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Facial expression recognition is the process of identifying the expression that is displayed by a person. It can be used to evaluate the mood of students during a class so that can help teachers improve the learning goal achievement. However, the recognition process in real environments such as in classrooms is still a challenging problem due to different expressions and illumination under arbitrary poses. In this paper we present a convolutional neural network based method that combining with gray level coocurence matrix. The result show that the proposed method can recognize three category of student facial expressions that represent good, bad, and neutral mood.