Self-determination across the secondary school years: how teachers and curriculum policy affect language learners’ motivation
Bibliographic record
Abstract
Motivation is argued to be a critical predictor of language learning success, but it is not clear whether motivation is equally relevant across compulsory and optional language education contexts. This study explored the motivation of adolescent Anglophone students of other languages across secondary school year groups with a particular interest in the impact of choice and curricular structure. Based on Self-Determination Theory, we developed a model that maintains that perceptions of autonomy support predict learners' sense of autonomy, in turn enhancings motivation. Through a survey of 1775 students aged 11-16, we tested whether this model holds for learners from different year groups, and in later years, across those in schools with and without mandatory language education. We found that all learners reported less autonomy frustration and were more likely to report a more autonomous form of language learning motivation if they perceived their language teacher as autonomy-supportive, but that as learners progressed through school perceptions of autonomy support declined. Further, we found that motivation was strongly associated with curriculum policies providing choice. These differences in motivational profiles across year group have implications for how teachers might support students' across different years and for programmatic adaptations that might facilitate students' learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".