A PILOT STUDY OF THE SITUATED GAME FOR AUTISTIC CHILDREN LEARNING ACTIVITIES OF DAILY LIVING
Bibliographic record
Abstract
Daily living skills are difficult for autistic children to learn because they have low motivation in learning new things. Some research had developed virtual environments to assist parents and teachers in teaching autistic children daily living skills. Educators still need to spend a lot of time in preparing personalized and more realistic tasks for children to practice in the virtual environments. The research team developed a situated game which is capable of generating personalized and non- repeated daily living activities for individual children. A small pilot had designed and conducted for verifying the effectiveness of the game and gathering the users’ (including parents and the autistic children) perceptions toward the game and the game-play. Questionnaire and interviews were used to collect user perceptions. While quantitative analysis method (with SPSS) was used to give readers an overview idea of what users felt, thematic analysis (with NVivo) was taken for analyzing interview transcripts and results could be the basis of our game’s future improvements. The results show that both of autistic children and their parents all gave positive feedback to the game. Suggestions for the game development for autistic children are also given based on the analysis results of questionnaire and parents’ interview.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".