Relationship Between Emotional Self-Control, Alexithymia and Educational Emotional with Academic Burnout in Students with Learning Disorder
Bibliographic record
Abstract
Background and Objectives: 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. Materials and Methods: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.Conclusions: 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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".