Game-based trench safety education: development and lessons learned
Bibliographic record
Abstract
In collaboration with a college teaching construction trades, the authors engaged in developing and deploying a serious game focussed on teaching trench health and safety lessons as an initial investigation into applying edutainment in the construction trades. This paper reviews the background of using interactive technology in construction trades training and presents the observations taken from the developers, teachers and students involved and subsequent conclusions drawn based on these observations. The broad lessons learnt indicate that serious games offer an engaging and innovative medium for delivering training to students who are more comfortable with hands-on learning for a hands-on trade. Although studies are still underway in assessing the long term benefits in retention, the students and teachers involved found the use of gaming technology to be an overall positive experience with some immediately demonstrable benefits. Furthermore, the potential for adopting serious games in educational programs will only grow as interactive computer technology only becomes more and more ubiquitous in society. This said, challenges remain in measuring the long term impact, and costs associated with developing and delivering the interactive content to the students and subsequently finding ways to reduce those costs and maximise the positive benefits attained using such technology. © 2011 The authors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".