Wheelchair Training as a Way to Enhance Experiential Learning Modules for Urban Planning Students: A Mixed-Method Evaluation Study
Bibliographic record
Abstract
This study assessed the effectiveness of a learning module we developed for planning students aimed to enhance their understanding for design issues in public spaces faced by wheelchair users. The module involves training of students to effectively navigate the environment in a wheelchair before they experience the real outdoor spaces. Through this evaluation study, we also attempted to add clarity to the problems observed from empirical studies about these ‘try-it-yourself’ exercises, such as potential stigmatisation and ableism. We employed a mixed-method study approach consisting of wheelchair skill tests, ‘walkabout’ audits, and a focus group, with 28 second-year undergraduate urban planning students. The cross-over design of the study involving two components of the module – (wheelchair) Skill Learning Experience (SLE) and Real-World Experience (RWE) – allowed us to assess the effect of the former on the performance of the latter and the module overall. The focus group also asked students’ perspectives about the module. Our findings suggest that the wheelchair skills training component likely contributed to a more nuanced and comprehensive understanding of design problems, while also fostering respect for wheelchair users. We believe that careful implementation is key to addressing potential negative consequences while optimising the benefit of experiential exercises.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".