Using learning analytics to understand esports students

Abstract

Electronic sports (esports) has advanced to become a media giant and an arena for competitions and career development. Due to this growth, more focus has been given to esports research, implementation of esports throughout the world, and development of esports curriculum. Introducing esports into schools has created huge opportunities for deeper analysis of esport and learning data to provide insight into the learning processes. By applying learning analytics methods, this research analyzes data that originate from students (N=149) in Swedish high schools. The data was divided between activity data and performance data. The analysis is guided by the learning theory concept self-regulation to analyze differences between user groups. Through exploratory analysis, multiple user groups were identified and then compared in their trends and results to measure the impact of self-regulated learning concepts. Furthermore, the student data was used in the design of a mid-fidelity prototype for a student-facing dashboard to provide feedback and recommendations. Findings reveal that concepts of self-regulated learning have a positive impact in terms of higher curriculum interaction, and also higher performance results in game matches. While the research finds that focus on features promoting self-regulated learning concepts is important, it is challenging to generalize the findings to recommend actions such as suggested session lengths. Future work should include a larger population sample and focus on the implementation of a student-facing dashboard tool to test its reception and usage.