Game elements from literature review of gamification in healthcare context

• Main Authors: Sakchai Muangsrinoon (Author), Poonpong Boonbrahm (Author)
• Format: Book
• Published: OmniaScience, 2019-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2013-6374; 10.3926/jotse.556
• Keywords: Gamification, dame elements, review, multi-label, classification, mining; Education; L; Special aspects of education; LC8-6691; Technology; T; Engineering (General). Civil engineering (General); TA1-2040
• Source: Journal of Technology and Science Education, Vol 9, Iss 1, Pp 20-31 (2019)

Gamification is a conceptual framework to apply game elements and techniques to improve the interesting process in non-game context. Gamification offers the motivation approach to motivate the player to handle the challenge tasks with game mechanics, game dynamics, and components. Nowadays, To discover the set of game elements and techniques from evaluating the existing related research is more opportunity for success in the exciting process. The core objective of this paper is to review the literature by using descriptive statistics of game elements with the review methodology and evaluate the model with multi-label classification with a dataset from this literature examined. The reviewed literature was first coded author-centrally. After each paper was scrutinized for the analysis, the perspective was pivoted, and further analyses were conducted concept-centrally. A systematic review has been conducted that proves the wide variety of game elements, being retrieved a total of fifteen terms of game elements from twenty-two selected papers that were screened from a total of eighty-two documents. Only a few terms are used: points, feedback, levels, leaderboards, challenges, badges, avatars, competition, and cooperation. However, only some can be considered actual elements mechanics and that have not a similar abstraction level. Additionally, the authors examined the relationship between game elements and STD: Competence, Autonomy, and Relatedness with a Data mining technique, Multi-label classification to discovery knowledge of game elements. The results indicated that rFerns algorithm provides the lowest Hamming Loss with 4.17%. Furthermore, It shows that Multi-label Rain Forest (rfsrc) in Algorithm adaptation method and Rain Forest (RF) in Problem transformation method provide the same Hamming Loss with 29.17%. Moreover, rFerns algorithm provides the highest accuracy with 87.5% for Competence, and 100% for Autonomy and Relatedness. Furthermore, It shows that Multi-label Rain Forest (rfsrc) in Algorithm adaptation method and Rain Forest (RF) in Problem transformation method provide the same Accuracy with 87.5% for Competence, and 62.5% for Autonomy and Relatedness. The results from this study will be used to design a gamified system in a healthcare context to promote physical activity.