Catch the Bus©: using a gamified application to introduce travel training for students with exceptionalities
Bibliographic record
Abstract
Gamified learning is becoming more prevalent in educational settings. In this study the authors use a gamified web-based application, Catch the Bus© (CtB) to teach public transportation skills. Navigating public transportation is key to independent living. Existing supports for public transportation training are inadequate for individuals with exceptionalities due to a lack of age appropriateness and immediate ridership feedback. Thus, this research explored the potential of CtB in terms of (i) alleviating anxiety and promoting confidence, and (ii) teaching public transportation skills such as problem solving, map reading, time management, and digital literacy in individuals with exceptionalities. Participants in this mixed-methods study included high school students with exceptionalities (e.g. social and generalized anxieties, dyslexia, autism) in a life skills course taken as preparation for transitioning to independent living. Data sources included pre- post-CtB training surveys and journal reflections of applied CtB training as participants navigated their city. Findings indicate CtB is an effective gamified digital tool for (i) teaching public transportation skills, (ii) promoting confidence with using public transportation, and (iii) alleviating public transportation-related anxiety. Interestingly, findings also revealed a disconnect between participants’ perceived and actual digital competencies, thus warranting further investigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".