Bibliographic record
Abstract
Robotics is an area of science, technology, engineering, and math (STEM) that has gained widespread and lasting popularity, and recent descriptive analysis suggests it may be the ideal forum for developing shared interests, enhancing communication, and coding skills with peers (Authors, 2018). Robots can naturally motivate children with ASD (Dautenhahn & Werry). When this interest in robotics is shared with peers, children with ASD may feel more motivated, comfortable, and competent with their peers, leading to the development of social/communication skills, mutual enjoyment, additional coding skills, and perhaps even friendships. In order to promote more positive outcomes in this vulnerable population, development of interventions to help children with their social competence should be of high priority. Four studies were conducted to examine the effectiveness of teaching coding of robotics to children and youth with ASD and their typically developing peers on their growth in social/communication skills and coding skills. Data were collected throughout the duration of the studies and analysis show that students can learn coding skills. While the descriptive study suggests social/communication skills also increased, additional research is needed to verify these initial findings. Results will be discussed in terms of implications for implementation and future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".