Online Student Engagement: The Overview of HE in Indonesia
Bibliographic record
Abstract
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 this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".