Adaptive learning game to personalize occupational health and safety training
Bibliographic record
Abstract
In 2012, the Association of Workers’ Compensation Boards of Canada recorded approximately a quarter-million workplace injuries, a staggering figure keeping in mind that some incidents go undocumented. It is important that organizations continue make Occupational Health and Safety (OHS) one of their top priorities. In this thesis, we discuss an implementation of an adaptive personalized learning support system within a game that is centered on health and safety training to promote the understanding of health and safety material. The design of the game incorporates a feedback loop that constantly evaluates the player’s performance while they complete learning challenges. As the players proceed within the game's environment their profile is constantly updated thus providing an insight into their strengths and weaknesses. The game is designed to adjust the challenges given to the player to focus on improving the player’s underperforming skills. The goal of this game is to promote health and safety in small and medium enterprises. Through this game we created a motivational designed application that helps to teach targeted health and safety information to the workers. The game was made in collaboration with the Public Services Health and Safety Association based in Toronto. The game aims to better the player’s health and safety performance in the Organizational Performance Metric and hone their underlying health and safety skills.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".