The Role of Social Media for Collaborative Learning to Improve Academic Performance of Students and Researchers in Malaysian Higher Education
Bibliographic record
Abstract
Social media is widely considered to improve the collaborative learning among students and researchers. However, there is a surprising lack of empirical research in Malaysian higher education to improve performance of students and researchers through the effective use of social media that facilitates desirable outcomes. Thus, this study offers a review of the empirical literature, and its distinctiveness stems from the focus on collaborative learning and engagement in literature, as dominated by higher education. This study also aims to explore factors that contribute to the enhancement of collaborative learning and engagement through social media. It is also unique in that it highlights that the effective use of social media depends on users in what is referred to as social interactivity to "collaborative learning, engagement and intention to use social media" - a phenomenon that relies on the theory of social constructivist learning. The findings showed that collaborative learning, engagement and intention to use social media positively and significantly relate to the interactivity of research group members (students and researchers) with supervisors to improve their academic performance in Malaysian higher education.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".