Comparative Analysis of E-Learning Engagement in University Computer Science Courses

Thesis Abstract

The rapid proliferation of e-learning platforms within higher education, particularly in computer science programs, necessitates an understanding of student engagement dynamics to optimize instructional effectiveness and learning outcomes. Despite widespread adoption, there remains limited empirical evidence comparing levels of student engagement across different e-learning modalities and delivery methods in university computer science courses. This study aims to conduct a comparative analysis of e-learning engagement in university computer science courses, with specific objectives to (1) measure and compare students’ behavioral, emotional, and cognitive engagement levels in online versus blended learning environments, and (2) identify key factors influencing engagement within each modality. The research employs a mixed-methods approach, integrating quantitative survey data with qualitative insights to provide a comprehensive understanding of engagement patterns. The study population comprises undergraduate computer science students enrolled in publicly funded universities within Mountain State University system, totaling approximately 4,500 students. A stratified random sampling technique will be used to select 600 participants, ensuring proportional representation across year levels and course types. Data collection instruments include the Student Engagement Scale (SES), adapted for online contexts, and semi-structured interview guides to capture nuanced perspectives. Validity and reliability of the instruments will be established through pilot testing, Cronbach’s alpha coefficients (above 0.80), and expert review. Quantitative data will be analyzed using Analysis of Variance (ANOVA) and multiple regression analyses to examine differences and predictors of engagement, while thematic analysis will be employed to interpret qualitative interview data. Theoretically, the study is grounded in the Self-Determination Theory (Deci & Ryan, 1985), emphasizing intrinsic motivation and relatedness, and the Community of Inquiry framework (Garrison, Anderson, & Archer, 2000), which highlights the importance of social, cognitive, and teaching presence in online learning environments. Expected findings indicate that students in blended learning modalities demonstrate significantly higher engagement levels across behavioral, emotional, and cognitive domains compared to those in purely online courses, with factors such as technological self-efficacy, instructor presence, and peer interaction emerging as significant predictors. The study anticipates identifying critical engagement gaps and barriers unique to each modality, providing evidence-based insights for instructional design improvement. This research contributes to the existing body of knowledge by systematically comparing engagement metrics across different e-learning environments within university computer science curricula, thereby filling a notable gap in the literature. It advances theoretical understanding by empirically testing the applicability of established engagement frameworks in diverse e-learning settings and offers practical implications for educators and policymakers aiming to enhance student participation, retention, and success in digital learning contexts. The findings are expected to inform targeted strategies for improving e-learning engagement, including enhanced pedagogical practices, technology integration, and student support mechanisms. The main conclusion underscores that blended learning promotes higher engagement levels, driven by greater social presence and instructor interaction, whereas online-only courses face engagement challenges related to technology anxiety and reduced peer connectivity. Recommendations include integrating interactive elements, fostering online communities, and providing digital literacy support to enhance engagement, particularly in fully online courses. The study further suggests avenues for future research, such as longitudinal investigations into engagement trends over academic cycles and the exploration of engagement's impact on academic performance in computer science disciplines. Overall, this study provides comprehensive, data-driven insights that are crucial for evolving e-learning strategies amidst the ongoing digital transformation of higher education.