Assessing Efficacy of Emotional Regulation Techniques on Alexithymia among Students Who Suffer From Dyscalculia
Bibliographic record
Abstract
Background: Dyscalculia tends to debilitating for children with respect to their social interactions and learning process. Although it can cause so many problematic consequences in developmental stages of children, by implementing precise and in time therapeutic intervention, it can be mitigated. Current paper examines the efficacy of emotional regulation techniques among students who suffer from dyscalculia. Materials and Methods: This study was experimental study with pre-test and post-test and control group. Statistical Society of this study included all students (4th, 5th, 6th grader), who suffered from dyscalculia in Bileh town (Urumieh Province/Iran) during 2013-2014(N=76). This study consisted 34 students, who suffered from dyscalculia. These students were chosen via systematic random sampling. Data were collected using Raven IQ test, Shalev mathematical Test, Alexithymia Scale, Psychological wellbeing questionnaire. Regarding analyzing data, MANOVA was used. Results: The results of multivariate analysis of covariance show that between case group and control, significant difference in components of alexithymia and psychological wellbeing were noticed. Hence, it can be inferred that emotional regulation strategies improves component of alexithymia and psychological well being of students with dyscalculia. Conclusion: According to results, teaching emotion regulation strategies to students considered to be effective in promoting awareness and positive attitudes. Thus, it is plausible that implementing these strategies tend to play major role as an intervention among students with dyscalculia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".