{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/students-engagement-level-detection-in-online","title":"Students Engagement Level Detection in Online e-Learning Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM","arxiv_id":null,"date":"2022-09-26","proceeding":"IEEE Access 2022 9","authors":["Tasneem Selim","Islam Elkabani","Mohamed A. Abdou"],"abstract":"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.","url_abs":"https://ieeexplore.ieee.org/document/9893134","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9893134","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"students-engagement-level-detection-in-online","repo_url":"https://github.com/TasneemMohammed/Engagement-Detection-Using-Hybrid-EfficientNetB7-Together-With-TCN-LSTM-and-Bi-LSTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"student-engagement-level-detection-four-class","task_name":"Student Engagement Level Detection (Four Class Video Classification)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/student-engagement-level-detection-four-class","task":"Student Engagement Level Detection (Four Class Video Classification)","dataset":"DAiSEE","model":"Hybrid EfficientNet B7 + Bi-LSTM","rank_in_archive_order":1,"of":3,"metrics":{"4-class test accuracy":"67.48%"},"uses_additional_data":false},{"leaderboard":"/sota/student-engagement-level-detection-four-class","task":"Student Engagement Level Detection (Four Class Video Classification)","dataset":"DAiSEE","model":"Hybrid EfficientNet B7 + LSTM","rank_in_archive_order":2,"of":3,"metrics":{"4-class test accuracy":"66.39%"},"uses_additional_data":false},{"leaderboard":"/sota/student-engagement-level-detection-four-class","task":"Student Engagement Level Detection (Four Class Video Classification)","dataset":"DAiSEE","model":"Hybrid EfficientNet B7 + TCN","rank_in_archive_order":3,"of":3,"metrics":{"4-class test accuracy":"64.67%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}