Effectiveness of Video Modeling in Teaching Computer Skills to Students with Intellectual Disabilities
Bibliographic record
Abstract
The purpose of this study is to determine whether video modeling is effective in teaching computer skills to students with intellectual disabilities. The study was designed with the multiple probe design across subjects, one of the single-subject research designs. The study was conducted with three female students with intellectual disabilities, who were 17–19 years old. Graphical analysis was used to analyze the data. The results show that video modeling was effective for them to acquire and retain skills for preparing a résumé, printing it out, and emailing it. However, the students had difficulties generalizing some of the skills on different computers and printers. It can be said that the reason for this originates from different designs of technological tools. In line with this, it is thought that removing the accessibility barrier in technological equipment will increase availability. The video modeling motivated students to learn computer skills. The participants reported that they could use these skills to do homework, apply for a job, and communicate with friends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".