Assessing the Impact of an Adapted Robotics Programme on Interest in Science, Technology, Engineering and Mathematics (STEM) among Children with Disabilities
Bibliographic record
Abstract
This study assessed the extent to which an adapted robotics programme fostered interest in science, technology, engineering and mathematics (STEM) among children with disabilities. This study included pre- and post-programme surveys. The sample involved 57 children with disabilities who participated in an adapted robotics programme held in a pediatric hospital. There were two main forms of the programme: junior group (aged 6–9) and intermediate group (aged 10–14). Statistical analyses showed that although both groups of children perceived they gained at least some knowledge about computing/robotics from the programme, juniors were significantly more likely to report learning a lot from the programme than intermediates. Further, the junior group showed a significant increased desire to pursue future careers in computing/robotics after the programme. However, the intentions of either group to actually study computing/robotics at school did not significantly increase. A thematic analysis of open-ended survey responses revealed that the intent of both groups of children for participating in the programme along with what they enjoyed the most during the programme was linked to STEM, socialisation and teamwork. Additionally, while the majority of the intermediate group liked everything about the programme, the majority of the junior group reported on some things they disliked.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".