Relationship Model among Learning Environment, Learning Motivation, and Self-Regulated Learning
Bibliographic record
Abstract
This study applies social capital theory, motivation theory, and systems theories to examine the role of the learning environment and motivation in learning to encourage self-regulation in learning especially effort regulation. This study examines the relationship among learning environment (i.e., student cohesiveness, teacher support, involvement, investigation, task orientation, cooperation, and equity), learning motivation (i.e., learning goal orientation, task value, and self-efficacy), and self-regulated learning in effort regulation. This study also examines the mediating role of learning motivation on relation between learning environment and self-regulation in learning effort. Respondents were 307 students of undergraduate program on business, management, and economics in Yogyakarta and Bandung, Indonesia. Self-report questionnaires were administered to respondents during their regular class periods. Results revealed that students’ perception of learning environment on all dimensions were significantly related to learning motivation and self-regulation in effort regulation. Students’ perception of learning environment especially task orientation dimension was significantly influenced on three dimensions of learning motivation. The result of this study also indicated that learning goal orientation and self-efficacy are the mediating variables in the relationship model. These results supported many of the hypothesized relationships. Further explanations are discussed regarding both the expected and unexpected outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".