Deep Analysis of Student Body Activities to Detect Engagement State in E-Learning Sessions

• Published in: Applied sciences 2023-02, Vol.13 (4), p.2591
• Main Authors: Khenkar, Shoroog Ghazee, Jarraya, Salma Kammoun, Allinjawi, Arwa, Alkhuraiji, Samar, Abuzinadah, Nihal, Kateb, Faris A.
• Format: Article
• Language: English
• Subjects: Accuracy; affective model; automatic engagement detection; body activities; Datasets; deep 3D CNN; Deep learning; E-learning systems; Emotions; Interactive systems; Knowledge bases (artificial intelligence); Learning; Online education; Online instruction; Performance evaluation; Prediction models; School environment; Student participation; Students; Video data
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042591

In this paper, we propose new 3D CNN prediction models for detecting student engagement levels in an e-learning environment. The first generated model classifies students’ engagement to high positive engagement or low positive engagement. The second generated model classifies engagement to low negative engagement or disengagement. To predict the engagement level, the proposed prediction models learn the deep spatiotemporal features of the body activities of the students. In addition, we collected a new video dataset for this study. The new dataset was collected in realistic, uncontrolled settings from real students attending real online classes. Our findings are threefold: (1) Spatiotemporal features are more suitable for analyzing body activities from video data; (2) our proposed prediction models outperform state-of-the-art methods and have proven their effectiveness; and (3) our newly collected video dataset, which reflects realistic scenarios, contributed to delivering comparable results to current methods. The findings of this work will strengthen the knowledge base for the development of intelligent and interactive e-learning systems that can give feedback based on user engagement.