Evaluation of e-learners’ concentration using recurrent neural networks

Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodolo...

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Bibliographic Details
Published in:The Journal of supercomputing 2023, Vol.79 (4), p.4146-4163
Main Authors: Jeong, Young-Sang, Cho, Nam-Wook
Format: Article
Language:English
Subjects:
Advances in Big Data and Deep Learning
Compilers
Computer Science
Distance learning
Eye movements
Interpreters
Processor Architectures
Programming Languages
Recurrent neural networks
Video data
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Summary:Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodology to predict e-learners’ concentration by applying recurrent neural network models to eye gaze and facial landmark data extracted from e-learners’ video data. One hundred eighty-four video data of ninety-two e-learners were obtained, and their frame data were extracted using the OpenFace 2.0 toolkit. Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to present a methodology to predict e-learners’ concentration in a natural e-learning environment.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04804-w