Self-Regulation in Higher Education: Students’ Motivational, Regulational and Learning Strategies, and Their Relationships to Study Success
Bibliographic record
Abstract
Abstract This study investigates how in the self-regulation of learning (SRL; Pintrich 2000; Zimmerman, 2000), the motivational and affective factors are related to regulation strategies of behaviour and context, and learning strategies - and identifies different profiles in SRL. The study also aims to explore which factors of SRL are related to study success and study progress during master degree studies. The data consist of undergraduate students’ (N = 1248) responses to IQ Learn self-report questionnaires, and of data (n = 229) retrieved from the university ’ s study register. The results revealed that the sub-processes of SRL: motivational and affective components, regulation strategies and learning strategies are systematically related with each other. In addition, motivational and affective factors, especially Intrinsic motivation predicted the use of strategies regulating behaviour and context and the use of learning strategies. Study success correlated slightly positively with accumulation of credits indicating that students with better grades proceed efficiently in their studies. Yet, accumulation of credits was evidenced to relate slightly and negatively with expectancy components of SRL and the use of deep learning strategies. Finally, three student profiles in SRL were encountered: (1) Aiming high with insufficient SRL, (2) Excellent in SRL, and (3) Distressed performers. Educational implications and the needs for future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".