Relationship Between Emotional Self-Control, Alexithymia and Educational Emotional with Academic Burnout in Students with Learning Disorder
Bibliographic record
Abstract
Introduction: One of the objectives of educational systems is to raise the level of students' ability to improve their academic performance. The aim of present study was to determine the relationship between emotional self-control, alexithymia, and academic excitement with academic burnout in students with learning disabilities. Method: The population of this study was all clients with learning disabilities who referred to the Learning Disabilities Centers in Tehran in 2021. The sampling method was cluster random sampling. Ten centers were selected from the centers of learning disabilities. Then, 150 students with the l learning disabilities were selected to participate in this study. The data was collected by Bresso Academic Burnout Questionnaire, Toronto Emotional Dysfunction Scale,Bakran Academic Emotion Questionnaire, and Weinberger & Schmaltz (srs) Self-Restraint Scale(1990). Results: The results showed that negative academic excitement, emotional self- control, and positive emotion were able to predict students' academic burnout. The collected data were analyzed using Pearson correlation coefficient test and stepwise multivariate regression analysis. Negative academic excitement (P<0.001, t= 4.91, β= 0.348) with the highest value of B and then self-control (P<0.001, t= 4.18,β=-0.288) and positive emotion (P<0.001, t= -3.42, β= -0.235) could predict academic burnout, respectively. Conclusion: Based on the findings, training the life skills related to emotion management, emotion regulation, and improving emotional intelligence should be widely and seriously considered. By training these skills, students' level of self-management can be improved and educational burnout can be prevented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".