Students’ affective content analysis in smart classroom environment using deep learning techniques

In the era of the smart classroom environment, students’ affective content analysis plays a vital role as it helps to foster the affective states that are beneficial to learning. Some techniques target to improve the learning rate using the students’ affective content analysis in the classroom. In t...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-09, Vol.78 (18), p.25321-25348
Main Authors: Gupta, Sujit Kumar, Ashwin, T. S., Guddeti, Ram Mohana Reddy
Format: Article
Language:English
Subjects:
Affect (Psychology)
Classrooms
Computer Communication Networks
Computer Science
Content analysis
Data Structures and Information Theory
Deep learning
Face recognition
Feedback
Moods
Multimedia Information Systems
Qualitative research
Special Purpose and Application-Based Systems
Students
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Summary:In the era of the smart classroom environment, students’ affective content analysis plays a vital role as it helps to foster the affective states that are beneficial to learning. Some techniques target to improve the learning rate using the students’ affective content analysis in the classroom. In this paper, a novel max margin face detection based method for students’ affective content analysis using their facial expressions is proposed. The affective content analysis includes analyzing four different moods of students’, namely: High Positive Affect, Low Positive Affect, High Negative Affect, and Low Negative Affect. Engagement scores have been calculated based upon the four moods of students as predicted by the proposed method. Further, the classroom engagement analysis is performed by considering the entire classroom as one group and the corresponding group engagement score. Expert feedback and analyzed affect content videos are used as feedback to the faculty member to improve the teaching strategy and hence improving the students’ learning rate. The proposed smart classroom system was tested for more than 100 students of four different Information Technology courses and the corresponding faculty members at National Institute of Technology Karnataka Surathkal, Mangalore, India. The experimental results demonstrate the train and test accuracy of 90.67% and 87.65%, respectively for mood classification. Furthermore, an analysis was performed over incidence, distribution and temporal dynamics of students’ affective states and promising results were obtained.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-7651-z