Video Prompting to Teach Robotics and Coding to Middle School Students With Autism Spectrum Disorder
Bibliographic record
Abstract
Video-based modeling is an evidence-based practice for teaching social and communication skills, functional and daily living skills, and some academic skills (i.e., math) to students with autism spectrum disorder. The efficacy of video-based modeling, however, has not yet been established for STEM skills related to science, technology, or engineering. Drawing on findings from a systematic review of video-based modeling to teach academic skills to students with autism spectrum disorder and/or intellectual disability, researchers used a single-case study design to examine the efficacy of video-based modeling for teaching robotics and coding to students with autism spectrum disorder. Specifically, researchers used a multiple probe across skills single-case research design replicated across three middle school participants to teach block-based coding of robots. This afforded three intraparticipant replications and three interparticipant replications. A functional relation between the use of systematic video prompting and mastery of robotics coding skills was demonstrated. Further, to substantiate the social and ecological validity of video-based modeling interventions for public school settings, a special education teacher implemented the intervention in a special education classroom. Additionally, questionnaires were disseminated to study participants and public school special educators naive to the study purpose and outcomes to assess the social validity (i.e., feasibility and effectiveness) of the intervention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".