A Proposed Paradigm for the Requirements for Designing and Using Digital Games-Based Learning by Educable Intellectual Disabled Children
Bibliographic record
Abstract
This study aims to shed further light on how to utilize digital games in education as a contribution to developing educable intellectual disabled children's teaching and learning practices via identifying the requirements for their design and use at those children's classes. Notably, the researchers focused on using digital games in promoting educable intellectual disabled children's learning experiences highlighting their different practical mechanisms and teaching practices based on literature review. Also, types of digital games capable of fulfilling the teaching requirements of those children were identified by making a list of the requirements for designing and using digital games-based learning at educable intellectual disabled children's classes consisting of 38 various requirements divided into 3 major dimensions, namely: (1) educational requirements for using digital games-based learning; (2) instructional design requirements for using digital games-based learning; and (3) practical application requirements for using digital games-based learning. Then, the proposed list was applied to a sample consisting of 25 faculty members at 5 Egyptian universities. Following data statistical analysis, it was revealed that the questionnaire total mean score is 2.73 with a relative weight of 90.1%. In a nutshell, such values verify that the questionnaire all proposed requirements are, indeed, very important for using digital games-based learning at educable intellectual disabled children's classes.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".