Studying Player Actions in the League of Legends: Game Analytics Techniques in Nontraditional Learning Contexts

• In Event: AERA Poster Session 6
  In Poster Session: Games for 21st Century Learning (San Diego)

Abstract

Humans interact in unprecedented ways within modern contexts; their actions are captured by countless variables and data across a myriad of digital systems. Researchers’ ability to document behavior, discern patterns, and understand learning within these systems relies on their ability to access and organize the data. Educational data mining and learning analytics techniques are well suited to this task. Presently, these techniques are applied within a non-traditional space (i.e., the game League of Legends) in a sequential mixed-methods design to examine the relationships among the patterns in the data and outcomes associated with play. Findings and implications for research are discussed, particularly in terms of large data sets in non-traditional settings.

Authors

• P.G. Schrader, University of Nevada - Las Vegas

• Mark Carroll, University of Nevada - Las Vegas

• Sara Ann Shaw, University of Nevada - Las Vegas