Student Engagement Monitoring in Online Learning Environment

Authors

  • Vijaya U. Pinjarkar Assistant Professor, K J Somaiya Institute of Technology, Sion, Mumbai, India.
  • Umesh S. Pinjarkar Assistant Professor,Saraswati College of Engineering, Kharghar, Mumbai, India.
  • Harsh Namdev Bhor Assistant Professor, K J Somaiya Institute of Technology, Sion, Mumbai, India.
  • Yogeshwari V. Mahajan Assistant Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering and Research, Ravet, Pune, Maharashtra, India.
  • Vishal Ratansing Patil Assistant Professor, Department of Computer Science and Engineering(AIML), Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India.
  • Satpalsing Devising Rajput Assistant Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India.
  • Parth Kothari UG Student, K J Somaiya Institute of Technology, Sion, Mumbai, India.
  • Dhruv Ghori UG Student, K J Somaiya Institute of Technology, Sion, Mumbai, India.
  • Harish Parshuram Bhabad Asst. Professor, Loknete Gopinathji Munde Institute of Engineering and Education Research, Nashik, Maharashtra, India.

Keywords:

Online Monitoring, Face Recognition, Student Engagement

Abstract

Students engagement is one of the most important factors in student achievement. Many schools are aware of this and have initiated programs to monitor how engaged students are in school. Tracking student engagement not only helps teachers assess their teaching methods, it also helps administrators know which aspects of the school environment need more attention. In order to measure student engagement, many schools can incorporate systems that track a child’s response time during individual lessons. We all know that the internet has changed education forever, and for the better. An accessible online world has allowed students to learn at their own pace in a more natural environment with new opportunities for collaboration, creativity, and growth. But what is not commonly understood is just how crucial student engagement on an online course can be to its success. Student engagement is fundamental to educational success. Engagement monitoring can help identify what students find interesting and engaging in the classroom, what they want, what makes them uncomfortable, and what they need.

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References

Dewan M. A. A., Lin F., Wen D., Murshed M. & Uddin Z., A Deep Learning Approach to Detecting Engagement of Online Learners, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp1895-1902, 2018.

Dash S., Akber DewanM. A., Murshed M., Lin F. , Abdullah-Al-Wadud M. & Das A., A Two-Stage Algorithm for Engagement Detection in Online Learning, 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1-4, 2019.

C. Arce-Lopera, J. J. Cardona and F. García, "Acoustic Monitoring System for Teacher and Student Engagement Evaluation," 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), 2020, pp. 1-4, doi: 10.23919/CISTI 49556.2020.9140442.

Y. Wang, A. Kotha, P. -h. Hong and M. Qiu, "Automated Student Engagement Monitoring and Evaluation during Learning in the Wild," 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), 2020, pp. 270-275, doi: 10.1109/CSCloud-EdgeCom49738.2020.00054.

M. Geetha, R. S. Latha, S. K. Nivetha, S. Hariprasath, S. Gowtham and C. S. Deepak, "Design of face detection and recognition system to monitor students during online examinations using Machine Learning algorithms," 2021 International Conference on Computer Communication and Informatics (ICCCI), 2021, pp. 1-4, doi: 10.1109/ICCCI50826.2021.9402553.

D. Preuveneers and W. Joosen, "Edge-Based and Privacy-Preserving Multi-Modal Monitoring of Student Engagement in Online Learning Environments," 2019 IEEE International Conference on Edge Computing (EDGE), 2019, pp. 18-20, doi: 10.1109/EDGE.2019.00017.

Alkabbany, A. Ali, A. Farag, I. Bennett, M. Ghanoum and A. Farag, "Measuring Student Engagement Level Using Facial Information," 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 3337-3341, doi: 10.1109/ICIP.2019.8803590.

J. Whitehill, Z. Serpell, Y. Lin, A. Foster and J. R. Movellan, "The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions," in IEEE Transactions on Affective Computing, vol. 5, no. 1, pp. 86-98, 1 Jan.-March 2014, doi: 10.1109/TAFFC.2014.2316163.

F. Orji and J. Vassileva, "Using Machine Learning to Explore the Relation Between Student Engagement and Student Performance," 2020 24th International Conference Information Visualization (IV), 2020, pp. 480-485, doi: 10.1109/IV51561.2020.

H. Mav, A. Mokashi, S. Nanduri and V. Pinjarkar, "Face Recognition and Adversarial Masking Techniques," 2022 3rd International Conference for Emerging Technology (INCET), 2022, pp. 1-7, doi: 10.1109/INCET54531.2022.9825044.

Abhay Gupta, Arjun D'Cunha, Kamal Awasthi, Vineeth Balasubramanian, “DAiSEE: Towards User Engagement Recognition in the Wild” arXiv:1609.01885 [cs.CV],

B. H. K. T., H. M. Perera, S. Kumarage, P. A. P. Savindri, D. Kasthurirathna and A. Kugathasan, "E-Learn Detector: Smart Behaviour Monitoring System to Analyze Student Behaviours During Online Educational Activities," 2021 3rd International Conference on Advancements in Computing (ICAC), 2021, pp. 19-24, doi: 10.1109/ICAC54203.2021.9671073.

S. Dash, M. A. Akber Dewan, M. Murshed, F. Lin, M. Abdullah-Al-Wadud and A. Das, "A Two-Stage Algorithm for Engagement Detection in Online Learning," 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), 2019, pp. 1-4, doi: 10.1109/STI47673.2019.9068054.

F. Orji and J. Vassileva, "Using Machine Learning to Explore the Relation Between Student Engagement and Student Performance," 2020 24th International Conference Information Visualisation (IV), 2020, pp. 480-485, doi: 10.1109/IV51561.2020.00083.

M. A. A. Dewan, F. Lin, D. Wen, M. Murshed and Z. Uddin, "A Deep Learning Approach to Detecting Engagement of Online Learners," 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018, pp. 1895-1902, doi: 10.1109/SmartWorld.2018.00318.

B. M. da Silva et al., "GuitarEasy - An Interactive Approach to the Study of Music Theory," 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), 2021, pp. 1-6, doi: 10.23919/CISTI52073.2021.9476472.

X. Li, C. Shao, Y. Zhou and L. Huang, "Face Mask Removal Based on Generative Adversarial Network and Texture Network," 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE), 2021, pp. 86-89, doi: 10.1109/RCAE53607.2021.9638866.

J. Whitehill, Z. Serpell, Y. -C. Lin, A. Foster and J. R. Movellan, "The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions," in IEEE Transactions on Affective Computing, vol. 5, no. 1, pp. 86-98, 1 Jan.-March 2014, doi: 10.1109/TAFFC.2014.2316163.

Zixiang Fei, Erfu Yang, Leijian Yu, Xia Li, Huiyu Zhou, Wenju Zhou, “A Novel deep neural network-based emotion analysis system for automatic detection of mild cognitive impairment in the elderly Neurocomputing”, Volume 468, Pages 306-316, ISSN 0925-2312, 2022.

V. Laijawala, A. Aachaliya, H. Jatta and V. Pinjarkar, "Classification Algorithms based Mental Health Prediction using Data Mining," 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020, pp. 1174-1178, doi: 10.1109/ICCES48766.2020.9137856.

V. Pinjarkar,Jain Amit, B. Bhasker, P.Srivastava, “Pertinent Exploration of Privacy Preserving Perturbation Methods” international Journal of Recent Technology and Engineering, 8(6), 2020, 1945-1949

Terdale, J. V. ., Bhole, V. ., Bhor, H. N. ., Parati, N. ., Zade, N. ., & Pande, S. P., ”Machine Learning Algorithm for Early Detection and Analysis of Brain Tumors Using MRI Images”, International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), (2023), 403–415. https://doi.org/10.17762/ijritcc.v11i5s.7057

Harsh Namdev Bhor, Mukesh Kalla, “TRUST-based features for detecting the intruders in the Internet of Things network using deep learning”, Computational Intelligence, 38(2), (2022), 438-462. https://doi.org/10.1111/coin.12473

Bhor, H.N., Kalla, M. “A Survey on DBN for Intrusion Detection in IoT”. In: Zhang, YD., Senjyu, T., SO–IN, C., Joshi, A. (eds) Smart Trends in Computing and Communications: Proceedings of SmartCom 2020. Smart Innovation, Systems and Technologies, vol 182. Springer, Singapore. https://doi.org/10.1007/978-981-15-5224-3_33

V. Pinjarkar, A. Jain., A. Bhasker, P Shrivastava , "Mental Health Disorder and Privacy Preserving Data Mining: A Survey", in book “The Role of IoT and Blockchain”, pp. 441–449, Taylor and Francis Group, (2022), 1 st Edition.

H. N. Bhor and M. Kalla, "An Intrusion Detection in Internet of Things: A Systematic Study," International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, (2020), pp. 939-944, doi: 10.1109/ICOSEC49089.2020.9215365.

Isabella Rossi, Reinforcement Learning for Resource Allocation in Cloud Computing , Machine Learning Applications Conference Proceedings, Vol 1 2021.

Deshpande, V. (2021). Layered Intrusion Detection System Model for The Attack Detection with The Multi-Class Ensemble Classifier . Machine Learning Applications in Engineering Education and Management, 1(2), 01–06. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/10

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Published

25.12.2023

How to Cite

Pinjarkar, V. U. ., Pinjarkar, U. S. ., Bhor, H. N. ., Mahajan, Y. V. ., Patil, V. R. ., Rajput, S. D. ., Kothari, P. ., Ghori, D. ., & Bhabad, H. P. . (2023). Student Engagement Monitoring in Online Learning Environment. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 292–298. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3902

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Research Article