CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

According to David (2015), Computer Science is the study of computers, computational systems,

and algorithms, including both hardware and software components. It involves designing and

analyzing algorithms, programming languages, data structures, artificial intelligence,

cybersecurity, networking, and software development (Tanenbaum & Bos, 2015). Computer

Science also focuses on problem-solving through computational thinking, which enables

individuals to design efficient solutions for real-world challenges (Denning, 2017).

According to Denning (2017) the significance of Computer Science is evident in its impact

across multiple sectors. In education, for example, e-learning platforms have changed how

students access knowledge. With digital classrooms, online courses, and interactive learning

tools, education has become more flexible and accessible, breaking geographical barriers and

allowing students to learn at their own pace. This has been particularly beneficial for students in

remote areas or those who require personalized learning experiences.

Beyond education, the healthcare industry benefits immensely from Computer Science (Uche et

al. 2015). Medical diagnostics and treatment have improved through technologies such as AI-

driven disease prediction, robotic-assisted surgeries, and electronic health records. These

innovations help doctors make more accurate diagnoses, provide better patient care, and

streamline administrative processes in hospitals. Similarly, financial institutions leverage

Computer Science to enhance banking services. Online transactions, fraud detection systems, and

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automated trading rely on complex algorithms to ensure efficiency and security in financial

operations.

According to Ezeliora (2015) entertainment and media have also been transformed by

advancements in Computer Science. Streaming services, video game development, and digital

content creation all rely on sophisticated computing technologies to deliver high-quality,

immersive experiences. From recommendation algorithms that suggest movies and music based

on user preferences to real-time graphics rendering in video games, these applications showcase

the power of computing in shaping entertainment.

Furthermore, socio-economic factors play a crucial role in determining how well students

perform in Computer Science. As highlighted by Oye, Salleh, and Iahad (2015), students from

low-income backgrounds often struggle with limited access to computers and the internet,

reducing their ability to fully benefit from e-learning opportunities. Additionally, teachers’

readiness and digital literacy impact students’ learning experiences. If educators are not well-

trained in online instructional methods, students may receive suboptimal guidance, leading to

poor academic outcomes (Selwyn, 2016).

Despite the undeniable importance of Computer Science and the benefits of e-learning, poor

academic performance among senior secondary school students in Umuahia North LGA, Abia

State, still exists (Ezeliora 2015). Several factors contribute to this challenge, including limited

access to digital resources, inadequate teacher training, poor internet connectivity, and students'

lack of motivation or technical skills to navigate online learning platforms. According to Uche et

al. (2015), while e-learning enhances knowledge acquisition, its effectiveness largely depends on

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students’ ability to engage actively with digital tools and the presence of supportive learning

environments.

Different scholars have defined e-learning in various ways. According to Clark and Mayer

(2016), e-learning refers to instruction delivered via digital devices with the goal of supporting

learning. Similarly, Holmes and Gardner (2016) describe e-learning as the use of internet

technologies to enhance knowledge and performance. E-learning encompasses various digital

tools, platforms, and strategies designed to provide educational content outside the traditional

classroom setting. In the context of Computer Science education, e-learning enables students to

access coding tutorials, software simulations, and virtual labs, which can improve their

understanding of key concepts.

Moreover, the shift to e-learning requires strong self-discipline and time management skills.

Research by Schunk (2017) suggests that students who lack self-regulation strategies tend to

underperform in virtual learning environments compared to those in traditional classroom

settings. In Umuahia North LGA, challenges such as irregular electricity supply and unstable

internet services further compound the problem, making it difficult for students to consistently

access online learning materials.

Additionally, while e-learning presents a transformative approach to improving academic

performance in Computer Science, poor performance persists due to infrastructural, economic,

and pedagogical barriers (Schunk 2017). Addressing these challenges requires targeted

interventions such as improving digital infrastructure, training teachers in online pedagogy, and

providing financial support to students from disadvantaged backgrounds.

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Over the years, various initiatives have been implemented to enhance e-learning and improve the

academic performance of senior secondary school students in Computer Science within Umuahia

North LGA, Abia State. Recognizing the potential of digital education, both the government and

private institutions have introduced policies and programs aimed at integrating technology into

the learning process. Despite these efforts, poor academic performance in computer studies has

persisted due to infrastructural, economic, and pedagogical challenges.

One of the key interventions has been the provision of digital learning resources. Schools have

attempted to introduce computer laboratories, and some have adopted multimedia teaching tools

such as video presentation, audio presentation, gaming and simulation etc. to enhance students’

understanding of Computer Science concepts. Government-led initiatives, such as the

distribution of educational tablets and online learning platforms, have also aimed to bridge the

digital divide. However, studies such as Oye, Salleh, and Iahad (2011) have shown that the mere

availability of digital tools does not automatically translate to improved learning outcomes, as

many students lack the technical skills and support needed to maximize these resources.

Teacher training programs have also been introduced to equip educators with the skills required

to effectively deliver computer studies lessons in an online or blended format. Workshops and

professional development sessions have focused on integrating technology into the curriculum.

Nonetheless, the challenge of inadequate teacher preparedness persists. Many teachers still

struggle with using digital platforms efficiently, which negatively affects their ability to engage

students effectively (Selwyn, 2015). Without strong digital literacy among educators, the full

potential of e-learning remains underutilized.

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According to Ezeliora (2015), E-learning takes different forms or types to suit various learning

needs. Synchronous e-learning involves real-time interaction through live classes, video

conferencing, or virtual discussions, allowing immediate feedback but requiring stable internet

access. In contrast, asynchronous e-learning offers flexibility, as students can access pre-

recorded lectures and online materials at their convenience, though it lacks instant support from

teachers. Blended learning combines online and face-to-face instruction, ensuring students

benefit from both digital and traditional classroom settings. Mobile learning (m-learning) allows

students to use smartphones and tablets for studying, making learning more accessible but

sometimes distracting. Meanwhile, adaptive e-learning personalizes learning experiences using

AI, adjusting content based on a student’s progress, though it requires strong digital

infrastructure (Ezeliora 2015).

According to Bernard et al. (2020) E-learning serves multiple purposes in the education system,

particularly in computer studies. It enables students to learn at their own pace, access a wide

range of learning materials, and practice programming skills using interactive simulations.

Schools and teachers use e-learning platforms to track student progress, provide instant feedback,

and engage students in collaborative projects. In addition, e-learning is used for teacher training,

helping educators improve their digital teaching skills.

Johnson (2022), revealed that beyond schools, e-learning is widely used in professional

development and certification programs. Many Computer Science professionals take online

courses to enhance their coding, networking, and cybersecurity skills. Universities also adopt e-

learning systems to offer distance learning programs, allowing students from different locations

to earn degrees without physical attendance.

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The importance of e-learning in modern education cannot be overstated. One of its key benefits

is accessibility. Students in remote areas, including those in Umuahia North LGA, can gain

access to high-quality educational resources that may not be available locally. E-learning also

provides flexibility, allowing students to learn at their convenience, which is particularly

beneficial for those with other responsibilities or limited internet access (Anderson, 2017).

Another crucial advantage is cost-effectiveness. Traditional education requires physical

infrastructure, textbooks, and transportation, whereas e-learning reduces these costs by providing

digital resources that can be accessed from anywhere. Additionally, e-learning enhances

engagement through interactive multimedia tools such as animations, quizzes, and virtual labs,

making learning more effective compared to traditional methods.

According to Ezeliora (2015), Education has evolved significantly over the past few decades due

to technological advancements. One of the most notable transformations is the adoption of e-

learning, which has reshaped the traditional classroom experience by integrating digital tools,

online resources, and virtual learning environments. E-learning refers to the use of electronic

media, information, and communication technologies (ICTs) to deliver educational content

remotely (Al-Qahtani & Higgins, 2016). With the increasing accessibility of the internet and

digital devices, e-learning has gained global recognition as a flexible and effective alternative to

conventional teaching methods (Anderson, 2017).

The importance of e-learning in secondary school education, particularly in subjects like

computer studies, cannot be overstated. Computer studies is a dynamic and rapidly evolving field

that requires continuous exposure to digital platforms, coding environments, and problem-

solving techniques. According to Bernard et al. (2020), e-learning provides students with

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personalized learning experiences, allowing them to engage with interactive content, video

tutorials, and virtual simulations, thereby enhancing their understanding of complex concepts.

Furthermore, studies by Means et al. (2021) suggest that students who engage in online learning

environments tend to perform better academically compared to those in traditional face-to-face

settings.

The origins of e-learning can be traced back to the early 20th century when educators and

scientists experimented with automated teaching machines. One of the first notable inventions

was Sidney Pressey’s teaching machine in the 1920s, which allowed students to answer multiple-

choice questions and receive immediate feedback. This laid the groundwork for future self-paced

learning systems.

This finding traces the evolution of e-learning from its early foundations in the 1950s with B.F.

Skinner’s programmed instruction to the rapid advancements of the digital age. Early

developments included mechanical teaching devices and later computer-based training systems

like PLATO in the 1960s. The rise of personal computers in the 1980s introduced computer-

based training (CBT), followed by the emergence of Learning Management Systems (LMS) in

the 1990s.

The expansion of the internet revolutionized e-learning in the 2000s, leading to web-based

learning platforms like Blackboard and Moodle. Massive Open Online Courses (MOOCs) gained

popularity, making education more accessible. The COVID-19 pandemic in 2020 accelerated the

adoption of online learning, highlighting both opportunities and challenges, such as digital

divides and teacher training issues.

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According to Emerson, (2024), E-learning has significantly influenced Computer Science

education, providing interactive learning tools that enhance engagement. However, its

effectiveness depends on factors like access to resources, teacher competency, and student

adaptability. While e-learning fosters independent learning, concerns remain about motivation,

accessibility, and distractions. Studies suggest that despite its benefits, disparities in digital

access must be addressed for e-learning to be truly effective.

The need for this study arises from the increasing integration of digital learning in secondary

education, particularly in computer studies. As schools transition from traditional methods to e-

learning, understanding its impact on students' academic performance becomes essential. The

study will explore how e-learning enhances or hinders students’ ability to grasp complex

programming concepts, problem-solving techniques, and computational thinking skills.

According to Taylor (2023), access to digital resources remains a challenge, especially for

students from disadvantaged backgrounds. While e-learning offers flexibility and interactive

learning opportunities, disparities in access to technology and internet connectivity could widen

the educational gap. Examining these issues will help identify solutions to ensure that all

students benefit from digital learning environments.

Engagement and motivation play a crucial role in academic success. E-learning provides

multimedia content, self-paced study options, and interactive platforms, yet distractions and lack

of direct supervision may affect students’ concentration and retention of knowledge.

Investigating these factors will help educators refine teaching approaches to maximize student

performance.

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Students in the Nigeria have shown increasing interest in e-learning due to its flexibility,

accessibility, and the way it aligns with their familiarity with digital technology. Many students

appreciate being able to learn at their own pace, revisit materials as needed, and access lessons

from any location, which supports a more personalized and convenient learning experience. The

integration of multimedia elements such as videos, interactive quizzes, and gamified features

makes online learning more engaging and stimulating compared to traditional classroom

methods. Additionally, e-learning platforms often provide a broader range of subjects and skills,

allowing students to explore topics beyond the standard curriculum and even earn certifications

that can enhance their future job prospects. The ability to collaborate with peers and

communicate with instructors through digital tools also adds a layer of connectivity that supports

active participation and learning. However, while many students are enthusiastic about the

benefits, some face challenges like limited internet access, lack of motivation, or difficulty

adapting to a less structured environment. Despite these challenges, the overall interest in e-

learning remains strong as it continues to evolve and cater to the needs of modern learners.

1.2 Statement of Problem

The ideal educational environment for senior secondary school students in computer studies

should be one where learning is interactive, engaging, and accessible to all students, regardless

of their socio-economic background. E-learning, in its most effective form, should provide

students with digital resources, virtual laboratories, and interactive learning tools that enhance

their understanding and practical application of Computer studies concepts. Ideally, students

should have access to stable internet connections, well-structured e-learning platforms, and

competent teachers who can effectively integrate technology into teaching.

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However, the current situation in Umuahia North LGA, Abia State, presents a different reality.

While e-learning has been introduced in some schools, its implementation faces significant

challenges. Many students lack access to essential digital devices and reliable internet

connectivity, making it difficult for them to fully engage in online learning. Additionally, some

teachers are not adequately trained to utilize e-learning platforms effectively, leading to gaps in

instructional quality. The shift from traditional classroom settings to digital learning

environments has also left some students struggling with self-discipline, motivation, and

comprehension, particularly in a technical subject like computer studies that requires hands-on

practice.

Over time, various measures have been put in place to address these challenges. Schools and

government agencies have made efforts to provide digital learning materials, train teachers on

the use of e-learning tools, and introduce alternative methods such as blended learning, which

combines online and in-person instruction. Private organizations and NGOs have also

contributed by donating computers and offering digital literacy programs. Despite these

initiatives, the problem persists due to infrastructural limitations, lack of consistent policy

implementation, and the financial constraints faced by students and schools in maintaining

access to digital learning resources.

The continued struggle with these challenges has significant effects on students' academic

performance. Those without access to proper e-learning tools are at a disadvantage compared to

their peers, leading to disparities in knowledge acquisition. The lack of proper engagement with

digital learning materials may result in poor understanding of Computer Science concepts,

reduced practical skills, and lower examination performance. Furthermore, students who are

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unable to adapt to self-paced learning may experience decreased motivation and increased

frustration, ultimately affecting their overall academic progress.

This research is necessary to examine the extent to which e-learning influences academic

performance in computer studies among senior secondary school students in Umuahia North

LGA. It seeks to identify the specific factors hindering the effectiveness of e-learning, assess

students' adaptability to digital learning environments, and evaluate the role of teachers in

ensuring successful e-learning integration.

A major gap in knowledge exists regarding the actual impact of e-learning on student

performance in computer studies within this specific area. While studies have explored e-

learning's benefits globally and nationally, there is limited research focused on how local

challenges such as infrastructural deficits, digital illiteracy, and economic constraints affect

students in Umuahia North LGA. Understanding these localized issues will help in developing

targeted solutions to improve e-learning effectiveness.

While e-learning holds great potential for enhancing computer studies education, its

effectiveness is hindered by several challenges that require urgent attention. This study aims to

bridge the knowledge gap by providing data-driven insights into the impact of e-learning on

students' academic performance, thereby informing better policies and interventions to optimize

digital learning in secondary education.

1.3 Purpose of the Study

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The purpose of this study is to investigate the impact of e-learning on academic performance of

senior secondary school students in computer studies in Umuahia North LGA, Abia State.

Specifically, it will

i. assess the impact of e-learning on academic performance of Computer science students in

secondary schools in Umuahia North LGA Abia State.

ii. determine the impact of e-learning on the improvement of the quality of education of

computer science students’ in Umuahia North LGA Abia State.

iii. to assess the interest of student in the use of e-learning

1.4 Research Questions

The study is guided by the following research questions:

1. What is the impact of E-Learning on the academic performance of computer science students

in secondary schools in Umuahia North LGA Abia State.

2. What is the impact of E-learning on the improvement of the quality of education of computer

science students in Umuahia North LGA Abia State?

3. What is the extent of studwnts’ interests on e-learning?

1.5 Significance of Study

The findings of this study will benefit a wide range of stakeholders, including senior secondary

school students in Umuahia North LGA, teachers, educationists, curriculum developers,

policymakers, parents, and future researchers. For students, the study may offer a deeper

understanding of how e-learning tools influence their academic performance, particularly in

Computer studies, helping them become more engaged and digitally literate.

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Teachers could also benefit from the study by gaining a better understanding of how to integrate

e-learning into their instructional strategies effectively. The findings may guide them in adopting

innovative teaching methods that enhance student participation and performance. Furthermore,

teachers may receive recommendations on how to address challenges such as digital accessibility

and engagement, leading to a more efficient and inclusive learning environment.

Educationists and curriculum developers may find the study useful in evaluating the role of e-

learning in secondary school Computer studies. The insights derived from the research could

help in designing policies that improve digital learning frameworks, ensuring that e-learning is

not only accessible but also aligned with educational objectives.

Policymakers and government authorities may also benefit from the study by understanding the

challenges and opportunities associated with e-learning in secondary education. The study’s

findings could inform policies on ICT infrastructure development, teacher training programs, and

strategies for bridging the digital divide among students in Umuahia North LGA and beyond.

Additionally, parents and guardians could gain insights into how e-learning affects their

children's education. This understanding may encourage them to support digital learning at

home, ensuring that students have the necessary resources and environment to benefit from e-

learning initiatives.

Lastly, researchers and scholars in the field of education and technology could find this study

useful for further investigations into e-learning effectiveness. The study may serve as a

foundation for future research aimed at improving digital learning in secondary schools,

especially in developing regions.

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By addressing these key areas, the study could contribute significantly to the improvement of

Computer studies, ensuring that students in Umuahia North LGA receive quality learning

experiences through effective e-learning strategies.

1.6 Scope of Study

The study is aimed at investigating impact of e-learning on the academic performance of

computer studies students in secondary schools in Umuahia North LGA, Abia State. Specifically,

the research will cover students in Senior Secondary One (SS1). The choice of Senior Secondary

One (SS1) students for this study is based on their unique position within the secondary school

academic structure. SS1 students have recently transitioned from junior to senior secondary

education, a period that often marks increased exposure to core academic subjects, including

computer studies. At this stage, students begin to engage with more advanced computer science

concepts, making it a critical point for assessing the effectiveness of e-learning interventions on

their academic performance.

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 CHAPTER TWO

REVIEW OF RELATED LITERATURE

2.0 Introduction

The impact of e-learning on academic performance has been widely studied across various

educational levels, with numerous studies highlighting its significance in enhancing students'

learning experiences and outcomes. This section reviews literature that explores the use of e-

learning in senior secondary schools, particularly in the context of Computer Science education

in Umuahia North LGA, Abia State. This is discussed under the following subheadings:

Conceptual Framework, Theoretical Framework, Review of Empirical Studies, Summary of

review of literature

2.1 Conceptual Framework

2.1.1 Concept of computer science

2.1.2 Importance of computer science

2.1.3 Concept of E-learning

2.1.4 Types of E-learning

2.1.5 The Impact of E-Learning on Academic Performance

2.1.6 How E-Learning Can Improve the Quality of Education in computer science students in

secondary schools.

2.2 Theoretical Framework

2.2.1 Constructivism Theory – Jean Piaget (1957) and Jerome Bruner (1985)

2.2.2 Facilitation Theory (The Humanist Approach) – Carl Rogers (2003)

2.3 Review of Empirical studies

2.4 Summary of Review of Literature

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2.1 CONCEPTUAL FRAMEWORK

2.1.1 Concept of Computer Science

According to David (2015), Computer science is the study of computation, information,

and automation. Computer science spans theoretical disciplines (such as algorithms, theory of

computation, and information theory) to applied disciplines (including the design and

implementation of hardware and software).

According to Yusuf (2015), computer science is the study of algorithms, data structures, and the

principles of computing that allow machines to process and manipulate information efficiently.

This definition highlights the mathematical and logical foundations of the field, emphasizing the

role of computation in problem-solving. Similarly, Simon (2020) defines computer science as the

systematic study of algorithms, including their design, analysis, implementation, and efficiency.

His perspective focuses on the theoretical aspects of computing and how algorithms drive the

development of computer applications.

Abelson and Sussman (2018) also describe computer science as the study of programs and

programming languages, emphasizing the development of software and the ways in which

different programming paradigms influence computing. Likewise, Nnamani (2021) argue that

computer science is the study of artificial processes and their capabilities, focusing on how

computational models can be used to mimic human intelligence and decision-making.

From an educational perspective, Wing (2016) defines computer science as the discipline that

promotes computational thinking, which involves problem formulation, pattern recognition,

decomposition, and algorithmic problem-solving. This definition underscores the significance of

computational thinking in fostering logical reasoning and creativity in various fields.

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Finally, computer science is a vast and interdisciplinary field with diverse definitions depending

on the perspective of the scholar or institution. Some definitions focus on its theoretical

foundations, such as algorithms, computation, and logic, while others highlight its practical

applications in software development, artificial intelligence, and human-computer interaction.

Despite the variations in definitions, all perspectives agree that computer science is

fundamentally concerned with the study of computation, problem-solving, and the efficient

processing of information through automated systems.

2.1.2 Importance of Computer Science

Nnamani (2021) emphasizes that computer science is crucial in developing algorithms and

computational models that drive the modern digital world. According to him, the ability to create

efficient computing systems allows for the automation of tasks, making work easier, faster, and

more accurate. He asserts that without computer science, modern computing technologies such

as artificial intelligence, machine learning, and big data analytics would not be possible.

David (2015) highlights the importance of computer science in problem-solving and decision-

making. He argues that computational thinking, a key aspect of computer science, enables

individuals to break down complex problems into smaller, manageable parts, which can then be

solved systematically using algorithms. This ability is not only beneficial in computing but also

in fields such as finance, healthcare, and engineering, where problem-solving skills are essential.

According to Yusuf (2015), computer science serves as the foundation for digital transformation

across industries. He states that the study of computational systems has led to the development of

revolutionary technologies such as the internet, cloud computing, and cybersecurity. Denning

also emphasizes that computer science is not just about programming but also about

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understanding the principles behind how systems work, which is vital in optimizing performance

and security.

Wing (2016) views computer science as an essential discipline for fostering computational

thinking, which she defines as a universal skill that enhances logical reasoning and creativity.

She argues that computational thinking is as important as literacy and numeracy in the modern

world, as it equips individuals with the ability to analyze data, design algorithms, and develop

innovative solutions to real-world problems.

From an educational perspective, Abelson and Sussman (2018) argue that computer science is

instrumental in preparing students for the digital age. They believe that understanding computer

science concepts enables students to become creators of technology rather than just consumers.

By learning programming languages, data structures, and artificial intelligence, students gain the

skills needed to build applications and systems that can address societal challenges.

2.1.3 Concept of E-Learning

Rosenberg (2021) defines e-learning as the use of internet technologies to deliver a broad array

of solutions that enhance knowledge and performance. He emphasizes that e-learning is not just

about digitalizing traditional learning materials but about creating an interactive, learner-centered

environment where individuals can access educational content at any time and from anywhere.

According to him, e-learning provides greater flexibility, allowing learners to study at their own

pace while benefiting from multimedia elements such as videos, animations, and simulations.

Garrison and Anderson (2023) describe e-learning as a learning system based on formalized

teaching but with the aid of electronic resources. They argue that while e-learning can occur

entirely online, it can also complement traditional classroom teaching in blended learning

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environments. Their definition recognizes the role of both synchronous (live) and asynchronous

(self-paced) learning modes in ensuring that students have a dynamic educational experience.

According to Clark and Mayer (2016), e-learning refers to the instruction delivered through

digital devices with the intent of supporting learning. They focus on the importance of

instructional design in e-learning, stating that effective digital learning should be engaging,

interactive, and structured to facilitate knowledge retention. Their definition highlights the use of

multimedia elements and adaptive learning technologies to enhance the overall effectiveness of

digital education.

Albert (2015) categorizes e-learning into three major types: synchronous, asynchronous, and

blended learning. He defines synchronous e-learning as real-time learning facilitated through

video conferencing, live chats, and virtual classrooms, whereas asynchronous e-learning involves

pre-recorded lectures, discussion forums, and digital learning materials that learners can access at

their convenience. Blended learning, as he explains, combines both online and face-to-face

instruction to maximize the benefits of both approaches.

Naidu (2016) describes e-learning as an innovative approach to education that utilizes digital

technologies, communication tools, and internet-based resources to provide flexible and

accessible learning experiences. His definition emphasizes the role of technology in overcoming

geographical barriers, allowing learners from different locations to access the same educational

opportunities without being constrained by time or physical presence.

Hassan (2016) sees e-learning as a structured learning experience delivered through digital

technology to facilitate knowledge acquisition. He argues that e-learning is not simply about

providing online access to content but involves interactive elements such as assessments,

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discussions, and real-time feedback to enhance learning outcomes. Horton further highlights the

importance of integrating e-learning within corporate training programs, where employees can

upskill and reskill through digital learning platforms.

2.1.4 Types of E-Learning

These types include synchronous e-learning, asynchronous e-learning, blended learning, mobile

learning, personalized learning, and collaborative e-learning. Each of these models offers unique

advantages and challenges in the digital learning landscape.

i. Synchronous E-learning

Synchronous e-learning is a real-time, interactive learning experience where instructors and

learners communicate simultaneously through digital platforms. According to Usman (2018),

synchronous e-learning fosters immediate feedback, real-time discussions, and collaborative

problem-solving, making it particularly beneficial for learners who require direct engagement

with instructors and peers. Tools such as video conferencing, live chat, and virtual classrooms

facilitate synchronous learning, ensuring that students receive instant clarification on

concepts. However, this model requires a stable internet connection and scheduled

participation, which may not always be feasible for learners in remote or underprivileged

regions.

ii. Asynchronous E-learning

Unlike synchronous learning, asynchronous e-learning allows learners to access educational

materials at their convenience without real-time interaction with instructors. Garrison (2023)

describes asynchronous learning as a flexible approach where learners engage with pre-

recorded lectures, discussion forums, e-books, and self-paced assessments. This model

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 enables students to learn at their own pace, making it ideal for individuals with different

learning speeds and schedules. Asynchronous e-learning is widely used in distance education

and corporate training programs, as it removes geographical and time constraints. However, it

lacks the immediacy of direct teacher-student interactions, which may lead to feelings of

isolation among learners (Usman, 2018).

iii. Blended Learning

Blended learning, also known as hybrid learning, combines both online and traditional face-

to-face instruction to optimize the learning experience. According to Graham (2006), blended

learning integrates digital tools with classroom interactions, allowing students to benefit from

the advantages of both e-learning and conventional teaching methods. This model enhances

flexibility while maintaining the social and cognitive benefits of physical classroom

engagement. Institutions adopting blended learning utilize learning management systems

(LMS) such as Moodle or Blackboard to distribute digital content, while in-person sessions

focus on discussions, hands-on activities, and mentorship. The effectiveness of blended

learning depends on the proper integration of online and offline components to ensure a

seamless learning experience (Bonk & Graham, 2022).

iv. Mobile Learning (M-learning)

Mobile learning refers to the use of smartphones, tablets, and other portable devices to access

educational content. Ally (2019) defines mobile learning as an extension of e-learning that

provides learners with immediate access to learning materials anytime and anywhere. This

model supports informal and on-the-go learning, making it particularly useful for students in

regions with limited access to traditional educational resources. Applications such as

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 Duolingo, Coursera, and Khan Academy enable learners to study through interactive mobile

platforms. However, issues such as screen size limitations, internet dependency, and

distractions from other mobile applications can impact the effectiveness of m-learning

(Traxler, 2020).

v. Personalized E-learning

Personalized e-learning tailors educational content to meet the individual needs, preferences,

and learning styles of students. According to Brusilovsky and Millán (2017), adaptive

learning technologies utilize artificial intelligence and data analytics to customize learning

paths based on students’ progress and performance. This type of e-learning enhances

engagement and motivation by offering customized assessments, interactive tutorials, and

learning recommendations. While personalized e-learning improves knowledge retention, it

requires advanced technological infrastructure and continuous data monitoring to function

effectively (Pappas, 2019).

vi. Collaborative E-learning

Collaborative e-learning emphasizes group interactions, teamwork, and knowledge sharing

through digital tools. Harasim (2022) explains that this model encourages learners to engage

in discussions, peer reviews, and co-creation of knowledge using online forums, wikis, and

social media platforms. Collaborative e-learning fosters a sense of community and enhances

critical thinking skills by allowing students to engage with diverse perspectives. Educational

institutions and businesses use collaborative tools such as Google Classroom, Microsoft

Teams, and discussion boards to facilitate cooperative learning. However, challenges such as

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 coordinating group schedules, ensuring equal participation, and managing conflicts may arise

in collaborative learning environments (Yusuf et al., 2016).

2.1.5 The Impact of E-Learning on Academic Performance

The impact of e-learning on academic performance has been widely debated among scholars,

with studies indicating both positive and negative effects.

i. Positive Impact of E-Learning on Academic Performance

One of the primary advantages of e-learning is its ability to provide flexible and self-paced

learning experiences. According to Means et al. (2020), students who engage in online learning

often perform better than those in traditional classroom settings due to personalized learning

experiences and access to a variety of digital resources. E-learning allows students to revisit

recorded lectures, access supplementary materials, and engage in interactive simulations, which

enhance comprehension and knowledge retention. This flexibility is particularly beneficial for

students with different learning styles, as they can choose the most effective approach to study

(Sun & Rueda, 2022).

E-learning also fosters independent learning and self-discipline. Research by John et al. (2020)

found that students using e-learning platforms develop critical thinking and problem-solving

skills more effectively than those in conventional learning environments. The ability to access

vast amounts of information online encourages students to conduct research and explore different

perspectives, which contributes to higher academic performance. Additionally, digital

23
assessment tools provide immediate feedback, helping learners identify their strengths and

weaknesses, thus improving their overall understanding of subjects (Bernard et al., 2015).

Moreover, e-learning enhances engagement and interactivity through multimedia content,

discussion forums, and virtual group projects. According to John (2018), interactive e-learning

methods, such as gamification, video-based learning, and adaptive quizzes, contribute to

increased motivation and participation, leading to better academic outcomes. Collaborative e-

learning environments, where students work together on projects and participate in peer

discussions, also promote deeper understanding and knowledge retention (Garrison & Vaughan,

2008).

ii. Negative Impact of E-Learning on Academic Performance

Despite its numerous benefits, e-learning also presents challenges that can negatively affect

academic performance. One significant issue is the digital divide, where students from low-

income backgrounds may lack access to reliable internet, computers, or digital learning tools.

According to Van Dijk (2020), the disparity in access to e-learning resources creates educational

inequalities, as students with limited technology experience difficulties in keeping up with online

coursework.

Another concern is reduced social interaction and lack of direct teacher support. While e-learning

provides flexibility, it often results in decreased face-to-face communication, which can lead to

feelings of isolation and reduced motivation (Benard, 2015). Some students struggle with self-

regulation and time management in e-learning settings, leading to procrastination and lower

academic achievement. Research by Zimmerman (2021) emphasizes that students who lack self-

discipline may find it difficult to complete online courses successfully.

24
 Furthermore, e-learning effectiveness is heavily dependent on the quality of instructional design.

Poorly structured courses, lack of engaging content, and insufficient instructor feedback can

hinder learning outcomes (Clark & Mayer, 2016). Technical difficulties, such as platform

malfunctions and cybersecurity threats, also pose barriers to effective online learning, potentially

disrupting students’ academic progress (Bawa, 2016).

2.1.6 How E-Learning Can Improve the Quality of Education in computer science students
in secondary schools.

E-learning has become a transformative force in the field of education, particularly in subjects

like computer science, where digital literacy and technological proficiency are essential. The

integration of e-learning into secondary school education has the potential to enhance the quality

of learning experiences for computer science students by offering greater accessibility,

personalized learning, interactive content, and real-world applications. With the increasing

reliance on technology in all aspects of society, it is imperative to explore how e-learning can

improve the teaching and learning of computer science in secondary schools.

i. Enhancing Accessibility and Learning Flexibility: One of the most significant advantages of

e-learning is its ability to provide students with unrestricted access to educational materials,

regardless of location or time constraints. Unlike traditional classroom settings, where learning

is confined to a specific period, e-learning allows students to access instructional content

through various digital platforms at their convenience. According to Pamela al. (2020),

students who engage with e-learning platforms benefit from greater control over their learning

pace, leading to improved comprehension and academic performance.

25
 In computer science education, where students must grasp complex concepts such as

programming, algorithms, and data structures, the flexibility of e-learning enables learners to

revisit lectures, practice coding exercises, and engage with online simulations repeatedly until

mastery is achieved. Research by Uche (2021) highlights that self-paced learning environments

foster deeper engagement and knowledge retention, as students can learn at a pace that suits

their cognitive abilities.

ii. Personalized Learning and Adaptive Technologies: E-learning platforms are designed to

cater to diverse learning needs by providing personalized and adaptive learning experiences.

Traditional classroom settings often follow a one-size-fits-all approach, which may not

accommodate the varying learning speeds and styles of students. However, e-learning systems

use artificial intelligence (AI) and machine learning algorithms to analyze students' progress

and adapt the curriculum accordingly (Clark & Mayer, 2016).

Bernard et al. (2015) argue that personalized learning enhances students’ motivation and self-

efficacy, ultimately leading to better academic outcomes. In computer science, adaptive

learning technologies can help students grasp programming concepts through interactive

coding exercises and instant feedback mechanisms, improving their problem-solving skills and

computational thinking.

iii. Interactive and Engaging Learning Experience: The interactive nature of e-learning

significantly improves the quality of computer science education by making learning more

engaging and dynamic. Unlike traditional teaching methods, which often rely on passive

lectures, e-learning incorporates multimedia content, virtual labs, and gamification to make

learning more interactive. According to Hassan (2018), the integration of videos, animations,

26
 quizzes, and coding simulations helps to enhance students' understanding of abstract concepts,

making learning more enjoyable and effective.

iv. Bridging the Gap Between Theory and Practice: One of the challenges in teaching computer

science in secondary schools is ensuring that students can apply theoretical knowledge to real-

world problems. E-learning addresses this issue by providing students with access to real-world

projects, industry case studies, and virtual internships. According to Olaniyi (2021),

experiential learning is crucial for developing technical proficiency and preparing students for

careers in the technology industry.

Many e-learning platforms offer project-based learning, where students work on real-world

coding assignments, software development projects, and hackathons. For example, GitHub

Education and Microsoft Learn provide students with access to industry-standard tools,

allowing them to collaborate on open-source projects and gain hands-on experience. Studies by

Abimbade (2023) indicate that students who engage in project-based e-learning demonstrate

higher levels of creativity, innovation, and problem-solving abilities.

v. Continuous Assessment and Immediate Feedback: Traditional assessment methods in

secondary schools, such as written exams and assignments, often fail to provide immediate

feedback, making it difficult for students to identify areas of improvement. E-learning

platforms, on the other hand, incorporate continuous assessment mechanisms, allowing

students to receive instant feedback on their performance. According to Stella (2015),

immediate feedback helps students correct mistakes in real-time, reinforcing learning and

reducing the likelihood of repeating errors.

2.2 THEORETICAL FRAMEWORK

27
This study is discussed within the framework of Jean Piaget's (1957) and Jerome Bruner's (1985)

Theory of Constructivism, and Carl Rogers' (1960) Facilitation Theory.

2.2.1 Constructivism Theory Jean Piaget (1957) and Jerome Bruner (1985)

Formalization of the theory of constructivism is generally propounded by Jean Piaget (1957) and

later expanded by Jerome Bruner in (1985). Constructivism emphasizes that learning is an active

process in which learners construct their own understanding and knowledge through experiences,

reflection, and interaction with their environment. This theory challenges the traditional view of

learning as a passive absorption of information and instead advocates for an approach where

learners actively engage in problem-solving, exploration, and critical thinking to make sense of

new knowledge.

Piaget's constructivist theory, originally developed to explain cognitive development in children,

asserts that individuals acquire knowledge by interacting with their environment, adapting new

experiences into their existing mental structures (assimilation) or modifying those structures to

accommodate new information (accommodation). He argued that learning occurs in stages and

that students should be given opportunities to discover concepts on their own rather than being

presented with ready-made solutions. His work laid the foundation for student-centered learning

approaches, emphasizing the importance of exploration, experimentation, and hands-on activities

in education.

Bruner expanded on Piaget’s ideas by introducing the concept of scaffolding, which refers to the

guidance provided by teachers, parents, or more knowledgeable peers to support a learner’s

development. He also introduced the spiral curriculum, suggesting that complex topics should be

introduced in a simplified form and revisited at progressively deeper levels as a learner’s

cognitive abilities expand. Bruner highlighted three modes of representation in learning: enactive

28
(learning through action), iconic (learning through visual representation), and symbolic (learning

through language and abstract thinking). These ideas revolutionized education by reinforcing the

notion that learning is an evolving process where students must be actively engaged in

constructing their knowledge.

This theory is particularly relevant to this study, which examines the impact of e-learning on the

academic performance of senior secondary school students in computer science. E-learning

environments are inherently constructivist, as they encourage students to learn by doing, engage

in interactive problem-solving, and explore knowledge through digital tools and resources.

Platforms such as virtual labs, coding simulations, and gamified learning environments enable

students to build their understanding by applying theoretical concepts to real-world problems.

Constructivism supports the use of technology in education because digital learning platforms

allow for individualized learning experiences, where students can progress at their own pace,

revisit concepts as needed, and engage in collaborative learning activities with peers across

different locations.

In the context of computer science education, constructivism plays a crucial role in shaping how

students develop programming skills, computational thinking, and problem-solving abilities.

Unlike traditional teacher-centered methods, where students passively receive information, e-

learning aligns with constructivist principles by promoting hands-on coding exercises, debugging

tasks, and collaborative software development. According to Nnachi (2020), Students do not just

memorize programming syntax; instead, they construct their knowledge through

experimentation, iteration, and real-world application. For example, online coding platforms like

Scratch, Codecademy, and Khan Academy follow a constructivist approach by providing

learners with interactive exercises that require them to explore, test, and refine their solutions.

29
Furthermore, constructivism supports the integration of discussion forums, online group projects,

and peer reviews in e-learning environments. These elements help students refine their

understanding by engaging in knowledge exchange and collaborative problem-solving, reflecting

Bruner’s emphasis on the social aspect of learning. By participating in online discussions,

students articulate their thought processes, receive feedback, and modify their perspectives based

on insights from their peers. This aligns with the constructivist idea that learning is not an

isolated process but one that thrives on interaction, dialogue, and shared experiences.

2.2.2 Facilitation Theory (The Humanist Approach) – Carl Rogers (1960)

The Facilitation Theory, also known as the Humanist Approach, which was propounded by Carl

Rogers. Developed in the mid-20th century, this theory emphasizes learner-centered education,

where students play an active role in their learning process rather than being passive recipients of

information. Rogers, a renowned psychologist, introduced this theory as part of his broader

humanistic perspective, which focuses on personal growth, self-actualization, and the intrinsic

motivation of learners. The Facilitation Theory challenges traditional authoritarian teaching

methods and instead promotes an environment where students are encouraged to explore,

question, and construct their understanding through self-directed learning and meaningful

interactions.

At the core of this theory is the belief that learning occurs most effectively when students feel

emotionally secure, valued, and respected. Rogers argued that the role of the teacher is not

merely to transmit knowledge but to act as a facilitator, guiding students to discover knowledge

on their own. This facilitative approach shifts the educational experience from a rigid, instructor-

led process to one that fosters curiosity, self-motivation, and experiential learning. According to

30
Rogers, when learners are actively engaged and feel a sense of ownership over their learning,

they develop deeper understanding and long-lasting knowledge. This aligns closely with modern

educational practices that emphasize student-centered learning, critical thinking, and problem-

solving skills.

Furthermore, e-learning fosters a student-centered environment where the teacher’s role shifts

from being the sole authority to a facilitator of knowledge. Online discussion forums, peer

collaboration tools, and interactive exercises encourage students to seek out information, engage

in critical discussions, and construct their understanding rather than passively absorbing content.

This aligns with Rogers' idea that meaningful learning occurs when students actively participate

in knowledge creation rather than simply receiving information from an instructor. By

integrating problem-solving tasks, project-based assignments, and real-world applications, e-

learning platforms encourage students to think critically and apply theoretical concepts in

practical contexts.

The relevance of Facilitation Theory to this study on the impact of e-learning on the academic

performance of senior secondary school students in Computer Science is profound. E-learning

environments inherently support the principles of this theory by providing students with

autonomy in their learning process. Unlike traditional classroom settings where teachers control

the pace and delivery of instruction, e-learning allows students to engage with educational

materials at their own pace, revisit difficult concepts, and explore supplementary resources.

2.3 REVIEW OF EMPIRICAL STUDIES

Onuka and Durowoju (2018) carried out a study to determine Effect of E-Learning on Academic

Performance of Secondary School Students in Computer Science in Igbo-eze North LGA, Enugu

31
State. A quasi-experimental pre-test post-test non-randomized control group design was adopted

for this study. The study was guided by two research questions and two null hypotheses

formulated to ensure a clear focus on the investigation. For data collection, the Computer

Science Achievement Test (CSAT) and Cognitive Ability Test (CAT) instruments were used,

and the study was conducted with a sample size of 91 students. The instruments underwent

validation by three subject-matter experts to ensure reliability and relevance. A reliability

coefficient of 0.73 for CSAT and 0.74 for CAT was obtained through the split-half method using

Pearson Product Moment Correlation. These values were later converted using the Spearman

Brown formula, resulting in reliability coefficients of 0.84 and 0.85, respectively. Data analysis

was conducted using the mean and standard deviation to answer the research questions, while

analysis of covariance (ANCOVA) was employed to test the hypotheses at a 0.05 significance

level. The results of the study revealed that students with low cognitive ability levels who

participated in e-learning performed significantly better than their counterparts with high and

average cognitive ability levels. Additionally, male students with average cognitive ability levels

demonstrated better performance compared to those with high and low cognitive ability levels.

Similarly, female students with low cognitive ability levels outperformed those with high and

average cognitive ability levels. The findings suggest that e-learning plays a crucial role in

shaping students' academic performance in Computer Science. However, it also highlights that

academic achievement is not entirely dependent on cognitive ability levels alone. Other factors

such as the accessibility of e-learning resources, students’ engagement with online learning

platforms, and instructional strategies employed by teachers may also contribute to variations in

performance.

32
Nwosu (2017) conducted a study to examine the role of e-learning in enhancing the quality of

computer science education in secondary schools in Abakaliki Urban, Ebonyi State, Nigeria. The

study adopted a pre-test post-test, control group, non-randomized quasi-experimental design to

evaluate the impact of e-learning on student achievement. The study population consisted of all

Senior Secondary School (SS II) students enrolled in secondary schools in Abakaliki Urban who

were studying computer science. From this population, four secondary schools were purposefully

selected out of the fourteen schools offering computer science in the area. The selected schools

were divided into two groups: two schools formed the treatment group, where e-learning

instructional methods were implemented, while the remaining two schools served as the control

group, where conventional teaching methods were used. A total of one hundred and sixty-three

(163) students participated in the study, providing a sample size that was considered adequate for

the research. To facilitate the study, two instructional packages were developed. One package

incorporated e-learning strategies and was implemented in the treatment group, while the other

followed traditional teaching methods for the control group. The primary instrument for data

collection was the Computer Science Achievement Test (CSAT), designed to assess students'

comprehension and retention of computer science concepts. The validity of the instrument was

ensured through expert reviews, and its reliability was determined using appropriate statistical

methods. Data analysis was carried out using mean and standard deviation to summarize the

students' performance scores across the two groups. Furthermore, the analysis of covariance

(ANCOVA) was employed to test the hypotheses and determine the statistical significance of

differences in students’ academic performance between the treatment and control groups at a

0.05 significance level. The results of the study revealed that students who were taught using e-

learning instructional methods performed significantly better than those taught using

33
conventional teaching methods. This finding underscores the effectiveness of e-learning in

improving academic achievement in computer science education. Additionally, the study found

that gender had no significant interaction effect with the teaching approach, suggesting that both

male and female students benefited equally from the e-learning instructional method.

Oyeniyi (2018) conducted a study to examine Students’ Interest and Engagement in E-Learning:

A Case Study of Computer Science Students in Secondary Schools in Ekiti State, Nigeria. The

research adopted an ex-post facto design of the survey type to explore the impact of e-learning

on students' engagement and interest in computer science education. The study was conducted in

Ekiti State, Nigeria, and focused on Junior Secondary III (JSS III) students in public secondary

schools. The targeted population for the study comprised Basic Science students, from which a

total of three hundred (300) students were selected as the sample. The sample included one

hundred and fifty (150) students from public secondary schools in urban areas (70 males and 80

females) and another one hundred and fifty (150) students from public secondary schools in rural

areas (72 males and 78 females). The selection of the sample was done randomly to ensure a fair

representation of both urban and rural school locations. For data collection, computerized result

sheets sent to each school by the Ekiti State Ministry of Education were gathered. The study

focused on the Ekiti State Junior WAEC results from the 2014-2017 May/June examinations for

all selected schools. The academic scores of the students forming the sample were extracted and

categorized as ‘Urban scores’ and ‘Rural scores’ to analyze their performance in Basic Science

as an indicator of their engagement in e-learning activities. To analyze the data, the study

formulated three research hypotheses, which were tested using the t-test statistical analysis at

P<0.05 level of significance. The statistical method allowed the researcher to determine

significant differences between students’ academic achievements based on their school location

34
and gender. The findings of the study revealed that there was no statistically significant

difference in the academic achievement mean scores of male and female students in urban school

areas. Similarly, there was no statistically significant difference in the academic achievement

mean scores of male and female students in rural school areas. However, the findings further

indicated that there was a statistically significant difference in the achievement mean scores of

students in urban and rural school locations, suggesting that environmental factors could

influence students’ engagement and performance in e-learning.

2.4 SUMMARY OF REVIEW OF RELATED LITERATURE

The researcher investigated that Computer science is a multidisciplinary field that focuses on

computation, algorithms, and software development, with significant contributions to digital

transformation, artificial intelligence, and decision-making. It is increasingly recognized as

essential across industries, with computational thinking becoming a vital skill. E-learning,

defined as the use of digital technologies to enhance education, provides flexibility, accessibility,

and interactivity through models such as synchronous, asynchronous, and blended learning. Its

effectiveness is shaped by factors like instructional design, learner engagement, and access to

resources. The Conceptual framework covers; Concept of computer science, Importance of

computer science, Concept of E-learning, Types of E-learning, The Impact of E-Learning on

Academic Performance, How E-Learning Can Improve the Quality of Education in computer

science students in secondary schools.

Some of the theories on which the study anchor is reviewed and discussed in line with the

present study. These theories are Constructivism theory and Facilitation Theory. Review of

empirical works, showed that some works have been carried out in some related areas to the

35
study at hand, for instance many studies associated with E-learning and the impact on student

academic performance has been done.

However, none of the works available to the researcher seems to have focused on the impact of

e-learning on the academic performance of computer science students in secondary schools in

Umuahia North LGA, Abia State. Hence, this study will be carried out to investigate the impact

of e-learning on the academic performance of computer science students in secondary schools in

Umuahia North LGA, Abia State.

CHAPTER THREE

METHODOLOGY

This part of the study is related to the methods and instruments that will be used in gathering and

analyzing data. It entails the research design; which is the blueprint of the study, population of

study; which helps in the choice of the sample, sampling technique and the research instrument,

procedure for analyzing data collected and method of data collection and analysis which involve

the strategy and procedure for summarizing and exploring relationships among variables being

considered in the investigation. They are presented under the following sub-headings; Design of

the Study, Area of the Study, Population of the Study, Sampling Size and Sampling Technique,

Instrument of Data Collection, Validity of the instrument, Reliability of the instrument, Method

of Data collection, Method of Data Analysis.

3.1 Design of the Study

36
This study will employ a descriptive survey design. Asika (2020) sees this design as suitable for

studies involving collecting data on opinions and feelings of respondents over a period of time. It

seeks to describe the variable associated with a phenomenon of interest.

3.2 Area of the Study

This study will be carried out in Umuahia North Local Government Area (LGA), Situated in

Abia State, southeastern Nigeria, Umuahia North LGA. The area encompasses the state's capital,

Umuahia. The area spans approximately 245 square kilometers. Geographically, it lies between

latitudes 5°31'54" N and 5°33'30" N, and longitudes 7°27'46" E and 7°29'47" E. The region

experiences a tropical climate with an average annual temperature of 27°C and distinct dry and

rainy seasons.

Umuahia North LGA was established in August 1991. It comprises several communities,

including Umuahia, Umukabia, Umuawa Alaocha, Amaogwugwu, Umuagu, Umuekwule,

Ofeme, Umuda Isingwu, and Nkwoegwu. As the administrative center of Abia State, Umuahia

hosts various governmental institutions and serves as a hub for educational activities.

According to the 2006 census, Umuahia North LGA had a population of 220,660. However,

more recent estimates suggest that the population has grown to approximately 324,900 by 2022.

The area is predominantly inhabited by the Igbo ethnic group, with Igbo language widely spoken

and Christianity being the prevalent religion.

The economy of Umuahia North LGA is diverse, with a significant emphasis on trade. The

region hosts notable markets such as the Ubani Main Market and the Industrial Market in

Azueke Ndume Ibeku, attracting numerous buyers and sellers of various commodities.

37
Additionally, the area features several banks, hotels, industries, and government-owned

establishments, contributing to its economic vitality.

Umuahia North LGA boasts a robust educational infrastructure, encompassing numerous

primary and secondary schools. The presence of these institutions underscores the area's

commitment to academic excellence and provides a conducive environment for studies focusing

on educational methodologies, such as the impact of e-learning on student performance.

3.3 Population of the study

The population of the study comprised all SS1 students in government senior secondary schools

in Umuahia North Local Government Area, Abia State. According to the Abia State Secondary

Education Management Board (SEMB), there is a total population of 1,000 SS1 students across

the 13 government secondary schools in the area. The focus on SS1 students is to ensure a

consistent academic level for evaluating the effect of e-learning on academic performance in

Computer Science.

3.4 Sampling Size and Sample Technique

The researcher will employ a random sampling technique to select Computer Science students

from government secondary schools in Umuahia North Local Government Area. Sample size of

Two Hundred and Eighty-Six (286) students out of 1000 students will be selected. The sample

size will be determined using the method of Yaro Yamane’s formula for a finite population:

N
n= 2
1+ N (e )

Where;
n= The sample size.
N= The finite Population.

38
e= Level of significant or (limit of tolerable error) which is taken as 0.05.
1= Unity (a constant).
Example Population of 1000
N=1000, e=0.05

1000 1000 1000

n= = = = 285.7 = 286
1+ 1000¿ ¿ 1+ 1000¿ ¿ 1+ 2.5

Out of the thirteen government secondary schools in Umuahia North, five schools were

randomly selected to participate in the study. The selected schools are:

1. Ibeku High School, Umuahia

2. Urban Girls Secondary School, Umuahia

3. Government College, Umuahia

4. Ndume Otuka Secondary School, Umuahia

5. Afugiri Secondary School, Umuahia

3.5 Instrument of Data Collection

A structured questionnaire will be designed and will be the main instrument for data collection.

The questionnaire will consist of two sections, section A and section B. Section A will comprise

of questions relating to biography of the respondents i.e. (age range, sex, class e.tc) that are clear

and not personal, while Section B will consist of item statement which are derived from the

research question. A total of 286 questionnaires will be distributed to selected senior secondary

school students in Umuahia North LGA. The responses will be measured using a four-point

Likert scale as follows: Strongly Agree (SA) = 4 points Agree (A) = 3 points Disagree (D) = 2

points Strongly Disagree (SD) = 1 point

3.6 Validity of the Instrument

39
The instrument will be face and content validated by three experts, two from Computer Science

Education Department and the other from Measurement and Evaluation Unit in Michael Okpara

University of Agriculture, Umudike, Abia State. Their corrections and inputs will be taken into

consideration while producing the final draft of the questionnaire.

3.7 Reliability of the Instrument

The reliability of the instrument will be determined using the test-retest method. A pilot study

will be conducted in a secondary school outside the study population to assess the consistency of

the questionnaire responses. The questionnaire will be personally administered by the researcher

to 30 senior secondary school students, ensuring they complete the instrument under similar

conditions as the main study. The completed questionnaires will be collected the same day after

the respondents have provided their answers. After an interval of two weeks, the same

questionnaire will be re-administered to the same set of students under similar conditions. The

responses from the first and second administrations will be analyzed using Pearson’s Product

Moment Correlation Coefficient to determine the reliability index. A reliability coefficient of

0.70 and above will be considered an indication of high reliability, confirming the instrument's

consistency for the study.

3.8 Method of Data Collection

The researcher will personally administer the questionnaires to the selected senior secondary

school students in Umuahia North Local Government Area. Before distributing the

questionnaires, the researcher will obtain permission from the school administrators and explain

the purpose of the study to the respondents to ensure their cooperation. Each selected student will

receive a copy of the questionnaire and will be given adequate time to complete it. The
40
completed questionnaires will be collected on the same day to minimize the risk of loss or non-

response. To ensure accuracy and completeness, the researcher will review the collected

questionnaires before leaving each school. This approach will help improve response rates and

ensure the quality of data collected for the study.

3.9 Method of Data Analysis

The data collected will be analyzed and the results would be presented using the weighted mean.

A four point liker scale item would be developed using a decision rule which consist of the

elements; SA- Strongly Agree, A- Agree, D- Disagree, SD- Strongly Disagree.

Formulae: The formulae used for calculation is as follows

X= ∑FX

Where X = Mean

X = Score

F = Frequency

N = Number of responses

∑= Sigma of summation

DECISION RULE

41
 SA = 4

A = 3

D = 2

SD = 1

-----

10

The mean value therefore X = 4+3+2+1

=10\4

=2.5

Therefore, mean ratings 2.5 and above indicates agree while mean ratings of 2.49 and below

indicates disagree.

42
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