The Interconnection of Motivation and Self Regulated Learning Among University Level EFL Students
Bibliographic record
Abstract
The main aim of this research is to investigate learners in higher education in a Turkish context, in terms of motivational components such as goal orientation, self efficacy, intrinsic value, test anxiety and self-regulated learning components such as cognitive strategy usage and self regulation. The study was carried out with 233 students in higher education enrolled in the English Language and Literature department. Descriptive, variance and correlation analyses were carried out to answer the research questions. The results showed that the participants were reported to have satisfactory level of goal orientation, self efficacy, intrinsic value, test anxiety, cognitive strategy usage and self regulation. ANOVA results indicated that there were statistically significant differences between the three types of students, regular (daytime), evening, and distance education, with regard to goal orientation and self-efficacy. Furthermore, correlation analysis suggested that there was a moderate level of correlation between self-regulation and cognitive strategy usage. This research on the whole, infers that self-regulated learning means empowering the student to take charge of their motivation and educational pathway, and that while doing so, teachers should keep in mind that the classroom remains a formal environment that still requires self-efficacy and self-regulation and these are all interrelated.
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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 | 0.000 |
| 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) | 0.001 | 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".