Instructional Media for Autistic Spectrum Disorder (ASD) Children: A Study on Need Analysis
Bibliographic record
Abstract
ASD children exhibit social functioning issues that indicate difficulty communicating with others and often do not understand why they have to have social interactions with other people. One way of helping them is to develop media based on their needs. This research aimed to analyze the specifications of the media required. The study is designed as a mixed-methods study. The data were collected using a questionnaire in the form of a survey and combined with interviews. The respondents to the survey consisted of 24 respondents from Malaysia, the Netherlands, Canada, and Indonesia, and include teachers from inclusive schools. The selection of the sample used purposive sampling techniques to ensure that they had background knowledge either in education, linguistics, or special education and were willing to be included in the research. The data were analyzed qualitatively and quantitatively. The results of the study show that the media have essential specifications that are: the media should be in the language that supports their learning; the media developed should be able to establish their purpose; the media developed should be able to provide learning support; the media should be in different variations to provide meaningful learning; the media should consider various factors that can affect their ability to access and benefit from different forms of media; the media should be meaningful for assessing students' progress and achievement; and the media should provide various functions. These specifications are the basis for developing the media based on the needs of students with ASD.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".