THE DEVELOPMENT OF SELF-DETERMINATION ACROSS THE LANGUAGE COURSE
Bibliographic record
Abstract
Abstract Research suggests that students put more effort into language learning when they feel that it is a voluntary and self-relevant activity or they enjoy the process of mastering that language (i.e., they have a more self-determined orientation). This orientation is fostered when learners feel autonomous, competent, and related to others in their learning environment. We followed 162 university students of French across one semester to examine these causal claims longitudinally. Latent growth curve modeling showed that feelings of autonomy, competence, and relatedness and self-determined motivation increased across the semester while engagement declined. Parallel processes growth curve modeling showed that declines in engagement across the semester were attenuated to the extent that self-determined motivation increased. Auto-regressive cross-lagged analysis showed that, contrary to expectation, more engagement as the semester started predicted greater self-determination mid-semester (instead of vice versa), but these relations became reciprocal from mid-semester on. These findings are consistent with a dynamic model of motivation that emphasizes the reciprocal interplay between motivational constructs over the duration of a language course. The implications of these findings for motivation theory and instructional practices are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 |
| 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".