The Role of Feedback Type and Task Performance on Concurrent Emotions and Interest During Game-Based Learning

Abstract

Building adaptive game-based learning (GBL) interventions (e.g., immediate feedback) has been a recent effort to maximize learning effectiveness. State-of-the-art algorithms often overlook the concurrent emotions and motivation of individuals and its impact on GBL interventions. This pilot study utilized a 3 (feedback type: results, elaborative, attribution) x 2 (graph type: misleading, non-misleading) within-subjects design with MediaWatch, a GBL environment built to improve critical graph literacy. At an Austrian university, 41 students’ concurrent emotions and motivation were measured using validated surveys immediately after different types of feedback on tasks during GBL. Results showed a significant improvement in graph literacy after GBL. Different types of feedback and task performance influenced concurrent emotions and interest, but individual differences accounted for the largest variability explained in emotions and interest. The findings suggest that within-subject variability is crucial for understanding concurrent emotions and motivation to feedback types and task performance during GBL.