Interplay of student characteristics multidimensional engagement and influencing factors in online computer science education

Digital technology is reshaping the landscape of higher education, especially in the field of computer science. As digital platforms become central to the learning process, understanding student engagement in these continuously evolving environments is increasingly vital. This study explores the current state of online learning among undergraduate computer science students, investigates factors influencing their engagement, and proposes strategies to enhance online education. The research framework is grounded in the TPACK model, behaviorist learning theory, learning engagement theory, and situated cognition learning theory, encompassing student characteristics, multidimensional online learning engagement, and key influencing factors. Data on participants' basic attributes, levels of engagement, and the major determinants of these engagement levels were collected via a questionnaire survey. Analyses using SPSS 25.0-employing t-tests, ANOVA, and Pearson correlation-revealed significant trends and relationships. Findings show a notable positive correlation between the duration of online learning and overall engagement, whereas gender, home location, and academic major exerted relatively limited influence. Subjective intention, attitude, and motivation emerged as crucial determinants, and interactions with instructors and Peers-reinforced by teaching approaches and feedback-played an essential role in fostering emotional involvement. Building on these insights, the study recommends initiatives to strengthen self-motivation, nurture meaningful online interactions, enhance technical support systems, and reinforce mechanisms for assessing learning outcomes. This work provides empirical evidence for a deeper understanding of online education while indicating directions for ongoing improvement.