Fostering Inductive and Deductive Learning in Oral Microbiology and Immunology With a Dual-Role Duel Card Game: Explanatory Sequential Mixed Methods Study
Bibliographic record
Abstract
Background: Game-based learning has emerged as an effective learning strategy in health care education. However, no games have been specifically designed to support cognitive improvement for diverse learning styles in oral microbiology and immunology. Objective: This study aimed to develop and evaluate an educational card game designed to support diverse learning styles in oral microbiology and immunology, using a duel-style format. Methods: An explanatory sequential mixed methods study was conducted with 40 third-year dental students, where half of them were assigned to the first group, starting as the host, while those in the other group began as the microbe. Participants alternated between the microbe and host roles during gameplay. Active engagement through playing as the microbe facilitated knowledge acquisition through observation, supporting inductive learning. On the other hand, the host role aimed to promote the application of knowledge for decision-making, facilitating deductive learning. Quantitative data were collected using pre- and postknowledge assessments and satisfaction questionnaires. Qualitative insights were obtained through semistructured interviews exploring learning experiences when playing as the microbe compared to the host. Results: Students demonstrated significant improvements in knowledge scores across the 3 assessments (P<.001), with no difference between groups (P=.85). They also perceived the game positively in all 3 aspects (usefulness, ease of use, and enjoyment). Qualitative findings revealed that role variation supported both inductive and deductive learning processes. Participants valued the combination of pedagogical and entertaining components, leading to the game's motivation and engagement. A conceptual framework demonstrated key emerging themes relevant to the game design and implementation, including learner profile, learning setting, game design, learning process, and learning outcomes. Conclusions: The card game demonstrated its potential in enhancing knowledge acquisition and student engagement in oral microbiology and immunology. Role-switching between the host and microbe was perceived by participants to facilitate different learning experiences. Further research is recommended to investigate long-term retention and broader practicality.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".