Assessing cognitive flexibility: Quantitative insights into the impact of adaptive learning technologies in special education
Bibliographic record
Abstract
This study employed quantitative methods to evaluate the influence of adaptive learning technology on the cognitive flexibility of students with special needs. Participants were recruited from special education schools using a purposive selection strategy. The Wisconsin Card Sorting Test (WCST) was utilized as a tool to assess cognitive flexibility. The data was analyzed using descriptive statistics, paired-samples t-test, correlation analysis, and regression analyses. The findings demonstrated a notable enhancement in WCST scores after the intervention, suggesting that adaptive learning technologies have a beneficial effect on cognitive flexibility. Regression studies revealed that various types of adaptive learning technologies had varied levels of efficacy, with Tech A showing the most significant beneficial impact. Surprisingly, demographic factors such as age, gender, and educational attainment demonstrated little and statistically insignificant associations with alterations in cognitive flexibility levels. The findings emphasize the potential of adaptive learning technologies as effective therapies for improving cognitive flexibility in kids with special needs. It underscores the significance of evaluating specific characteristics and design principles to maximize their efficacy.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".