Use of Intelligent Student Mood Classification System (ISMCS) to achieve high quality in education

Kamil Dimililer ()
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Kamil Dimililer: Near East University

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 1, No 47, 662 pages

Abstract: Abstract Quality in education can be raised through the integration of technology. In this respect, technology has been more involved in contemporary education. A recent and promising way to integrate technology is to employ face tracking devices into classes. Facial analysis determines emotions of students revealing their levels of interest in the course. Teachers can benefit from these emotion recognition systems by redesigning their courses in accordance with the interests of their students for the purpose of high quality education. The purpose of this paper is to evaluate the effectiveness of a back-propagation neural network in recognizing different faces based on Scale Invariant Feature Transform as feature extractor of an average of 128 features to be applied in the intelligent system to see the effect on education and virtual learning environments. The developed framework consists of two main phases which are the image processing phase and the classification phase. In the image processing phase, the face images are passed through image processing techniques then the images are classified using neural networks. The results indicated that the proposed intelligent face recognition system had a significant accuracy rate which clarifies that teachers’ teaching methodologies and strategies change according to students’ mood during learning processes.

Keywords: Intelligent education system; Quality education; Face recognition; Scale Invariant Feature Transforms; Back-propagation neural networks (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11135-017-0644-y

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