Track: Data Analytics and Big Data

Abstract

Serious game has been introduced as an interactive educational tool for teaching and learning processes. It incorporates non-entertainment elements into an interactive game environment. Although serious game offers various benefits to support teaching and learning in a variety of contexts, measuring the learner's skills and knowledge improvement is difficult. Two main problems are: 1) how to understand the learner’s skill and performance improvement, and 2) how to capture and analyse the data. This work aims to tackle the second problem by designing a flexible serious game analytics framework. It focuses on identifying the relevant data to evaluate learner’s skills and knowledge improvement for a specific intended learning outcome, as well as, gathering this data in a serious game framework that can be configured based on the intended learning outcomes of the game. Comprehensive literature reviews and field study, including observations and semi-structured interviews, were conducted to consider different gameplay data and identify the necessary gameplay data for a particular learning outcome. Based on the findings, a serious game framework is developed. This framework would be implemented as a configurable setting for the smart serious game analytics engine.