Exploring students' experience with game-based learning: a descriptive study.
Bibliographic record
Abstract
Background: Game-based learning aims to promote student engagement and boost motivation in the classroom. However, creating long-term motivation in an education game is challenging and requires a balance between "fun" and "educational" objectives. The gaming platform Gimkit allows educators to create, host, and play quiz-based games in class and host game-based homework in learning management systems. Gimkit was introduced in 2 dental hygiene courses at a Canadian university: one was offered in person; the other was asynchronous online. This study aimed to explore students' perception of game-based learning experiences, their choice of game modes, and their source of motivation. Methods: Students from the second and third years of the dental hygiene program were invited to participate in a voluntary online survey to collect their perceptions of either the live quiz game or the game-based homework assignments, their choice of game mode, and their motivation to play. Descriptive statistics were applied to analyze the survey data. Results: Thirty-five percent (n = 15) of the in-person class and thirty percent (n = 14) of the online class completed the voluntary survey. All participants from the online and in-person groups strongly agreed that they improved their knowledge by playing the game. Discussion: Students were largely motivated extrinsically and played the game to learn course content. Students from the in-person class were driven towards Gimkit live quiz games by in-class competition. For online students, the "challenge of the game" was the most attractive feature of Gimkit. Conclusion: Game-based learning with Gimkit can motivate dental hygiene students and promote self-determination.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".