The Importance of an Autonomy-Supportive Workplace and Engaged Students for Language Teachers’ Self-Determination and Engagement
Bibliographic record
Abstract
Teachers often face pressures both from above and from below as they adhere to their supervisors’ requirements and work with students who may be disengaged from the learning process. These interpersonal influences are argued to thwart instructors’ teaching motivation and result in a more controlled teaching experience and a more controlling teaching style, whereas supportive supervisors and engaged learners could promote a self-determined motivational orientation and greater teaching engagement. Regression analyses of survey responses collected from 63 English language instructors in Canada showed that supportive supervisors helped satisfy English language teachers’ psychological needs for autonomy, competence, and relatedness, which in turn promoted self-determined motivation and teaching engagement. Student disengagement undermined the satisfaction of instructors’ needs and led to more controlled teaching motivation, but student engagement enhanced teachers’ self-determined motivation and/or teaching engagement. These results underscore the importance of supervisory attentiveness to instructors’ psychological needs. As well, they suggest that whereas teachers might be inspired by engaged students, they might also need to find strategies to cope with the potentially negative impact of disaffected students on their own teaching motivation and engagement.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".