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The Journal of Supercomputing

Erschienen in:

22.09.2022

Evaluation of e-learners’ concentration using recurrent neural networks

verfasst von: Young-Sang Jeong, Nam-Wook Cho

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2023

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Abstract

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.

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Titel: Evaluation of e-learners’ concentration using recurrent neural networks
verfasst von: Young-Sang Jeong
Nam-Wook Cho
Publikationsdatum: 22.09.2022
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 4/2023
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-022-04804-w

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