Exploring Maker Cultures and Pedagogies to Bridge the Gaps for Students with Special Needs
Bibliographic record
Abstract
Through this ethnographic study, the researchers investigate the efficacy of using "makerspace" pedagogies with students who are identified as having special needs. These pedagogies include the transferable skills and global competencies as outlined by the Ontario Ministry of education. The research questions address how teachers view changes in his/her special education students' behaviour and learning based on their participation in maker-related activities, including, but not limited to coding, programmable robots, and circuits, in the classroom. Teachers were supported through professional development by our STEAM 3D Maker Team at the Faculty of Education and then subsequent visits made to each of 20 different schools investigated how maker pedagogies were being employed. Qualitative data was collected in the form of digital video and audio recordings, photographs, observational field notes, and individual and focus group interviews. The data suggest that the use of maker pedagogies can facilitate a number of improved outcomes for students with exceptionalities, including confidence and perseverance, engagement and motivation, self-regulation, collaborative skills, and increased academic achievement.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".