Learning analytics for children’s using augmented reality games / Fatin Nasuha Abdul Razak, Amirrudin Kamsin and Hameedur Rahman - Abdul Razak, Fatin Nasuha; Kamsin, Amirrudin; Rahman, Hameedur (UiTM Press, 2024)
Data science approaches, which are increasingly used in virtually every sector, can be applied to the quantity of information collected from children’s interactions with augmented reality (AR) games. Data science tools can significantly improve AR game evaluation and help teachers and institutions to make evidence-based decisions. The collection, analysis, and visualisation of player interactions with AR games is referred to as game learning analytics (GLA). The main purpose of this study is to evaluate the effectiveness of children’s performances in AR games by using learning analytics (LA). The data acquired from these analytics can be used to improve AR games, better comprehend player behaviours and strategies, and improve player assessment. To examine the benefits of these technologies, the methods that can be used to implement LA for children in AR games are presented in this paper. The findings suggest that LA provides significant benefits to children while also assisting educators in evaluating students based on their game involvement.