Factors Affecting General and Online Academic Achievement of University Students: Online Self-Regulated Learning, Online Self-Efficacy, and Motivation Scores
Bibliographic record
Abstract
This study aims to determine whether university students' levels of online self-regulated learning, online technologies self-efficacy, and motivated strategies for learning predict their general academic achievement and online academic achievement. In this study, the scanning design and the prediction research design were used. The participants of this research consisted of 55 undergraduate students studying in different departments of a university in Western Canada. The data were collected with “Online Technologies Self-Efficacy Scale (OTSES)” developed by Barnard et al., (2009); “Motivated Strategies for Learning Questionnaire (MSLQ)” developed by Pintrich et al., (1991); “Online Self-regulated Learning Questionnaire (OSLQ)” developed by Miltiadou and Yu (2000); and “demographic form”. This study did not determine a significant relationship between university students' total scores of OSLQ, OTSES, MSLQ, and their GPAs. Instead, the study found that university students' total scores of OSLQ, OTSES, MSLQ did not significantly predict their GPAs and online GPAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".