Converting an Entire Course Into a Game by Implementing Gated Pathways
Bibliographic record
Abstract
BACKGROUND: Games are well recognized for enhancing student engagement, yet most reported game-based interventions in higher education remain limited to single, one-time activities. Comprehensive integration of gaming concepts across an entire course is still uncommon, particularly in health professional education. APPROACH: In this context, a game-based strategy was embedded into an entire dental hygiene (DH) course using a gated pathway and rewards. In a real game, a gated pathway requires players to complete specific tasks before unlocking the next level. In the learning management system (LMS), the same game-based concept was applied by restricting students' access to weekly content until they completed reviewing lecture materials and passed a quiz. Successful completion of the quiz unlocked the next week's materials. Quiz games designed in Gimkit were integrated as rewards for students. EVALUATION: Student engagement data were collected from the LMS, and exam performance was compared with the previous cohort, where no gated pathway was used. Voluntary, anonymous surveys captured students' perceptions. Eighty-eight percent of the survey respondents agreed that the gated pathway helped them complete their tasks on time, and 87% found this intervention helpful for their studies. IMPLICATIONS: This study demonstrates the successful integration of game-based concepts across a full DH course, with positive effects on engagement and performance. Clinical educators can adapt this approach to offer students a better learning experience in a game-like course.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".