Using Learning Analytics to Understand How to Design Effective Digital Educational Games

• In Event: Advanced Technologies for Learning SIG Individual Paper Session

Abstract

This study aimed to understand learners’ behavioral patterns in using a digital educational game through analyzing big data. The dataset consisted of 3,063,267 lines of raw log data from 3,963 middle school students across nine U.S. states. The findings showed significant positive relationships between students' performance scores and their tool usage both in frequency and duration of tool use. This was further confirmed by the relationships between different levels of students’ performance and tool usage: the more frequently and longer that students used the tools, the higher their performance scores. There were significant usage differences between students with high performance scores and those with low performance scores. These evidence-based findings are used to inform the game’s next iteration of design.

Authors

• Min Liu, The University of Texas at Austin

• Chenglu Li, The University of Texas at Austin

• Zilong Pan, The University of Texas at Austin

• Xin Pan, The University of Texas at Austin