Identifying Key Principles and Commonalities in Digital Serious Game Design Frameworks: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Digital serious games (DSGs), designed for purposes beyond entertainment and consumed via electronic devices, have garnered attention for their potential to enhance learning and promote behavior change. Their effectiveness depends on the quality of their design. Frameworks for DSG design can guide the creation of engaging games tailored to objectives such as education, health, and social impact. OBJECTIVE: This study aims to review, analyze, and synthesize the literature on digital entertainment game design frameworks and DSG design frameworks (DSGDFWs). The focus is on conceptual frameworks offering high-level guidance for the game creation process rather than component-specific tools. We explore how these frameworks can be applied to create impactful serious games in fields such as health care and education. Key goals include identifying design principles, commonalities, dependencies, gaps, and opportunities in the literature. Suggestions for future research include empathic design thinking, artificial intelligence integration, and iterative improvements. The findings culminate in a synthesized 4-phase design process, offering generic guidelines for designers and developers to create effective serious games that benefit society. METHODS: A 2-phase methodology was used: a scoping literature review and cluster analysis. A targeted search across 7 databases (ACM, Scopus, Springer, IEEE, Elsevier, JMIR Publications, and SAGE) was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Studies included academic or industry papers evaluating digital game design frameworks. Cluster analysis was applied to categorize the data, revealing trends and correlations among frameworks. RESULTS: Of 987 papers initially identified, 25 (2.5%) met the inclusion criteria, with an additional 22 identified through snowballing, resulting in 47 papers. These papers presented 47 frameworks, including 16 (34%) digital entertainment game design frameworks and 31 (66%) DSGDFWs. Thematic analysis grouped frameworks into categories, identifying patterns and relationships between design elements. Commonalities, dependencies, and gaps were analyzed, highlighting opportunities for empathic design thinking and artificial intelligence applications. Key considerations in DSG design were identified and presented in a 4-phase design baseline with the outcome of a list of design guidelines that might, according to the literature, be applied to an end-to-end process of designing and building future innovative solutions. CONCLUSIONS: The main benefits of using DSGDFWs seem to be related to enhancing the effectiveness of serious games in achieving their intended objectives, such as learning, behavior change, and social impact. Limitations primarily seem to be related to constraints associated with the specific contexts in which the serious games are developed and used. Approaches in the future should be aimed at refining and adapting existing frameworks to different contexts and purposes, as well as exploring new frameworks that incorporate emerging technologies and design principles.
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 | 0,086 | 0,236 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,009 |
| Bibliométrie | 0,047 | 0,032 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,013 | 0,015 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».