The Effectiveness of Attribution Retraining on achievement motivation and attributional style of Children specific learning disabilities
Bibliographic record
Abstract
The current study intended to examine the effectiveness of group attributional retraining on achievement motivation and attributional style of children specific learning disabilities in Tehran.This study utilized a quasi-experimental design with pretest-posttest and control group. Using purposive and convenience sampling method, 30 individuals (18 male and 12 female) and(age=8-12) were selected from all students with learning disabilities in Tehran (2015-2016), and they were equally assigned into two groups (experimental and control). The experimental group received group attributional retraining for 11 sessions of 45 minutes in six weeks while the control group only received the regular education in learning disabilities centers. Before and after the intervention, Children Attributional Style Questionnaire, and Hermans Achievement Motivation Questionnaire were administered for both groups. The data were analyzed through multivariate analysis of covariance. Attributional retraining resulted in a significant increase achievement motivation and improved optimistic attributional style and reduced pessimistic attributional style in students with learning disabilities in experimental group. According to the findings, one may conclude that attributional retraining may increase achievement motivation and improved optimistic attributional style and reduced pessimistic attributional style in students with specific learning disabilities.
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 | 0.001 | 0.004 |
| 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".