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.

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Anatomy is considered one of the foundation studies for all of the health science students especially medical and nursing students. Anatomy of the hand is complicated. It composes of bones, nerves, blood veins, muscles, and tendon. Memorising all the details about all those parts is tedious work and need much imagination. With the advances in computer graphics and human-computer interaction techniques, understanding how those body parts move is easy to understand in a visual presentation. Augmented Reality (AR) is the technique that allowed the computer-generated objects to overlay on top of the real world. In this study, we concentrate on studying the bones only. We have selected the Leap Motion, which is the device that can detect the hands and fingers, like a tracking device, and marker-based AR technique for displaying the computer generated bones on top of the real hand. Since the Leap Motion detects the hands and shows the bone in real time, so when a user moves the hands such as waving, all the 3D virtual bones move to the new position just like the real hand. Besides using this tool as the educational tool to help the students have better learning about anatomy, it can also be used as an assessment tool for anatomy class as well. Results from testing this tool with volunteer students indicate that it helps them to understand the hand anatomy better and faster than traditional ways.

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