The Effectiveness of Emotional Intelligence Training on Alexithymia Components in Students with Learning Disabilities
Bibliographic record
Abstract
Objective: This study aimed to investigate the impact of emotional intelligence training on reducing alexithymia symptoms in students with learning disabilities. Methods and Materials: The study employed an experimental design with a pretest-posttest framework and a control group. Sixty middle school students diagnosed with learning disabilities and alexithymia were randomly assigned to experimental (n = 30) and control (n = 30) groups. The experimental group participated in an 8-week emotional intelligence training program, consisting of 16 sessions focusing on self-awareness, emotional regulation, empathy, and relationship management. Data were collected using the Bar-On Emotional Quotient Inventory (EQ-i) and the Toronto Alexithymia Scale (TAS-20). ANCOVA and descriptive statistics were used for data analysis. Findings: Results revealed significant improvements in the experimental group compared to the control group. Posttest scores for the experimental group showed an increase in emotional intelligence (M = 90.23, SD = 4.89) and a reduction in alexithymia symptoms, including difficulty identifying emotions (t = 45.56, p < .001) and difficulty describing emotions (t = 48.26, p < .001). ANCOVA results demonstrated a significant difference between the groups, with a large effect size (η² = 0.74). The control group showed minimal changes in both emotional intelligence (M = 83.95, SD = 5.28) and alexithymia symptoms. Conclusion: The findings indicate that emotional intelligence training is an effective intervention for reducing alexithymia symptoms and enhancing emotional competencies in students with learning disabilities. These results support the integration of emotional intelligence training into educational and therapeutic programs to improve emotional and social outcomes in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".