Improving Motor Skills of Students with Disabilities via Engineering Education
Bibliographic record
Abstract
Schools in Montreal have adopted variouskinds of technologies and programs to help students withlearning or speech impairments succeed based on one ofthe Quebec’s Education Minister’s mandates, notablythrough integrating these tools into the classroom. At LaSocieté des Handicapés du Québec, students (ages 3-18)get the opportunity to interact with robots by building andprograming them. The purpose of the study is to observehow teaching robotics can develop their motor skills orother cognitive abilities.Over the course of 12 weeks, students (ages 3-4)were given building instructions and kits, and asked tobuild and program their robots. Students with roboticsexperience were grouped and considered as a baseline.The other students were separated into two other groupsand had no prior experience. All three groups hadstudents with and without special needs. The study aimedto analyze how motor skills and various other abilitiesimproved over the 12 weeks in comparison to thechildren’s performance during the first session of theterm. The number of lessons required for the three groupsto reach similar results was also tracked. Overall, allstudents showed significant improvement in motorabilities. Both groups with no experience were able toreach similar precision and accuracy results as those withexperience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".