Development and Evaluation of a Case-Based Serious Game for Diagnosis and Treatment Planning in Orthodontic Education: Quasi-Experimental Study
Notice bibliographique
Résumé
Background: Orthodontic education requires effective training in diagnosis and treatment planning, but traditional teaching methods may lack engagement and opportunities in offering a safe learning environment. Serious games are gaining momentum in dental education due to their positive educational impact in enhancing learner knowledge and motivation. However, their application in orthodontic diagnosis and treatment planning training remains unexplored. Objective: The aim of this study was to develop and evaluate a simulation-based serious game for training orthodontic diagnosis and treatment planning in virtual patients (OrthoVirt), examining its impact on student knowledge and satisfaction. This study also explored whether prior gaming experience influenced learning outcomes. Methods: A quasi-experimental study was conducted with 32 fourth-year dental students, who were requested to complete a preknowledge assessment, 3 simulated patients within OrthoVirt, a postknowledge assessment, and a satisfaction survey. Participants were categorized as gamers (n=16) or nongamers (n=16) based on self-reported weekly gaming time. The primary outcome was knowledge improvement, analyzed using 2-tailed paired t tests (Cohen dz). Group comparisons were conducted using 2-tailed independent t tests (Cohen d). User satisfaction was measured using a validated questionnaire based on the technology acceptance model. A stricter significance threshold (P<.01) and effect size metrics (Cohen d and Cohen dz) were used to account for the small sample size, multiple comparisons, and exploratory nature of the study. Results: Both gamer and nongamer groups showed significant knowledge improvement after using OrthoVirt (mean score increased from 10.75 (SD 2.75) to 14.75 (SD 1.81) out of 20; P<.001). The mean scores of the gamer group increased from 10.31 (SD 3.07) to 15.19 (SD 1.83) while those of the nongamer group rose from 11.19 (SD 2.40) to 14.31 (SD 1.74). No statistically significant differences were found between groups in pre- and postknowledge assessments as well as improvement scores (P>.01), suggesting that the educational benefit was consistent regardless of gaming background. Participants from both groups rated OrthoVirt positively, particularly for "perceived ease of use." However, "perceived enjoyment" was rated slightly lower than other aspects, with nongamers scoring it 3.60 (SD 0.81) and gamers 3.45 (SD 0.73) out of 5, indicating a potential area for design enhancement. Overall satisfaction ratings were similar between the 2 groups (P>.01). Conclusions: OrthoVirt demonstrated potential as a supplementary tool for diagnosis and treatment planning in orthodontic education, with statistically significant improvements observed in learner knowledge. While feedback was generally positive, these findings should be interpreted with caution due to the quasi-experimental design and small sample size. Future development should focus on improving user enjoyment and engagement through entertaining design elements. Further research should explore how OrthoVirt can be integrated as a case discussion tool alongside lectures, with the potential to enhance learning not only in orthodontic education but also across other areas of dental training.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».