Comparative Analysis of E-Learning Effectiveness in Computer Science Education Across Schools

Thesis Abstract

The rapid integration of e-learning platforms into computer science education necessitates a comprehensive evaluation of their effectiveness across diverse educational settings. This study addresses the persistent variability in e-learning outcomes among secondary and tertiary institutions by investigating the factors influencing its success and identifying best practices. The primary aim is to conduct a comparative analysis of e-learning effectiveness in computer science education across different schools, with specific objectives to assess student performance, engagement levels, and perceptions of e-learning tools; to examine the influence of instructional strategies; and to explore contextual factors such as infrastructural support and teacher preparedness. The study employs a mixed-methods research design, integrating quantitative and qualitative approaches to provide a holistic understanding of the phenomenon. The target population consists of computer science students and instructors from twenty secondary schools and ten higher education institutions across a metropolitan region with established e-learning programs. A stratified random sampling technique was used to select 600 students (300 from secondary schools and 300 from tertiary institutions) and 50 instructors, ensuring representative variation across educational levels and geographical distribution. Data were collected through structured questionnaires measuring student academic performance, engagement, and perceptions of e-learning effectiveness, complemented by semi-structured interview protocols with instructors to capture instructional and infrastructural insights. Quantitative data were analyzed using descriptive statistics, Analysis of Variance (ANOVA), and multiple regression analyses to identify significant differences and predictors of e-learning effectiveness across institutions. Qualitative data from interviews were subjected to thematic analysis, exploring contextual factors influencing e-learning experiences. Theoretical frameworks guiding the analysis include the Technology Acceptance Model (TAM) to evaluate user acceptance and Engagement Theory to understand student participation in digital learning environments. It is anticipated that the findings will reveal statistically significant differences in student performance and engagement levels attributable to variations in infrastructural adequacy, instructional methods, and student attitude towards e-learning. The study expects to identify key predictors of e-learning success, such as technological self-efficacy and pedagogical approaches, and to delineate context-specific challenges faced by institutions. These results will contribute to the body of knowledge by establishing empirical benchmarks and informing best practices for e-learning implementation in computer science education. The study concludes with recommendations for policymakers, educators, and institutions to optimize e-learning strategies, enhance infrastructural support, and foster positive student perceptions. It advocates for tailored teacher training programs and infrastructural investments to bridge the effectiveness gap observed across institutions. Furthermore, it suggests avenues for further research, particularly longitudinal studies to monitor the sustained impact of e-learning interventions and experimental designs to test targeted pedagogical innovations. In sum, this research aims to advance understanding of e-learning efficacy and foster evidence-based improvements in computer science education through contextualized, data-driven insights.