instruction?

97
iv. What would you hope in getting feedback from computer science
instruction?
5. Lastly, I will ask you questions around the school’s vision. Is there a larger computer
science vision at the site?
a. How involved are you with the process of creating the vision?
b. Does your school have computer science goals, in one year, five year?
c. How much do the goals come into meetings with your colleagues?
i. How do you work with your team to set goals?
6. Is there anything else you would like to add to our discussion today?
(Probes if Needed: Can you give me an example? What were you thinking at that time? What did
you/he/she say next? Can you please clarify? I would love to hear more about that.)
Closing and Follow Up
Thank you so much for meeting with me today and sharing your thoughts and
experiences on computer science integration. I have prepared a small token of appreciation for
your time. You will receive a $10 gift card to Amazon in your staff box. If I have any follow up
questions, may I contact you via email? I will now stop the recording. Thank you for
participating in the interview today!

98
Appendix B
Interview Protocol for Focus Group
I would like to begin by thanking you for your time. I appreciate your willingness to meet
with me to answer some questions regarding your role with computer science implementation at
the site. The purpose of the research is for inquiry as well to further examine teachers’ efficacy
with computer science. This interview should take about one hour. Thank you for bringing a
copy of a coding lesson plan per request (voluntary.)
Before we begin the interview, I would like to provide you with a quick overview on
what we will discuss today. Please feel free at any time to ask me questions regarding today’s
interview. I will be having 1:1 conversation with teachers who implement coding in their
classrooms. I will ask questions regarding your thoughts on computer science/coding
implementation, your experience with computer science/coding implementation, your experience
with professional development on coding, your thoughts on implementation support and thoughts
and involvement with the school’s vision. Do you have any questions about today’s
conversation?
I would like to review the logistics of the interview process. Your participation is
voluntary. Everything that we talk about here today is confidential. No names will be provided to
anyone or any names associated with the conversation, findings and/or results. No data collected
will be shared with district personnel, including other staff members or administration. The data
will not be used or distributed for future studies. Data will be stored electronically on a
password-protected device. Refusal or a change of mind to participate is done without penalty.
You may also discontinue participation of this interview at any time.

99
I have a recorder to capture our conversation. This will allow me to have a conversation
freely without worrying about taking notes. If at any time you would like for me to stop the
recording, please let me know, and I will turn off the recorder. May I have your permission to
record our conversation?
(I will begin recording. I will mention the date, time, and interviewee’s position)
Interview Questions
We will begin the interview with questions on your thoughts and experience in education,
specifically around computer science. But before we begin, may I ask how long you have been in
the teaching profession? (Demographic)
1. For Focus Group: Lastly, I will ask you questions around the school’s vision. Is there
a larger computer science vision at the site?
a. How involved are you with the process of creating the vision?
b. Does your school have computer science goals, in one year, five year?
c. How much do the goals come into meetings with your colleagues?
i. How do you work with your team to set goals?
2. Is there anything else you would like to add to our discussion today?
(Probes if Needed: Can you give me an example? What were you thinking at that time? What did
you/he/she say next? Can you please clarify? I would love to hear more about that.)
Closing and Follow Up
Thank you so much for meeting with me today and sharing your thoughts and
experiences on computer science integration. I have prepared a small token of appreciation for
your time. You will receive a $10 gift card to Amazon in your staff box. If I have any follow up

100
questions, may I contact you via email? I will now stop the recording. Thank you for
participating in the interview today!

101
Appendix C
Document Analysis Protocol
Research Question Type of Personal Document Submitted
To what extent is the school meeting its goal?

How does the document support knowledge
influencers in the area of:

● Factual Knowledge
● Procedural Knowledge
● Professional Development

• Lesson plans
• Schedules
• Grade level meeting minutes
• Student Activities
• Presentations
• Other:
What increases knowledge and motivation to
help build teacher efficacy in the area of
computer science instruction?

How does the document support knowledge
and motivational influencers in the area of:

● Active Learning
● Transfer of Learning
● Self-efficacy
● Goal Orientation

• Lesson plans
• Schedules
• Grade level meeting minutes
• Student Activities
• Presentations
• Other:
In what ways does the organizational culture
support teacher efficacy in the area of
computer science integration?

How does the document support
organizational influencers in the area of:

● Feedback
● Trust
● Collective Goals
● Shared Responsibility
● Collective Accountability

• Lesson plans
• Schedules
• Grade level meeting minutes
• Student Activities
• Presentations
• Other:

Abstract (if available)

Abstract This study evaluates the effectiveness of coding implementation in the classroom by examining instructional practices that promote teacher efficacy. This study closely examined teachers’ role in building the program’s structure and content, delivering coding instruction, and measuring students’ proficiency with integrating coding skills to content areas. An evaluation of the Computer Science Pathways Program was conducted through a qualitative study. Based on the study’s purpose to seek knowledge, motivational and organizational influences as well as their impact on teacher efficacy, the research was conducted by gathering data through individual teacher interviews, a focus group, and document analysis. The research questions explored how the organization was meeting its goal, how knowledge and motivational factors influence teachers’ ability to learn and provide quality coding instruction, and how organizational processes support the goal. In this study, the stakeholder population of focus was third through eighth grade teachers who participate in the Computer Science Pathways Program at a K8 public school. The study focused on teachers who are committed to the program using the TechSmart Coding Curriculum and received coding training through professional development. This study used inter-method mixing of data collection through interviews, a focus group, and document analysis to gather data on stakeholder participation towards the organizational goal. The researcher’s role was to organize the collection of data, analyze the data, establish themes and interpret the meaning based on the research questions and conceptual framework. Based on Creswell and Creswell’s (2018) five-step process for data analysis, this study followed each step to interpret the data. A full report with the study’s analysis and recommendations was provided to the site that may serve as next steps. A published dissertation may also assist other school sites with computer science integration.

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Asset Metadata

Creator Lee, Julienne Mi (author)

Core Title Evaluation study: building teacher efficacy in K8 computer science integration

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Publication Date 06/18/2020

Defense Date 05/22/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag computer science integration,OAI-PMH Harvest,teacher efficacy

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Min, Emmy Jungwon (committee chair), DeMark, Alison (committee member), Freking, Frederick (committee member)

Creator Email julienml@usc.edu,juliennelee@me.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-320282

Unique identifier UC11664047

Identifier etd-LeeJulienn-8601.pdf (filename),usctheses-c89-320282 (legacy record id)

Legacy Identifier etd-LeeJulienn-8601.pdf

Dmrecord 320282

Document Type Dissertation

Rights Lee, Julienne Mi

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA