The Role of Learning Approaches as Mediator between Peer Social Support and Self-Regulated Learning among Engineering Undergraduates
Bibliographic record
Abstract
This study examines the relationship between peer perceived social support, learning approaches and dimensionsin self-regulated learning. This study employed a quantitative method through a survey using questionnaireswhich were distributed to a total of 93 engineering undergraduates from the Universiti Teknikal MalaysiaMelaka. The Revised Study Process Questionnaire, LASSI and Multidimensional Perceived Social Support wereused to measure students’ beliefs about self-regulatory processes, knowledge, learning approaches, andperceived social support from peers. Statistical test for mediation was conducted using a series of regressionanalyses. Results indicate that out of nine dimensions of self-regulated learning, only three dimensions i.e.information processing (r=.22, p<.05), motivation (r=.37, p<.01), and self-testing (r=.32, p<.01) that were foundto be significantly associated with only deep learning approach dimension. Peer perceived social support wasfound to be associated with students’ information processing (r=.31, p<.01) and motivation (r=.26, p<.01). Itshows that peers also have significant role in the development of students’ ability in processing the informationand promote students’ needs of achievement. Based on the findings, the following theoretical and practicalapplications are suggested in order to be applied specifically among engineering undergraduates.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".