Personality Traits as Predictors of Self-Regulated Learning and Academic Engagement among College Students in Ghana: A Dimensional Multivariate Approach
Bibliographic record
Abstract
The study explored personality traits as they predicted self-regulated learning and academic engagement among college students in Ghana. A sample of 652 (return rate was 87.0%) was drawn from an accessible population of 17,396. Adapted versions of Taiwanese Short Self-Regulation Questionnaire (22 items; α = 0.84), University Student Engagement Inventory (15 items; α = 0.81), and Big-Five Personality Inventory (30 items; α = 0.70) were used for the data collection. The data collected were analysed using multivariate multiple regression. The study revealed that student-teachers exhibited lower levels of self-regulated learning and academic engagement. Again, openness, conscientiousness, extraversion, and agreeableness aspects of the personality traits predicted self-regulated learning and academic engagements of students. Findings from this study serve as a beacon for teacher education programs in Ghana to scale up their efforts in ensuring that preservice teachers are able to self-regulate their learning. As preservice teachers who will soon be practicing, they cannot help their students self-regulate their learning if they themselves have low levels of self-regulation and engagement. Students’ success can only be realized when learners are able to manage their own learning and engage in academic activities.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".