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Computer-Based Learning for Students with Disabilities: Teacher Understanding

and Needs Following the COVID-19 Pandemic

A Dissertation Submitted to the Faculty of

Immaculata University

Donna M. Freydlin

In partial fulfillment of the requirements

for the degree of

Doctor of Education

Immaculata, Pennsylvania April 2024

iii

Donna M. Freydlin

Abstract

This qualitative study sought to uncover and analyze teacher understanding and needs

following the COVID-19 pandemic regarding computer-based/ remote implementation

and instruction for students identified with a disability. The purpose of this study was to

investigate teachers’ perspectives, understandings, and needs on the implementation of

computer-based/ remote instruction for students with disabilities following the recent

pandemic. The 41 participants were certified public school teachers in south central

Pennsylvania who have taught on a computer-based/ remote platform within the last three

years (post-pandemic). Data found indicated teachers have varying views on the

implementation of computer-based/ remote instruction for students identified with

disabilities. Data demonstrated a decrease in the percentage of computer-based

instruction throughout the three years noted within this study. Participants felt that their

district had the ability to accommodate/ modify within their district’s educational

platform, but many participants felt unprepared for computer-based/ remote instruction

for students identified with disabilities. The data presented a need for additional

professional development relevant to computer-based/ remote instruction for students

with disabilities, as well as all learners. From the data provided, new standards on the

preparation, implementation, and professional development for educating students with

disabilities on a computer-based educational platform may be developed. Educational

leaders may facilitate appropriate training to better meet the needs and success of all

teachers and their students.

Acknowledgements

It is with sincerest gratitude that I thank and recognize the following people who

have supported and assisted me with the completion of this dissertation throughout the

entire doctoral program:

I would like to first and foremost thank my chairperson, Dr. Laura Eisemann for

supporting me when I needed it. Dr. Eisemann provided encouragement, knowledge, and

insight throughout the entire writing process. Dr. Eisemann always knew exactly what I

needed at the exact time. Thank you.

I would like to thank my dearest committee members, Dr. Rian Brown-Beasley

and Dr. Jaime Linn Brown. You each provided much needed feedback and guidance

throughout this process. Your encouragement gave me the strength to continue forward. I

am honored to have worked with you and hope to get the opportunity to work with you

again in the future.

Last and certainly not least, I want to thank my family. Without your unwavering

support, encouragement, and love I would not be able to be where I am today. Many a

night I wanted to give up and you were there for me with logic and reasoning. I am

forever thankful for your faith in me. Stan, you know how much you supported me

through this multi-year process toward a doctorate. From watching our kids, reading my

drafts, to drying my tears, thank you. To my children (Leonid, Dmitriy, and Anton),

thank you for letting mom have some “mom time” to reach her potential. I know it was

not easy for mom to be busy during the evenings, thank you. My dog, Fuzzy, thank you

for the cuddles. Mom and dad, you never gave up encouraging me to reach for the stars,

thank you. Theresa, thank you for supporting my/our family when I/we needed it.

Table of Contents

Abstract .............................................................................................................................. iv

Acknowledgements .............................................................................................................v

Table of Contents .............................................................................................................. vi

List of Tables ......................................................................................................................x

Chapter One – Introduction ................................................................................................1

Overview .................................................................................................................1

Statement of the Problem ........................................................................................2

Need for the Study ..................................................................................................4

Limitations ..............................................................................................................6

Research Questions .................................................................................................7

Definition of Terms .................................................................................................7

Summary ...............................................................................................................11

Chapter Two – Literature Review .....................................................................................13

Introduction ...........................................................................................................13

History of Computer-Based Learning ...................................................................15

Challenges of Computer-Based Learning .............................................................19

Lack of Access ......................................................................................................20

Student Technology Skills ....................................................................................21

vii

Time Management and Self-Regulation ...............................................................22

Computer-Based Adaptations and Accommodations ...........................................23

Computer-Based Learning Planning .....................................................................27

Computer-Based Learning Teacher Training .......................................................29

Mental Health ........................................................................................................34

Teacher Perspectives of Computer-Based/ Remote Learning ..............................38

Summary ...............................................................................................................40

Chapter Three – Methodology and Procedure ...................................................................43

Introduction ...........................................................................................................43

Setting ....................................................................................................................43

Participants .............................................................................................................44

Instruments ............................................................................................................45

Survey ...................................................................................................................45

Interview ...............................................................................................................46

Reliability and Validity .........................................................................................48

Design of Study .....................................................................................................49

Procedure ..............................................................................................................50

Data Analysis ........................................................................................................51

Summary ...............................................................................................................52

Chapter Four – Results ......................................................................................................54

viii

Introduction ...........................................................................................................54

Demographics .......................................................................................................55

Compilation of Data ..............................................................................................65

Research Question One .........................................................................................65

Survey Responses .................................................................................................66

Interview and Open-Ended Survey Responses .....................................................75

Research Question Two .........................................................................................81

Survey Responses .................................................................................................82

Interview and Open-Ended Survey Responses ...................................................107

Research Question Three ....................................................................................109

Survey Responses ...............................................................................................110

Interview and Open-Ended Survey Responses ...................................................114

Conclusion ..........................................................................................................117

Chapter Five – Discussion ..............................................................................................118

Summary of the Study ........................................................................................118

Summary of the Results ......................................................................................119

Research Question One .......................................................................................120

Research Question Two ......................................................................................123

Research Question Three ....................................................................................129

Limitations Found in the Study ...........................................................................131

Relationship to the Research ...............................................................................133

Recommendations for Future Research ..............................................................137

Conclusion ..........................................................................................................139

References .......................................................................................................................141

Appendices ......................................................................................................................158

Appendix A – Immaculata University RERB Review Board Comments Form ..............158

Appendix B – Computer-Based Instruction for Students with Disabilities Survey ........159

Appendix C – Interview Information ..............................................................................173

Appendix D – Interview Questions .................................................................................177

Appendix E – Information Letter to Superintendents .....................................................179

Appendix F – Teacher Email Invitation ..........................................................................182

List of Tables

Table 4.1 Demographic Information for Survey Participants ...........................................57

Table 4.2 Average Number of Students Taught ...............................................................59

Table 4.3 Demographic Information of General Education Teachers and Special

Education Participants ......................................................................................................60

Table 4.4 Demographic Information of General Education Participants through Grade

Span ...................................................................................................................................61

Table 4.5 Demographic Information of Special Education Participants through Grade

Span ...................................................................................................................................63

Table 4.6 Demographics Information for Interview Participants .....................................64

Table 4.7 Grade Span Awareness of Disability Categories by Current Role in Education

............................................................................................................................................67

Table 4.8 Disability Categories .........................................................................................68

Table 4.9 Percentage of Computer-Based Instruction ......................................................69

Table 4.10 Percentage of Computer-Based Instruction General Education .....................70

Table 4.11 Percentage of Computer-Based Instruction Special Education .......................70

Table 4.12 Percentage of Computer-Based Instruction by Grade Span 2020-2021 .........72

Table 4.13 Percentage of Computer-Based Instruction by Grade Span 2021-2022 .........73

Table 4.14 Percentage of Computer-Based Instruction by Grade Span 2022-2023 .........74

Table 4.15 Interview Questions One and Five Comments ................................................77

Table 4.16 Interview Questions One, Three, Five, and Seven Responses Relevant to

Theme ...............................................................................................................................81

Table 4.17 Educational Platforms .....................................................................................84

Table 4.18 Educational Platform by Current Role and Grade Span .................................85

Table 4.19 District's Computer-Based Platform's Capacity to Accommodate by Current

Role and Grade Span ........................................................................................................87

Table 4.20 Current Presentation Accommodations/ Modifications Available .................88

Table 4.21 Current Presentation Accommodations/ Modifications Available by Current

Role and Grade Span ........................................................................................................89

Table 4.22 Current Response Accommodations/ Modifications Available .......................90

Table 4.23 Current Response Accommodations/ Modifications Available by Current Role

and Grade Span .................................................................................................................92

Table 4.24 Current Timing Accommodations/ Modifications Available .........................93

Table 4.25 Current Timing Accommodations/ Modifications Available by Current Role

and Grade Span ..................................................................................................................95

Table 4.26 Computer-Based Activities/ Assignment Frequency ......................................96

Table 4.27 Computer-Based Activities Frequency by Current Role in Education ............97

Table 4.28 Computer-Based Activities Frequency by Grade Six to Eight Grade Span ...99

Table 4.29 Computer-Based Activities Frequency by Grade Nine to 12 Grade Span ...100

Table 4.30 Computer-Based Assignments Frequency by Current Role in Education ....102

Table 4.31 Computer-Based Assignments Frequency by Grade Six to Eight Grade Span

..........................................................................................................................................104

Table 4.32 Computer-Based Assignments Frequency by Grade Nine to 12 Grade Span

..........................................................................................................................................106

Table 4.33 Responses from Research Question Two Relevant to Theme ......................109

xii

Table 4.34 Professional Development While Implementing Computer-Based Instruction

by Current Role and Grade Span ....................................................................................111

Table 4.35 Additional Professional Development Needed for Success by Current Role

and Grade Span ...............................................................................................................113

Table 4.36 Responses from Research Question Three Relevant to Theme ....................116

Chapter One- Introduction

Overview

With the recent global pandemic, computer-based instruction for students

identified with disabilities has increased dramatically (Averett, 2021). It has never been

more evident that education and educational platforms have evolved throughout history.

From one-room multi-grade schoolhouses to virtual learning, education has changed with

time. Teachers have been faced with the ever-changing needs of educating the leaders of

tomorrow. With the introduction and growth of computer-based educational platforms,

questions of equity for students with disabilities have increased (Tate & Warschauer,

2022).

Miks and Mcllwaine (2020) indicate that, 1.6 billion or 91% of students globally

were impacted by school closures during the 2019-2020 school year. During the 2017-

2018 school year, the National Center for Educational Statistics reported that about 19%

of elementary and secondary schools provided online course work for students (National

Center for Education Statistics, 2021). Many schools were forced to make drastic changes

to instructional approaches and educational platforms as the world encountered a global

pandemic. Burbio Inc. (2022) conducted a survey in August of 2021 about the 2020-2021

school year in the United States and found that 55% of students learned through the use

of a computer-based or remote educational platform and 25% participated in some form

of hybrid learning. Many of these online students were those identified with disabilities

(Averett, 2021).

The National Center for Education Statistics (2022) reported that during the 2020-

2021 school year, the United States had an estimated 7.2 million or 15% of public-school

students identified as students with disabilities. The Individuals with Disabilities

Education Act (IDEA, 2004) guaranteed the right to a free and appropriate public

education (FAPE) for children with disabilities (Individuals with Disabilities Education

Act, 2004). School districts were faced with how to appropriately educate all these

students. “Americans with disabilities are three times as likely as those without a

disability to say they never go online (15% to 5%)” (Perrin & Atske, 2021, para. 5).

These statistics, compounded by almost instantaneous redirection to online education

caused by the pandemic, provided a staggering concern for school systems which are

legally and morally required to provide an education to students with disabilities

(Individuals with Disabilities Education Act, 2004).

School districts and teachers are faced with the daily challenge of how to provide

accessible education and services for students with disabilities. IDEA and Chapter 14

require that equitable services must be provided for all students (Individuals with

Disabilities Education Act, 2004). These services extend beyond direct instruction and

include the provision of appropriate accommodations for students with disabilities. While

accommodations have historically been challenging for school systems across the nation

(Mason-Williams et al., 2020), schools are presented with the increased complexity of

providing FAPE within the computer-based/ remote learning environment.

Statement of the Problem

With the increase of computer-based/remote learning for all students, teachers

have been presented with increased complexities to the long standing and existing

challenge associated with providing instruction that meets the needs of students with

disabilities. “Teachers using the Internet as either the primary or sole medium of

interaction with students are additionally charged with implementing new pedagogical

strategies as part of a reconceptualization of teaching and learning” (Basham et al., 2015,

p. 43). Students must increase their self-regulation of their own learning due to the

remote aspect of computer-based educational learning. “For all students--- but for

students with disabilities in particular-- self-regulation strategies cannot be presumed to

exist and can be encouraged by the effective use of online-specific instructional strategies

and learning supports embedded in online systems” (Basham et al., p. 43). Students with

disabilities may face a variety of challenges when participating in computer-based/remote

learning. Teachers must be prepared for the challenges of instructing students with

identified disabilities, as well as students with diverse technological skills.

Educators who teach students with disabilities may be tasked with complex

challenges and may feel inadequately prepared (Basham et al., 2015). “Teachers whose

experience and expertise is primarily with brick-and-mortar practices are often

unprepared to transition to using online offerings” (Basham et al., p. 44). Further

complicating the matter, research indicated that evidence-based practices from traditional

classroom instruction are not necessarily transferable to online instruction (Crouse et al.,

2018). Additionally, and worthy of note, is the pressure that teachers feel in the delivery

of school district approved curriculum. “Teachers feel pressured to teach to the content

requirements and approved curriculum, which does not always include teaching specific

strategies or executive functioning skills” (Basham et al., p. 45).

Students identified with a disability may require specific instruction,

accommodations, and modifications to be successful within a computer-based/remote

learning experience. Online materials must be appropriate for all students. “Teachers

need to be prepared to gather data, use data, and make data-based decisions” (Basham et

al., 2015, p. 56). Notwithstanding, little research exists on the use and implementation of

computer-based learning for students identified with disabilities (Rice & Dykman, 2018).

The purpose of this study was to explore teachers’ perspectives on the

implementation of computer-based/ remote instruction for students with disabilities. In

identifying teacher perspectives on the implementation of computer-based/remote

instruction, an increased awareness of the online learning experiences of teachers who are

instructing students with disabilities may be used to improve the online learning

experiences and equity for all students, including those identified with disabilities. The

data presented may increase awareness of the need for a multi-layered approach to

instructing students with special needs. The feedback gathered from this study may lead

toward future professional development opportunities for educators of students with

disabilities who are implementing computer-based/remote instruction. The increased

awareness of online learning experiences of teachers instructing students with disabilities

may be used to improve the online learning experiences and equity for all students.

Need for the Study

Research indicated that teachers may not be adequately prepared to accommodate

and modify for students with disabilities while instructing with computer-based/ remote

instruction (Crouse et al., 2018). Rice and Dykman (2018) found that teachers felt they

had “limited skills for helping students persist in fully online learning beyond pacing

guides” (p. 200). In contrast, teachers within brick-and-mortar schools teach self-

regulation and executive functioning skills, which have been found to increase student

persistence (Rice & Carter, 2015).

Despite the challenges associated with remote/online instruction, teachers have a

positive view on the learning potential for students with disabilities while utilizing fully

online asynchronous computer-based instruction (Marteney & Bernadowski, 2016). Greer

et al. (2014) research findings asserted that “Flexibility and individualization through

online learning platforms and tools may offer greater opportunities to facilitate inclusion

of students with disabilities” (p. 160). Greer et al. further asserted that teachers felt they

lacked the training needed to reach this potential. They continued that “nearly two-thirds

(64%) of online teachers surveyed indicated that their highest need for professional

development centered on strategies for meeting the needs of students with special needs

in online learning” (p. 152). Most professional development provided represents

accessibility, but not how to develop and implement online accommodations

(Archambault & Kennedy, 2014). For teachers to be successful, they “must have the

skills to integrate pedagogy and evidence-based practices with online technology,

facilitate online communication and collaboration, carry out new roles and

responsibilities, and fulfill all or part of the special education requirements” (Greer et al.,

p. 152). Educators of students with disabilities may require professional development to

acquire computer-based instructional skills that vary from traditional brick-and-mortar

teaching practices.

Further investigation is required to analyze and understand the teachers’

perspectives of computer-based instruction for students with disabilities. From the data

provided, new standards on the preparation, implementation, and professional

development for educating students with disabilities on a computer-based educational

platform may be developed. Educational leaders may facilitate appropriate training to

better meet the needs and success of all teachers and their students.

Limitations

This study focused on teachers of students with disabilities who are participating

in a computer-based/remote learning experience. The participants were located in south

central Pennsylvania. This study included both elementary and secondary teachers’

perspectives. The sample size of 41 participants may present a limitation. With few

participants, the information and data gathered was only representative of a small portion

of teachers employed within the participating south central public school districts.

The geographic region presented limitations to this study. The data provided was

representative of the region and may not represent the perspectives of teachers in general

within Pennsylvania, the United States, or around the world. The results of the study only

represent a small portion of the Commonwealth of Pennsylvania.

Study participants were anonymous, therefore limitations to the data provided

may occur. Such a limitation may be the exact course taught or the type of computer-

based/remote learning that they provide. Therefore, feedback to any specific coursework,

as well as type of computer-based/remote learning platform was limited.

A final limitation noted, was that this research was completed in 2023, three years

after the recent global pandemic. During the global pandemic, emergency computer-

based/remote teaching was utilized for most teachers and students. Therefore, the recent

pandemic and school closures may have impacted the participants’ responses toward

computer-based/remote instruction for students with disabilities.

Research Questions

This study’s purpose was to investigate teachers’ perspectives, understandings,

and needs on the implementation of computer-based/remote instruction for students with

disabilities following the recent pandemic. The following questions were addressed in

this study:

1. What are teachers’ perceptions on the implementation of computer based/remote

instruction for students with disabilities?

2. What strategies do educators use when teaching students with disabilities on a

computer based/ remote platform?

3. What are teachers’ perceptions on the implementation of professional

development for computer based/ remote instruction for students with disabilities?

Definition of Terms

The following definitions were used for the purpose of this study:

Accessibility- Defined by the United States Department of Education (2020) as

equal access to electronic information and data provided to individuals with disabilities as

compared to those who were not identified with disabilities, unless there was an undue

burden imposed on the agency.

Blended Learning- Learning opportunities that incorporated both face-to-face and

online learning opportunities (U.S. Department of Education, 2021).

Coronavirus Disease 2019 (COVID-19)- “An infectious disease caused by the

SARS-CoV-2 virus” (World Health Organization, 2023, para. 1).

Children’s Online Privacy Protection Act- Defined by Public Law 105-277,

which provided parents control over what information websites can collect from children

under the age of 13 (Federal Trade Commission, 2021).

Communication Literacy- Digitally supported communication that is almost

instant in nature and required users to be more aware of the nature and implications of

what they communicate and how they communicate (Rice & Ortiz, 2021, p. 210).

Digital Literacy- “The skills associated with using technology to enable users to

find, evaluate, organize, create, and communicate information” (Department of

Education, 2022, para. 7).

Distance Learning- Described by the working definition of the Pennsylvania

Department of Education as “The delivery of instruction for academic credit when the

instructor is physically located at an address that differs from the physical location of the

student at the time of instruction” (Pennsylvania Department of Education, 2023, para. 7).

Free and Appropriate Public Education (FAPE)- Defined by the United States

Department of Education (2010) as free and appropriate education for all students and is

a guaranteed right of all students identified as student with a disability.

Hybrid learning- “Cross between the traditional face to face classroom format and

online only instruction” (Lamport & Hill, 2012, p. 49).

Individual Education Program (IEP)- A written plan for the provision of services

for the education of students who are disabled or gifted (Pennsylvania Department of

Education, 2021).

Individuals with Disability Education Act (IDEA)- Defined by US Public Law

101-476, which guarantees a free and appropriate public education for students with

disabilities within the United States (Individuals with Disabilities Education Act, 2004).

Information Literacy- “Vast amounts of information that users must evaluate and

use to make meaning” (Rice & Ortiz, 2021, p. 210).

Least Restrictive Environment (LRE)- Defined as by the United States Department

of Education in Public Law Section 300.114 as “the maximum extent appropriate,

children with disabilities, including children in public or private institutions or other care

facilities, are educated with children who are nondisabled; and special classes, separating

schooling, or other removal of children with disabilities from the regular educational

environment occurs only if the nature or severity of the disability is such that education in

regular classes with the use of supplemental aids and services cannot be achieved

satisfactorily” (para. a).

Online Educational Platform- The software or learning management system

utilized in providing online computer-based instruction (Gross, 2014).

Perception- “The process or result of becoming aware of objects, relationships,

and events by means of the senses, which includes such activities as recognizing,

observing, and discriminating” (American Psychological Association, 2023, para. 1).

Professional Development- “Activities to develop an individual’s skills,

knowledge, and expertise and other characteristics as a teacher” (UNESCO, 2020, para.

1).

Reasonable Accommodation- Modifications or adjustments to the tasks,

environment, or to the way things are done for individuals with disabilities to have an

equal opportunity to participate (U.S. Department of Education, 2023).

Reasonable Modification- Public entities are required to make reasonable

modifications in policies, practices, or procedures when necessary to avoid discrimination

on the basis of disability, unless making the modifications would fundamentally alter the

nature of the service, program, or activity (U.S. Department of Education, 2023).

Regular Education Teacher- “Professional employee or temporary professional

employee who provide direct instruction to students related to a specific subject or grade

level” (Pennsylvania Department of Education, 2023, para. 1, bullet 1).

Remote Learning- Learning that occurred when the student and instructor were

not located at the same location. This may have been a separation of time or location

from the traditional classroom setting (UNESCO, 2020).

Self-Regulation- The ability to inhibit, override, or alter dominant impulses to

modify thought, feeling and behavior (de Ridder et al., 2012).

Special Education Teacher- “Teachers who work with students who have a wide

range of learning, mental, emotional, and physical disabilities” (Bureau of Statistics and

Labor, 2022, para. 1).

Student with a Disability- Defined in Public Law 108-446, the Individuals with

Disabilities Education Improvement Act of 2004 (IDEA), as a child “(i) with mental

retardation, hearing impairments (including deafness), speech or language impairments,

visual impairments (including blindness), serious emotional disturbance, orthopedic

impairments, autism, traumatic brain injury, other health impairments, or specific

learning disabilities: and (ii) who, by reason thereof, needs special education and related

services” (para. 4).

Technological Literacy- Defined by Rice and Ortiz (2021) as the skills-based

vocational competency for computing (p. 210).

Trauma- “Emotional response to a terrible event like an accident, rape, or natural

disaster” (American Psychological Association, 2023, para. 1).

Virtual or Online Learning- Online learning opportunities that ranged from

supplementing classroom instruction on an occasional basis to students enrolled in full-

time programs (U.S. Department of Education, 2021).

Summary

Computer-based instructional platforms are now prevalent throughout the world.

With advancements in technology and a recent global pandemic, teachers are faced with

new challenges each day. One advancement in technology is the use of online educational

platforms. In an interview by Li and Lalani (2020), Wang Tao, the Vice President of

Tencent Cloud and Vice President of Tencent Education, stated “I believe that the

integration of information technology in education will be further accelerated and that

online education will eventually become an integral component of school education”

(para. 9). Teachers must be prepared for such a transition to computer-based/remote

instruction.

Existing research has found that teachers do not feel adequately prepared for

implementing computer-based instruction for students with disabilities (Rice & Carter,

2015). Research findings will validate the need for standards for the implementation of

computer-based learning for all students, but especially for students with disabilities. This

qualitative research study was designed to increase our understanding of teachers’

perspectives of implementing computer-based instruction for students with disabilities.

Chapter Two will focus on a literature review that includes relevant information

on the history of computer-based/remote instruction, as well as studies that relate to the

implementation and professional development for educators instructing within computer-

based/remote educational platforms. The studies will include information pertaining to

the instruction of all students, including those identified with disabilities.

Chapter Two - Literature Review

Introduction

“We need technology in every classroom and in every student and teacher’s hand,

because it is the pen and paper of our time, and it is the lens through which we experience

much of our world.” (Warlick, 2006, para. 5). Through technological advancements, K12

education has changed, grown, and continued to evolve throughout history. Prior to the

global pandemic, technology changed the availability of computer-based learning

opportunities. With the move toward computer-based/remote learning in the post-

pandemic world, technology has been more present in education and everyday life.

Computer-based learning is here to stay (Miller, 2014). Nambiar (2020) expressed that

online learning has made education accessible and available to all. This means that

teachers must prepare all students, including those identified with disabilities, to use the

skills necessary to be successful in both a traditional face-to-face and a computer-based

setting.

Educators have taught and must continue to teach 21st century skills such as

"learning skills (creativity, and innovation; critical thinking, and problem solving;

communication and collaboration), literacy skills (information literacy, media literacy,

information and communication technology (ICT) literacy) and life skills (flexibility and

adaptability; leadership and responsibility)” (Gonzalez-Perez & Ramirez-Montoya, 2022,

p.2). A “partnership of paper and digital settings” (Usiskin, 2018, p. 861) has been

required. Prensky (2001) emphasized that educators need to know how to teach “both

Legacy and Future” (p.4). Traditional methods, as well as the methods of the future

(utilizing technology) are key factors in current and future education. Van Laar et al.

(2017) uncovered through a literature review that “beyond skills, knowledge and attitude

are viewed as essential to thrive in the knowledge society” (p. 582). Van Laar et al.

continued that seven core 21st century digital skill dimensions were found: “technical,

information management, communication, collaboration, creativity, critical thinking and

problem solving” (p. 582). Five contextual 21st century digital skills dimensions were

found as well: “ethical awareness, cultural awareness, flexibility, self-direction and

lifelong learning” (p. 582). The demand for an information-rich and technology-based

society has been met through digital skills and 21st century skills taught to students (Van

Laar et al., 2017). Curran and Ribble (2017) explored how elementary and secondary

schools, as well as colleges can embed digital citizenship into their curriculum and

instruction to create prepared students to “lead with empathy and respect, to create

solutions and be problems solvers, and value the participatory nature of digital

citizenship” (p. 36).

One way to prepare students and technology users for a society full of new

innovations that was determined to be successful was through digital citizenship. Curran

and Ribble (2017) described digital citizenship as more than just teaching tools or rules of

what can and cannot be done online. “Digital citizenship skills are the norms of

appropriate, responsible behavior with regard to technology use" (Curran & Ribble, 2017,

p. 36). Egresitz (2020) added that these skills are vital for navigating the web safely and

effectively. Educators have prepared students for the world in which they live through the

skills taught. Egresitz stated that “there is a distinction between process-related skills and

a deeper and more thorough understanding of what the action means in the greater

context [within technology use]” (p. 9). Students are also prepared technology users

through the use of analytics. Egresitz expressed that technology educators prepare

students for the digital world they live in through analytical skills taught. The world has

transitioned to increased computer use, and to continue to advance, educators must teach

students to troubleshoot the challenges of computer-based learning. Andrei (2022)

emphasized that “teachers should embrace this challenge with great patience” (p. 5).

This literature review will focus on the concept and history of computer-based

learning and the differences in the types of computer-based learning. It will also identify

some of the challenges of computer-based learning for all students, including those

identified with disabilities, as well as their educators. These challenges provide insight

into teacher perceptions and understanding of computer-based learning for students

identified with disabilities. This literature review will also review planning required for

teachers participating in computer-based learning, as well as teacher training needs for

computer-based learning through the pedagogical shift from traditional brick-and-mortar

to computer-based learning.

History of Computer-Based Learning

Even prior to the global pandemic, computers were present in education. The first

computers were used at Harvard University in 1944 and they were primarily used for

science and mathematics research (Molnar, 1997). Computers were utilized as a

supplement to instruction (Fouts, 2000). Technology became its own academic discipline

in 1958 (Ferguson, 1974). By 1959, computers were used in more areas of education,

such as undergraduate work in mathematics, engineering, and science, as well as

expanding within elementary reading programs (Molnar). Software and usage changed,

and computers transitioned from a supplement to a tool (Fouts). The use of the computer

allowed for more real-world (size and type) problems to be solved without the use of a

slide rule (Molnar; Suppes & Jerman, 1969). The use of computers and technology

improved feedback and individualization of instruction. Molnar (1997) found that

computer-assisted programs were utilized in trying to “free students from the lock-step

process of group-paced instruction and developed individualized, instructional strategies

that allowed the learner to correct his responses through rapid feedback” (Molnar, p. 64).

By 1974, two million students used computers in their classes (Molnar). By 1975, 55% of

secondary schools had access to computers and 23% were instructing on computers

(Molnar). The journal Computers & Education was established in 1976 (Zawacki-Richter

& Latchem, 2018). Computer-based instruction was being utilized in the United States

and became its own field titled Instructional and Educational Technology (Zawacki-

Richter & Latchem). The computer transitioned from an “instructional delivery medium

to technology as a transformational tool and integral part of the learning environment”

(Fouts, p. 9). Personal computers (PCs) were owned by millions of users by 1976

(Zawacki-Richter & Latchem). Microsoft released Word in 1983 and Windows in 1985,

which increased the use of computers with educational media and instructional design

(Zawacki-Richter & Latchem). In the late 1980s, laptop computers were created, which

allowed for more mobility and connectivity (Zawacki-Richter & Latchem). “In 1990 Tim

Berners-Lee invented the World Wide Web and created the first webpage” (Zawacki-

Richter & Latchem, p.141). Multimedia PCs were created in the 1990s, as well as the

smartphone, search engines, and networking opportunities (Zawacki-Richter & Latchem).

Kulik and Kulik (1991) completed an updated meta-analysis which confirmed that

computer-based education could increase final examination scores from fifteen to twenty

percentile points and reduce the time necessary to achieve goals by one-third. With the

launch of the iPhone in 2007, the Web became mobile and the following year the first

Massive Open Online Course (MOOC) was created (Zawacki-Latchem). “In 2011

Stanford University offered three free online courses to over 160,000 students around the

world” (Zawacki-Richter & Latchem, p. 144). Through the development of technology in

education, distance learning opportunities were created (Molnar; Watts, 2016).

Distance learning began as asynchronous coursework opportunities where

students were in a different location than the instructor, completing the work at their own

pace (Moore et al., 2011). Education had become, according to Molnar (1997),

“flexitime” and “flexiplace” (p. 66). With items pre-recorded for participants, Buxton

(2014) reported in a nonexperimental post intervention study, that it added convenience

with the anytime-anywhere format. In a meta-analysis, Watts (2016) asserted that

“asynchronous interaction has been the traditional method for engaging students in their

distance education courses, but as technology has evolved, synchronous media have

become an increasing focus for engagement in online courses” (p. 23). Synchronous

distance learning utilized an approach where participants completed coursework at the

same time through live-streaming and instantaneous feedback (Giesbers et al., 2014).

This may have included virtual lectures and active participation. A third option was a

combination of asynchronous and synchronous learning opportunities called blended

learning. Blended learning has also supplemented traditional brick-and-mortar education

(Powell et al., 2015). Blended learning referred to a learning situation in which there was

a combination of face-to-face instruction within a brick-and-mortar school, as well as

opportunities for online educational materials and supplemental instruction (Powell et

al.).

During the global pandemic, 91% of students were impacted by school closures

during the 2019-2020 school year (Miks & Mcllwaine, 2020). Within a qualitative study

of educators’ perceptions and experiences of the transition from brick-and-mortar to

online teaching during the COVID-19 pandemic, Pederson and Scotch (2021) found that

educators were forced to pivot from traditional brick-and-mortar instruction to computer-

based instruction almost overnight. Despite the challenging and unplanned transition

from brick and mortar to cyber instruction, the United States Department of Education

(2020) advised school districts that they must continue to provide a free and appropriate

public education to traditional students and students with disabilities through “. . .

distance instruction provided virtually, online, or telephonically” (p. 2). In an effort to

comply, and to continue educating students, school districts made drastic changes to

traditional education. Hodges et al. (2020) contended that this drastic switch should be

titled “Emergency Remote Teaching” (para. 5). This distinguished it from “high-quality

online teaching” (Hodges et al., para. 6). The Pew Research Center reported that in the

United States approximately 46% of students were learning via computer-based

instruction during the 2020-2021 school year (Menasce & Igielnick, 2020, para. 7).

Instruction occurred through synchronous learning opportunities, asynchronous learning

opportunities, or a combination of the two. Within a qualitative study, Averett (2021)

reported that with increased student participation in computer-based learning, came an

increase of students identified with disabilities enrolled in computer-based learning

programs. This influx of participation has presented multiple challenges. Some of the

challenges noted within this review of literature were: the digital divide, lack of access,

communication, student technology skills, time management, self-regulation, computer-

based adaptations and accommodations, computer-based learning planning, computer-

based learning teacher training, and mental health (Averett).

Challenges of Computer-Based Learning

With the transition and influx of computer-based/remote instruction, new

challenges emerged. One such challenge was the digital divide. “The ‘digital divide’—

the gap between people who have sufficient knowledge of and access to technology and

those who do not—can perpetuate or even worsen socioeconomic and other disparities

for already underserved groups” (Moore & Vitale, 2018, p. 1). As more technology is

integrated within education, the digital divide became more apparent (Ames, 2021).

Prensky (2001) created the terms digital natives which are those who have had access and

use of digital technology throughout their lifetime and digital immigrants, which are

those that were not born into the digital world and must learn, adopt, and adapt to the new

technology (p. 2). Some students, especially those from digital immigrant families, were

faced with challenges, such as sharing a device with family members to complete

assignments or participate in computer-based instruction. Prior to the COVID-19

pandemic, eighty-five percent of students had access to two or more devices during their

school year, whereas fifteen percent of students had access to one or less, and twenty-

eight percent of students were provided a device through their school (Moore et al.,

2018).

Lack of Access

Nambiar (2020) completed a qualitative study that surveyed teachers and their

perspective of some of the challenges to online learning and found that teachers reported

that students use a variety of reasons for not being present for online class. Several

reasons reported were connectivity issues, poor video or audio quality, or network issues.

“Nearly half (47%) of students who report relying on one device at home depend

exclusively on a monthly cellular data plan for home internet access” (Moore et al., 2018,

p. 6). It was found that prolonged school closure further exacerbated preexisting

educational disparities (Chen et al., 2022). Chen et al. further discovered that “Students

within lower-income households experience a “digital gap” due to the lack of reliable

access to the Internet and other digital resources (e.g. computers) at home, potentially

affecting their learning” (p. 722). Chen et al. uncovered through their survey that children

of upper-middle and high-class were more likely to engage in distance learning than their

counterparts from middle, low-income, or lower-middle income classes. To conclude,

Chen et al. revealed that there were “inequitable consequences of the pandemic for low-

income families and families of color” (p. 735). The “lack of resources such as computers

and the Internet accessibility and stability, might have partially prevented students in

low-income and lower-middle households from engaging in long-distance learning”

(p.736). Ames (2021) affirmed that, even if devices and networks were obtained and

maintained for student use, that would be only a small portion of what would be required

to support a student’s well-being and education within computer-based/remote

instruction. To try to alleviate connectivity and the cost of internet, mobile hotspots were

released to improve internet access in many areas during the pandemic (Davis, 2015).

However, there was still inadequate internet access which allowed the digital gap to

grow.

Student Technology Skills

Also concerning and coinciding with the lack of adequate access was the lack of

technological skills students possessed in general (Hauser et al., 2012). Buzzetto-

Hollywood et al. (2018) utilized a multi-methodological research approach that included

placement scores, survey results, and pre- and post-test results, which revealed many

students have not been taught the skills necessary to utilize all the course features or

content within their online program. Buzzetto-Hollywood et al. also stated that there was

a gap between the students’ skill levels and the computer skills necessary for success.

Rice and Ortiz (2021) completed a qualitative research study that reported that parents

may also have lacked the skills necessary to assist their children with their schoolwork

and accessing their child’s progress. “Barriers to accessing education extend into the

home as many caregivers may have little knowledge of and experience in the delivery of

educational programs” (Stenhoff et al., 2020, p. 212). Martzoukou and Abdi (2017)

concluded through a literature review that a shift in digital literacy has occurred, from the

simple tasks of typing or attaching files to a more sophisticated and advanced state of

finding, sorting, creating, evaluating, and critiquing. Rice and Ortiz concluded that there

are different categories of digital literacy. Digital literacy included computer-information

technological literacy, technological literacy, information literacy, and communication

literacy. Students and parents have varying levels of digital literacy (Rice & Ortiz).

Therefore, the planning involved in the development of computer-based learning has

multi-layers.

Time Management and Self-Regulation

In addition to the lack of adequate access and technological literacy, students may

often still be developing time management and self-regulation skills as their education

becomes more computer-based/remote. Zimmerman and Kulikowich (2016) utilized a

quantitative research approach that identified that time management and self-regulation

(self-management) were key factors in a student’s readiness for online learning. Meeting

deadlines with very few reminders (Zimmerman & Kulikowich) was difficult for those

lacking self-regulation skills. Within a qualitative investigation, Bork and Rucks-

Ahidiana (2013) uncovered that “one instructor noted that online students needed to be

independent learners” (p. 12). Hart et al. (2019) completed a quantitative study of Florida

virtual students and teachers and concluded that skeptics of virtual learning were worried

that students who are less skilled in self-directed learning, may lean toward

procrastination and decreased success within online learning.

Bork and Rucks-Ahidiana (2013) determined that many instructional participants

felt that the students were responsible for their own learning (self-regulation) and the

participating instructors rarely brought up strategies that may be used to foster this skill

or characteristic within the survey. Nambiar (2020) expressed that the inability to monitor

and control students and their behavior during online learning was a concern for teachers.

The inability to assess student understanding, focus on instruction, and background

distractions were noted as major concerns within the teacher perspective of online

learning (Nambiar). In a qualitative study administered by Smith, Burdette, et al. (2016)

of parents of students diagnosed with a disability who were fully enrolled in online

learning revealed parents often are placed in the teacher role when utilizing an online

learning platform. This, in turn, affected the parent-child dynamics when parents

provided the missing prompting from an instructor (Smith, Burdette, et al.).

To alleviate this concern, teachers and online program management, were

recommended to consider the typical levels of prompting support required for students to

engage with curricular materials in the classroom and then adapt to the computer-based

learning platform (Stenhoff et al., 2020). One suggestion from Stenhoff et al. was for

teachers to embed additional stimulus prompts into materials or use various accessibility

features (e.g., pointers, an enlarged cursor, highlighted text, picture cues, and color-coded

materials) to facilitate student attention and engagement. In addition to the built-in

features of the learning management software, additional software was available that

permit teachers to embed interactive questioning tools (e.g., Kahoot, Zeetings, and Boom

Cards). Stenhoff et al. suggested the use of additional software when necessary.

MacSuga-Gage and Simonsen (2015) completed a multi-phase literature review and

concurred that utilizing a variety of questions, as well as multiple opportunities and

modalities for response, increased engagement and demonstrated effective management

practices.

Computer-Based Adaptations and Accommodations

Another challenge found was the success and availability of adaptations and

accommodations for students with disabilities who were enrolled in online educational

platforms. Accommodations and adaptations are required under the Individuals with

Disabilities Education Act (IDEA, 2004). Gin et al. (2022) found through qualitative

research utilizing a survey that many students with disabilities enrolled in a computer-

based learning environment were unable to receive the same accommodations they would

in a brick-and-mortar setting. Students reported the following missing accommodations

within the online educational programs: “reduced distraction testing environment,

additional test time, and note-taking assistance” (Gin et al., p. 2). Gin et al. also

uncovered those students felt “it was up to the instructor’s discretion about whether to

provide an accommodation or accept a particular student’s request for accommodation”

(p. 5). Kunkes (2020) completed a quantitative survey study which reported that 41% of

students had accommodation needs change with their transition to online learning. The

top three reported accommodations needs identified were: a need for increased time on

exams due to distractions at home, a need for greater flexibility with assignments because

of difficulty managing workloads, and a need for more asynchronous learning due to

extended periods of computer use (Kunkes). Gin et al. found that “students with new

challenges (within the computer-based/remote instruction) received new

accommodations, but they did not perceive that they were properly accommodated,

which reveals that the new accommodations may not be sufficient” (p. 6). Stenhoff et al.

(2020) and Gin et al. both agreed that teachers and instructors were placed in a situation

where communication was key amongst disability services, students, and instructors.

Pfiefer et al. (2023) completed a qualitative study of students identified with a

disability and were registered for accommodations. The research found that students who

were diagnosed with non-apparent disabilities found themselves in situations where they

must disclose their disability to obtain accommodations or were placed in a situation

where they had to explain their use of accommodations to people assuming they do not

have a disability or require the requested accommodations. The need to self-disclose or

advocate for themselves may have influenced the receipt of accommodations. Prior to

online learning, many students had their special education teacher and parents to

advocate for the accommodations needed (Terras et al., 2015). Through qualitative

research, Terras et al. noted students may not know what accommodations were available

through online learning or what accommodations would be of benefit to their learning

experience. Many students would self-accommodate by being skillful users of technology

and create workarounds to meet their needs instead of requesting accommodations

formally (Terras et al.).

Students’ need for self-advocacy increased with the need for accommodations or

overlooked accommodations (Pfeifer et al., 2023). Communication with instructors was

vital for student success regarding accommodation access (Pfeifer et al.). Educators who

communicated a protocol for how students should advocate for accommodations

demonstrated higher success (Pfeifer et al.). Terras et al. (2015) emphasized that “It is

important that instructors make every effort to be approachable and to create a learning

environment which avoids barriers to accommodation requests” (p. 337). Madaus et al.

(2021) also expressed through quantitative research that communication was vital to

student success. Terras et al. found that ultimately, academic success was a joint

responsibility of online instructors, educational systems, and the students themselves.

Successful communication should be clear, consistent, early, proactive, and flexible

(Madaus et al.). Terras et al. suggested straightforward communication as the most

beneficial for all students.

Boettcher and Conrad (2016) reported that creating a supportive online course

community was also essential for success. Boettcher and Conrad continued to emphasize

planning by suggesting the use of balanced dialogue within coursework from student to

teacher, student to student, and student to resources. Nambiar’s (2020) study uncovered a

teacher-student disconnect that brought a lack of interaction within the online learning

environment. Through a cross-sectional study, it was found that students required direct

steps to complete, as well as opportunities for clarification of ambiguities to increase

success (Lischer et al., 2021). Reeves et al. (2018) argued that maintaining a continuous

online presence in the online learning place was also vital for developing the course

community and opening communication pathways.

As with all new experiences, communication, adaptations, and accommodations

required time to develop. Nambiar’s (2020) study identified time as a concern for

teachers. Within Nambiar’s study, some teachers noted that online teaching was more

time consuming to start, with the creation of PowerPoint Presentations, extra worksheets,

and other supplemental materials needed for online classes. Stenhoff et al. (2020)

concurred that creating accessible computer-based instruction was time consuming.

Teachers also noted within the study that lessons might lack a personal touch within an

online educational platform (Nambiar). Stenhoff et al. emphasized the need for planning

time as follows:

In the classroom, teachers have access to curricula and materials that are used

across multiple students with ‘in the moment’ adaptations, whereas in a distance

format, they may need to commit additional time to prepare materials that are

modified for each student. (p. 212)

Therefore, it was found that teachers need to plan for multiple levels of instructional

support for students identified with disabilities.

Computer-Based Learning Planning

Computer-based teachers should plan for multiple layers of support and learn how

the educational platform may be utilized to meet these needs of students identified with

disabilities. Ferri et al. (2020) utilized a two-step qualitative research method that

consisted of an online discussion forum, as well as research from secondary online

resources, which found that “more attention is necessary on how technology and learning

can be integrated effectively, including the vital role of teachers, and the students’ needs”

(p. 86). Ferri et al. continued by stating that teachers were found to need to manage

several operational environments simultaneously. This was new for some teachers who

were not trained in teaching within an online platform.

To be effective, teachers required a framework for planning online instruction.

One instructional design model developed by Smaldino et al. (2019) called the ASSURE

model was developed to assist in lesson planning. ASSURE is an acronym that stands for

the steps of that model. The steps of the model are to analyze the learners; state standards

and objectives; select strategies, technology, media, and materials; utilize technology,

media, and materials; require learner participation; and evaluate and revise (Reeves et al.,

2018, p. 41). The model looked at the learner in detail. This allowed planning to match

the learner. Teachers assessed students’ knowledge and prior skills, as well as state the

intended outcome and expectations. Reeves et al. (2018) suggested that teachers use the

ASSURE model as a framework for planning online instruction. The ASSURE model,

which is multi-layered, was beneficial for those working with a diverse learning

population through analyzing the learner and individualizing the lesson.

Another method that assisted in planning computer-based/remote instructional

lessons was the Universal Design for Learning (UDL) (Smith, 2020). Universal design

for learning is “goal driven, and student-centered” (Basham et al., 2020, p. 81). Basham

et al. continued to explain that UDL leads to “proactive and iterative design cycles”

(p.81). Smith stated, “the principles of the universal design for learning (UDL) provide

accessibility to all students in an online instructional environment, but especially for

students with disabilities” (p.170). UDL provided for accommodations directly within the

framework of the method. It allowed for flexibility and different modalities of learning

(Smith). This foundation provided the required multi-layered planning, accessibility, and

equity for all students, including those identified with disabilities (Smith). “Universal

Design for Learning rests on principles and guidelines that can be embedded in virtually

any learning environment or experience (e.g., physical, blended, online” (Basham et al.,

p. 82).

Multi-layered planning included utilizing different modalities, such as teacher

recorded directions in addition to provided written directions. In the classroom, the

teacher supported the students by utilizing physical cues such as pointing to the storyline,

but in the print materials sent home or on the computer screen, the teacher utilized color

to highlight repeated storylines as cues. Peimani and Kamelipour (2021) completed a

case study and found that the use of technology improved access and inclusion within

lecture and discussion materials for those identified with disabilities. A mix of audio,

screensharing, visual, and text benefitted all learners (Peimani & Kamelipour). Peimani

and Kamelipour stated that successful and effective adaptation required accessibility to

new and relevant technology. Beasley and Beck (2017) established that teachers noted

different learning styles as a need for differentiation and accommodation and the use of

different teaching modalities may meet this need. Adapted materials or supplemental

materials may also be required. Some adaptations and differentiation suggested by

Beasley and Beck within their qualitative study were within the content, product, and

process. Other adaptations were required after evaluating assessment data and assignment

completion (Beasley & Beck). Marteney and Bernadowski (2016) completed a qualitative

research study and found that online education has made it easier for students with visual

limitations (69%), auditory limitations (83%), and physical limitations (92%), where 53%

of teachers responded that it was easier to make accommodations within online learning,

where twenty-eight percent disagreed and responded that they were not able to

adequately meet the needs of students with disabilities. The most common

accommodations were extended time on assignments, breaks, and reduced writing tasks

(Marteney & Bernadowski). Ferri et al. (2020) also concluded that new approaches to

maintain a student’s attention and participation on a screen for a long period of time were

needed. A multi-layered approach was necessary to meet each student’s individual needs

hence teachers required appropriate training to implement this.

Computer-Based Learning Teacher Training

A review of recent literature found that teachers were not adequately prepared to

modify and adapt for students identified with a disability while utilizing computer-based

learning (Basham et al., 2015). “They lacked confidence in both themselves and their

colleagues to effectively use online learning environments in their teaching, and were

particularly unprepared to teach students with disabilities” (Greer et al., 2014, p. 150).

Heinrich et al. (2019) uncovered that many regular education teachers did not have access

to the Individualized Education Plan (IEP) for online students enrolled in their courses.

Within their quantitative study, Heinrich et al. had several teachers suggest that students

with disabilities attend a virtual support room, where a special education teacher could

work with the students on their online coursework. Heinrich et al. did note that this would

only be beneficial if the special education teacher has the technical knowledge and

content knowledge required to assist the student on their online course. Further training

was required for both special education and regular education instructors to meet the

growing need of online learners who are diagnosed with a disability (Heinrich et al.).

Having training in the online educational platform for all instructors improved outcomes

for both teachers and students.

Rice and Carter (2015) also uncovered through a qualitative study that some

teachers felt a skill deficit for identifying time management skills to assist students

beyond the pacing guide. Heinrich et al. (2019) concurred regarding the lack of

opportunity or ability to make adjustments or supplement core content, as well as the lack

of opportunity or ability to adjust instructional delivery and accommodations within the

online educational platform. Archambault and Kennedy (2014) determined that few

teacher preparation programs address online learning as an individual context.

Archambault and Kennedy continued to emphasize that the dynamics of a fully online

program were unknown, therefore teacher preparation programs did not focus on

developing that skill set. Barbour (2016) determined that teacher preparation programs

lacked available models on which to design online coursework. Archambault and

Kennedy also discovered a lack of online field experience prior to graduation provided

for prospective future teachers.

Furthermore, teachers’ levels of exposure to and knowledge of technology varied

within districts. Tremmel et al. (2020) stated that every teacher must receive training to

become comfortable with an online learning format. As Lischer et al. (2021) completed a

cross-sectional study and stated, “Digital literacy is no longer a “nice to have” but

dispensable competence for both lecturers and students” (p. 597). Martin et al. (2019)

found a need for technical skills competency within their qualitative study participant

responses. Participants expressed the need to be able to run the learning management

system, basic technological skills (email, toggle windows, upload files, create videos,

etc.), as well as create and write in a technical style (Martin et al.). Martin et al. continued

by noting that the writing required for online coursework is a drastically different means

of communication and titled this web writing.

Teachers noted that their lack of computer skills inherently affected their use of

the online teaching platform effectively (Nambiar, 2020). Pressley et al. (2021) reported

in an exploratory study that teachers who received training and support using district

technology and instructional platforms were more successful. Professional development

created for all teachers on the use of technology provided an opportunity for teachers to

feel more confident in their use of technology, which provided higher quality and

effective instruction for students, as well as increased student learning and development

(Leech et al., 2020). Leech et al. utilized a survey-based quantitative study which

continued to affirm the need for additional professional development for teachers

regarding technology troubleshooting for devices, as well as the learning platform. This

type of professional development demonstrated benefits to teacher-student-parent

communication and engagement of online learning (Leech et al.). One way professional

development was presented was with presentations provided by teachers within the

district. These presentations provided an opportunity for teachers to learn from other

teachers (Pressley et al.). Martin et al. (2019) also emphasized the importance of

professional development. Participants found that those who participated in professional

development and engaged in online learning opportunities were more likely to become

expert online instructors (Martin et al.).

Nambiar (2020) noted that teachers within their study did express that the

opportunity to utilize online learning opportunities did help them explore more

innovative teaching methods, as well as improve their time management of evaluating

students and grading given work. In a study completed by Martin et al. (2019),

participants noted that online instructors had a willingness to learn. Instructors

demonstrated a willingness to learn how to use online technology and experiment with

new technology (Martin et al.). Online instructors required a willingness to allot time to

learn how to teach online, as well as an understanding of the time commitment required

to be a medium within an online course (Martin et al.). Peimani and Kamelipour (2021)

concluded that professional development fostered the acquisition of creativity,

collaboration, critical thinking, and problem-solving skills. The need for increased

professional development and digital learning innovation was established (Peimani &

Kamelipour, 2021).

Marteney and Bernadowski (2016) found through qualitative research that

teachers had a positive outlook on the ability to use computer-based instruction for

students with disabilities. Enthusiasm about the development of digital technology was

documented. Digital technology increasingly became more personalized, remote,

adaptive, and data-driven (Lischer et al., 2021). The demonstrated capability to adapt,

modify, and use different instructional modalities while teaching within a computer-

based learning platform, improved inclusion, as well as assisted in meeting each student’s

individual needs. Nambiar’s (2020) study agreed regarding online learning providing

more opportunities to utilize visual data such as graphs, charts, and videos. However,

teachers lacked the confidence sometimes to move forward with online content creation.

Greer et al. (2014) found that “nearly two-thirds (64%) of online teachers surveyed

indicated that their highest need for professional development was in how to meet the

needs of students with special needs in online learning” (p. 152). Providing teacher

training including adequate technological training was vital and should be a prerequisite

for successful online course implementation (Nambiar).

Teachers continued to request more training to better meet this need. “Although

instructional curricula often have been determined by a combination of the IEP team’s

recommendations and state academic standards, teachers must consider ways to target

those curricula in less time and often with fewer resources” (Stenhoff et al., 2020, p.

214). Stenhoff et al. continued through the encouragement for teachers to consider

carefully planning the distribution or embedment of some skills within instruction in

another skills area. It was important that teachers continued to collect data during online

instructional delivery. Rice and Carter (2015) also identified monitoring student progress

as an area of training need. Many districts mandated the use of specific platforms or

online tools; therefore, teacher preparation programs must do a better job of training

current and future teachers. Teaching the components and features available within

computer-based software was vital to improve adaptations and modifications for students

identified with disabilities (Crouse et al., 2018; Tremmel et al., 2020).

Mental Health

A final need for teachers who utilized a computer-based instructional platform

was additional training required in dealing with students’ mental health. Many students

were found to have trauma related to the school closures or the recent reopening of

schools. The closures caused unexpected disruption of traditional teaching and learning

within schools (Adedoyin & Soykan, 2020). Routine adjustments were found to be a true

struggle for students (Bartlett et al., 2020). Isolation from activities, school closures,

travel cancellations, and the transition to learning online have affected students’

psychological and emotional development (Bartlett et al.)

Ahmad et al. (2022) used a quantitative research approach and found that mental

health negatively impacts motivation. Students selected online learning for many

different reasons. One reason found was the anonymity of online coursework. Many

students appreciated the privacy a computer-based instructional platform had to offer

(Los Angeles Business Journal, 2020). The longer-wait time available between prompt

and response, allowed students to increase confidence and was less intimidating (Los

Angeles Business Journal). A second reason students choose a computer-based

instructional platform was that students may have been bullied in another school or

educational setting (The Foundation for Blended and Online Learning, 2017). The

Foundation for Blended and Online Learning stated that bullying can quickly lead to

anxiety and depression and noted that students may also choose online learning due to

health issues, such as a debilitating injury or serious illness. Young parents, those with

behavior concerns, high absenteeism, those returning from incarceration, as well as those

requiring credit recovery, also chose an online educational option in higher numbers than

their peers (Heinrich et al., 2019).

Mental health also affects online learners regarding loneliness, anxiety, and

feeling overwhelmed. Feldman (2020) argued that students’ anxiety had negative effects

on performance. Loneliness also correlated to social difficulties, which may have led to

academic decline (Kotera et al., 2021). McIntyre et al. (2018) completed a quantitative

study and concurred that feelings of loneliness were the strongest predictor of poor

mental health, as well as academic distress. An increase in interactive activities reduced

the feeling of loneliness (McIntyre et al.). In a qualitative interview-based study

completed by Kotera et al., students with disabilities noted they had less social anxiety

and loneliness when participating in an online education platform because their disability

was less visible. The participants noted that there was less stigma about disabilities when

working online (Kotera et al.). Caprara and Caprara (2021) completed a literature review

and discovered the real-time visual presence of teachers was an important factor in

maintaining good mental health. Another area of concern was the abundance of online

material which could lead to confusion or a student feeling overwhelmed. Adedoyin and

Soykin (2020) confirmed that students had difficulty discerning important updates or

notices from irrelevant information. Students and instructors received many emails,

which increased stress levels and may have resulted in decreased mental health

(Adedoyin & Soykin).

Another mental health concern was the effects of excess use of screen time on

cognitive and physical functioning. Researchers have found that the overuse of

technology and screen time has negatively impacted social skills (Kim et al., 2009). Kim

et al. research “suggests that individuals who are not psychosocially healthy (e.g., are

lonely) have difficulty not only maintaining healthy social interaction in real lives, but

also regulating their internet use” (p. 454). Excessive technology use may also have

affected a student’s ability to form their identity (Salo et al., 2018). Within the qualitative

study that included narrative interviews that Salo et al. completed, participants expressed

that the use of social networking sites, services, and technology caused a strain on social

relationships, as well as identity problems. Salo et al. asserted that social networking

sites, services, and technology created tension between participants’ conceptions of

themselves, as well as how they communicated their conceptions to others. Educators’

awareness of technology strain and the increased education of students on the negative

effects of social networking sites and services were required (Salo et al.). Abi-Jaoude et

al. (2020) also found a correlation between use of social media and an increase in mental

health distress and treatment in North America. Through a review of cross-sectional

surveys completed in the United States and Germany of university students, Abi-Jaoude

et al. found that students who spent more time on social media were more likely to have

feelings of envy, “fear of missing out,” body image concerns, self-harm, and suicidal

behavior (p. E137). One survey reviewed found that 54% of teens felt they spent too

much time on their cell phones and half were going to reduce their use (Abi-Jaoude et al.,

p. E140). One suggestion mentioned by Abi-Jaoude et al. was to encourage parents to

follow the American Academy of Pediatrics guidelines for social media use, as well as a

“Family Media Use Plan” to help support and structure social media use (p. E139). There

is also a “Family Media Tool Kit” created by a partnership between the American

Academy of Pediatrics and Common Sense Media that also defined strategies to help

mitigate the negative effects of social media and use of smartphones for adolescents

(Abi-Jaoude et al.).

Researchers also noted the negative impacts of technology such as improper

images, unwanted contact, or exposure to explicit information (Halupa, 2016). Halupa

continued by stating the overuse of technology has led to lack of sleep, changes in eating

patterns, and other physical complaints- such as weight gain or weight loss. The overuse

of technology has also led to mental overload, a disconnect between people, as well as a

decrease in concentration or time on task (Halupa). Young children should avoid

excessive use of technology. Researchers have found that children with excessive

technology use may fail to reach developmental milestones at the same rate as those who

do not partake in excessive use (Halupa). Halupa also noted the possibility of decreased

motor development and decreased sensory development in children who use technology

excessively. Teachers noted that some students may have had a technology addiction,

where technology interfered with everyday life, school, and relationships with family and

friends (Halupa). Salo et al. (2018) agreed; the overuse and overdependence of

technology hindered concentration and sleep, which affected overall health and

wellbeing. This overdependence and reliance of technology has become present in nearly

all aspects of daily life.

Professional training on trauma and other challenges for students, including those

who are returning to the structured school environment was required (Tremmel et al.,

2020). Teachers received training on how to best support returning students as they

transition back to the school and begin to re-engage with peers in a structured

environment or continue within the online platform was found to be beneficial for both

students and teachers (Tremmel et al.). Ribeiro (2020) expressed that the attitudes of

faculty, administration, and students shifted. With this shift, educators required updated

training and support.

Teacher Perspectives of Computer-Based/Remote Learning

In a review of literature, it was found that few preparation programs for teachers

included coursework on online learning in its own context (Kennedy & Archambault,

2014). Teacher preparation programs were found to be unsure what full-online education

entailed or looked like (Kennedy & Archambault). Barbour (2016) uncovered that many

teacher preparation programs did not have available models to support design and

instruction of virtual school experiences for new teachers. Furthermore, “Teacher

preparation that is specific to online learning and specific to students with disabilities is

even more scarce” (Crouse et al., 2018, p. 125). Rice and Ortiz (2016) found that students

with disabilities were largely untracked within online learning. This lack of information

caused minimal preservice training and professional development on online learning for

those identified with disabilities (Rice, 2018). Rice also found that professional

development for instructing students identified with disabilities in a fully online school

was minimal and informal in nature. Of the studies found, many did not discuss strategies

that could be utilized within online learning for students with disabilities (Crouse et al.).

Smith, Basham et al. (2016) found within their study that building relationships

with students and parents online was essential to student success within online learning.

Crouse et al. (2018) completed a qualitative study that identified “an increase in parent

communication functioned as a critical strategy for providing support” (p. 133). Crouse et

al. continued “the teachers felt that through their efforts to contact the parents of students

with disabilities more often than students who were not struggling, they were exhibiting

the vigilance needed to support the student” (p. 133). Furthermore, teachers utilized the

following strategies when working with students with disabilities: remove assessment

questions, reduce the task load, monitor student progress, provide encouragement, and

reteach concepts (Course et al.). The study uncovered that “sometimes the teachers made

these types of supports on their own and sometimes they pulled from supplementary

resources provided by the vendors” (p. 131). They also found that “none of the teachers

could generate the content and lessons on their own” (p.132), but teachers did use a

variety of instructional groupings as a strategy to meet the needs of students with

disabilities (Course et al.).

Online educational opportunities were found to be expanding (Basham et al.,

2015). This caused some concern “that students with disabilities are not accessing these

opportunities or receiving appropriate services, and that significant variation exists

among the states and territories” (Basham et al., p. 27). Massengale and Vasquez (2016)

found that it was difficult in the online environment for an instructor to identify which

students have disabilities. Crouse et al. (2018) found within their study that teachers

“reported they had received no direct preparation for teaching in the online environment”

(p. 135). Teachers also reported that the lack of initial preparation was initially a barrier,

but their teaching experience prevailed as teacher’s modified their traditional practices to

meet the online environment (Crouse et al.). Hamilton et al. (2020) reported that half of

the 42% of the teachers surveyed who educate students with severe disabilities reported

they did not receive adequate support. Teacher’s naturally wanted to “bridge the gaps

between online learning and traditional settings” and “demonstrate agency in their online

teaching roles since participants saw their previous experiences in traditional educational

settings as assets” (p. 136-137). One way to bridge the gap was professional

development. Teachers reported seeking professional development opportunities to

connect with colleagues and learn new strategies to improve their teaching and student

success (Crouse et al.).

Summary

In conclusion, education and educational technology has evolved with time.

Miller (2014) declared that computer-based instruction is here to stay. The United

Nations Educational, Scientific, and Cultural Organization’s (UNESCO) Global

Education 2030 Agenda is to “ensure inclusive and equitable quality education (including

computer-based) and promote lifelong learning opportunities for all” (UNESCO, 2006).

Educators required more professional development in the area of technology, educational

platforms, and how to make appropriate accommodations and modifications while

utilizing computer-based learning. Educators presented with opportunities for training in

areas that are vital for their and their students’ online success was beneficial. There are

clear pedagogical differences between teaching within an in-person educational

experience and a computer-based program (Martzoukou & Abdi, 2017). Teachers had

varied levels of technology literacy and must be better prepared to reduce the feeling of

inadequacy in today’s online world (Crouse et al., 2018).

Educators must also be provided professional development in student

engagement. Student engagement on an online educational platform was different than

within a traditional brick and mortar setting (Greer et al., 2014). To better meet the needs

of engagement and learning for all students, educators must be given opportunities to

learn the educational platform and features. Providing training opportunities to improve

adaptation and accessibility created an equitable environment for all students, including

those with varied needs (Archambault & Kennedy, 2014).

Finally, training regarding the modern mental health concerns of today and the

future was found to be beneficial to educators (Ahmad et al., 2022). As described above,

educators must meet the ever-changing needs of students. Students experienced different

levels of mental health throughout their online learning experience (Tremmel et al.,

2020). Training in areas such as communication, learning styles, self-advocacy,

inclusion, anxiety, loneliness, depression, and trauma are beneficial for both students and

educators (Abi-Jaoude et al., 2020). Educators required training to better understand

student’s needs, as well as maintain good mental health (McIntyre et al., 2018).

The lack of sufficient research (Rice & Dykman, 2018) and evidence of teacher

preparation training, professional development, and support for those instructing students

identified with disabilities on a computer-based/remote instructional method within K-12

education has led to the formation of this study. Many of the previous studies were on the

post-secondary level (Crouse et al., 2018). There is a need for more current and relevant

data on the teacher perspectives of K12 educators who are instructing students identified

with a disability on a computer-based/remote platform.

In the following chapter, the current methodology for this study exploring teacher

perspectives and understanding of computer-based instruction for students with

disabilities will be defined. Chapter Three will include the setting, participants, and

instruments of the study. The reliability and validity of the study will also be included.

The procedure used to conduct the study, as well as the data analysis utilized to discuss

the research questions will be discussed.

Chapter Three- Methodology and Procedure

Introduction

The purpose of this qualitative study was to investigate teachers’ perspectives on

the implementation of computer-based/remote instruction for students with disabilities. In

identifying teacher perspectives on the implementation of computer-based/remote

instruction, an increased awareness of the online learning experiences of teachers who are

instructing students with disabilities may be used to improve the online learning

experiences and equity for all students, including those identified with disabilities.

This chapter describes the methodology of the study. This includes the setting,

participants, and instruments of the study. This chapter also includes the reliability and

validity of the study. The procedure that was used to conduct the study, as well as the

data analysis is also discussed.

Setting

This study took place in south central Pennsylvania. For this study only public-

school districts were considered. The setting for this study was three south central

Pennsylvania suburban public-school districts. These school districts ranged in size from

approximately 3,000 to 6,800 students enrolled. These schools were selected due to

similar size, and location (suburban within the same geographical area). This study

invited participation from elementary and secondary public-school teachers within the

districts. Participants were public-school certified teachers who are implementing or have

implemented computer-based/remote instruction for students with disabilities.

Participants

The participants in this study were currently employed public-school certified

teachers within the three participating public-school districts in south central

Pennsylvania. The participants consisted of teachers, with a range in level of experience,

as well as grades taught within the K-12 public school districts. Participants were asked

to voluntarily complete a survey, which included information on demographics. This

demographic information included the participants’ teaching position, experience in

current position, experience within computer-based/remote instruction, grade taught, as

well as number of students identified with disabilities taught within the past three years.

The survey also contained questions related to the three research questions of the study.

Upon submission of the survey, participants were asked if they would like to complete a

follow-up interview. If so, the participant completed the second link provided with their

contact information for follow-up. A panel of experienced educational experts reviewed

the survey and interview questions for clarity within the study and provided feedback

prior to the start of the study. Feedback on the readability, clarity, and conciseness, as

well as connectivity to the research questions was provided from each expert. From the

feedback provided, revisions were made. Upon approval from Immaculata University’s

Research Ethics Review Board (RERB), the survey and interview invitations were sent

out (Appendix A). The study had 41 participants complete the survey and four

participants volunteer for an additional interview. The researcher analyzed the first three

interviews to determine if any themes are present to reach data saturation. Data saturation

is “when a researcher stops collecting data because fresh data no longer sparks new

insights or reveals new properties” (Creswell & Creswell, 2018, p. 270). If data saturation

was found, no more interviews would be completed.

Instruments

The study investigated teachers’ perspectives on the implementation of computer-

based/remote instruction for students with disabilities to improve the online learning

experiences and equity for all students, including those identified with disabilities. The

researcher collected data through two instruments: a researcher developed, mixed

question type online Google Form survey, as well as researcher developed, interview.

The researcher collected data through the Google Form survey responses and voluntary

recorded interviews completed through Zoom. The interviews consisted of open-ended

semi-structured questions. The interviews were transcribed for accuracy and clarification,

as well as data collection. All survey and interview data was collected and completed

electronically. Transcripts from the interviews, as well as data from the open-ended

Google Form survey responses were uploaded to NVivo software for qualitative analysis

of coding and thematic analysis to remove researcher bias.

Survey

A researcher developed, Google Forms survey (Appendix B) consisting of thirty-

one questions was disseminated by a building designee to participants through an

introduction email and link. The survey consisted of demographic questions, check-all-

that-apply, short answer, and open-ended questions. There were thirty-one questions on

the survey and took participants approximately fifteen minutes or less to complete. The

survey consisted of consent to participate and demographic information (questions one,

two, three and four) that included: grade taught, teaching experience, experience in

computer-based/remote instruction, and type of position held (general educator,

specialist, or special educator). Questions five through thirty-one of the survey collected

data on the three research questions. Survey questions five, six, seven, eight, nine, ten,

eleven, twelve, thirteen, fourteen, fifteen, and eighteen were designed for data collection

on the first research question; survey questions sixteen, seventeen, nineteen, twenty,

twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-

seven, and twenty-eight were created to provoke responses to the second research

question; and survey questions eighteen (also answers research question one), twenty-

nine, and thirty were designed to prompt the third research question. Question thirty-one

provided information regarding any of the three research questions dependent on the

response given. A link to the Google Form survey was included in an email sent to

participants through their building designee.

At the conclusion of the Google Form survey, upon submission, a link was

provided for those who would like to volunteer to participate in a semi-structured follow-

up interview. The link guided participants to a separate Google Form (Appendix C) that

provided the researcher with the contact information to set up an interview. If the

participant agreed to participate in an interview, they were contacted for availability for

an interview through Zoom.

Interview

Participants were offered an opportunity to participate in a follow-up interview

(Appendix D) that consisted of seven semi-structured open-ended questions following the

submission of their Google Form survey. The interview took approximately 20 minutes

or less to complete and allowed the participant to further elaborate on their perceptions of

computer-based/remote instruction for students identified with disabilities, as well as

provided more evidence to answer the three research questions. The interview was

conducted virtually through Zoom with confidentiality provided through the program

settings. Demographic information was collected within the introduction section of the

interview. Interview question one provided information and evidence on the teacher’s

experience with teaching students with disabilities through computer-based learning

within the last three years, which connected to research question one. The interview

questions two and six provided additional evidence and data on research question number

three of this study in regard to professional development provided prior to instructing

through computer-based/remote learning, as well as professional development

suggestions for the future. The accommodations and modifications utilized for students

identified with a disability was also asked in question three, which connected to research

question number one of this study. The researcher asked about strategies used to instruct

students identified with disabilities in question four, which connected and provided more

data to answer research question number two of this study. The interview asked about the

challenges the participant experienced while instructing students with disabilities through

computer-based/remote instruction in question five, which provided additional data in

response to research question number one of the study. The final question (seven) asked

for any additional comments relevant to the implementation of computer-based/remote

instruction for students identified with disabilities, which provided additional information

to the research question number one of the study. The interviews were recorded and

transcribed through the Zoom program. Then corrections of identified errors while

listening to the recording by the researcher were documented. The participants received

the transcript of the interview for accuracy, correction, and clarification. The transcripts

were uploaded to NVivo software for coding and thematic analysis.

Reliability and Validity

Creswell and Creswell (2018) defined reliability as “whether scores to items on an

instrument are intentionally consistent (i.e., are the item responses consistent across

constructs?), stable over time (test-retest correlations), and whether there was consistency

in test administration and scoring” (p. 269). To ensure reliability, two different sources of

data were utilized within this study. The same Google Form survey and interview

questions were provided to all participants in the same way. This was through an

introductory email from their building designee that included the Google Form survey

link and information on how to volunteer for a one-on-one interview on the research

topic. The survey and interview questions were written in a clear, concise manner. All

interviews were transcribed utilizing the same method: transcription through the virtual

program settings. The transcription was checked for clarity by the researcher and verified

by the participants for accuracy and consistency following each interview. The

transcripts, as well as Google Form survey open-ended responses were uploaded to

NVivo software for coding and thematic analysis. Creswell and Creswell define validity

strategies as “procedures (e.g., member checking, triangulating data sources) that

qualitative researchers use to demonstrate the accuracy of their findings and convince

readers of this accuracy” (p. 272). Consistency was ensured through the Google Form

survey responses and interview data collected throughout the study. Triangulation was

also utilized to ensure the validity of the study through commonalities or themes

discovered from the data collected.

To confirm the reliability and validity of the survey and the interview, a panel of

experienced educational experts reviewed the survey and interview questions and

provided feedback. Feedback on comprehensibility, readability, clarity, and conciseness

was provided. Connections to the research questions were also noted. This confirmed the

items connected to one or more of the research questions, as well as verified the

reliability of the survey and interview instruments. From the experienced educational

expert panel feedback, revisions were made to the survey and interview questions prior to

the Research Ethics Review Board (RERB) approval and the start of the study.

Design of Study

This study was a qualitative research study, which is “a means for exploring and

understanding the meaning of individuals or groups ascribe to a social or human

problem” (Creswell & Creswell, 2018, p. 268). This study was designed to investigate

teachers’ perspectives on computer-based/remote instruction for students with

disabilities. This study utilized survey and interview methods to gather data. This study

consisted of a mixed type question survey that contained questions related to teachers’

experiences with computer-based/remote instruction for students with disabilities. The

survey included questions about demographics, computer-based/remote instruction,

strategies, professional development, and other comments. The data was analyzed and

coded into themes, which are discussed in Chapters Four and Five of this study. In

addition to the Google Form survey, participants were offered an opportunity to

participate in a semi-structured interview to provide additional data in relation to the

three research questions, as well as further clarification. All participants of the Google

Form survey were kept anonymous and confidential, while all participants of the

interview were kept confidential. The results of this study were kept secure through the

entire process on a password protected computer and will be destroyed by deletion five

years after the conclusion of the study. The results of this study may increase educators’

understanding of teachers’ perspectives of implementing computer-based/remote

instruction for students with disabilities. This may increase administrators’ and teacher

preparation programs’ awareness of the challenges, strategies, and professional

development needs of teachers who are implementing computer-based/remote instruction

for students with disabilities.

Procedure

To begin the study, email communication to solicit permission to tentatively

conduct a study following Immaculata University’s Research Ethics Review Board

(RERB) approval was sent to three south central Pennsylvania public-school district

superintendents (Appendix E). After approval from Immaculata University’s RERB, the

researcher sent out formal communication to the three south central Pennsylvania school

districts to obtain written consent to conduct the study within their school district. Upon

approval from the school district superintendents, the researcher contacted the building

designee to explain the purpose of the study and to create procedures for the study’s

completion. The building designee forwarded a created invitation to participate

(Appendix F) to the teachers within their building. The invitation contained the purpose

of the study, consent to participate voluntarily, and the Google Form survey link. The

survey was a thirty-one-question mixed-type survey that began with consent and

demographics questions and proceeded into questions that related to the three research

questions of the study. Forty-one participants responded to the survey. At the conclusion

of the survey, participants were provided a thank you and a link to a separate Google

Form to volunteer for a follow-up semi-structured interview. Five participants

volunteered for a follow-up interview. One volunteer did not meet the interview

requirements, therefore was not included in the study. Those that volunteered for an

interview provided their contact information on that Google Form. Participants had eight

weeks to complete the survey. Interviews were completed at the first convenient time

agreed upon.

Completed Google Form surveys were returned to the researcher electronically

and anonymously. Upon submission of the survey, a prompt at the final screen of the

survey asked the participant if they were interested in volunteering for an interview.

Interviews were offered through Zoom. Privacy and confidentiality were protected during

the study process, as well as upon completion. All interviewees were assigned a random

number and coded to ensure confidentiality and reduce bias. Interviews were recorded for

later transcription, analyzing, and coding. All transcriptions were given to participants for

review to confirm accuracy, note any corrections, and provide additional clarification.

The transcripts of interviews, as well as open-ended Google Form survey responses were

uploaded to NVivo software for coding and thematic analysis. All data was organized,

analyzed, and coded to identify themes and findings. All data was kept secure during the

study on a password protected computer, as well as for five years after publishing and

then will be destroyed by deletion at that time.

Data Analysis

The demographic data from the Google Form survey was reviewed by the

researcher to analyze the participants’ responses. This demographic information included

the participants educator position, experience in current position, and experience in

computer-based/remote instruction. The Google Form survey asked for the participant’s

grade taught, the number of students instructed through computer-based/remote

instruction, as well as number of students taught through computer-based/remotion

instruction who are identified with a disability within the last three years. Participants

were provided a random code to provide anonymity/confidentiality throughout the survey

and interview process. The Google Form survey collected data from check-all-that-apply,

short answer, and open-ended questions. The check-all-that-apply questions provided

data where the NVivo software and the researcher may run basic mathematical

calculations to determine the number and percent of the responses provided.

Following the completion of the survey, data from the short answer and open-

ended Google Form survey questions, as well as the interview questions were compared

and then coded for themes. These themes were analyzed for connections to the research

questions of the study. Conclusions to the data collected were thoroughly investigated.

Upon completion, the data collected from the Google Form surveys, as well as the

interviews were stored on a password protected computer in a secure location for five

years and then destroyed at that time, through deletion.

Summary

The qualitative study investigates teachers’ perspectives on the implementation of

computer-based/remote instruction for students with disabilities. This study gathered data

on teachers’ perspectives from three south central Pennsylvania public school districts.

Data was collected through Google Form surveys, as well as virtual interviews conducted

on Zoom. The data was collected, analyzed, and coded into themes. These themes reveal

and identify teachers’ perspectives of the implementation of computer-based/remote

instruction for students identified with a disability within the three south central

Pennsylvania public school districts. A description of this study’s methods and procedure

was provided in Chapter Three.

In identifying teacher perspectives on the implementation of computer-

based/remote instruction, an increased awareness of the online learning experiences of

teachers who are instructing students with disabilities may be found. These conclusions

may be used to improve the online learning experiences and equity for all students,

including those identified with disabilities. The feedback gathered from this study may

lead toward future professional development opportunities for educators of students with

disabilities who are implementing computer-based/remote instruction. The data was

analyzed, coded, and organized by themes. The results were analyzed in Chapter Four

and discussed in Chapter Five.

Chapter Four - Results

Introduction

The purpose of this qualitative study was to explore teachers’ perspectives on the

implementation of computer-based/ remote instruction for students with disabilities. With

the recent global pandemic, computer-based instruction for students identified with

disabilities has increased dramatically (Averett, 2021). As the world encountered a global

pandemic, many schools were forced to make drastic changes to instructional approaches

and educational platforms. It is vital to understand the teachers’ perceptions on the

implementation of computer-based learning to provide appropriate future professional

development to improve the online learning experience and equity for all. This chapter

presents the results of the study organized by each research question.

The study focused on general and special education teachers currently educating

students in kindergarten through 12th grade in a public-school setting. This study took

place in three south central suburban Pennsylvania public school districts during the

2023-2024 school year. Forty-one certified public-school teachers completed the mixed-

type question researcher-developed Google Form survey consisting of 31 questions. It is

important to note that one additional participant selected “no” to their participation in the

Google Form survey, therefore that survey data was not included in the research findings.

The total number of survey participant data was 41 (N = 41).

Teachers’ perceptions of the implementation of computer-based learning for

students identified with disabilities were collected through a researcher-created Google

Form mixed-type question survey, as well as structured interviews conducted by the

researcher through Zoom. Questions one through four consisted of the participant’s

demographic information. Questions five through 31 consisted of questions developed to

answer the three research questions of this study. Participants also had the opportunity to

volunteer for a Zoom interview with the researcher. Five participants volunteered to

participate in an interview via Zoom. The interview consisted of an introduction where

demographic information was collected, as well as seven researcher-developed open-

ended questions to elaborate on information to answer the three research questions. It is

important to note that one interview participant did not meet the qualifying criteria,

therefore that interview was not included in the research findings. The total number of

interview participant data was four (N = 4).

The survey data was organized and analyzed through Google Forms. Interviews

were recorded on Zoom and transcribed by Zoom. The researcher and interviewee

reviewed the transcripts for accuracy. Open-ended and interview transcriptions were

uploaded to NVivo software for qualitative analysis of coding and thematic analysis to

remove researcher bias. The data was utilized to answer the three research questions. This

chapter presents the results of the Google Form survey and Zoom interview data

collected within this study.

Demographics

This study was conducted during the 2023-2024 school year at three suburban

school districts in south central Pennsylvania. Forty-one south central suburban certified

public-school teachers participated in the study by completing the Google Form survey.

One additional participant did select “no” to their participation in the Google Form

survey. It is important to note that all data were calculated based on the 41 total teacher

participants that responded to all questions. Total participant data (N = 41) will be

reported in both numerical and percentage terms. In addition to the 41 participants who

completed the online Google Form survey, five participants also volunteered for a Zoom

interview with the researcher. One volunteer did not meet the interview criteria and was

not included in the research findings. For this study, general education and specialist

teachers (art, music, etc.) were combined into the category of general education teachers.

Google Form survey data from question number four determined that a total of 26

(63.4%) participants were general education teachers. Google Form survey question

number one asked for participants to list their current teaching experience. Google Form

survey question number two asked for participants to report their teaching experience

utilizing computer-based learning. Google Form survey question number three asked

participants to state what grade they currently teach. Data determined that seven (17.1%)

participants selected “other,” which includes kindergarten through fourth grade,

kindergarten through sixth grade, as well as speech and language therapy. A summary of

the data is found in Table 4.1.

Table 4.1

Demographic Information for Survey Participants

Participant Responses

Variables

Total Responses

Current Role in Education

General Education Teacher

(51.2%)

Specialist (Art, Music, etc.)

(12.2%)

Special Education Teacher

(36.6%)

Grade Instructed

Kindergarten – 2nd

(2.4%)

3rd – 5th

(7.3%)

6th – 8th

(19.5%)

9th – 12th

(53.7%)

Other

(17.1%)

Years of Experience as a Teacher

0-2 years

(0.0%)

3-5 years

(12.2.%)

6-10 years

(12.2%)

11-15 years

(14.6%)

16-20 years

(24.4%)

21 or more years

(36.6%)

Computer-Based Instruction Experience

0-2 years

(19.5%)

3-5 years

(29.3%)

6-10 years

(17.1%)

11-15 years

(24.4%)

16-20 years

(7.3%)

21 or more years

(2.4%)

Note. N = 41. Percentages may not add up to 100% due to rounding.

Google Form survey question number five asked for an average number of

students taught through computer-based learning during the 2020-2021 school year.

Participants noted an average of 82 students were taught utilizing computer-based

instruction during the 2020- 2021 school year. The responses ranged from zero to 800.

Google Form survey question number eight asked how many students they taught

utilizing computer-based learning who were identified with a disability. Data indicated an

average of 21.6 (26.3%) students were identified with a disability during the 2020-2021

school year. Google Form survey question number six asked for an average number of

students taught through computer-based learning during the 2021-2022 school year.

Participants reported an average of 63.8 students were taught utilizing computer-based

instruction during the 2021-2022 school year. The Google Form survey responses ranged

from zero to 800. Google Form survey question number nine asked for an average

number of students taught through computer-based learning who were identified with a

disability. Data indicated an average of 12.7 (19.9%) students were identified with a

disability during the 2021-2022 school year. Google Form survey question number seven

asked participants an average number of students taught through computer-based learning

during the 2022-2023 school year. Participants noted that an average of 58.4 students

were taught through computer-based instruction during the 2022-2023 school year. The

responses ranged from zero to 750 students. Google Form survey question number 10

asked for an average number of students taught through computer-based learning during

the 2022-2023 school year who were identified with a disability. Data indicated an

average of 11.9 (20.4%) students were identified with a disability during the 2022-2023

school year. A summary of the data is found in Table 4.2.

Table 4.2

Average Number of Students Taught

Participant Responses

Variables

Total Responses

Average Number of Students Taught Through Computer-Based

Instruction in the last 3 Years

2020-2021

(3280/40- one was unsure)

2021-2022

63.8

(2551/40- one was unsure)

2022-2023

58.4

(2393/41)

Average Number of Students Taught Through Computer-Based

Instruction in the Last 3 Years Identified with a Disability

2020-2021

21.6

(862/40- one was unsure)

2021-2022

12.7

(509/40- one was unsure)

2022-2023

11.9

(489/41)

Note. N = 41. Percentages may not add up to 100% due to rounding.

To further investigate the differences between the demographic information of

general and special education teacher participants, the researcher analyzed the

demographic information for each group. Total participant data (N = 41) was reported in

both numerical and percentage terms. A summary of the demographic data can be found

in Table 4.3. Demographic information on their teaching experience was asked in Google

Form survey question number one. Furthermore, Google Form survey question number

two presented what teaching experience participants had teaching with computer-based

instruction. Google Form survey question number three compared data on grade

instructed. Google Form survey question number four asked if participants were a general

education teacher, specialist (art, music, etc.), or a special education teacher. For this

study, general education and specialist teachers (art, music, etc.) were combined into the

category of general education teachers. Responses indicated that 26 (63.4%) participants

were general education teachers, and 15 (36.6%) participants were special education

teachers. General education participant data (n = 26) and special education participant

data (n = 15) is reported in both numerical and percentage terms. Data indicated that

more general education participants had 21 or more years of experience compared to

special education participants.

Table 4.3

Demographic Information of General and Special Education Participants

General Education

Teachers

Special Education

Teachers

Variables

Total Responses

Current Role in Education

General Education Teacher

(63.4%)

Special Education Teacher

(36.6%)

Years of Experience as a Teacher

0 – 2 years

(0%)

3 – 5 years

(7.7%)

(20%)

6 -10 years

(11.5%)

(13.3%)

11 -15 years

(15.4%)

(13.3%)

16 – 20 years

(19.2%)

(33.3%)

21 or more years

(46.2%)

(20%)

Computer-Based Instruction Experience

0 – 2 years

(11.5%)

(33.3%)

3 – 5 years

(26.9%)

(33.3%)

6 – 10 years

(19.2%)

(13.3%)

11 - 15 years

(26.9%)

(20%)

16 – 20 years

(11.5%)

(0%)

21 or more years

(3.8%)

(0%)

Grade Instructed

Kindergarten – 2nd

(2.4%)

(0%)

3rd – 5th

(7.3%)

(0%)

6th - 8th

(14.6%)

(13.3%)

9th – 12th

(29.3%)

(66.7%)

Other

(9.8%)

(20%)

Note. N = 41 for Current Role in Education, n = 26 for General Education Teachers and n = 15 for special education teachers. Percentages may

not add up to 100% due to rounding.

The researcher further examined the differences between the demographic

information of the following grade spans: kindergarten through fifth, sixth through

eighth, ninth through 12th, and “other,” which is a combination of grade levels for general

education participants. General education participant data from the Google Form survey

question three indicated that four (15.4%) general education participants (n = 4) instruct

kindergarten through fifth grade, six (23.1%) general education participants (n = 6)

instruct grades six through eight, 12 (46.2%) general education participants (n = 12)

instruct grades nine through 12, and four (15.4%) general education participants (n = 4)

instruct “other,” a combination of various levels are reported in both numerical and

percentage terms. Total general education participant data (n = 26) is represented in both

numerical and percentage terms. General education participant demographic data was

assessed regarding participants’ teaching experience and teaching experience utilizing

computer-based learning. A summary of the demographic data is found in Table 4.4.

46.2% of the general education participants were found within the grades nine to 12

grade span, which could skew data.

Table 4.4

Demographic Information of General Education Participants through Grade Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

General Education Teachers

(15.4%)

(23.1%)

(46.2%)

(15.4%)

Years of Experience as a Teacher

0 – 2 years

(0%)

3 – 5 years

(0%)

(7.7%)

(0%)

6 – 10 years

(3.8%)

(0%)

(3.8%)

11 – 15 years

(0%)

(3.8%)

(7.7%)

(3.8%)

16 – 20 years

(3.8%)

(7.7%)

(3.8%)

21 or more years

(7.7%)

(11.5%)

(23.1%)

(3.8%)

Computer-Based Instruction Experience

0 – 2 years

(7.7%)

(0%)

(3.8%)

(0%)

3 – 5 years

(11.5%)

(3.8%)

(11.5%)

(0%)

6 – 10 years

(0%)

(7.7%)

(3.8%)

11 – 15 years

(3.8%)

(7.7%)

(11.5%)

(3.8%)

16 – 20 years

(0%)

(3.8%)

(7.7%)

(0%)

21 or more years

(0%)

(3.8%)

(0%)

Note. N = 26 for Current Role in Education, n = 4 for kindergarten-5 grade span, n = 6 for 6-8 grade span, n = 12 for 9-12 grade span, and n = 4 for “other” grade

span. Percentages may not add up to 100% due to rounding.

The researcher similarly examined the difference between the information of the

kindergarten through fifth grade span, six through eighth grade span, ninth through 12th

grade span, and “other” grade span of special education participants. Fifteen special

education teachers participated in the Google Form survey. Total special education

participation data (n = 15) from Google Form survey question three will be reported in

both numerical and percentage terms. The kindergarten through grade five grade span

participant data (n = 2), six through eighth grade span data (n = 2), nine through 12 grade

span data (n = 10), and the “other” grade span data (n = 1) are reported in both numerical

and percentage terms. Google Form survey question one asked participants to identify

how many years of experience they have in education. Google Form survey question two

asked participants to identify how many years of teaching experience they had utilizing

computer-based learning. Google Form survey question three asked participants to note

what grade level they currently teach. A summary of the demographic data can be found

in Table 4.5. 66.7% of the special education participants fell within the grades nine to 12

grade span.

Table 4.5

Demographic Information of Special Education Participants through Grade Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

Special Education Teachers

(13.3%)

(66.7%)

(6.7%)

Years of Experience as a Teacher

0 – 2 years

(0%)

3 – 5 years

(6.7%)

(0%)

6 – 10 years

(0%)

(13.3%)

(0%)

11 – 15 years

(0%)

(6.7%)

(6.7)

16 – 20 years

(0%)

(33.3%)

(0%)

21 or more years

(6.7%)

(0%)

Computer-Based Instruction Experience

0 – 2 years

(6.7%)

(13.3%)

(6.7%)

3 – 5 years

(6.7%)

(20%)

(0%)

6 – 10 years

(0%)

(13.3%)

(0%)

11 – 15 years

(0%)

(20%)

(0%)

16 – 20 years

(0%)

21 or more years

(0%)

Note. N = 15 for Current Role in Education, n = 2 for kindergarten-5 grade span, n = 2 for 6-8 grade span, n = 10 for 9-12 grade span, and n = 1 for

“other” grade span. Percentages may not add up to 100% due to rounding.

A summary of the interview demographic data for all participants can be found in

Table 4.6. The interview consisted of an introduction for demographic information

followed by seven researcher developed open-ended questions. Five participants

volunteered for an interview. One volunteer did not meet the interview criteria, therefore

that interview data was not included in the research findings. The total number of

interview participants data was four (N = 4) and will be reported in both numerical and

percentage terms. Interview data indicated that four (100%) interview participants were

general education teachers. Interview data indicated an average of 82 students were

instructed through computer-based learning each year. Of that 82-student average, an

average of 14.8 (18%) students were identified with a disability.

Table 4.6

Demographics Information for Interview Participants

Participant Responses

Variables

Total Responses

Current Role in Education

General Education Teacher

(100%)

Specialist (Art, Music, etc.)

(0%)

Special Education Teacher

(0%)

Grade Instructed

Kindergarten – 2nd

(0%)

3rd – 5th

(25%)

6th – 8th

(0%)

9th – 12th

(75%)

Other

(0%)

Years of Experience as a Teacher

0 - 2 years

(0%)

3 – 5 years

(0%)

6 - 10 years

(0%)

11 - 15 years

(0%)

16 – 20 years

(25%)

21 or more years

(75%)

Computer-Based Instruction Experience

0 - 2 years

(0%)

3 – 5 years

(0%)

6 -10 years

(0%)

11-15 years

(75%)

16 – 20 years

(0%)

21 or more years

(25%)

Average Number of Students Taught through Computer-

Based Instruction in the last 3 Years

9-12A (participant 1)

111

9-12B (participant 2)

132

9-12C (participant 3)

K-5A (participant 4)

Average Number of Students Taught Through Computer-

Based Instruction in the Last 3 Years Identified with a

Disability for Interview Participants

14.8

9-12A (participant 1)

9-12B (participant 2)

9-12C (participant 3)

K-5A (participant 4)

Note. N = 4. Percentages may not add to 100% due to rounding.

Compilation of Data

This study included 41 voluntary participants who completed a Google Form

survey. Of the participants, five volunteered to participate in a Zoom interview with the

researcher. One interview volunteer did not meet the interview criteria, therefore that

interview data was not included in the research findings. Four (9.8%) interviews were

conducted. Interviews were organized by the researcher according to earliest availability.

All (100%) interviews were conducted with general education participants. One (25%)

interview was conducted with a participant from the kindergarten to grade five grade

span and three (75%) interviews were conducted within the grades nine through 12 grade

span. Seven scripted interview questions were asked by the researcher to expand and

elaborate on Google Form survey responses to provoke voluntary participant perceptions

concerning the implementation of computer-based instruction for students identified with

disabilities post-pandemic. Interviews were coded to preserve the anonymity of

participants and responses were labeled as the grade span and letter for each participant

(i.e. K-5A, 9-12A, etc.).

Research Question One. What are teachers’ perceptions on the

implementation of computer based/remote instruction for students with disabilities?

The purpose of research question one was to identify teacher perspectives on the

implementation of computer-based/ remote instruction post-pandemic. Twelve Google

Form survey questions corresponded to research question one. Google Form survey

question number 31 may also answer research question one dependent on the response

provided. The interview included four questions that respond and elaborate on research

question one of the study, as well as the possibility of responses from interview question

seven dependent on response.

Survey Responses. The mixed-type Google Form survey questions five, six,

seven, eight, nine, 10, 11, 12, 13, 14, 15, and 18 explored the implementation of

computer-based/ remote instruction. This included implementation for those identified

with disabilities. Google Form survey question number 31 may also answer research

question one dependent on the response provided. All 41 participants responded to each

question. Google Form survey question 11 asked participants their awareness of the

disability categories for the students instructed. Data indicated that 39 (95.1%)

participants were aware of the disability category of students instructed, whereas 2

(4.9%) participants were unaware. The researcher further investigated the data by general

education and special education participants and found that 24 (92.3%) general education

participants were aware of the disability category of the students instructed, whereas two

(7.7%) general education participants were unaware of the disability category of the

students instructed. Fifteen (100%) special education participants were aware of the

disability categories of the students instructed.

The researcher further examined the differences between the awareness of

disability categories within the following grade spans: kindergarten through fifth, sixth

through eighth, ninth through 12th, and “other,” which is a combination of grade levels

for general education and special education participants. The researcher first analyzed the

general education participant data. Total general education participant data (n = 26) is

represented in both numerical and percentage terms. General education participant data

was as follows: kindergarten through fifth grade span participant data (n = 4), six through

eighth grade span participant data (n = 6), ninth through 12th grade span participant data

(n = 12), and “other” grade span participant data (n = 4) is reported in both numerical and

percentage terms. The researcher then analyzed the special education participant data.

Total special education participation data (n = 15) from survey question 11 will be

reported in both numerical and percentage terms. Special education participant data was

as follows: kindergarten through grade five grade span participant data (n = 2), sixth

through eighth grade span data (n = 2), ninth through 12th grade span data (n = 10), and

the “other” grade span data (n = 1) is reported in both numerical and percentage terms.

Google Form survey question 11 asked participants of their awareness of the

disability categories of the students instructed. General education participant data

indicated two (7.7%) general education grade nine through 12 participants were unaware

of the disability categories for students taught. Similarly, the researcher investigated the

special education data from Google Form survey question 11. A summary of the

demographic data can be found in Table 4.7.

Table 4.7

Grade Span Awareness of Disability Categories by Current Role in Education

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

(63.4%)

General Education Teachers

(15.4%)

(23.1%)

(46.2%)

(15.4%)

Awareness of Disability Categories

Yes

(15.4%)

(23.1%)

(38.5%)

(15.4%)

(0%)

(7.7%)

(0%)

Current Role in Education

(36.6%)

Special Education Teachers

(13.3%)

(66.7%)

(6.7%)

Awareness of Disability Categories

Yes

(13.3%)

(66.7%)

(6.7%)

(0%)

Note. N = 41 for Current Role in Education. n = 26 for General Education Teachers (n = 4 for kindergarten-5 grade span, n = 6 for 6-8 grade span, n = 12 for 9-12 grade span, and

n = 4 for “other” grade span). n = 15 for Special Education Teachers (n = 2 for kindergarten-5 grade span, n = 2 for 6-8 grade span, n = 10 for 9-12 grade span, and n = 1 for

“other” grade span). Percentages may not add up to 100% due to rounding.

Google Form survey question 12 asked for participants to list the disability

categories of students instructed. Table 4.8 displays the disability categories noted by

participants. The 41 Google Form survey participants responded with 175 total disability

categories taught. Total participant data (N = 175) is represented in numerical and

percentage terms.

Table 4.8

Disability Categories

Participant Responses

Variables

Total Responses

Disability Categories

Intellectual Disability

(43.9%)

Hearing Impairment

(14.6%)

Speech and Language Impairment

(68.3)

Visual Impairment

(12.2%)

Emotional Disturbance

(61%)

Autism

(70.7%)

Other Health Impairment

(51.2%)

Specific Learning Disability

(80.5%)

Deafness

(0%)

Multiple Disabilities

(17.1%)

Unsure

(7.3%)

Note. N = 175 responses. Percentages may not add to 100% due to participants being able

to select multiple categories.

Google Form survey question numbers 13, 14, and 15 asked participants to state

what percentage of computer-based instruction occurred during each school year (2020-

2021, 2021-2022, 2022-2023) respectively. A summary of the findings can be found in

Table 4.9.

Table 4.9

Percentage of Computer-based Instruction

2020-2021

2021-2022

2022-2023

Variable

Total Responses

Percentage

(4.9%)

(9.8%)

(19.5%)

1-24%

(14.6%)

(19.5%)

(36.6%)

25-49%

(12.2%)

(29.3%)

(7.3%)

50-74%

(24.4%)

(17.1%)

(12.2%)

75-99%

(26.8%)

(14.6%)

100%

(17.1%)

(9.8%)

Note. N= 41. Percentages may not add to 100% due to rounding.

The researcher further examined the percentage of computer-based instruction

between the general education participants and the special education participants. Total

participant data (N = 41) is represented in numerical and percentage terms. Total general

education participant data (n = 26) is represented in both numerical and percentage terms.

Total special education participation data (n = 15) from Google Form survey questions

13, 14, and 15 are reported in both numerical and percentage terms. The data is

summarized in Tables 4.10 and 4.11. Data indicated that general education participants

noted a slight decrease (7.7%) in the percentage of computer-based instruction

throughout the three-year time span, whereas special education participants noted a

higher decline (13.3%) of computer-based instruction throughout the same time span.

Data indicated a turn toward supplemental use of computer-based instruction.

Table 4.10

Percentage of Computer-Based Instruction General Education

2020-2021

2021-2022

2022-2023

Variable

Total Responses

Percentage

(3.8%)

(7.7%)

(15.4%)

1-24%

(15.4%)

(19.2%)

25-49%

(11.5%)

(26.9%)

(11.5%)

50-74%

(11.5%)

(15.4%)

75-99%

(34.6%)

(19.2%)

(23.1%)

100%

(23.1%)

(15.4%)

Note. N= 26. n = 26 for General Education Participants. Percentages may not add to

100% due to rounding.

Table 4.11

Percentage of Computer-Based Instruction Special Education

2020-2021

2021-2022

2022-2023

Variable

Total Responses

Percentage

(6.7%)

(13.3%)

(26.7%)

1-24%

(13.3%)

(26.7%)

(66.7%)

25-49%

(13.3%)

(33.3%)

(0%)

50-74%

(46.7%)

(20%)

(6.7%)

75-99%

(13.3%)

(6.7%)

(0%)

100%

(6.7%)

(0%)

Note. N= 15. n = 15 for Special Education Participants. Percentages may not add to 100%

due to rounding.

The researcher continued to analyze the data regarding grade span information for

the general education and special education participants for each school year. Total

participant data (N = 41) is represented in numerical and percentage terms. Total general

education participant data (n = 26) is represented in both numerical and percentage terms.

Kindergarten through fifth grade span participant data (n = 4), sixth through eighth grade

span participant data (n = 6), ninth through 12th grade span participant data (n = 12), and

“other” grade span participant data (n = 4) is reported in both numerical and percentage

terms. Total special education participation data (n = 15) from Google Form survey

questions 13, 14, and 15 are reported in both numerical and percentage terms. Special

education participant data was as follows: kindergarten through grade five grade span

participant data (n = 2), sixth through eighth grade span data (n = 2), ninth through 12th

grade span data (n = 10), and the “other” grade span data (n = 1) is reported in both

numerical and percentage terms. The data is summarized in Tables 4.12, 4.13, and 4.14.

The data indicated that overall, the upper grade spans (grades six through eight and

grades nine through 12) utilized computer-based learning more frequently than the lower

grade spans (kindergarten through grade five). In general education, kindergarten through

grade five displayed an overall reduction of time spent utilizing computer-based

instruction over the three-year time span. General education grade span six through eight

and nine through 12 displayed a steadier use of computer-based instruction over the

three-year time frame. Special education grade span data demonstrated a reduction of

computer-based instruction over the three-year time span. The largest reduction was

between the 2021-2022 and 2022-2023 school years.

Table 4.12

Percentage of Computer-Based Instruction by Grade Span 2020-2021

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

(63.4%)

General Education Teachers

(15.4%)

(23.1%)

(46.2%)

(15.4%)

(3.8%)

(0%)

1-24%

(3.8%)

(7.7%)

(0%)

25-49%

(0%)

(3.8%)

(0%)

(7.7%)

50-74%

(3.8%)

(0%)

(7.7%)

(0%)

75-99%

(0%)

(11.5%)

(19.2%)

(3.8%)

100%

(3.8%)

(11.5%)

(3.8%)

Current Role in Education

(36.6%)

Special Education Teachers

(13.3%)

(66.7%)

(6.7%)

(0%)

(6.7)

(0%)

1-24%

(0%)

(13.3%)

(0%)

25-49%

(0%)

(6.7%)

(0%)

50-74%

(6.7%)

(33.3%)

(0%)

75-99%

(6.7%)

(0%)

(6.7%)

100%

(0%)

(6.7%)

(0%)

Note. N = 41 for Current Role in Education. n = 26 for General Education Teachers (n = 4 for

kindergarten-5 grade span, n = 6 for 6-8 grade span, n = 12 for 9-12 grade span, and n = 4 for

“other” grade span). n = 15 for Special Education Teachers (n = 2 for kindergarten-5 grade span,

n = 2 for 6-8 grade span, n = 10 for 9-12 grade span, and n = 1 for “other” grade span).

Percentages may not add up to 100% due to rounding.

Table 4.13

Percentage of Computer-Based Instruction by Grade Span 2021-2022

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

(63.4%)

General Education Teachers

(15.4%)

(23.1%)

(46.2%)

(15.4%)

(3.8%)

(0%)

(3.8%)

(0%)

1-24%

(7.7%)

(0%)

(7.7%)

25-49%

(0%)

(19.2%)

(3.8%)

50-74%

(3.8%)

(7.7%)

(0%)

75-99%

(0%)

(19.2%)

(0%)

100%

(0%)

(11.5%)

(3.8%)

Current Role in Education

(36.6%)

Special Education Teachers

(13.3%)

(66.7%)

(6.7%)

(0%)

(6.7)

(0%)

1-24%

(0%)

(6.7%)

(13.3%)

(0%)

25-49%

(0%)

(26.7%)

(6.7%)

50-74%

(6.7%)

(13.3%)

(0%)

75-99%

(6.7%)

(0%)

(6.7%)

(0%)

100%

(0%)

Note. N = 41 for Current Role in Education. n = 26 for General Education Teachers (n = 4 for

kindergarten-5 grade span, n = 6 for 6-8 grade span, n = 12 for 9-12 grade span, and n = 4 for

“other” grade span). n = 15 for Special Education Teachers (n = 2 for kindergarten-5 grade span,

n = 2 for 6-8 grade span, n = 10 for 9-12 grade span, and n = 1 for “other” grade span).

Percentages may not add up to 100% due to rounding.

Table 4.14

Percentage of Computer-Based Instruction by Grade Span 2022-2023

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

(63.4%)

General Education Teachers

(15.4%)

(23.1%)

(46.2%)

(15.4%)

(7.7%)

(0%)

(3.8%)

1-24%

(3.8%)

(7.7%)

25-49%

(0%)

(11.5%)

(0%)

50-74%

(0%)

(3.8%)

(11.5%)

(0%)

75-99%

(3.8%)

(15.4%)

(0%)

100%

(0%)

(11.5%)

(3.8%)

Current Role in Education

(36.6%)

Special Education Teachers

(13.3%)

(66.7%)

(6.7%)

(13.3)

(0%)

1-24%

(6.7%)

(46.7%)

(6.7%)

25-49%

(0%)

50-74%

(0%)

(6.7%)

(0%)

75-99%

(0%)

100%

(0%)

Note. N = 41 for Current Role in Education. n = 26 for General Education Teachers (n = 4 for

kindergarten-5 grade span, n = 6 for 6-8 grade span, n = 12 for 9-12 grade span, and n = 4 for “other”

grade span). n = 15 for Special Education Teachers (n = 2 for kindergarten-5 grade span, n = 2 for 6-8

grade span, n = 10 for 9-12 grade span, and n = 1 for “other” grade span). Percentages may not add up

to 100% due to rounding.

Google Form survey question number 18 asked participants if they received

professional development prior to starting instruction through computer-based learning.

All 41 participants responded to the Google Form survey question. Twenty-eight (68.3%)

participants stated they had received professional development prior to the

implementation of computer-based/ remote instruction, whereas 13 (31.7%) stated they

were not provided professional development prior to implementing computer-based/

remote instruction.

The researcher further analyzed the data for professional development prior to the

implementation of computer-based instruction through participants’ current role in

education, as well as grade span. There was no statistical difference noted in the data

between the general education and special education participant roles nor across the

different grade spans.

Interview and Open-Ended Survey Responses. Additional information related

to the implementation of computer-based learning was elicited by attaining responses to

open-ended survey question 31 and interview questions one, five, and seven. Fourteen

(34.1%) participants responded to open-ended Google Form survey question 31. Seven

(50%) comments related to research question one. One (7.1%) participant responded that

a student taught had an Orthopedic Impairment, which was not a disability listed within

Google Form survey question 12.

Google Form survey question 31 gave participants an opportunity to leave any

additional comments relevant to computer-based instruction for students identified with

disabilities. Six (42.9%) of 14 participant responses related to the impact of computer-

based learning for students identified with disabilities. Three (50%) of the comments

were that computer-based instruction for students identified with disabilities was

“difficult due to logging off, walking away, and students being unfocused.” Three (50%)

other independent comments were made with no statistical significance.

The first interview question asked participants about experiences within the last 3

years (post-pandemic) of teaching students with disabilities through computer-based

learning. All four (100%) interview participants responded by sharing at least one

comment. The fifth interview question asked participants to share any challenges of

computer-based instruction for students with disabilities. All four (100%) participants

responded by sharing at least one challenge. The researcher grouped the responses based

on common themes. Twenty-two comments were noted for interview question one.

Thirty-nine comments were noted for interview question five. A total of 61 comments

were made by participants within interview questions one and five. Total participant data

(N = 61) is represented in numerical and percentage terms. Table 4.15 is a summary of

the responses. The comments are in order from the most frequent to least frequent

comment.

The researcher first analyzed the positive responses. Data indicated that there

were 22 (36.1%) positive comments on the impact of computer-based learning provided

within the responses of interview questions one and five. Total positive participant data

(n = 22) is represented in numerical and percentage terms. Please note two (3.3%)

positive items only received one comment and were classified under “other” within Table

4.15.

The researcher then analyzed interview questions one and five comments for

negative comment frequency. Data indicated that 39 (63.9%) negative comments on the

impact of computer-based learning were provided within the responses of interview

questions one and five. Total negative participant data (n = 39) is represented in

numerical and percentage terms. Table 4.15 summarizes the comments. Please note three

(4.9%) negative items only received one comment and were classified as “other” within

Table 4.15.

Table 4.15

Interview Questions One and Five Comments

Participant Responses

Variables

Total Responses

Comments

(100%)

Positive

(36.1%)

Talk and instant translation

(4.9%)

Equalize learning

(4.9%)

Works well for some groups

(3.3%)

Self-pace

(3.3%)

One-on-one instruction

(3.3%)

Effective for those with

disabilities

(3.3%)

Confidence boost

(3.3%)

Mask their needs with technology

(3.3%)

Teach students to use technology

(3.3%)

“Other”

(3.3%)

Negative

(63.9%)

Distraction (YouTube, Netflix,

etc.)

(21.3%)

Reliance on others to get work

done

(8.2%)

Current software- missing the

mark

(6.6%)

Hide they are struggling

(4.9%)

Students do not have to push hard

(4.9%)

Share work

(3.3%)

Time management

(3.3%)

Scaffolding not taken away

(3.3%)

Need to be able to lock-down

devices

(3.3%)

“Other”

(4.9%)

Note. N= 61. n = 22 Positive Comments and n = 39 Negative Comments. Participants made more than one

comment. Percentages may not add to 100% due to rounding.

The seventh interview question asked participants to share any additional

comments relevant to the implementation of computer-based learning for students

identified with disabilities. All four participants responded to the question and shared at

least one comment. There were a total of 16 comments made for interview question

seven. Five (31.3%) comments were made in relation to research question one. The

comments were that computer-based/ remote instruction may: “impact their brain

development and maturation”, may be “very beneficial,” “handwriting is neglected,” the

amount of “screentime,” and that “teachers are constantly on computers (grading,

questions, lesson planning/creation, etc.”

The third interview question asked participants to share the accommodations and

modifications that they utilize while instructing students with disabilities through

computer-based learning. All four participants responded to the question and shared at

least one accommodation/ modification. A total of 19 comments were relevant to

accommodations/ modifications. Total participant data (N = 19) is represented in

numerical and percentage terms. The researcher grouped the responses based on the

common responses. A summary of the responses is found in Table 4.16. The responses

are listed from most frequent response to less frequent response. Three (15.8%) responses

for accommodation/ modification were “one-on-one instruction,” three (15.8%) responses

were that the “online textbook is read aloud,” three (15.8%) responses were the ability for

“grouping of students,” and two (10.5%) responses were the ability to “publish/ assign

items to individual students per need.” Multiple participants selected an accommodation/

modification that only received one response. These included: “Tutorials for different

software,” “Self-paced,” “Deadline management,” “Closed captioning for videos,”

“Allows for students to move forward or backward for review,” “Answer keys provided,”

“Videos/ visuals,” and “Google classroom.”

The researcher compared the responses from Google Form open ended questions

and interview questions one, three, five, and seven and found common themes. The

themes are summarized in Table 4.16 in order from most frequent to least frequent

response. Within interview questions one, three, five, and seven, all four (100%)

interview participants identified various elements of one major theme: (1) the impact of

computer-based learning. This theme was then broken down into two subthemes: (1) the

impact on students and (2) the impact on teachers. Within each subtheme there were two

categories: (1) positive impacts and (2) negative impacts. A total of 86 references to the

theme were found amongst the four interviews. Total participant data (N = 86) is

represented in numerical and percentage terms.

Thirty-eight (44.2%) comments referenced the impact of computer-based learning

on students. Total participant data (n = 38) is represented in numerical and percentage

terms. Of the 38 responses, 17 (44.7%) referenced positive impacts on students, whereas

21 (55.3%) referenced negative impacts on students. Total positive impacts on student

participant data (n = 17) and the total negative impacts on student participant data (n =

21) are represented in numerical and percentage terms. The top comment that fit the main

theme relevant to the impact of computer-based learning was, “It is easier for them

(students) to mask their needs I think through technology.” Statements were coded and

categorized as positive and negative in nature in total, as well as by interview

participants. A total of 13 (15.1%) responses involved a statement of “distractibility.”

One participant stated, “(students) like their iPads for watching videos and playing

games.” Another participant stated, “I’m hiding that I’m off-task, I’m hiding that I’m not

focusing” in relation to students and their use of technology within the classroom. The

top comment of subtheme (1) the impact on students was “there is a reliance on others.”

The top comment of category (1) positive was “it can be a way to equalize learning” and

the top comment of this category (2) negative was “they (students) hide that they are

struggling.”

Twenty-eight (32.6%) comments referenced the impact of computer-based

learning on teachers. Total participant data (n = 28) is represented in numerical and

percentage terms. Of the 28 responses, eight (28.6%) referenced positive impacts on

teachers, whereas 20 (71.4%) referenced negative impacts on teachers. Total positive

participant data (n = 28) and total negative participant data (n = 20) are represented in

numerical and percentage terms. The top comment that fit the main theme of the impact

of computer-based learning was, “You have to teach them (students) number one how to

use the technology.” Statements were coded and categorized as positive and negative in

nature in total, as well as by interview participants. A total of three responses involved a

statement of “beneficial.” One participant stated, “Very beneficial.” Another stated,

“there are so many more options available.” The top comment of subtheme (1) the impact

on teachers was “there needs to be more of a lockdown (on devices)” in relation to

students veering off the lesson. The top comment of category (1) positive was

“Everybody’s able to approach it at their own pace” and the top comment of category (2)

negative was “We provide them with so much now, to help support them, that we never

take those supports away.”

It is important to note that interview questions one, three, five, and seven had 20

responses that were related to the impact of computer-based learning but were ambiguous

and may fall under both the impact of students, as well as teachers, therefore they were

not included directly within the tabled data.

Table 4.16

Interview Questions One, Three, Five, and Seven Responses Relevant to Theme

9-12A

9-12B

9-12C

K-6A

Variables

Total Responses

Themes

Impact of Computer-Based

Learning

(100%)

Subtheme:

Impact on Students

(44.2%)

Subthemes:

Positive. 17

(44.7%)

(17.6%)

(5.9%)

(47.1%)

(29.4%)

Negative. 21

(55.3%)

(52.4%)

(19%)

(9.5%)

(19%)

Subtheme:

Impact on Teachers

(32.6%)

Subthemes:

Positive. 8

(28.6%)

(25%)

(37.5%)

(12.5%)

Negative. 20

(71.4%)

(40%)

(25%)

(10%)

(25%)

Note. N = 86. n = 38 for the Impact on Students, n = 17 for the Positive Impact on Students, n = 21 for the

Negative Impact on Students, n = 28 for the Impact on Teachers, n = 8 for the Positive Impact on Teachers,

n = 20 for the Negative Impact on Teachers. Percentages may not add to 100% due to rounding.

Research Question Two. What strategies do educators use when teaching

students with disabilities on a computer based/ remote platform? The purpose of

research question two was to identify computer-based/ remote platform strategies

educators are utilizing during instruction of students identified with disabilities. Twelve

survey questions corresponded with research question two. Google Form survey question

number 31 may also answer research question two dependent on the response provided.

The interview included one question that asked interviewees to respond and elaborate on

research question two of the study, as well as the possibility of responses from interview

question seven dependent on response.

Survey Responses. The mixed-type Google Form survey questions 16, 17, 19,

20, 21, 22, 23, 24, 25, 26, 27, and 28 explored the types of strategies educators utilize for

students identified with disabilities instructed through computer-based learning. Survey

question number 31 may also answer research question two dependent on the response

provided. All 41 (100%) participants responded to each question. All 41 participants

responded to the Google Form survey questions. Total participant data (N = 41) is

represented in numerical and percentage terms.

Google Form survey question 16 asked if the participant’s school provided “one-

one-one” computers or iPads since the fall of 2020. Total participant data (N = 41) is

represented in numerical and percentage terms. Forty (97.6%) participants responded that

their school provided “one-on-one” computers or iPads since the fall of 2020, whereas

one (2.4%) participant responded that they were not provided “one-to-one” computers or

iPads since the fall of 2020. Google Form survey question number 17 asked participants

that responded “no” to question 16 to elaborate if they were provided access to computers

within the classroom. Of those who responded that they were not provided “one-on-one”

computers or iPads since the fall of 2020, one (100%) responded they were provided

classroom computers. Total “no” participant data (n = 1) is represented in numerical and

percentage terms.

The researcher further analyzed the one-on-one iPad or computer data by current

role in education and grade span. Total participant data (N = 41) is represented in

numerical and percentage terms. Total general education participant data (n = 26) is

represented in both numerical and percentage terms. All general education participants

were provided with a device in each grade span. Total special education participation data

(n = 15) from Google Form survey question 16 will be reported in both numerical and

percentage terms. The one special education participant that was not provided “one-on-

one” iPads or computers since the fall of 2020 but was provided a computer within the

classroom. That participant was within the grades nine through 12 grade span. No other

relevant findings were discovered.

Google Form survey question number 19 asked participants to provide the

computer-based educational platform that their school district has utilized from the fall of

2020 to the spring of 2023. All 41 participants responded by sharing at least one

computer-based educational platform utilized. A total of 66 different computer-based

educational platforms were stated. Total participant data (N = 66) is represented in

numerical and percentage terms. The researcher grouped the responses based on common

theme. A summary of the responses is found in Table 4.17. The responses are listed from

most frequent response to less frequent response. Multiple (18) responses received one

comment each and did not demonstrate any significance towards research question two of

the study. These were categorized as “Other.” The “Other” responses include:

“Accelerate education,” “Alek,” “In-House LMS,” “Online Databases,” “Star

Reading/Math,” “IXL,” “Sum Dog,” “Prodigy,” “Mystery Science,” “Discovery

Education,” “Gimkit,” “Gizmo,” “Sora,” “Teams,” “Canva,” “WVA,” “iReady,” and

“Unsure.”

Table 4.17

Educational Platforms

Participant Responses

Variables

Total Responses

“Educational Platforms”

(100%)

Schoology

(31.8%)

Seesaw

(10.6%)

Edgenuity

(9.1%)

Google Classroom

(9.1%)

Apple iPads

(3.0%)

Moodle

(3.0%)

Google Drive

(3.0%)

Zoom

(3.0%)

“Other”

(27.3%)

Note. N = 66. Participants were able to make multiple responses, therefore percentages may not add to 100%.

The researcher further investigated the educational platform use by current role in

education and grade level span. Total participant response data (N = 66) is represented in

numerical and percentage terms. Total general education participant data (n = 46) is

represented in both numerical and percentage terms. Kindergarten through fifth grade

span participant data (n = 13), sixth through eighth grade span participant data (n = 11),

ninth through 12th grade span participant data (n = 16), and “other” grade span participant

data (n = 6) is reported in both numerical and percentage terms. Total special education

participation data (n = 20) from Google Form survey question 19 will be reported in both

numerical and percentage terms. Special education participant data was as follows:

kindergarten through fifth grade span participant data (n = 3), sixth through eighth grade

span data (n = 2), nine through 12th grade span data (n = 14), and the “other” grade span

data (n = 1) is reported in both numerical and percentage terms. The data is summarized

in Table 4.18. Data indicated that both general education participants and special

education participants utilized Schoology the most frequently in the upper grade spans.

Data also indicated that Seesaw was the most frequently utilized educational platform in

the kindergarten through grade five grade span. Google Classroom and Google programs

were also frequently used educational platforms within the general education grade six to

eight grade span.

Table 4.18

Educational Platform by Current Role and Grade Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Current Role in Education

(63.4%)

General Education Teachers

(28.3%)

(23.9%)

(34.8%)

(13%)

Educational Platforms

Schoology

(2.2%)

(19.6%)

(0%)

Seesaw

(4.3%)

(0%)

(2.2%)

Edgenuity

(0%)

(4.3%)

(0%)

(2.2%)

Google Classroom

(2.2%)

(8.7%)

(4.3%)

(0%)

Apple iPads

(0%)

(4.3%)

(0%)

Moodle

(0%)

(2.2%)

(0%)

Google Drive

(0%)

Zoom

(0%)

“Other”

(19.6%)

(2.2%)

(4.3%)

(8.7%)

Current Role in Education

(30.3%)

Special Education Teachers

(15%)

(10%)

(70%)

(5%)

Educational Platforms

Schoology

(5%)

(40%)

(5%)

Seesaw

(%)

(0%)

Edgenuity

(0%)

(%)

(0%)

Google Classroom

(0%)

Apple iPads

(0%)

Moodle

(0%)

Google Drive

(0%)

Zoom

(0%)

“Other”

(0%)

(5%)

(20%)

(0%)

Note. N = 66. Participants were able to make multiple responses, therefore percentages may not add to 100%.

Google Form survey question 20 asked participants to rate the computer-based

platform’s capacity of accommodations for students identified with disabilities. All 41

participants answered the question. One (2.4%) participant stated that their district’s

platform did not offer the capacity to accommodate, nine (22%) participants stated it was

offered less than 25% of the time, six (14.6%) participants stated it was offered

26050%of the time, 13 (31.7%) participants stated it was offered 51-75% of the time and

12 (29.3%) stated it was offered more than 75% of the time.

The researcher further analyzed the district’s capacity to accommodate by the

participants current role in education and grade level spans. Total participant data (N =

41) is represented in numerical and percentage terms. Total general education participant

data (n = 26) is represented in both numerical and percentage terms. Kindergarten

through fifth grade span participant data (n = 4), sixth through eighth grade span

participant data (n = 6), ninth through 12th grade span participant data (n = 12), and

“other” grade span participant data (n = 4) is reported in both numerical and percentage

terms. Total special education participation data (n = 15) from Google Form survey

question 20 will be reported in both numerical and percentage terms. Special education

participant data was as follows: kindergarten through grade five grade span participant

data (n = 2), sixth through eighth grade span data (n = 2), ninth through 12th grade span

data (n = 10), and the “other” grade span data (n = 1) is reported in both numerical and

percentage terms. Table 4.19 summarizes the findings. General education and special

education participants noted that the capacity to accommodate was overall higher with

the upper grade level span of grades nine through 12. A higher percentage of special

education participants (100%) noted the ability for a district’s educational platform to

accommodate than general education participants (96.2%).

Table 4.19

District’s Computer-Based Platform’s Capacity to Accommodate by Current Role and

Grade Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

District’s Platform’s Capacity to Accommodate

via Computer-Based

(100%)

Current Role in Education

General Education Teachers

(63.4%)

0- Not offered

(0%)

(3.8%)

(0%)

1- Offered less than 25% of the time

(7.7%)

(3.8%)

(7.7%)

(3.8%)

2- Offered 26-50% of the time

(3.8%)

(0%)

(%)

(3.8%)

3- Offered 51-75% of the time

(0%)

(7.7%)

(19.2%)

(0%)

4- Offered more than 75% of the time

(3.8%)

(7.7%)

Current Role in Education

Special Education Teachers

(36.6%)

0- Not offered

(0%)

1- Offered less than 25% of the time

(0%)

(13.3%)

(2.4%)

2- Offered 26-50% of the time

(0%)

(2.4%)

(0%)

3- Offered 51-75% of the time

(2.4%)

(26.7%)

(0%)

4- Offered more than 75% of the time

(2.4%)

(20%)

(0%)

Note. N = 41. n = 26 for General Education participants, n = 15 for special education participants. Percentages may not add to 100%

due to rounding.

Google Form survey question 21 asked participants to respond on what

presentation accommodations/ modifications are available currently within the computer-

based educational platform being utilized. All 41 participants answered the question. A

total of 120 presentation accommodations/ modifications were mentioned. Total

participant data (N = 120) is represented in numerical and percentage terms. The results

are summarized in Table 4.20. If a participant selected “other,” they were to elaborate in

survey question number 22. Eight (19.5%) participants responded to Google Form survey

question 22 and made 17 additional comments. Two (11.8%) participants noted “reduced

answer choices” and two (11.8%) participants noted “more images.” Multiple responses

received one comment each. These were found to have no significance in relation to

research question two of the study. The responses included: “Extended time,” “Dual

Monitor,” “Read Aloud by Device/ Program,” “Visual Timelines,” “Modified

Worksheets,” “Demonstration Videos with Subtitles,” “Untimed Assessments,” “Multiple

Attempts at Assessments,” “Direct Meetings with Instructors,” “Simplified Language,”

“Translation,” “Spell Check,” and “Various Versions Easily Assigned.”

Table 4.20

Current Presentation Accommodations/ Modifications Available

Participant Responses

Variable

Total Responses

Presentation Accommodations/ Modification

120

(100%)

Read aloud directions

(70.7%)

Read aloud problems/assignments

(61%)

Signing of directions

(2.4%)

Use of audio version

(41.5%)

Clarification of directions

(26.8%)

Large print

(58.5%)

None that I am aware of

(12.2%)

Other

(19.5%)

Note. N = 120. Percentages may not add to 100% due to participants being able to select multiple responses.

The researcher further examined the presentation accommodations/ modifications

by current role in education and grade span. Total participant data (N = 41) is represented

in numerical and percentage terms. The total number of presentation accommodation/

modification responses was 120 (N = 120). Total general education participant data (n =

77) is represented in both numerical and percentage terms. Total special education

participation data (n = 43) from Google Form survey question 21 and 22 will be reported

in both numerical and percentage terms. Table 4.21 summarizes the findings. The grade

span with the most frequent use of presentation accommodations/ modifications was the

grades nine through 12 within both roles in education. There was no significant

difference found between general education and special education participants

investigated.

Table 4.21

Current Presentation Accommodations/ Modifications Available by Current Role and

Grade Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Presentation Accommodations/ Modifications

120

(100%)

Current Role in Education

General Education Teachers

(64.2%)

Read aloud directions

(2.6%)

(5.2%)

(11.7%)

(3.9%)

Read aloud problems/

assignments

(2.6%)

(3.9%)

(9.1%)

(3.9%)

Signing of directions

(0%)

(1.3%)

(0%)

Use of audio version

(2.6%)

(5.2%)

(2.6%)

Clarification of directions

(1.3%)

(0%)

(5.2%)

(2.6%)

Large print

(2.6%)

(5.2%)

(9.1%)

(2.6%)

None that I am aware of

(2.6%)

(1.3%)

(0%)

(1.3%)

Other

(0%)

(1.3%)

(3.9%)

(1.3%)

Current Role in Education

Special Education Teachers

(35.8%)

Read aloud directions

(4.7%)

(2.3%)

(18.6%)

(0%)

Read aloud problems/

assignments

(4.7%)

(2.3%)

(16.3%)

(0%)

Signing of directions

(0%)

Use of audio version

(0%)

(11.6%)

(0%)

Clarification of directions

(0%)

(9.3%)

(0%)

Large print

(0%)

(2.3%)

(18.6%)

(0%)

None that I am aware of

(0%)

(2.3%)

(0%)

Other

(0%)

(2.3%)

Note. N = 120. n = 77 for General Education participants, n = 43 for special education participants. Percentages may not add to 100%

due to participants being able to select more than one option.

Google Form survey question 23 asked participants to respond on what response

accommodations/ modifications are available currently within the computer-based

educational platform being utilized. All 41 participants answered the question. The

results are summarized in Table 4.22. The 41 participants responded with 94 response

accommodations/ modifications for question 23. Total participant data (N = 94) is

represented in numerical and percentage terms. If a participant selected “other,” they

were to elaborate in the Google Form survey question number 24. Four (9.8%)

participants responded that nine “other” response accommodations/ modifications were

available on their district’s platform. Two (22.2%) comments noted “audio/voice record,”

one (11.1%) noted “image submission,” one (11.1%) noted “reading aloud,” one (11.1%)

noted “highlight,” one (11.1%) noted “cross out,” one (11.1%) noted “sticky notes,” one

(11.1%) noted “reduction of question items and choices,” and one (11.1%) noted “create/

upload different options” as response accommodations offered.

Table 4.22

Current Response Accommodations/ Modifications Available

Participant Responses

Variables

Total Responses

Response Accommodation/Modification

(100%)

Voice to Text

(70.7%)

Multiple Choice

(73.2%)

Word Bank

(63.4%)

None that I am aware of

(12.2%)

Other

(9.8%)

Note. N = 94. Percentages may not add to 100% due to rounding.

The researcher further examined the response accommodations/ modifications by

the participants current role in education and grade span. Total participant response data

(N = 94) is represented in numerical and percentage terms. Total general education

participant data (n = 57) is represented in both numerical and percentage terms.

Kindergarten through fifth grade span participant data (n = 8), sixth through eighth grade

span participant data (n = 15), ninth through 12th grade span participant data (n = 26), and

“other” grade span participant data (n = 8) is reported in both numerical and percentage

terms. Total special education participation data (n = 37) from Google Form survey

question 23 and 24 are reported in both numerical and percentage terms. Special

education participant data was as follows: kindergarten through grade five grade span

participant data (n = 4), sixth through eighth grade span data (n = 4), ninth through 12th

grade span data (n = 28), and the “other” grade span data (n = 1) is reported in both

numerical and percentage terms. Table 4.23 summarizes the findings. Data indicated that

within both the general education and special education participant responses, the most

response accommodations/ modifications were found in the grade nine through grade 12

span, whereas the least response accommodations/ modifications were found in the

“other” grade span.

Table 4.23

Current Response Accommodations/ Modifications Available by Current Role and Grade

Span

K- 5

Grade

Span

6-8

Grade

Span

9-12

Grade

Span

“Other”

Grade

Span

Variables

Total Responses

Response Accommodations/

Modifications

(100%)

Current Role in Education

General Education Teachers

(60.6%)

Voice to Text

(5.3%)

(8.8%)

(14%)

(5.3%)

Multiple Choice

(3.5%)

(8.8%)

(14%)

(3.5%)

Word Bank

(3.5%)

(7%)

(14%)

(1.8%)

None at that I am aware of

(1.8%)

(0%)

(1.8%)

(3.5%)

Other

(0%)

(1.8%)

(0%)

Current Role in Education

Special Education Teachers

(39.4%)

Voice to Text

(2.7%)

(5.4%)

(18.9%)

(0%)

Multiple Choice

(5.4%)

(2.7%)

(27%)

(0%)

Word Bank

(2.7%)

(0%)

(27%)

(0%)

None that I am aware of

(0%)

(2.7%)

Other

(0%)

(2.7%)

(0%)

Note. N = 94. n = 57 for General Education participants, n = 37 for special education

participants. Percentages may not add to 100% due to participants being able to select

more than one option.

Google Form survey question 25 asked participants to respond on what timing

accommodations/ modifications are available currently within the computer-based

educational platform being utilized. All 41 participants answered the question. There

were 93 (100%) timing accommodations/ modifications selected. Total participant data

(N = 93) is represented in numerical and percentage terms. The results are summarized in

Table 4.24. If a participant selected “other,” they were to elaborate in the Google Form

survey question number 26. Two (4.9%) participants responded to the Google Form

survey question 26 and four responses were provided each with one (25%) response.

These were: “sticky note questions to return to,” “pause,” “attempts can be added,” and

“choice removed once picked from the word bank.”

Table 4.24

Current Timing Accommodations/ Modifications Available

Participant Responses

Variables

Total Responses

Current Timing Accommodations/

Modifications Available

(100%)

Extended Time

(70.7%)

Breaks

(43.9%)

Fragmenting/Chunking of Test

(39%)

Unlimited Time

(51.2%)

None that I am Aware of

(17.7%)

Other

(4.9%)

Note. N = 93. Percentages may not add to 100% due to rounding.

The researcher further examined the timing accommodations/ modifications by

the participants current role in education and grade span. Total participant timing data (N

= 93) is represented in numerical and percentage terms. Total general education

participant data (n = 61) is represented in both numerical and percentage terms.

Kindergarten through fifth grade span participant data (n = 10), sixth through eighth

grade span participant data (n = 14), ninth through 12th grade span participant data (n =

27), and “other” grade span participant data (n = 10) is reported in both numerical and

percentage terms. Total special education participation data (n = 32) from Google Form

survey question 25 and 26 are reported in both numerical and percentage terms. Special

education participant data was as follows: kindergarten through grade five grade span

participant data (n = 6), sixth through eighth grade span data (n = 2), nine through 12th

grade span data (n = 23), and the “other” grade span data (n = 1) is reported in both

numerical and percentage terms. Table 4.25 summarizes the findings. Data indicated that

extended time and unlimited time are the most frequently used timing accommodations/

modifications for both the general education and special education participants. There

was no significant difference found between general education and special education

grade level spans investigated.

Table 4.25

Current Timing Accommodations/ Modifications Available by Current Role and Grade

Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Timing Accommodations/ Modification

(100%)

Current Role in Education

General Education Teachers

(65.6%)

Extended Time

(3.3%)

(6.6%)

(13.1%)

(6.6%)

Breaks

(3.3%)

(8.2%)

(3.3%)

Fragmenting/ Chunking of

Text

(3.3%)

(4.9%)

(8.2%)

(3.3%)

Unlimited Time

(3.3%)

(4.9%)

(9.8%)

(3.3%)

None that I am aware of

(3.3%)

(1.6%)

(0%)

Other

(0%)

(3.3%)

(0%)

Current Role in Education

Special Education Teachers

(34.4%)

Extended Time

(6.3%)

(0%)

(28.1%)

(0%)

Breaks

(6.3%)

(0%)

(15.6%)

(0%)

Fragmenting/ Chunking of

Text

(3.1%)

(0%)

(9.4%)

(0%)

Unlimited Time

(3.1%)

(18.8%)

(0%)

None that I am aware of

(0%)

(3.1%)

(0%)

(3.1%)

Other

(0%)

Note. N = 93. n = 61 for General Education participants, n = 32 for special education participants. Percentages may not add to 100% due to

participants being able to select more than one option.

Google Form survey question number 27 asked participants to select how

frequently computers are regularly used for different skills. Google Form survey question

28 asked participants to rate how frequently they used different computer-based activities

as a regular part of students’ assignments. All 41 participants responded to the question.

Total participant data (N = 41) is represented in numerical and percentage terms. Table

4.26 summarizes the responses for Google Form survey questions 27 and 28. Each

participant rated the frequency of each skill or activity. Total participant data (N = 41) is

represented in numerical and percentage terms.

Table 4.26

Computer-Based Activities/ Assignment Frequency

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency

Develop Skills of Independent

Learning

(7.3%)

(14.6%)

(34.1%)

(43.9%)

Provide Additional Instruction

and Practice Opportunities

(4.9%)

(9.8%)

(43.9%)

(41.5%)

Allow Students to Learn/Work

at Their Own Pace During

Lessons

(7.3%)

(17.1%)

(29.3%)

(46.3%)

Multidisciplinary Approach

(combine subjects)

(34.1%)

(29.3%)

(22%)

(14.6%)

Create Opportunities for

Learning by Simulation (Real-

World Simulation Programs)

(24.4%)

(36.6%)

(24.4%)

(14.6%)

Obtain Information from the

Internet

(9.8%)

(39%)

(41.5%)

Computer-based Activities Frequency Assignments

Operating a Computer (Saving

Files, Printing, etc.)

(9.8%)

(12.2%)

(29.3%)

(48.8%)

Writing Documents with Word

Processor (Typing, Editing,

Layout, etc.)

(14.6%)

(19.5%)

(29.3%)

(36.6%)

Making Illustrations with

Graphical Programs

(22%)

(41.5%)

(24.4%)

(12.2%)

Calculating with Spreadsheet

Programs (Sheet Creation, Use

of Formulas, Organizing

Information)

(51.2%)

(29.3%)

(7.3%)

(12.2%)

Writing Programs (e.g. Logo,

Pascal, etc.)

(63.4%)

(17.1%)

(9.8%)

Communicating via e-mail

with Teachers and Other

Students

(9.8%)

(14.6%)

(24.4%)

(51.2%)

Sending, Searching For, and

Using Electronic Forms of

Information

(14.6%)

(19.5%)

(29.3%)

(36.6%)

Using Educational Software

(e.g. Taking Tests, Exercises,

etc.)

(4.9%)

(17.1%)

(24.4%)

(53.7%)

Note. N = 41. Percentages may not add to 100% due to rounding.

The researcher further examined the computer-based activities participant data by

current role in education. All 41 participants responded to the question. Total participant

data (N = 41) is represented in numerical and percentage terms. Twenty-six (63.4%) were

general education participants (n = 26) and are represented in numerical and percentage

terms. Fifteen (36.6%) participants were special education participants (n = 15) and are

represented in numerical and percentage terms. Table 4.27 summarizes the findings.

Overall, one of the most common computer-based activity was to “provide additional

instruction and practice opportunities” for both general education and special education

participants. “Learn/ work at their own pace during lessons” was tied for the most

common computer-based activities for the special education participants. General

education participants noted “obtaining information from the internet” as their top

computer-based activity. General education participants also noted “developing skills of

independent learning” as a common computer-based activity. Special education

participants noted “obtaining information from the internet” as a common computer-

based activity as well.

Table 4.27

Computer-Based Activities Frequency by Current Role in Education

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency

(100%)

General Education

(63.4%)

Develop Skills of

Independent Learning

(3.8%)

(11.5%)

(42.3%)

Provide Additional

Instruction and Practice

Opportunities

(7.7%)

(11.5%)

(46.2%)

(34.6%)

Allow Students to

Learn/Work at Their Own

Pace During Lessons

(3.8%)

(19.2%)

(34.6%)

(42.3%)

Multidisciplinary Approach

(combine subjects)

(34.6%)

(30.8%)

(26.9%)

(7.7%)

Create Opportunities for

Learning by Simulation

(Real-World Simulation

Programs)

(26.9%)

(30.8%)

(11.5%)

Obtain Information from

the Internet

(3.8%)

(7.7%)

(50%)

(38.5%)

Special Education

(36.6%)

Develop Skills of

Independent Learning

(13.3%)

(20%)

(46.7%)

Provide Additional

Instruction and Practice

Opportunities

(0%)

(6.7%)

(40%)

(53.3%)

Allow Students to

Learn/Work at Their Own

Pace During Lessons

(13.3%)

(20%)

(53.3%)

Multidisciplinary Approach

(combine subjects)

(33.3%)

(26.7%)

(13.3%)

(26.7%)

Create Opportunities for

Learning by Simulation

(Real-World Simulation

Programs)

(20%)

(46.7%)

(13.3%)

(20%)

Obtain Information from

the Internet

(20%)

(13.3%)

(20%)

(46.7%)

Note. N = 41. n = 26 for general education participants and n = 15 for special education participants. Percentages may not add to 100%

due to rounding.

The researcher further examined the computer-based activities participant data by

grade span. All 41 participants responded to the question. Total participant data (N = 41)

is represented in numerical and percentage terms. Twenty-six (63.4%) were general

education. Fifteen (36.6%) participants were special education participants. Four (9.8%)

participants were general education teachers within the kindergarten through grade five

grade span (n = 4) and are represented in numerical and percentage terms. Two (4.9%)

participants were special education teachers within the kindergarten to grade five grade

span (n = 2) and are represented in numerical and percentage terms. There was no

significant difference noted in the data between the general education and special

education participant roles across the kindergarten through grade five grade span.

The researcher continued examining the computer-based activities participant data

by grade span. All 41 participants responded to the question. Total participant data (N =

41) is represented in numerical and percentage terms. Twenty-six (63.4%) were general

education. Fifteen (36.6%) participants were special education participants. Table 4.28

summarizes the findings for the sixth through eighth grade span. Six (14.6%) participants

were general education teachers within the grade six through eight grade span (n = 6) and

are represented in numerical and percentage terms. Two (4.9%) participants were special

education teachers within the kindergarten to grade five grade span (n = 2) and are

represented in numerical and percentage terms. Data indicated that within both roles in

education, “developing skills of independent learning,” “providing additional instruction

and practice opportunities,” and “allow students to learn/ work at their own pace during

lessons” increased in frequency within the grade six to grade eight span.

Table 4.28

Computer-Based Activities Frequency by Grade Six to Eight Grade Span

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency

(100%)

General Education

(14.6%)

Develop Skills of

Independent Learning

(0%)

(66.7%)

(33.3%)

Provide Additional

Instruction and Practice

Opportunities

(16.7%)

(0%)

(50%)

(33.3%)

Allow Students to

Learn/Work at Their Own

Pace During Lessons

(0%)

(66.7%)

(33.3%)

Multidisciplinary Approach

(combine subjects)

(33.3%)

(16.7%)

(33.3%)

(16.7%)

Create Opportunities for

Learning by Simulation

(Real-World Simulation

Programs)

(33.3%)

(16.7%)

(50%)

(0%)

Obtain Information from

the Internet

(0%)

(50%)

Special Education

(4.9%)

Develop Skills of

Independent Learning

(0%)

(50%)

Provide Additional

Instruction and Practice

Opportunities

(0%)

(50%)

Allow Students to

Learn/Work at Their Own

Pace During Lessons

(50%)

(0%)

(50%)

Multidisciplinary Approach

(combine subjects)

(50%)

(0%)

(50%)

Create Opportunities for

Learning by Simulation

(Real-World Simulation

Programs)

(50%)

(0%)

(50%)

Obtain Information from

the Internet

(50%)

(0%)

Note. N = 41. n = 6 for general education participants and n = 2 for special education participants. Percentages may not add to 100% due to

rounding.

The researcher further examined the computer-based activities participant data by

grade span. All 41 participants responded to the question. Total participant data (N = 41)

is represented in numerical and percentage terms. Twenty-six (63.4%) were general

education. Fifteen (36.6%) participants were special education participants. Table 4.29

summarizes the findings for the grade nine through grade 12 span. Twelve (29.3%)

participants were general education teachers within grade nine through 12 grade span (n

= 12) and are represented in numerical and percentage terms. Ten (24.4%) participants

were special education teachers within the kindergarten to grade five grade span (n = 10)

and are represented in numerical and percentage terms. Data within the grade nine

100

through grade 12 span indicated that general education participants utilized all the

different activities at a higher frequency than the special education participants. The

largest difference noted between the general education participant data and the special

education participants is within the frequency of “learn/ work at their own pace during

lessons.” Special education participants noted a 60% daily frequency, whereas the

general education participants noted 41.7% daily frequency.

Table 4.29

Computer-Based Activities Frequency by Grade Nine to 12 Grade Span

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency

(100%)

General Education

(29.3%)

Develop Skills of

Independent Learning

(0%)

(25%)

(33.3%)

(41.7%)

Provide Additional

Instruction and Practice

Opportunities

(8.3%)

(25%)

(41.7%)

(25%)

Allow Students to

Learn/Work at Their Own

Pace During Lessons

(0%)

(33.3%)

(25%)

(41.7%)

Multidisciplinary Approach

(combine subjects)

(50%)

(33.3%)

(16.7%)

(0%)

Create Opportunities for

Learning by Simulation

(Real-World Simulation

Programs)

(16.7%)

(41.7%)

(25%)

(16.7%)

Obtain Information from

the Internet

(0%)

(16.7%)

(50%)

(33.3%)

Special Education

(24.4%)

Develop Skills of

Independent Learning

(10%)

(20%)

(50%)

Provide Additional

Instruction and Practice

Opportunities

(0%)

(50%)

Allow Students to

Learn/Work at Their Own

Pace During Lessons

(0%)

(10%)

(30%)

(60%)

Multidisciplinary Approach

(combine subjects)

(40%)

(30%)

(0%)

(30%)

Create Opportunities for

Learning by Simulation

(Real-World Simulation

Programs)

(0%)

(60%)

(20%)

Obtain Information from

the Internet

(10%)

(0%)

(20%)

(70%)

Note. N = 41. n = 12 for general education participants and n = 10 for special education participants. Percentages may not add to 100% due to

rounding.

101

The researcher continued examining the computer-based activities participant data

by grade span. All 41 participants responded to the question. Total participant data (N =

41) is represented in numerical and percentage terms. Twenty-six (63.4%) were general

education. Fifteen (36.6%) participants were special education participants. Four (9.8%)

participants were general education teachers within the “other” grade span (n = 4) and are

represented in numerical and percentage terms. One (2.4%) participant was a special

education teacher within the “other” grade span (n = 1) and are represented in numerical

and percentage terms. There was no statistical difference noted in the data between the

general education and special education participant roles across the “other” grade span,

with the exception of more frequency within general education participants due to larger

sample size reported.

The researcher further examined the computer-based assignments participant data

by current role in education. All 41 participants responded to the question. Total

participant data (N = 41) is represented in numerical and percentage terms. Twenty-six

(63.4%) were general education participants (n = 26) and are represented in numerical

and percentage terms. Fifteen (36.6%) participants were special education participants (n

= 15) and are represented in numerical and percentage terms. Table 4.30 summarizes the

findings.

102

Table 4.30

Computer-Based Assignments Frequency by Current Role in Education

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency Assignments

(100%)

General Education

(63.4%)

Operating a Computer

(Saving Files, Printing,

etc.)

(3.8%)

(11.5%)

(30.8%)

(53.8%)

Writing Documents with

Word Processor (Typing,

Editing, Layout, etc.)

(7.7%)

(23.1%)

(34.6%)

Making Illustrations with

Graphical Programs

(11.5%)

(46.2%)

(30.8%)

(11.5%)

Calculating with

Spreadsheet Programs

(Sheet Creation, Use of

Formulas, Organizing

Information)

(46.2%)

(38.5%)

(3.8%)

(11.5%)

Writing Programs (e.g.

Logo, Pascal, etc.)

(65.4%)

(19.2%)

(11.5%)

(3.8%)

Communicating via e-mail

with Teachers and Other

Students

(7.7%)

(15.4%)

(23.1%)

(53.8%)

Sending, Searching For,

and Using Electronic Forms

of Information

(11.5%)

(15.4%)

(34.6%)

(38.5%)

Using Educational

Software (e.g. Taking

Tests, Exercises, etc.)

(3.8%)

(19.2%)

(26.9%)

(50%)

Special Education

(36.6%)

Operating a Computer

(Saving Files, Printing,

etc.)

(20%)

(13.3%)

(26.7%)

(40%)

Writing Documents with

Word Processor (Typing,

Editing, Layout, etc.)

(26.7%)

(13.3%)

(20%)

(40%)

Making Illustrations with

Graphical Programs

(40%)

(33.3%)

(13.3%)

Calculating with

Spreadsheet Programs

(Sheet Creation, Use of

Formulas, Organizing

Information)

(60%)

(13.3%)

Writing Programs (e.g.

Logo, Pascal, etc.)

(60%)

(13.3%)

(6.7%)

(20%)

Communicating via e-mail

with Teachers and Other

Students

(13.3%)

(26.7%)

(46.7%)

Sending, Searching For,

and Using Electronic Forms

of Information

(20%)

(26.7%)

(20%)

(33.3%)

Using Educational

Software (e.g. Taking

Tests, Exercises, etc.)

(6.7%)

(13.3%)

(20%)

(60%)

Note. N = 41. n = 26 for general education participants and n = 15 for special education participants. Percentages may not add to 100% due to

rounding.

The researcher continued examining the frequency of computer-based assignment

participant data by grade span. All 41 participants responded to the question. Total

participant data (N = 41) are represented in numerical and percentage terms. Twenty-six

103

(63.4%) were general education. Fifteen (36.6%) participants were special education

participants. Four (9.8%) participants were general education teachers within the

kindergarten to grade five grade span (n = 4) and are represented in numerical and

percentage terms. Two (4.9%) participants were special education teachers within the

kindergarten to grade five grade span (n = 2) and is represented in numerical and

percentage terms. There was no statistical difference noted in the data between the

general education and special education participant roles across the kindergarten through

grade five grade span.

The researcher continued examining the frequency of computer-based assignment

participant data by grade span. All 41 participants responded to the question. Total

participant data (N = 41) is represented in numerical and percentage terms. Twenty-six

(63.4%) were general education. Fifteen (36.6%) participants were special education

participants. Table 4.31 summarizes the findings for the grade six to grade eight span. Six

(14.6%) participants were general education teachers within the grade six to eight grade

span (n = 6) and are represented in numerical and percentage terms. Two (4.9%)

participants were special education teachers within the grade six to grade eight span (n =

2) and are represented in numerical and percentage terms. Data indicated that general

education participants within the grades sixth through eighth grade span utilized

computer-based activities more frequently than special education participants within the

same grade span. The two most frequent computer-based activities for both roles in

education within the grade six to grade eight span were “operating a computer (savings

files, printing, etc.)” and “writing documents with word processor (typing, editing,

layout, etc.).”

104

Table 4.31

Computer-Based Assignments Frequency by Grade Six to Eight Grade Span

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency

Assignments

(100%)

General Education

(14.6%)

Operating a Computer

(Saving Files, Printing,

etc.)

(0%)

(33.3%)

(66.7%)

Writing Documents with

Word Processor (Typing,

Editing, Layout, etc.)

(0%)

(16.7%)

(66.7%)

Making Illustrations with

Graphical Programs

(0%)

(83.3%)

(0%)

(16.7%)

Calculating with

Spreadsheet Programs

(Sheet Creation, Use of

Formulas, Organizing

Information)

(16.7%)

(66.7%)

(0%)

(16.7%)

Writing Programs (e.g.

Logo, Pascal, etc.)

(50%)

(16.7%)

(33.3%)

(%)

Communicating via e-mail

with Teachers and Other

Students

(0%)

(16.7%)

(33.3%)

(50%)

Sending, Searching For,

and Using Electronic Forms

of Information

(0%)

(16.7%)

(33.3%)

(50%)

Using Educational

Software (e.g. Taking

Tests, Exercises, etc.)

(0%)

(33.3%)

(66.7%)

Special Education

(4.9%)

Operating a Computer

(Saving Files, Printing,

etc.)

(50%)

(0%)

(50%)

Writing Documents with

Word Processor (Typing,

Editing, Layout, etc.)

(50%)

(0%)

(50%)

Making Illustrations with

Graphical Programs

(50%)

(0%)

Calculating with

Spreadsheet Programs

(Sheet Creation, Use of

Formulas, Organizing

Information)

(100%)

(0%)

Writing Programs (e.g.

Logo, Pascal, etc.)

(50%)

(0%)

(50%)

Communicating via e-mail

with Teachers and Other

Students

(0%)

(100%)

(0%)

Sending, Searching For,

and Using Electronic Forms

of Information

(50%)

(0%)

Using Educational

Software (e.g. Taking

Tests, Exercises, etc.)

(0%)

(50%)

(0%)

(50%)

Note. N = 41. n = 6 for general education participants and n = 2 for special education participants. Percentages may not add to 100%

due to rounding.

105

The researcher continued examining the frequency of computer-based assignment

participant data by grade span. All 41 participants responded to the question. Total

participant data (N = 41) is represented in numerical and percentage terms. Twenty-six

(63.4%) were general education. Fifteen (36.6%) participants were special education

participants. Table 4.32 summarizes the findings for the grade nine to grade 12 span.

Twelve (29.3%) participants were general education teachers within the grade nine to 12

grade span (n = 12) and are represented in numerical and percentage terms. Ten (24.4%)

participants were special education teachers within the grade nine to grade 12 span (n =

10) and are represented in numerical and percentage terms. Data indicated that both

general education participants and special education participants utilized “operating a

computer (saving files, printing, etc.)” and “word processor (typing, editing, layout, etc.)

more frequently within the grade nine to grade 12 span. “Communicating via e-mail with

teachers and other students” was utilized by both general education and special education

participants. Special education participants utilized “using educational software (e.g.

taking tests, exercises, etc.)” more frequently within the grade nine to grade 12 span.

106

Table 4.32

Computer-Based Assignments Frequency by Grade Nine to 12 Grade Span

Never

Monthly

Weekly

Daily

Variables

Total Responses

Computer-based Activities Frequency Assignments

(100%)

General Education

(29.3%)

Operating a Computer

(Saving Files, Printing,

etc.)

(0%)

(16.7%)

(66.7%)

Writing Documents with

Word Processor (Typing,

Editing, Layout, etc.)

(0%)

(33.3%)

Making Illustrations with

Graphical Programs

(8.3%)

(41.7%)

(8.3%)

Calculating with

Spreadsheet Programs

(Sheet Creation, Use of

Formulas, Organizing

Information)

(41.7%)

(8.3%)

Writing Programs (e.g.

Logo, Pascal, etc.)

(75%)

(16.7%)

(8.3%)

(0%)

Communicating via e-mail

with Teachers and Other

Students

(0%)

(8.3%)

(25%)

(66.7%)

Sending, Searching For,

and Using Electronic Forms

of Information

(0%)

(25%)

(41.7%)

(33.3%)

Using Educational

Software (e.g. Taking Tests,

Exercises, etc.)

(0%)

(25%)

(33.3%)

(41.7%)

Special Education

(24.4%)

Operating a Computer

(Saving Files, Printing,

etc.)

(0%)

(20%)

(30%)

(50%)

Writing Documents with

Word Processor (Typing,

Editing, Layout, etc.)

(10%)

(20%)

(50%)

Making Illustrations with

Graphical Programs

(30%)

(20%)

Calculating with

Spreadsheet Programs

(Sheet Creation, Use of

Formulas, Organizing

Information)

(40%)

(20%)

Writing Programs (e.g.

Logo, Pascal, etc.)

(50%)

(20%)

(10%)

(20%)

Communicating via e-mail

with Teachers and Other

Students

(0%)

(10%)

(20%)

(70%)

Sending, Searching For,

and Using Electronic Forms

of Information

(0%)

(20%)

(30%)

(50%)

Using Educational

Software (e.g. Taking Tests,

Exercises, etc.)

(0%)

(30%)

(70%)

Note. N = 41. n = 12 for general education participants and n = 10 for special education participants. Percentages may not add to 100%

due to rounding.

107

The researcher continued examining the frequency of computer-based assignment

participant data by grade span. All 41 participants responded to the question. Total

participant data (N = 41) is represented in numerical and percentage terms. Twenty-six

(63.4%) were general education. Fifteen (36.6%) participants were special education

participants. Four (9.8%) participants were general education teachers within “other”

grade span (n = 4) and are represented in numerical and percentage terms. One (2.4%)

participant was a special education teacher within the “other” grade span (n = 1) and are

represented in numerical and percentage terms. There was no statistical difference noted

in the data between the general education and special education participant roles across

the “other” grade span.

Google Form survey question 31 asked participants to list any additional

comments regarding computer-based learning for students identified with disabilities.

Two (14.3%) comments out of 14 responses were relevant to accommodations/

modifications and research question two. One (2.4%) participant out of 41 commented on

a need for further use of the applications for reading text to students. One (2.4%)

participant out of 41 commented they have created all accommodations and

modifications themselves as a classroom teacher through Schoology.

Interview and Open-Ended Survey Responses. The fourth interview question

asked for participants to share the computer-based strategies they utilize while instructing

students identified with disabilities. Four (9.8%) out of 41 participants responded with

nine (100%) strategies. The most common strategy was “team-based” learning/

collaboration, which received two (22.2%) of the comments from participants. Seven

(77.8%) comments each received one comment which did not demonstrate any statistical

108

significance to the research. These were: “strategic on when to use computers,” “online

textbook that is read aloud,” “enlarge text,” “amplify sound,” “split screening,” “seat

location,” and “speech to text,”

The researcher compared the Google Form survey open ended response and

interview question four responses and found common themes. Table 4.33 summarizes the

findings. All four (100%) interview participants identified various elements of one major

theme: (2) Implemented Adaptations. A total of 337 responses/ comments were made

related to accommodations/ modifications from the 41 participants. Total participant data

(N = 337) is represented in numerical and percentage terms. Data indicated a total of 120

(34.7%) presentation accommodation/ modification references to the theme were found,

with 17 (4.9%) additional presentation accommodations/ modifications were mentioned

within the “other” responses, 94 (27.2%) response accommodation/ modification

references were found, with nine (2.6%) additional response accommodations/

modifications mentioned within the “other” responses, and 93 (26.9%) timing

accommodation /modification references found, with four (1.2%) additional timing

accommodations/ modifications mentioned within the “other” responses. Participants

responded with nine (2.6%) comments related to accommodations/ modifications. Total

participant data (N = 346) is represented in numerical and percentage terms. Interview

participant data (n = 9) is represented in numerical and percentage terms.

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Table 4.33

Responses from Research Question Two Relevant to Theme

9-12A

9-12B

9-12C

K-6A

Variables

Total Responses

Themes

Implemented Adaptations

346

(100%)

Presentation

120

(34.7%)

Presentation “Other”

(4.9%)

Response

(27.2%)

Response “Other”

(2.6%)

Timing

(26.9%)

Timing “Other”

(1.2%)

Interview

(2.6%)

Per Participant

(11.1%)

(44.4%)

(22.2%)

Note. N = 346. n = 9 for Interview Question Four Responses per Participant. Percentages

may not add to 100% due to rounding.

Research Question Three. What are teachers’ perceptions on the

implementation of professional development for computer based/ remote instruction

for students with disabilities? The purpose of research question three was to identify

teachers’ perception on the implementation of professional development for computer-

based/remote instruction for students identified with disabilities. Three survey questions

corresponded with research question three. Google From survey questions 18, 29, and 30.

Google Form survey question number 31 may also answer research question three

dependent on the response provided. The interview included two questions that respond

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and elaborate on research question three of the study, as well as the possibility of

responses from interview question seven dependent on response.

Survey Responses. Google Form survey question 18 was already discussed

within research question one. Survey question 29 asked participants if they received any

professional development or training while implementing computer-based instruction

within the last three years. All 41 participants responded to Google Form survey question

29. Thirty-two (78%) participants responded that they had received professional

development, whereas nine (22%) responded they had not.

The researcher further examined the professional development while

implementing computer-based instruction data by participant current role and grade span.

Forty-one participants answered Google Form survey question number 18. Total

participant data (N = 41) is represented in numerical and percentage terms. Twenty-six

general education participants responded (n = 26) and are represented in numerical and

percentage terms. Fifteen special education participants responded (n = 15) and are

represented in numerical and percentage terms. Table 4.34 summarizes the findings.

There was no significant difference found between general education and special

education participants. There was more professional development noted in the grade nine

through grade 12 span.

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Table 4.34

Professional Development While Implementing Computer-Based Instruction by Current

Role and Grade Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Professional Development While

Implementing Computer-Based

Instruction

(100%)

Current Role in Education

General Education Teachers

(63.4%)

Yes 19

(73.1%)

(7.7%)

(19.2%)

(38.5%)

(7.7%)

No 7

(26.9%)

(7.7%)

(3.8%)

(7.7%)

Current Role in Education

Special Education Teachers

(36.6%)

Yes. 13

(86.7%)

(6.7%)

(13.3%)

(66.7%)

(0%)

No. 2

(13.3%)

(6.7%)

(0%)

(6.7%)

Note. N = 41. n = 26 for general education participants and n = 15 for special education

participants. Percentages may not add to 100% due to rounding.

Google Form survey question 30 asked participants what additional professional

development they need for success. All 41 participants responded with at least one

comment. One participant made two comments. Total participant data (N = 42) is

represented in numerical and percentage terms. Seven (16.7%) participants noted they

required no additional professional development, seven (16.7%) participants noted they

need “updates with apps and systems as they improve, update, or become outdated,” five

(11.9%) participants noted a need for “time to create and modify assignments,” three

(7.1%) participants noted a need for additional professional development to be “linked

with documentation of disabilities and ways to accommodate,” two (4.8%) participants

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noted a need for additional professional development on the “use of Schoology features,”

and two (4.8%) participants noted a need for professional development “for instructing

students with disabilities.” It should be noted that 16 responses received one comment

each. These included: “more awareness of programs available,” “gamification,” “subject-

related for developing computer-based instruction,” “tech support,” “communication with

stakeholders regarding student needs,” “trial time before implementation,” “AIMsweb

implementation,” “reading comprehension programs,” “managing online content,”

“intervention programs,” “handwritten vs digital,” “collaboration with colleagues,”

“feature training for software,” “discovery alt. text,” “regular education program training

for special educators,” and “personalized virtual learning.”

The researcher further examined the professional development needs from the

comments from the Google Form survey question 30 by participant’s current role in

education, as well as grade span. All 41 participants responded with at least one

comment. One participant gave two comments. Total professional development needed

for success comments (N= 42) is represented in both numerical and percentage terms.

Twenty-seven (64.3%) comments were provided by the general education participants (n

= 27), whereas 15 (35.7%) comments were provided by the special education participants

(n= 15), which each are represented in both numerical and percentage terms. A summary

of the findings is found in Table 4.35. Professional development needs in relation to

“students with disabilities” was noted within the special education participant data only.

There were no other differences noted in the data between the general education and

special education participant roles nor across the different grade spans.

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Table 4.35

Additional Professional Development Needed for Success by Current Role and Grade

Span

K- 5

Grade Span

6-8

Grade Span

9-12

Grade Span

“Other”

Grade Span

Variables

Total Responses

Professional Development Needed for

Success Comments

(100%)

Current Role in Education

General Education Teachers

(64.3%)

None

(3.7%)

Updates with Apps and

Systems as Improve,

Update, or Outdated

(0%)

(3.7%)

(11.1%)

(0%)

Time to Create and Modify

Assignments

(3.7%)

(7.4%)

(3.7%)

Linked Documentation of

Disability and Ways to

Accommodate

(0%)

(3.7%)

(0%)

Use of Schoology Features

(3.7%)

(%)

(0%)

For Students with

Disabilities

(0%)

Other

(3.7%)

(25.9%)

(7.4%)

Current Role in Education

Special Education Teachers

(35.7%)

None

(0%)

(13.3%)

(6.7%)

Updates with Apps and

Systems as Improve,

Update, or Outdated

(6.7%)

(0%)

(13.3%)

(0%)

Time to Create and Modify

Assignments

(0%)

Linked Documentation of

Disability and Ways to

Accommodate

(0%)

(6.7%)

(0%)

Use of Schoology Features

(0%)

For Students with

Disabilities

(6.7%)

(0%)

(6.7%)

(0%)

Other

(0%)

(6.7%)

(26.7%)

(0%)

Note. N = 42. Participants could make more than one comment. Percentages may not add to 100% due to multiple

responses.

Survey question 31 asked participants to share any other comments on computer-

based/remote learning for students identified with disabilities. One (2.4%) participant

commented that they require more training on computer-based instruction even for

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supplemental use. Total participant data (N = 41) is represented in numerical and

percentage terms.

Interview and Open-Ended Survey Responses. The second interview question

asked participants to share the professional development provided prior to instructing

through computer-based learning. All four participants responded to the question and

stated eight (100%) different professional development comments. Two (25%) comments

noted having received “none/zero,” two (25%) comments noted receiving “self-practice

on their own,” and two (25%) comments noted receiving “independent professional

development,” which was found outside of the district offerings. One (12.5%) participant

noted receiving “mandated training on specific programs provided” and one (12.5%)

participant noted receiving “mini sessions.”

The sixth interview question asked participants to share suggestions towards

professional development and training to improve computer-based instruction for

students with disabilities. All four participants provided at least one suggestion. The four

participants made 15 comments in relation to professional development suggestions. Four

(26.7%) participants suggested a need for “more exposure to different programs,” two

(13.3%) participants suggested a need for “tech sessions related to just disabilities. Nine

(60%) professional development suggestions received one (6.7%) participant comment.

These include: “small trainings,” “refreshers at the beginning of the year,” “make it

relevant to your current class,” “tech sessions from special educators for general

educators,” “top ten apps sessions,” “more needs done in general,” “collaborations with

districts/ IU,” “sessions on appropriateness (for students) of technology pieces,” and

“cheat sheet that matches a disability with programs/ application options.”

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The seventh interview question asked for any additional comments relevant to the

implementation of computer-based learning for students with disabilities. Multiple

responses were related to research question three in relation to professional development.

The four interview participants made seven comments in relation to professional

development within their question seven responses. The most frequent professional

development suggestion was “be realistic and introduce, but just test it for a while,”

which received two (28.6%) participant suggestions. Five (71.4%) additional suggestions

were made each with one (14.3%) comment. These included: “when we introduce a new

update, you must ensure the tech is there,” “tech troubleshooting for when it is down,”

“we need to learn more, implement more,” “try to be proactive and ready for the future of

technology,” and “we need some realism on how technology is assisting versus

replacing.”

The researcher compared the Google Form survey and interview questions two,

six, and seven responses in relation to the third research question and found common

themes. Table 4.36 summarizes the findings. All four (100%) interview participants

identified various elements of one major theme: (3) Latent and Emerging Professional

Development Insights. A total of 37 interview responses/comments were made related to

professional development from the 41 participants. An additional 42 responses from

Google Form survey question 30 also were related to emergent professional development

insight. Total participant data (N = 79) is represented in numerical and percentage terms.

The 79 (100%) responses were then divided into two subthemes: (1) Latent Professional

Development Insights and (2) Emergent Professional Development Insights. Data

indicated a total of 23 (62.2%) participant references were related to the subtheme of

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latent professional development insight. Total latent professional development participant

data (n = 56) is represented in numerical and percentage terms. Fourteen (17.7%)

participant interview references were related to the subtheme of emergent professional

development insight, as well as an additional 42 (53.2%) participant references from

Google Form survey question 30 for a total of 56 (70.9%) latent professional

development participant responses. Total emergent professional development participant

data (n = 56) is represented in numerical and percentage terms. The latent and emergent

professional development responses were also broken down by participant responses as

shown in Table 4.36.

Table 4.36

Responses from Research Question Three Relevant to Theme

9-12A

9-12B

9-12C

K-6A

Variables

Total Responses

Themes

Latent and Emergent

Professional

Development Insights

(100%)

Latent

(29.1%)

Per Participant

(30.4%)

(13.0%)

(26.1%)

(30.4%)

Emergent

(70.9%)

Per Participant.

(17.7%)

(42.9%)

(21.4%)

(14.3%)

Google Form Survey

Question Thirty

Responses

(53.2%)

Note. N = 79. n = 23 for Latent Reponses, n = 14 for Emergent Responses, n = 42 for

Google Form Survey Question 30 Responses. Percentages may not add to 100% due to

rounding.

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Conclusion

The purpose of this qualitative study was to identify teachers’ perspectives on the

implementation of computer-based/ remote instruction for students with disabilities post-

pandemic. The study took place in south central Pennsylvania. Three suburban school

districts participated in the study. Forty-one certified public-school teachers participated

in the Google Form survey and four volunteered for a one-on-one Zoom interview. The

Google Form survey consisted of 31 mixed-type questions. The interview consisted of a

demographic introduction and seven researcher-created, open-ended response questions.

The interviews were recorded and transcribed through the Zoom application. The

transcriptions of the interviews, as well as open-ended survey responses were then

uploaded into the NVivo for analysis to remove researcher bias. NVivo identified

common themes to the participant responses. The survey and interview data were coded

for themes to answer the three research questions. The combination of the data from the

Google Form survey and the information collected from the interviews identified

common perceptions among the participants of the study relevant to the implementation

of post-pandemic, computer-based learning within the three south central suburban

Pennsylvania public schools. The data presented answered the three research questions of

the study. These findings and implications are discussed in Chapter Five. Chapter Five

also includes the limitations of the study, relationship to other research, as well as

recommendations for future research.

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Chapter Five – Discussion

Summary of the Study

This qualitative study sought to ascertain the perceptions of teachers on the

implementation of computer-based/ remote instruction for students with disabilities post-

pandemic. With an increase in computer-based/ remote instruction for all students,

teachers have been presented with the increased complexity of providing instruction to

meet the needs of students identified with disabilities. Historically, teachers have felt

inadequately prepared for these challenges (Basham et al., 2015). This study examined

teacher attitudes toward the implementation of computer-based/ remote instruction for

students with disabilities. The study focused on public school teachers instructing grades

kindergarten through twelfth grade in south central Pennsylvania. The study focused on

the following three post-pandemic school years, 2020-2021, 2021-2022, and 2022-2023.

Teacher perspectives of the implementation of computer-based/ remote instruction

provided an increased awareness of the online learning experiences of teachers. These

insights may be used to improve the online learning experiences for all. Data from the

study may lead to future professional development opportunities and increased equity for

all students.

This qualitative study was directed by three research questions. Research question

one examined teacher perceptions on the implementation of computer-based/ remote

instruction for students with disabilities. Research question two identified strategies that

teachers utilize to instruct students with disabilities on a computer-based/ remote

platform. Research question three analyzed teacher perceptions on the implementation of

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professional development for computer-based/ remote instruction or students identified

with disabilities.

Data were collected from south central Pennsylvania public-school teachers who

have instructed students through a computer-based/ remote instruction platform, post-

pandemic within the last three school years. Data was collected from general education

and special education teachers currently teaching grades kindergarten through twelfth.

Forty-one teachers participated through a Google Form mixed-type question survey. For

this study, specialist area teachers (art, music, etc.) were categorized as general education

teachers. Twenty-six general education teachers participated, as well as 15 special

education teachers. Five volunteered for a Zoom interview with the researcher. One of

those volunteers did not meet the interview requirements, therefore that data was not

included in the study. The interview consisted of an introduction which acquired

demographic information, as well as seven researcher-created open-ended responses

connected to the three research questions of the study. These interview questions were

created to allow for interview volunteers to expand, clarify, or enhance their Google

Form survey responses. The Zoom interviews were recorded and transcribed through the

Zoom program. The Google Form survey open-ended responses and interview transcripts

were then uploaded to NVivo for analysis. The data were coded, organized, and analyzed

according to themes identified in relation to the three research questions.

Summary of the Results

This study intended to highlight general education and special education teachers’

perspective of the implementation of computer-based/ remote instruction for students

identified with disabilities post-COVID-19 pandemic. Three research questions framed

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the research on understanding teacher perspectives and needs in relation to the

implementation of computer-based/ remote instruction for students with disabilities. Data

were collected and analyzed through a researcher created mixed-type question Google

Form survey, as well as researcher created open-ended interview questions.

Research Question One. What are teachers’ perceptions on the

implementation of computer based/remote instruction for students with disabilities?

Research question one sought to determine teacher attitudes toward the implementation

of computer-based/ remote instruction for students identified with disabilities post-

pandemic. Teachers who participated in the survey were currently employed kindergarten

through twelfth grade teachers from three south central Pennsylvania public school

districts. Twelve (five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen,

fifteen, and eighteen) Google Form survey questions corresponded to research question

one. Google Form survey question number thirty-one may also answer research question

one dependent on the response provided. All 41 participants responded to the Google

Form survey questions. The interview included three questions (one, three, and five) that

respond and elaborate on research question one of the study, as well as the possibility of

responses from interview question seven dependent on response. Analysis of the data

collected from the participants helped the researcher draw several conclusions.

Data yielded from the Google Form survey responses indicated that the average

number of students instructed through computer-based/ remote instruction was 82 for the

2020-2021 school year and 21.6 (25.3%) were students identified with disabilities.

Responses indicated a reduced average of 63.8 students taught through computer-based/

remote instruction during the 2021-2022 school year, whereas the average number of

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students identified with disabilities was 12.7 (19.9%). Data indicated computer-based/

remote instruction continued to decrease during the 2022-2023 school year to an average

of 58.4 students with an average of 11.9 (20.4%) identified with disabilities. 95.1% of

participants were aware of the disability category of the students instructed. 100% of the

special education participants knew the disability categories of the students instructed.

During the 2020-2021 school year, 39 (95%) participants utilized computer-based/

remote instruction in some way, 37 (90%)) participants utilized computer-based/ remote

instruction during the 2021-2022 school year, and 33 (80%) participants utilized

computer-based/ remote instruction in some way during the 2022-2023 school year. The

researcher noted a decline in the use of computer-based/ remote instruction over the

three-year timeframe post-pandemic of this study. The data indicated that overall, the

upper grade spans (grades six through eight and grades nine through 12) utilized

computer-based learning more frequently than the lower grade spans (kindergarten

through grade five). General education grade span kindergarten through grade five

displayed an overall reduction of time spent utilizing computer-based instruction over the

three-year time span. General education grade span six through eight and nine through 12

displayed a steadier use of computer-based instruction over the three-year time span.

Special education grade span data demonstrated a reduction of computer-based

instruction over the three-year time span. The largest reduction was between the 2021-

2022 and 2022-2023 school years. The 2020-2021 school year involved school closure in

response to the global pandemic. Schools began reopening and offering hybrid

opportunities within the 2021-2022 school year. By the 2022-2023 school year, most

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schools had reopened and returned to the traditional instructional model with in-person

education.

The Google Form survey also asked participants to note if they had received

professional development prior to starting instruction through computer-based learning.

68.2% stated they had received professional development prior to starting computer-

based instruction, whereas 31.7% stated they had not. There was no difference noted in

the data between the general education and special education participant roles nor across

the different grade spans.

Data yielded from the interview responses indicated that each participant was able

to respond with at least one experience and challenge of computer-based learning for

students identified with disabilities. Twenty-two comments were noted for experiences of

computer-based/ remote instruction and thirty-nine comments were noted as challenges

of computer-based/ remote instruction. Thirty-eight (44.2%) comments referenced the

impact of computer-based learning on students. Seventeen (44.7%) comments referenced

positive impacts on students, whereas 21 (55.3%) referenced negative impacts on

students. Twenty-eight (32.6%) comments referenced the impact of computer-based

learning on teachers. Eight (28.6%) comments referenced positive impacts on teachers,

whereas 20 (71.4%) referenced negative impacts on teachers. The researcher analyzed the

comments provided into themes. Data from the interview responses also indicated a total

of 19 accommodation and modification comments were made for students instructed

through computer-based/ remote instruction.

Further analysis of the Google Form survey responses and interview responses

identified one major theme within the responses from research question one: (1) the

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impact of computer-based/ remote learning. This major theme was then divided into two

subthemes: (1) the impact on students and (2) the impact on teachers. Each of these

subthemes was then broken down into further subthemes: (1) positive impacts and (2)

negative impacts.

In summary, participants responded through experiences and challenges to

computer-based/ remote instruction. Overall, there was not a drastic difference between

the impact on students (44.2%) and teachers (32.6%). There were however more

responses in relation to the negative impacts on each respectively. 55.3% of the impact

statements toward students were negative in nature, whereas 71.4% of the impact

statements toward teachers were negative. Overall, the data indicated that the impact of

computer-based/ remote instruction was negative in nature.

Research Question Two. What strategies do educators use when teaching

students with disabilities on a computer based/ remote platform? Research question

two sought to identify what strategies teachers are using when instructing students

identified with disabilities through computer-based/ remote instruction. Google Form

survey had 12 questions (16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, and 28) that were

created to provoke responses to the second research question. Responses from Google

Form survey question number 31 may also provide information on the second research

question dependent on response. All 41 participants responded to the Google Form

survey questions. The interview included one question (four) that was created to provide

additional data in response to research question two of the study. Interview question

seven may also provide data in relation to research question two of the study response

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dependent. Analysis of the data collected from the participants helped the researcher

draw several conclusions.

Data yielded from the Google Form survey indicated 97.6% of participants have

“one-on-one” computers or iPads for use of computer-based/ remote instruction since the

fall of 2020. The one participant that selected “no” to having “one-on-one” computers or

iPads did note that they were provided classroom computers. When analyzed by current

role of education and grade span, data indicated that all general education participants

were provided “one-on-one” iPads or computers, whereas one (2.4%) special education

participant within the grade nine through grade 12 span was not but had a classroom

computer provided. Data for computer-based/ remote instruction on an educational

platform or programming indicated that 66 different responses were provided by the

participants. 31.8% utilize Schoology, 10.6% utilize Seesaw, 9.1% utilize Edgenuity, and

9.1% utilize Google Classroom. 98% of participants noted that their current educational

platform or program has the capacity to accommodate students identified with

disabilities. Data analyzed by current role and grade span indicated that both general

education participants and special education participants utilized Schoology the most

frequently in the upper grade spans. Data also indicated that Seesaw was the most

frequently utilized educational platform in the kindergarten through grade five grade

span. Google Classroom and Google programs were also frequently used educational

platforms within the general education grade six to eight grade span.

Data analyzed by current role in education and grade span indicated that general

education and special education participants noted that the capacity for their district’s

educational platform to accommodate was overall higher with the upper grade level span

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of grades nine through 12. A higher percentage of special education participants (100%)

noted the ability for a district’s educational platform to accommodate than general

education participants (96.2%).

The Google Form survey asked participants about current presentation

accommodations and modifications provided. Participants made 120 different

presentation accommodations/ modifications responses with an additional 17 “other”

presentation accommodations/ modifications. Data analyzed by current role in education

and grade span noted that the grade span with the most frequent use of presentation

accommodations/ modifications was grades nine through 12. There was no significant

difference found between general education and special education participants

investigated.

The Google Form survey asked participants to note response accommodations/

modifications currently provided. Participants made 94 different response

accommodations/ modifications, as well as an additional nine “other” response

accommodations/ modifications. Data analyzed by current role in education and grade

span indicated that within both the general education and special education participant

responses, the most response accommodations/ modifications were found in the grade

nine through grade 12 span, whereas the least response accommodations/ modifications

were found in the “other” grade span. The most common response accommodations

overall were “voice to text,” “multiple choice,” and “word bank.”

The Google Form survey asked participants to note current timing

accommodations/ modifications available. Participants noted 93 different timing

accommodations/ modifications currently available, with an additional four “other”

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timing accommodations and modifications. The most common timing accommodation

overall was “extended time” with “breaks” following in second. Data analyzed by current

role in education and grade span indicated that extended time and unlimited time are the

most frequently used timing accommodations/ modifications for both the general

education and special education participants. There was no significant difference found

between general education and special education grade level spans investigated.

The Google Form survey asked about computer-based activities and assignments

frequency. The most common computer-based activity daily was “allowing students to

learn/work at their own pace during lessons.” The most frequent computer-based activity

weekly was “provide additional instruction and practice opportunities.” The most

frequent monthly computer-based activity was to “create opportunities for learning by

simulation (real-world simulation programs).” The least used computer-based activity

was “multidisciplinary approach (combine subjects).” Data analyzed by current role in

education noted one of the most common computer-based activity was to “provide

additional instruction and practice opportunities” for both general education and special

education participants. “Learn/ work at their own pace during lessons” was tied for the

most common computer-based activity for the special education participants. General

education participants noted “obtaining information from the internet” as their top

computer-based activity. General education participants also noted “developing skills of

independent learning” as a common computer-based activity. Special education

participants noted “obtaining information from the internet” as a common computer-

based activity as well. Data analyzed through grade span noted no statistical significance

within the grade kindergarten through grade five grade span. Data within the grade six

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through grade eight span noted within both roles in education, “developing skills of

independent learning,” “providing additional instruction and practice opportunities,” and

“allow students to learn/ work at their own pace during lessons” increased in frequency.

Data within the grade nine through grade 12 span indicated that general education

participants utilized all the different activities at a higher frequency than the special

education participants. The largest difference noted between the general education

participant data and the special education participants is within the frequency of “learn/

work at their own pace during lessons.” Special education participants noted a 60% daily

frequency, whereas the general education participants noted 41.7% daily frequency.

Within the “other” grade span, there was no statistical difference noted in the data

between the general education and special education participant roles, with exception of

more frequency of use within general education participants due to larger sample size

reported.

The most frequent daily computer-based assignment was “using educational

software (e.g. taking tests, exercises, etc.).” The most frequent weekly computer-based

assignment was tied between “operating a computer (saving files, printing, etc.),”

“writing documents with a word processor,” and “sending, searching for, and using

electronic forms of information.” The most frequent monthly computer-based assignment

is “making illustrations with graphical programs.” The least used computer-based activity

was “writing programs (e.g. Logo, Pascal, etc.).” Data analyzed by current role in

education as well as grade spans were noted. There was no statistical difference noted in

the data between the general education and special education participant roles across the

kindergarten through grade five grade span. Data from the grade six to grade eight span

128

indicated that general education participants utilized computer-based activities more

frequently than special education participants within the same grade span. The two most

frequent computer-based activities for both roles in education within the grade six to

grade eight span were “operating a computer (savings files, printing, etc.)” and “writing

documents with word processor (typing, editing, layout, etc.).” Data within the grade nine

to grade 12 span indicated that both general education participants and special education

participants utilized “operating a computer (saving files, printing, etc.)” and “word

processor (typing, editing, layout, etc.) more frequently. “Communicating via e-mail with

teachers and other students” was utilized by both general education and special education

participants. Special education participants utilized “using educational software (e.g.

taking tests, exercises, etc.)” more frequently within the grade nine to grade 12 span.

There was no statistical difference noted in the data between the general education and

special education participant roles across the “other” grade span.

Data yielded from the interview responses indicated that nine of the comments

reference strategies utilized for students identified with disabilities through computer-

based instruction. All four interview participants responded to the question. The most

frequent comment was “team-based/ collaborative learning.” The researcher analyzed

the comments provided into themes.

In summary, participants responded about the accommodations, modifications,

and strategies utilized during computer-based/ remote instruction. Overall,

accommodations, modifications, and strategies were utilized by each of the 41

participants during computer-based/ remote instruction to meet their students’ needs.

Further analysis of the Google Form survey and interview responses identified one major

129

theme within the responses from research question two: (2) implemented adaptations. A

total of 337 responses/ comments connected to research question two.

Research Question Three. What are teachers’ perceptions on the

implementation of professional development for computer based/ remote instruction

for students with disabilities? Research question three sought to identify teachers’

perspectives on the implementation of professional development for computer-based/

remote instruction for students with disabilities. The Google Form survey had three

questions (eighteen- also answers research question one, twenty-nine, and thirty)

designed to prompt the third research question. Google Form survey question number 31

may also provide information regarding the third research question response dependent.

All 41 participants responded to the Google Form survey questions. The interview had

two questions (two and six) that were created to provide additional evidence and data on

research question three of the study. All four interview participants answered the

questions. Interview question seven may also provide additional data in relation to

research question three of the study response dependent. Analysis of the data collected

from the participants helped the researcher draw several conclusions.

Data yielded from the Google Form survey indicated 78% of participants were

provided professional development while implementing computer-based/ remote

instruction, whereas 22% were not. The Google Form survey also asked participants for

additional comments on professional development needed for success with computer-

based/ remote instruction. Forty-two additional comments were made. The most frequent

comments were “none,” “updates with apps and systems to improve, update, or inform of

outdated technology,” and “time to create and modify assignments for computer-based/

130

remote instruction.” Professional development needs in relation to “students with

disabilities” was noted within the special education participant data only. There were no

other differences noted in the data between the general education and special education

participant roles nor across the different grade spans. One additional comment was left by

a participant to Google Survey question number 31. The participant would like more

training on computer-based instruction even for supplemental use.

Data yielded from the eight interview responses indicated two (25%) interview

participants did not receive any professional development prior to the start of computer-

based instruction, whereas two (25%) participants had “self-paced professional

development on their own,” and two (25%) completed “independent professional

development (not by the district).” Interview participants were asked for suggestions for

future professional development. Fifteen comments were made by the interview

participants. The most frequent responses were “more exposure to different programs”

and “tech sessions related just to disabilities.” Interview question seven asked for any

additional comments in relation to the implementation of computer-based/ remote

instruction for students with disabilities. The most frequent response was to “be realistic

and introduce, but just test it for a while.”

Further analysis of the Google Form survey and interview responses identified

one major theme within the responses from research question three: (3) latent and

emerging professional development insights. A total of 37 interview responses/comments

were made related to professional development from the 41 participants. An additional 42

responses from Google Form survey question thirty also were related to emergent

professional development insight. This major theme was then divided into two

131

subthemes: (1) latent professional development insight and (2) emergent professional

development insight. Data indicated a total of 23 (62.2%) participant references were

related to the subtheme of latent professional development insight. Fourteen (17.7%)

participant interview references were related to the subtheme of emergent professional

development insight, as well as an additional 42 (53.2%) participant references from

Google Form survey question thirty for a total of 56 (70.9%) latent professional

development participant responses.

In summary, participants provided 79 comments in relation to professional

development insight. 29.1% were in relation to latent professional development insight,

whereas 70.9% were in relation to emergent professional development insight. Overall,

participants provided valuable information on professional development provided prior

and during computer-based instruction. Participants also provided important insight on

future professional development required for success with computer-based/ remote

instruction for students identified with disabilities. This data will provide school districts

vital information on the professional development needs of teachers instructing students

identified with disabilities through computer-based/ remote instruction.

Limitations Found in the Study

This qualitative study utilized an online statement Google Form survey and Zoom

interviews with the researcher. The data was confined to the three public school districts

participating and their current teachers. Data was limited to general education and special

education teachers. Forty-one participants completed the Google Form survey and four

completed the voluntary Zoom interview. Data was limited also due to lack of middle

school participants within the interview process. The interview process only had general

132

education volunteers. The lack of special education interview data limits the results of the

study. Data was also limited to a small sample size of a specific geographic location of

south central Pennsylvania. The results cannot be generalized for the general education

and special education teachers of another geographic location. The data may also have

been limited due to the timing of the collection at the beginning of a school year.

Potential participants may not have responded based on beginning of the school year

priorities at the time of the study. Participant responses may also have bias based on the

individual experience teachers exposed to during the COVID-19 pandemic. Districts may

have placed different restrictions, resources, and support through remote instruction

during the COVID-19 pandemic and thereafter.

This study asked participants to provide their perception of the implementation of

computer-based/ remote instruction post-pandemic for students identified with

disabilities. The study was limited by the accuracy of the participants’ responses of self-

reporting the data. The study did not include directly from the students participating in

computer-based/ remote instruction identified with disabilities, who may have different

perspectives from teachers.

A final limitation is that the researcher of this study worked as a teacher within

one of the same public school districts where some of the participants taught. Although

the researcher did not work directly with any of the teachers at the time of the study,

participants may have wanted their responses to reflect negatively or positively on the

school district even though all responses were anonymous. It is important to note that the

researcher did not disperse the study information, nor have access to who received the

133

information, and has no financial interest through this research. The researcher also was

not in an influential role (supervisory, lead teacher, etc.) during this research study.

Relationship to the Research

This qualitative study conducted through a Google Form survey and researcher

created interviews acquired data to understand the perspectives of teachers on the

implementation of computer-based/ remote instruction for students identified with

disabilities. Key themes in this study emerged from the data collected and research on

this topic were factors that contributed to the teachers’ perspectives of the

implementation of computer-based/ remote instruction for students with disabilities.

These themes included the impact of computer-based learning for students and teachers,

implemented adaptations, and latent and emerging professional development insights.

With the growth of computer-based educational platforms, the quest of equitable

education for all has expanded (Tate & Warschauer, 2022). The Individuals with

Disabilities Education Act (IDEA) guarantees the right to a free and appropriate public

education (FAPE) for children with disabilities (Individuals with Disabilities Education

Act, 2004). IDEA and Chapter 14 require equitable services for students with disabilities

(Individuals with Disabilities Education Act, 2004). School districts are faced with the

challenge of how to provide accessible education services for all students including those

participating in computer-based instruction (Mason-Williams et al., 2020). The results of

this study indicated that even though computer-based/ remote instruction was on a

decline, an average of 58.4 students were enrolled within computer-based learning during

the 2022-2023 school year. Of those an average of 11.9 were diagnosed with a disability

during the 2022-2023 school year. The increase of computer-based learning throughout

134

recent years has presented increased complexities to instruction for those identified with

disabilities. “Teachers using the Internet as either the primary or sole medium of

interaction with students are additionally charged with implementing new pedagogical

strategies as part of the reconceptualization of teaching and learning” (Basham et al.,

2015, p.43). Crouse et al. (2018) found that research indicated that evidence-based

practices from traditional classroom instruction are not necessarily transferable to online

instruction. Crouse et al. found within their study that teachers “reported they had

received no direct preparation for teaching in the online environment” (p. 135). Like

Crouse et al., the results of this study indicated that 68.3% of participants received

professional development prior to the implementation of computer-based/ remote

instruction, while 31.7% of participants did not, which leads to a need for professional

development.

Basham et al. (2015) and Crouse et al. (2018) found that teachers felt they were

not adequately prepared to modify and adapt for students identified with disabilities while

utilizing computer-based learning. This study’s results indicated a need for future

professional development on time to create and modify assignments (11.9%) for

computer-based/ remote instruction, as well as for accessibility/ accommodations (7.1%).

Rice and Dykman (2018) noted that teachers felt they had “limited skills for helping

students persist in fully online learning beyond pacing guides” (p.200). Teachers felt a

deficit in time management skills needed to assist students beyond the given pacing

guides (Rice and Carter, 2015). Greer et al. (2014) noted that teachers may lack

confidence and may feel unprepared to teach students with disabilities within online

learning environments. Greer et al. continued in finding that teachers felt they lacked the

135

training needed to reach their potential within online learning. “Nearly two-thirds (64%)

of online teachers surveyed indicated that their highest need for professional development

was in how to meet the needs of students with special needs in online learning” (p. 152).

The interview data of this study indicated that 13.3% of participants require tech sessions

(professional development) related to just disabilities, whereas the Google Form survey

data indicated that 7.1% of participants require professional development linked to

disabilities and ways to appropriately accommodate. Similar to the study conducted by

Nambiar (2020) noted that providing teacher training including adequate technological

training was vital and should be a prerequisite for successful online course

implementation, this study found that 26.7% of participants require more exposure to

different programs and platforms.

Archambault and Kennedy (2014) noted that most professional development was

provided on accessibility and not on how to develop and implement online

accommodations. The study’s results indicate that 11.9% of participants require more

time to create, develop, and modify assignments for accessibility and accommodations

for computer-based/ remote instruction. It was noted that educators may require

professional development in computer-based instructional skills and teaching practices

(Greer et al., 2014). This study’s participants noted a need for professional development

on how to modify for specific disabilities or accommodations. Henrich et al. (2019)

found that further training was required for both special education and regular education

instructors to meet the growing need of online learners who were diagnosed with

disabilities. Henrich et al. continued by noting that having training in the online

educational platform for all instructors improved the outcomes for both teachers and

136

students. This study’s results indicated that many districts provided mandated technology

training prior to the implementation of computer-based/ remote instruction, but a need for

refreshers year to year that are directed to teacher’s current class lists. The study revealed

that many participants found independent (not provided from their school district)

professional development that met their current classroom needs. Tremmel et al. (2020)

concurred that teachers must receive training to become comfortable with the online

learning format. Similar to the study conducted by Martin et al. (2019), this study

affirmed that participants expressed the need to be able to run learning management

systems, have basic technological skills, as well as create and write in a technical manner

required for the online instruction. Pressley et al. (2021) noted that teachers who received

training and support using the district’s technology and instructional platforms were more

successful. Correspondingly, this study indicated the same results. Leech et al. (2020)

stated that professional development created for all teachers on the use of technology

provided an opportunity for teachers to feel more confident in their use of technology,

which provided higher quality and effective instruction for students, as well as increased

student learning and development. This study found that participants want to “try to be

more proactive and ready for the future of technology.” Martin et al. affirmed that

participants found that those who participated in professional development and engaged

in online learning opportunities were more likely to become expert online instructors.

Professional development fostered the acquisition of creativity, collaboration, critical

thinking, and problem-solving skills (Peimani & Kamelipour, 2021). Peimani and

Kamelipour noted a need for increased professional development and digital innovation.

137

The results of this study noted that participants want to “try more, learn more, and

implement more.”

Recommendations for Future Research

The goal of this qualitative study was to identify the perceptions of teachers on

the implementation of computer-based/ remote instruction for students identified with

disabilities post-COVID-19 pandemic. With an increase of online learning post-

pandemic, there is an increasing need to better understand the ways to improve access

and equity for all students, including those identified with disabilities. Teacher

perspectives are a unique component in understanding the current implementation,

benefits, and challenges to computer-based/ remote instruction for students identified

with disabilities. South central Pennsylvania public school teachers within three districts

provided data through mixed-type Google Form survey responses and researcher-created

structured interview responses conducted by the researcher. Based on the results of this

study, further research exploring the following dimensions of teacher perceptions of the

implementation of computer-based/ remote instruction is recommended.

The data collected in the present study consisted of only a small sample size of

three south central Pennsylvania public school district teachers. Additional research

concerning the perceptions of other school districts of geographic regions could provide

further valuable perspectives.

In addition to the small geographic location, participants in this study consisted of

only currently employed public school teachers from grades kindergarten to twelfth.

Additional research concerning the perception of non-public school teachers, including

those employed within cyber education, could provide valuable perspectives.

138

In addition to the perceptions of general education and special education teachers,

additional input from relevant school community members such as building principals,

school counselors, related service providers, parents, and students could provide valuable

perspectives.

This study focused on the past three school years post-COVID-19 pandemic

(2020-2021, 2021-2022, and 2022-2023). Additional research into the impact of the

COVID-19 pandemic on public education may prove useful in improving the

implementation of computer-based/ remote instruction for students identified with

disabilities.

This study identified a variety of different educational programs and platforms

being utilized for computer-based/ remote instruction either full-time or supplemental.

Study participants noted a need for additional professional development and training on

pairing disabilities with appropriate technology/ programs, accessibility features for

available programs/ platforms, training specific to instructing students with disabilities

via computer-based/ remote instruction, as well as general exposure to technology and

what is available for use. Additional research into the different educational programs and

platforms including the advantages and disadvantages may prove to be useful in

improving the overall experience on computer-based/ remote instruction for all students

including those identified with disabilities. Future research on the appropriateness of

programs/ platforms for specific disabilities would prove useful for those implementing

computer-based/ remote instruction either full-time or through supplemental use in the

future.

139

Conclusion

This qualitative study explored teacher perspectives of the implementation of

computer-based/ remote instruction for students identified with disabilities. The data for

the study were collected through a researcher-created, mixed-type question Google Form

survey and follow-up voluntary Zoom interviews consisting of researcher-created

structured open-ended responses. The data were analyzed surrounding the three research

questions.

The data collected provided insight into understanding teacher perceptions

regarding the implementation of computer-based/ remote instruction post-pandemic for

students identified with disabilities. The data collected also provided insight on teachers’

needs regarding the implementation of computer-based/ remote instruction. Results of the

study revealed that teachers have varying views on the implementation of computer-

based/ remote instruction for students identified with disabilities. General and special

education participants noted a similar reduction in the enrollment of those participating in

online learning. Data demonstrated a decrease in the percentage of computer-based

instruction throughout the three years noted within this study. Results indicated that most

participants (68.3%) received professional development prior to the implementation of

computer-based/ remote instruction, as well as during implementation (78%).

Additionally, teachers felt that their district had the ability to accommodate/ modify

within their district’s educational platform. Many teachers felt unprepared for computer-

based/ remote learning for students identified with disabilities. This included a need for

professional development of what accommodations/ modifications already exist within

140

their district’s choice educational platform. Teachers also noted that the online learning

environment may not be appropriate for all learners.

The overall results of this study revealed varying teacher perspectives on the

implementation of computer-based/ remote learning for students identified with

disabilities. The data presented a need for additional professional development relevant to

computer-based/ remote instruction for students with disabilities, as well as all learners.

Data presented in this study supports the need for professional development directly

related to online learning, accommodations/ modifications, and technological training on

their district’s educational platforms.

The research demonstrated that teachers wish for more professional development

directly related to online instruction for students with disabilities. This extension of

research offered insight into what the implementation of computer-based/ remote

instruction looks like to teachers of those identified with disabilities. The findings of the

study highlighted the importance of relevant professional development on the

implementation of computer-based/ remote instruction. By incorporating the

recommendations in this study, school leaders can better support teachers and students

through the challenges of computer-based/ remote learning and provide effecting training

and new standards on the preparation and implementation of online instruction for

students with disabilities. Educational leaders may facilitate appropriate training to better

meet the needs for success of all teachers and their students.

141

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Appendix A

IMMACULATA UNIVERSITY RESEARCH ETHICS REVIEW BOARD

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Appendix B

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Appendix C

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Appendix D

Interview Questions

Code for Interviewee:

Grade Taught: Subject Taught:

Years of Teaching Experience:

Years Teaching Through Computer-Based Learning:

Average Number of Students Taught in Last 3 Years (per year):

Average Number of Students Taught with an Identified Disability in the Last 3 Years (per

year):

Date:

Start Time: End Time:

1) What has your experience been within the last 3 years (post-pandemic) of

teaching students with disabilities through computer-based learning?

2) Please share the professional development you were provided prior to instructing

through computer-based learning.

3) Please share the accommodations and modifications that you utilize while

instructing students with disabilities through computer-based learning.

4) Please share what strategies you utilize for computer-based learning for students

with disabilities.

5) Please share the challenges of computer-based learning for students with

disabilities.

6) Please share your suggestions towards professional development and training to

improve computer-based instruction for students identified with disabilities.

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7) Please share any other comments relevant to the implementation of computer-

based learning.

179

Appendix E

Information Letter to Superintendents

Date: May 22nd 2023

Title of Project: Computer-Based Learning for Students with Disabilities:

Teacher Understanding and Needs Following the COVID-19 Pandemic

Student Researcher: Donna M. Freydlin

Immaculata University, Department of Education

(443-859-4550)

My name is Donna Freydlin and I am presently a graduate student in the Educational

Leadership Doctoral Program at Immaculata University. I am writing to request your

school district’s tentative participation with a research study titled, “COMPUTER-

BASED LEARNING FOR STUDENTS WITH DISABILITIES: TEACHER

UNDERSTANDING AND NEEDS FOLLOWING THE COVID-19 PANDEMIC.” I am

currently in the process of obtaining approval through Immaculata’s Research Ethics

Review Board. I have created a brief survey that asks questions regarding teachers’

experience with instructing students with disabilities through computer-based instruction,

as well as professional development provided. The purpose of this study is to examine

teacher perceptions of computer-based learning for students identified with disabilities.

This study intends to examine the professional development provided, accommodations

provided, and strategies utilized by computer-based instructors for students with

disabilities. There are no known or anticipated risks from participating in this study.

180

Upon approval from the Research Ethics Review Board, an additional letter of request

will be sent for your formal approval to conduct research within your school district.

By completing the online survey, teachers will have voluntarily agreed to participate as a

participant in this study, which will be carried out by the aforementioned researcher,

under the supervision of Dr. Laura Eisemann, faculty advisor. The participants have the

right to withdraw at any time in the process with no penalty by not completing the

survey, interview, or emailing the researcher that they are no longer interested in

participating. The researcher will then remove their data at that time. Please allow me to

thank you in advance for your cooperation. In the event that you need any additional

information regarding this research project, you may contact me at 443-859-4550 or

dfreydlin@mail.immaculata.edu. For questions about the rights of research participants,

you may contact the Chair of the Research Ethics Review Board, Dr. Marcia Parris at

610-647-4440 x3222 or mparris@immaculata.edu. Information about this study will be

available upon approval from the Research Ethics Review Board. All survey data will be

stored in a secure website database available only to me, the sole researcher. The data

collected during this study will be destroyed after five years.

The results will be reported in aggregated format and individual identities will not be

known. We know that time is valuable and limited; therefore, I sincerely appreciate your

district’s participation in this research study. If interested, I will forward you a copy of

my study when completed.

181

If you have any questions, please do not hesitate to contact me at

dfreydlin@mail.immaculata.edu or 443-859-4550, or the research advisor (Dr. Laura

Eisemann) at leisemann@immaculata.edu or 610-647-4400 ext. 1005.

If you agree to participate in my study, please respond by email to

dfreydlin@mail.immaculata.edu. I will reach out with a formal letter of approval to

conduct research upon my RERB approval.

Sincerely,

Donna M. Freydlin & Dr. Laura Eisemann, Faculty Advisor

182

Appendix F

Teacher Email Invitation

Dear Teachers,

My name is Donna Freydlin and I am presently a graduate student in the Educational

Leadership Doctoral Program at Immaculata University. I am writing to request your

participation with a research study titled, “COMPUTER-BASED LEARNING FOR

STUDENTS WITH DISABILITIES: TEACHER UNDERSTANDING AND NEEDS

FOLLOWING THE COVID-19 PANDEMIC”. I have created a brief survey that asks

questions regarding your experience with instructing students with disabilities through

computer-based instruction, as well as professional development provided. The purpose

of this study is to examine teacher perceptions of computer-based learning for students

identified with disabilities. This study intends to examine the professional development

provided, accommodations provided, and strategies utilized by computer-based

instructors for students with disabilities. There are no known or anticipated risks from

participating in this study. This online survey will be kept anonymous and confidential.

If you are interested, please complete the online survey.

Computer-Based Instruction for Students with Disabilities Survey Link

Start by selecting “Yes.” It should take you approximately 20 minutes to complete the

survey.

By completing the online survey, you will have voluntarily agreed to participate as a

participant in this study, which will be carried out by the aforementioned researcher,

183

under the supervision of Dr. Laura Eisemann, faculty advisor. You have the right to

withdraw at any time in the process with no penalty by not completing the survey,

interview, or by emailing the researcher that you are no longer interested in participating.

The researcher will then remove your data at that time. Please allow me to thank you in

advance for your cooperation. In the event that you need any additional information

regarding this research project, you may contact me at 443-859-4550 or

dfreydlin@mail.immaculata.edu. For questions about your rights as a research

participant, you may contact the Chair of the Research Ethics Review Board, Dr. Marcia

Parris at 610-647-4440 x3222 or mparris@immaculata.edu. All survey data will be stored

in a secure website database available only to me, the sole researcher. The data collected

during this study will be destroyed after five years.

The results will be reported in aggregated format and individual identities will not be

known. I ask that you please complete the survey by October 15th 2023. We know your

time is valuable and limited; therefore, I sincerely appreciate your participation in this

research study. If interested, I will forward you a copy of my study when completed.

If you have any questions, please do not hesitate to contact me at

dfreydlin@mail.immaculata.edu or 443-859-4550, or the research advisor (Dr. Laura

Eisemann) at leisemann@immaculata.edu or 610-647-4400 ext. 1005.

Sincerely,

Donna M. Freydlin & Dr. Laura Eisemann, Faculty Advisor

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