Ludifier un simulateur d’examen en recourant à des badges – Effets sur la participation, la perception et la performance
Bibliographic record
Abstract
Cet article décrit, analyse et évalue la ludification d’un simulateur d’examen au travers de l’installation d’une dynamique d’octroi de badges. Destiné à des étudiants de première année en médecine, cet outil a pour objectif de les familiariser avec la résolution de QCM et de favoriser la maîtrise des prérequis et de la matière enseignée dans le cadre d’un cours de physique. Cette recherche met en évidence l’impact positif des badges sur la fréquentation du simulateur d’examen, la perception de sa contribution à l’étude et les performances à l’examen. This article describes, analyses and questions the gamification of an examination simulator through the implementation of a dynamic system of badges used as awards. This tool targets first-year medicine students. Its purpose is to introduce students to MCQ tests and to improve their command of the pre-requisite knowledge and new topics taught in physics classes. This research highlights the positive impact of the examination simulator on participation, performance, and perception. Keywords : Gamification, badges, science education, physics, MCQ, higher education, undergraduate, e learning Peer reviewed
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".