Game Experience and Learning Effects of a Scoring-Based Mechanic for Logistical Aspects of Pediatric Emergency Medicine: Development and Feasibility Study
Notice bibliographique
Résumé
Background Using serious games for learning in operations management is well established. However, especially for logistics skills in health care operations, there is little work on the design of game mechanics for learning engagement and the achievement of the desired learning goals. Objective This contribution presents a serious game design representing patient flow characteristics, systemic resource configurations, and the roles of the players based on a real Swedish emergency ward. The game was tested in a set of game-based learning practices in the modalities of a physical board game and an online multiplayer serious game that implemented the same game structure. Methods First, survey scores were collected using the Game Experience Questionnaire Core and Social Presence Modules to evaluate the experience and acceptance of the proposed design to gamify real processes in emergency care. Second, lag sequential analysis was applied to analyze the impact of the game mechanics on learning behavior transitions. Lastly, regression analysis was used to understand whether learning engagement attributes could potentially serve as significant predicting variables for logistical performance in a simulated learning environment. Results A total of 36 students from courses in engineering and management at KTH Royal Institute of Technology participated in both game-based learning practices during the autumn and spring semesters of 2019 and 2020. For the Core Module, significant differences were found for the scores for negative affect and tension compared with the rest of the module. For the Social Presence Module, significant differences were found in the scores for the psychological involvement – negative feelings dimension compared with the rest of the module. During the process of content generation, the participant had access to circulating management resources and could edit profiles. The standard regression analysis output yielded a ΔR2 of 0.796 (F14,31=2725.49, P<.001) for the board version and 0.702 (F24,31=2635.31, P<.001) for the multiplayer online version after the learning engagement attributes. Conclusions The high scores of positive affect and immersion compared to the low scores of negative feelings demonstrated the motivating and cognitive involvement impact of the game. The proposed game mechanics have visible effects on significant correlation parameters between the majority of scoring features and changes in learning engagement attributes. Therefore, we conclude that for enhancing learning in logistical aspects of health care, serious games that are steered by well-designed scoring mechanisms can be used.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».