Online Student Engagement: The Overview of HE in Indonesia




student engagement, online learning, mixed-method, Indonesia


The use of technology in higher education learning has been shown to increase student engagement. However, how its application can increase student engagement is still largely unreported in Indonesia, especially during and after COVID-19, when online learning was used massively and suddenly. This study aims to examine students’ engagement with online learning using a sequential explanatory mixed-method study design that is expected to produce in-depth information. The study involved a number of n = 775 students, with 149 participants who identified themselves as male (19.3%) and 626 participants who identified themselves as female (80.7%). The age range of the participants was 18 to 22 years (M-age = 20.12). Quantitative data analysis was carried out using descriptive tests and ANOVA variance tests, while qualitative data analysis was carried out using thematic analysis. Integration of quantitative and qualitative data analyses results was conducted using a joint display approach. The results showed that 94.45% (n = 732) of students had low engagement scores. Gender and field of study were found to have no effect on the level of student engagement in online learning (F 1,775 = 3.259, p = .071, η2 = .004). Data integration results showed that online learning reduces emotional attachment, participation, and performance, although it does not reduce students’ skill engagement. Based on student experience, online learning is considered less effective than in-person learning. Students with higher self-regulation show engagement in online learning. The online learning model needs an effective formula for increasing student engagement, in addition to help students develop self-regulation skills.


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How to Cite

Kristiana, I. F., Prihatsanti , U., Simanjuntak, E., & Widayanti, C. G. (2023). Online Student Engagement: The Overview of HE in Indonesia. The International Review of Research in Open and Distributed Learning, 24(3), 34–53.



Research Articles