Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving

In the present 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 also examined how high and low performers differed in cognitive engagement levels when performing surface and deep learning behaviors. We found that students' facial behaviors were powerful predictors of their cognitive engagement states. In particular, we found that the SVM (Support Vector Machine) model demonstrated excellent capacity for distinguishing engaged and less engaged states when 17 informative facial features were added into the model. In addition, the results suggested that high performers did not differ significantly in the general level of cognitive engagement with low performers. There was also no difference in cognitive engagement levels between high and low performers when they performed shallow learning behaviors. However, high performers showed a significantly higher level of cognitive engagement than low performers 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.