Automated Detection of Cognitive Engagement to Inform the Art of Staying Engaged in Problem-Solving

• In Event: Applications of Artificial Intelligence in Education

Abstract

In this paper, we used supervised machine learning algorithms to predict students’ cognitive engagement states from their facial behaviors as 61 students solved a clinical reasoning problem in an intelligent tutoring system. We examined how high and low performers differed in cognitive engagement levels in general and when performing surface and deep learning behaviors during clinical reasoning. We found that students’ facial behaviors could predict their cognitive engagement. Furthermore, high performers showed a significantly higher level of cognitive engagement than low performers but only when conducting deep learning behaviors. This study advances our understanding of how students regulate their engagement to succeed in problem-solving. This study also has significant methodological implications for the automated measurement of cognitive engagement.

Authors

• Shan Li, McGill University

• Susanne P. Lajoie, McGill University

• Juan Zheng, McGill University

• Hongbin Wu, Peking University

• Huaqin Cheng, Peking University