Development of a mobile application to increase motivation, engagement & teaching activity of clinical faculty using gamification principles
Bibliographic record
Abstract
Background: Clinician teachers (CT) have historically felt undervalued and underappreciated. One technology used to increase motivation is gamification: the process of inserting elements of game-playing into activities that are not usually associated with games. We developed a mobile application that rewards CTs using in-app gamification techniques to increase CTs motivation. This program was implemented specifically in a regional campus setting, Mississauga, Ontario. Methods: A cross-platform application that rewards physicians for their clinical teaching hours was created. This consisted of a star grading criteria where each physician was awarded depending on the number of hours completed. End-user perceptions of the application were evaluated using a survey with a Likert scale and open-ended questions. Survey results were collated with descriptive statistics and thematic analysis. Results: The TutorTracker application was developed implementing a live gamification algorithm. It allows physicians to view their hours completed, rewards obtained, and add additional hours. The majority of CTs agreed or strongly agreed that the application was user-friendly, easy to navigate and enjoyed the rewards provided. Major themes that emerged were regarding additional features and full integration of such an application for rewarding teaching efforts. Conclusions: Gamification principles have been implemented in a cross-platform application allowing CTs to be rewarded for their teaching. The next steps would be to formally quantify the effects and advantages of using the application to increase the motivation of tutors.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".