Investigating Cognitive Biases in Self-Explanation Behaviors during Game-based Learning about Mathematics

The study investigates the impact of cognitive biases on middle-school students' affective experiences while learning about math in a game-based learning environment (GBLE). The study focused on students' confrustion, an affect construct that unifies the several manifestations of confusion and frustration. We studied confrustion in the context of students self-explaining erroneous examples, where they had to find and fix common errors in given math problems and self- explain their problem-solving processes either with or without scaffolding. Text replays were utilized to examine student interactions during game-based learning and identify behaviors that emerged in response to cognitive biases and affect and its impact on learning and performance outcomes. The results revealed that students who demonstrated more pseudo-confidence in their self-explanations had higher self-reported self-efficacy, but were more likely to submit incongruent responses, exhibit confrustion, make errors, and take longer to finish the game. Overall, the findings show that students were vulnerable to cognitive biases and did not always respond in ways that accurately reflected their approach to solving math problems. The insights into how students approach and learn from math games inform the design and implementation of GBLEs by addressing cognitive biases.