Self-Determination and Classroom Engagement of EFL Learners: A Mixed-Methods Study of the Self-System Model of Motivational Development
Bibliographic record
Abstract
This study examines the antecedents and outcomes of classroom engagement of 412 Turkish English as a Foreign Language (EFL) learners. Grounded in self-determination theory and the self-system model of motivation, this mixed-methods study examined the relations between context (perceived autonomy-support from the instructor), self (basic psychological needs), action (behavioral, emotional, agentic, and cognitive engagement), and outcome (achievement and absenteeism). The results of structural equation modeling supported the hypothesized model and showed that learners’ perception of their teachers’ autonomy-support within the classroom predicted their need satisfaction, which in turn predicted self-determined engagement. Engagement predicted achievement and absenteeism within English courses. Semi-structured interviews showed patterns consistent with the quantitative results, and also that students felt their engagement would best be supported in classes with a positive social atmosphere. As well, their comments underscored the important role of language teachers in supporting learners’ psychological need satisfaction, classroom engagement, and positive academic outcomes. The findings suggest strategies for English language educators to bolster students’ engagement within the classrooms, including students who seem to be unmotivated, reluctant language learners.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".