Abstract
This paper concentrates on testing Facial Expression Recognition metrics such as anger, contempt, disgust, happiness, neutral, sadness, and surprise to determine which of them is the most appropriate to provide effective feedback in an Intelligent Tutoring System. We discuss how to exploit this tool to develop an intelligent system to deep the students’ understanding of basic scientific principles and concepts during the learning process, in order to improve the teaching in distance education. In fact, despite of the growth of E-learning environments, it’s already difficult to detect the student’s emotions and attention so we look up in this paper the relevant metrics with a best accuracy for generating the proper feedback in response to the student’s expression during experimental learning sessions.
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Khalfallah, J., Ben Hadj Slama, J. (2017). Relevant Metrics for Facial expression recognition in Intelligent Tutoring System. In: Popescu, E., et al. Innovations in Smart Learning. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-2419-1_17
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DOI: https://doi.org/10.1007/978-981-10-2419-1_17
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