Using Video Games to Enhance Motivation States in Online Education: Protocol for a Team-Based Digital Game
Bibliographic record
Abstract
BACKGROUND: Video and computer games for education have been of interest to researchers for several decades. Over the last half decade, researchers in the health sector have also begun exploring the value of this medium. However, there are still many gaps in the literature regarding the effective use of video and computer games in medical education, particularly in relation to how learners interact with the platform, and how the games can be used to enhance collaboration. OBJECTIVE: The objective of the study is to evaluate a team-based digital game as an educational tool for engaging learners and supporting knowledge consolidation in postgraduate medical education. METHODS: A mixed methodology will be used in order to establish efficacy and level of motivation provided by a team-based digital game. Second-year medical students will be recruited as participants to complete 3 matches of the game at spaced intervals, in 2 evenly distributed teams. Prior to playing the game, participants will complete an Internet survey to establish baseline data. After playing the game, participants will voluntarily complete a semistructured interview to establish motivation and player engagement. Additionally, metrics collected from the game platform will be analyzed to determine efficacy. RESULTS: The research is in the preliminary stages, but thus far a total of 54 participants have been recruited into the study. Additionally, a content development group has been convened to develop appropriate content for the platform. CONCLUSIONS: Video and computer games have been demonstrated to have value for educational purposes. Significantly less research has addressed how the medium can be effectively utilized in the health sector. Preliminary data from this study would suggest there is an interest in games for learning in the medical student body. As such, it is beneficial to undertake further research into how these games teach and engage learners in order to evaluate their role in tertiary and postgraduate medical education in the future.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".