Integrating Serious Games in the Educational Experience of Students with Intellectual Disabilities
Bibliographic record
Abstract
The purpose of this paper is to present a series of observations made by researchers and educators on the integration of serious games in the educational experience of users with intellectual disabilities (ID). Data were gathered from four different studies and different games were used, in order to identify a successful model of games based learning application. Moreover, results that highlight the motivational importance of playful integration towards the promotion of self determination in students with ID, will be presented. According to the authors’ findings, special education can be benefited from the successful integration of digital games in the educational scenario, creating a safe and personalized educational environment for the students, as well as a valuable motivational tool for the educator - especially when the educator takes a threefold role, able to support a hybrid model of digital and non digital play. Trying to assist the educational efforts of special education teachers, the authors will present the results of a series of case studies and applications, the role of the educator, as well as practical considerations that resulted in the sketch of a model of playful game-based learning integration.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".