Learning Analytics for Data-Driven Decision Making: Enhancing Instructional Personalization and Student Engagement in Online Higher Education

Abstract

This study examines the use of learning analytics to enhance instructional personalization and student engagement in online higher education. The research focuses on the engagement levels of students based on different access methods (mobile and non-mobile), the relationships among engagement indicators, and the strategies for instructional personalization. Quantitative research methodology is employed to analyse and measure students’ engagement levels. The findings indicate that students using non-mobile devices exhibit higher engagement in terms of average minutes, item accesses, and content accesses, while mobile access shows higher engagement in terms of course accesses, course interactions, and average interactions. Significant correlations are observed among engagement indicators, highlighting the importance of course interactions, content accesses, and assessment accesses in promoting student engagement. Accordingly, a critical model for effective student engagement in online learning courses is proposed.