Gamification of Professional Development for First Year Engineering Students
Bibliographic record
Abstract
Approximately 1.23 billion people play video games. Gamification is the study of what motivates gamers to invest thousands of hours into these games, and more importantly attempts to derive principles of gamification that can be applied to motivate people to participate in non-video game tasks with equal zeal. Education is one area where gamification is being explored.One gamification principle is to give participants a clear indication of their progress. In video games this is often depicted as ‘points’. The typical grade system could be interpreted as a type of point system, but one without much flexibility. Implementation of a bonus point system as an overlay to the standard grade system may allow for more flexibility.In this study this gamification principle was used to motivate students in a first year design course to participate in optional professional development activities and to foster an active online peer feedback and instruction community. With relatively minor modifications and repackaging of an existing evaluation methods students were motivated to give optional oral presentations, attend optional skill development workshops, and to contribute extensively to an online learning community.This implementation of gamification was found to have a net positive effect on student participation in Professional Development activities. Where it succeeded and where it failed will be explored.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".