The Impact of Learned Helplessness and Intervention Strategies on Academic Outcomes of Students with Learning Disabilities
Bibliographic record
Abstract
This study investigates the learned helplessness and intervention strategies in determining academic outcomes for students with learning disabilities. A quantitative research design was utilized where structured surveys were conducted on three main variables, which included learned helplessness, academic performance, and the moderating role of intervention strategies. The participants were approximately 100 students from different schools in the Balqa Governorate, Jordan, who were selected through stratified random sampling to ensure representation of gender, academic year, and urban/rural settings. Thus, it can be deduced that, with an overwhelming calculated statistical significance (P-values of 0.000), the two intervening methods in combination with learned helplessness profoundly shape academic performance. This is further supported by powerful T-statistics (4.002 and 5.601). Nonetheless, the moderating effect proved to be statistically nonsignificant (P = 0.084, T = 1.729), which indicates that the moderator was not important in centering the relationship between the predictors and academic results. This finding highlights the need for future research to examine why commonly applied intervention strategies may not buffer the negative effects of learned helplessness, especially considering intervention effort and time, educator preparation, and responsiveness to learner profiles. Gaining insight into these factors may help in developing more tailored and contextually appropriate intervention strategies for learners with learning disabilities.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".