Students Engagement Level Detection in Online e-Learning Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM

Students engagement level detection in online e-learning has become a crucial problem due to the rapid advance of digitalization in education. In this paper, a novel Videos Recorded for Egyptian Students Engagement in E-learning (VRESEE) dataset is introduced for students engagement level detection...

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Bibliographic Details
Published in:	IEEE access Vol. 10; pp. 99573 - 99583
Main Authors:	Selim, Tasneem, Elkabani, Islam, Abdou, Mohamed A.
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Bidirectional LSTM Colleges & universities Computational modeling Datasets Deep learning Digitization Distance learning EfficientNet B7 Electronic learning engagement level detection Feature extraction Learning Long short term memory LSTM Online instruction online learning Online services Students TCN Three-dimensional displays Training data Videos
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Summary:	Students engagement level detection in online e-learning has become a crucial problem due to the rapid advance of digitalization in education. In this paper, a novel Videos Recorded for Egyptian Students Engagement in E-learning (VRESEE) dataset is introduced for students engagement level detection in online e-learning. This dataset is based on an experiment conducted on a group of Egyptian college students by video recording them during online e-learning sessions. Each recorded video is labeled with a value from 0 to 3 representing the level of engagement of each student during the online session. Moreover, three new hybrid end-to-end deep learning models have been proposed for detecting student's engagement level in an online e-learning video. These models are evaluated using the VRESEE dataset and also using a public Dataset for the Affective States in E-Environment (DAiSEE). The first proposed hybrid model uses EfficientNet B7 together with Temporal Convolution Network (TCN) and achieved an accuracy of 64.67% on DAiSEE and 81.14% on VRESEE. The second model uses a hybrid EfficientNet B7 along with Long Short Term Memory (LSTM) and reached an accuracy of 67.48% on DAiSEE and 93.99% on VRESEE. Finally, the third hybrid model uses EfficientNet B7 along with a Bidirectional LSTM and achieved an accuracy of 66.39% on DAiSEE and 94.47% on VRESEE. The results of the first, second and third proposed models outperform the results of currently existing models by 1.08%, 3.89%, and 2.8% respectively in students engagement level detection.
ISSN:	2169-3536 2169-3536
DOI:	10.1109/ACCESS.2022.3206779