Self-directed Learning in Preparatory-year University Students: Comparing Successful and Less-successful English Language Learners
Bibliographic record
Abstract
There is consensus among those involved in teaching English as a foreign language (EFL) in the Saudi educational context that students’ achievement in language learning is below expectations. Much research has been directed towards finding the reasons for low achievement amongst learners. However, very few studies have looked at parameters of learners’ agency and learners’ responsibility in the learning process. This study examines learners’ efforts at self-directed learning, measured with reference to a set of behavioral and metacognitive constructs. The primary objective is to diagnose efficiency problems in EFL learning and compare successful learners to those who fail to progress from one academic language level to the next. A secondary objective in this study is to find out if the General Aptitude Test (GAT) score is a predictor of success in language learning. The findings reveal significant differences between successful learners and less-successful learners in aggregate self-directedness scores. However, while the analysis of the component constructs shows statistically significant differences between successful and less-successful learners in the self-management and study time measures, differences in the self-monitoring and motivation measures were non-significant. The lack of significant differences between some of the measures is attributed to the relative baseline similarity of the two groups. Moreover, the GAT measure yielded a counter-intuitive result; namely that less-successful learners had higher GAT scores than the successful ones, though the difference wasn’t statistically significant. The study concludes with implications for further research; for example, calling for a larger scale investigation of self-directedness, as well as other meta-cognitive strategies, and the possible relationship of these to GAT scores. Academic coaching of self-directedness and self-regulation strategies for college students is recommended.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".