How Does Cooperative Learning Affect Self-Confidence in Children with Mild Intellectual Disabilities in Saudi Arabia? Systematic Review
Bibliographic record
Abstract
Despite national policies promoting inclusion in Saudi Arabia, a significant gap between policy and practice persists, with many students with disabilities educated in segregated settings. Collaborative learning is an evidence-based strategy known to support inclusion; however, there is a lack of synthesized evidence on its effectiveness in the Saudi context. Hence, this study aimed to systematically identify, appraise, and synthesize all available evidence on the effects of collaborative learning strategies on academic and/or social-emotional outcomes for students with disabilities in Saudi Arabia. A systematic review was conducted following PRISMA guidelines. Key databases, including ERIC, Scopus, and PsycINFO, were searched using a predefined strategy. Studies were selected based on PICOS criteria, with data extracted and methodological quality appraised using the QualSyst tool. The search yielded 80 records, from which three studies met the full inclusion criteria. The evidence was positive instead although some limitations. The studies included a quasi-experiment, a qualitative case study, and a mixed-methods design. These studies reported improvements in academic outcomes. Perceived benefits for social-emotional skills, including self-confidence and collaboration, were also noted. However, the methodological quality of the evidence base was moderate, limited by small sample sizes and a lack of controlled trials. The study concluded that there is a profound scarcity of high-quality research on this topic in Saudi Arabia. While the limited available evidence is promising, it must be interpreted with caution. This review highlights an urgent need for more rigorous primary research, particularly studies that objectively measure the impact of collaborative learning on the self-confidence of students with mild intellectual 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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".