Using Gameplay Data for Identifying User’s Learning Experience in an Environment of Digital Gesture Based-game

Abstract

The digital game has become one of the most popular tools for teaching and learning. While digital game has accepted as a powerful tool for enhancing the process of teaching and learning, the nature of user learning experience is not well understood. In this regard, the goal of this study is to propose a method for identifying user learning experience using gameplay data. This study investigates possibility of identifying user learning experience namely, user learning behavior and user learning styles in environment of digital gesture based-game. This work presents computational theory of perception for identifying user learning behavior and user learning styles. The results showed that GLMP1 method is worthy to identify user learning behavior. Additionally, GLMP2 method offers 18% accuracy to identify user learning style. In conclusion, two proposed methods have the potential for identifying user learning experience in the environment of digital gesture based-game and another digital game.