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.

在本文中，我们使用监督式机器学习算法根据他们的面部表情预测学生的认知参与状态，因为61名学生在智能补习系统中解决了临床推理问题。我们还研究了在进行表面和深度学习行为时，高绩效和低绩效者在认知参与水平上的差异。我们发现，学生的面部行为是他们认知参与状态的有力预测指标。特别是，当将17种信息丰富的面部特征添加到模型中时，我们发现SVM（支持向量机）模型具有出色的区分参与状态和较少参与状态的能力。此外，结果表明，与低绩效者相比，高绩效者在认知参与的总体水平上没有显着差异。高绩效者和低绩效者在进行浅层学习行为时的认知参与水平也没有差异。但是，在进行深度学习行为时，高绩效者表现出比低绩效者更高的认知参与度。这项研究提高了我们对学生如何调节自己的参与以成功解决问题的理解。这项研究对认知参与的自动测量也具有重要的方法学意义。这项研究提高了我们对学生如何调节自己的参与以成功解决问题的理解。这项研究对认知参与的自动测量也具有重要的方法学意义。这项研究提高了我们对学生如何调节自己的参与以成功解决问题的理解。这项研究对认知参与的自动测量也具有重要的方法学意义。