Investigating Real-time Predictors of Engagement

Abstract

Engagement is a worthwhile psychological construct to examine in the context of video games and online training. In this context, previous research suggests that the more engaged a person is, the more likely they are to experience overall positive affect while performing at a high level. This research builds on theories of engagement, Flow Theory, and Cognitive Load Theory, to operationalize engagement in terms of cognitive load, affect, and performance. An adaptive algorithm was then developed to test the proposed operationalization of engagement. Using a puzzle-based video game, player performance and engagement was compared across three conditions: adaptive gameplay, a traditional linear gameplay, and choice-based gameplay. Results show that those in the adaptive gameplay condition performed higher compared to those in the other two conditions without any degradation of overall affect or self-report of engagement.