Enhancing Serious Game Design: Expert-Reviewed, Stakeholder-Centered Framework
Notice bibliographique
Résumé
BACKGROUND: Traditional serious game design methods often overlook stakeholder needs. This study integrates stakeholder theory and enterprise architecture (EA), along with the Architecture Development Method, to propose a novel framework for serious game design. Crafted to aid practitioners, researchers, and specialists in leveraging resources more effectively, the framework is validated through a design science research methodology. Expert reviews have further refined its features, making it a robust tool for enhancing serious game design and implementation. OBJECTIVE: This paper introduces a framework for designing serious games, covering stakeholder analysis, requirements gathering, and design implementation planning. It highlights the importance of expert review in validating and refining the framework, ensuring its effectiveness and reliability for use in serious game design. Through critical assessment by experts, the framework is optimized for practical application by practitioners, researchers, and specialists in the field, ensuring its utility in enhancing serious game development. The next step will be to validate the framework empirically by applying it to a serious game development project. METHODS: We developed and validated a conceptual framework for serious game design by synthesizing stakeholder theory and EA through literature review, concept mapping, and theory development by way of a design science research approach. The framework is iteratively refined and validated via expert review, drawing on insights from professionals experienced in serious games, stakeholder theory, and EA. This method ensures the framework's practical relevance and effectiveness in addressing real-world design challenges. RESULTS: An expert review by 29 serious game practitioners validated the framework's success in stakeholder management, confirming its stakeholder-centered effectiveness. Although the experts praised its structured approach, they suggested clearer guidance for game design elements. In addition, the experts, while acknowledging the framework's complexity, saw its depth as valuable for efficient management. The consensus calls for a refined balance between detailed functionality and user-friendly design, with the framework's impact on stakeholder capabilities revealing a spectrum of professional needs. CONCLUSIONS: This paper presents a framework for creating effective and organizationally aligned serious games. Evaluated across execution, practical, and EA levels, it is logical but varies in ease of understanding, with experts calling for more accessibility at the EA level. It enhances stakeholder efficiency and management but is criticized for rigidity and a need for flexibility. Recommendations include streamlining the framework, enhancing clarity, reducing administrative tasks, and incorporating clear guidelines on technology use, motivational elements, and operational tools. This aims to help stakeholders produce more targeted and adaptable game designs. The next iteration will be developed after application to a project and team feedback.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,003 | 0,003 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».