Factors Affecting the General Academic Achievement of University Students: Gender, Study Hours, Academic Motivation, Metacognition and Self-Regulated Learning
Bibliographic record
Abstract
This study aimed to determine the effects of university students' gender, weekly study hours, academic motivation, metacognition, and self-regulated learning levels on their overall academic achievement and to examine whether academic motivation, metacognition and self-regulated learning total scores predicted their GPAs. This study utilized a survey and prediction research design to analyze the research questions posed. The participants of the study consisted of 86 undergraduate students attending various programs of a university in Western Canada. The research data were collected using the “Metacognitive Awareness Inventory (MAI)” developed by Schraw and Dennison (1994), the “Self-regulated learning perception scale (SASR)” developed by Dugan and Andrade (2011), the "Academic Motivation Scale (AMS-C 28) College Version" developed by Vallerand, Pelletier, Blais, Brière, Senécal and Vallières (1992), and the “demographic form”. We found a significant relationship between the university students' self-regulated learning, metacognition and academic motivation scores, and their grade point averages (GPAs). We also determined that the total scores related to the university students’ self-regulated learning, metacognition and academic motivation significantly predicted their GPAs, and that the gender and weekly study hours of the university students did not have a significant effect on their self-regulated learning, metacognition, academic motivation and academic GPA.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".