Evaluation of e-learners' concentration using recurrent neural networks
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manus...
| Veröffentlicht in: | The Journal of supercomputing. - 1998. - 79(2023), 4 vom: 30., Seite 4146-4163 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | The Journal of supercomputing |
| Schlagworte: | Journal Article Concentration E-learner E-learning Gated recurrent units(GRU) Long short-term memory (LSTM) Recurrent neural networks (RNN) |
| Zusammenfassung: | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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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| Beschreibung: | Date Revised 02.02.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0920-8542 |
| DOI: | 10.1007/s11227-022-04804-w |