Chinese students’ boredom and burnout and their prediction by autonomy supportive learning climate: a latent growth curve modeling
Bibliographic record
Abstract
Boredom is a prevalent emotional experience in everyday life that negatively impacts an individual's well-being, mental health, and social performance. Students’ burnout is an additional manifestation of poor mental health. Among various elements influencing students’ boredom and burnout, educational-related concepts are prominent as the autonomy-supportive learning climate (ASLC) has been at the center of attention recently as a factor associated with a great level of significant learner consequences. Based on Self-determination Theory (SDT), the importance of a positive ASLC, learning encouragement, and constructive motivation from teachers has been approved in learning as it has a positive effect on learners’ achievement. Accordingly, this study makes efforts to investigate the efficacy of an ASLC that promotes autonomy in reducing students’ boredom and burnout. To this end, 798 respondents from three colleges and universities, who were taught in an ASLC context, participated in this study. They filled out the three questionnaires, boredom, burnout, and ASLC at the onset, middle, and end of the semester. The results through the Latent Growth Curve Modeling (LGCM) as a dynamic research approach, revealed that positive changes in ASLC over time are linked to further reductions in students’ boredom and burnout. Similarly, it can be stated that students with higher perceived levels of ASLC tended to experience greater changes in their levels of boredom and burnout over the course. ASLC uniquely predicts about 50% of the variance in boredom scores and about 61% of the variance in burnout scores. Finally, some implications for academic stakeholders are provided.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".