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Evaluation of Teaching Effectiveness based on Classroom Micro-Expression Recognition

Volume 14, Number 11, November 2018, pp. 2877-2885
DOI: 10.23940/ijpe.18.11.p33.28772885

Xiaoxu Guoa, Juxiang Zhoub, and Tianwei Xuc

aSchool of Information, Yunnan Normal University, Kunming, 650500, China
bKey Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, 650500, China
cGraduate Faculty, Yunnan Normal University, Kunming, 650500, China

(Submitted on August 22, 2018; Revised on September 16, 2018; Accepted on October 10, 2018)

Abstract:

The improvement of teaching quality has been a persistent theme in education. To improve the quality of teaching in the classroom, teachers need to interact with students, pay attention to each student’s emotional changes, and closely follow each student’s changes in learning status, so as to make effective adjustments for teaching content. However, students’ responses often cannot be captured in time due to the limitations of the teacher in the classroom. Advances in computer and Internet technology as well as the development and maturation of image processing and artificial intelligence have provided technical support for the evaluation system of facial expression recognition in intelligent classrooms. In this paper, we propose an effective method to evaluate teaching effectiveness based on facial micro-expression recognition. An evaluation system is also designed and realized based on analyzing the change of classroom micro-expressions and the concentration of students. In such an evaluation system, face detection, tracking, and micro-expression recognition technology are applied to analyze the emotional changes during the learning process. Then students’ attention in class will be timely fed back to teachers, which can help teachers adjust teaching methods and strategies in a timely manner to improve teaching quality. In an informational teaching environment with general monitoring equipment, our proposed system can automatically track and analyze the degree of student’s concentration in the teaching process. Furthermore, it can also track the specified objects and analyze the change of their learning status in a certain period of time, which can help teachers conduct expediently multi-dimensional evaluation and guidance.

 

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