Human personality toward digital gameplay analytics for edutainment-based instructional design

Abstract

Compare between current electronic learning material and digital games, students or learners can spend a whole day on games as game addicted. They are immersed in games by games' context, rules, rewards, multisensory cues, mechanism, interactivity, sound and color but it hardly happens with electronic learning materials. Nonetheless, individual inclination in games is different according to one's preference and game features. This paper explores gameplay behaviors and player characteristics regard as human personality which can be utilized to design edutainment-based instructional material. The experimental results show that decision tree using forward selection with balanced class distribution achieved the highest accuracy upto 93.49% among four styles of digital gameplay.