Smart E - Learn Tracer
Volume 6 - Issue 5, May 2023 Edition
[Download Full Paper]
Vimaleshwaran. S, Thanojan. S, Dias J J J, Niyas Inshaf
Machine learning, E-learning, Video processing, Artificial Intelligence, Natural Language Processing.
The COVID-19 pandemic has accelerated the shift towards online education, which presents a range of challenges for educators, including difficulties in monitoring student participation, tracking attendance, generating and evaluating questions, and dealing with external distractions. To address these issues, we propose a comprehensive online video classroom web application that leverages machine learning techniques. The application includes a machine learning approach to monitor and analyze student concentration, an attendance checker based on neural network technology, and natural language processing (NLP) to generate and evaluate questions. Additionally, an outside voice processor using neural network technology will filter out background noise to improve the clarity of both the teacher's voice and the student's responses. Our proposed solution will provide educators with a powerful tool to enhance the learning experience for students, helping them to engage with their students more effectively, streamline question generation and evaluation, and create a more productive learning environment.
. H. R. Tavakoli, A. Rezaee, and M. Ghahremani, "The impact of artificial intelligence on education and learning process: A systematic review," International Journal of Educational Technology in Higher Education, vol. 16, no. 1, p. 39, Oct. 2019, doi: 10.1186/s41239-019-0176-6.
. J. Wang, Y. Li, X. Zhan, and Y. Liu, "Development and Research on Artificial Intelligence Education Based on Machine Learning," in 2018 4th International Conference on Education and Training Technologies (ICETT), Shanghai, China, 2018, pp. 17-21, doi: 10.1109/ICETT.2018.8528628.
. K. M. S. Mahmud, M. J. Hossain, and M. A. Azim, "Deep learning approaches in education: A systematic literature review," IEEE Access, vol. 7, pp. 90445-90462, 2019, doi: 10.1109/ACCESS.2019.2923124.
. X. Wang, M. Gao, X. Yu, and Z. Wang, "Smart education using artificial intelligence: A review," in 2019 IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Cagliari, Italy, 2019, pp. 167-171, doi: 10.1109/AIKE.2019.8853391.
. R. Akter, S. Mahmud, and R. Islam, "Integrating Artificial Intelligence and Adaptive Learning to Promote Student-Centric Education," in 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 2019, pp. 41-46, doi: 10.1109/ICREST.2019.8714369.
. N. M. Aljohani and M. A. Alaboudi, "Artificial Intelligence Applications in Distance Education: A Comprehensive Review," IEEE Access, vol. 7, pp. 128538-128555, 2019, doi: 10.1109/ACCESS.2019.2932873.
. L. Cai, X. Huang, and Y. Lin, "Artificial intelligence for intelligent education: A review," in 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Yokohama, Japan, 2020, pp. 1066-1070, doi: 10.1109/TALE48900.2020.9368292.
. M. A. Anwar and M. H. R. Bappy, "An Artificial Intelligence-Based Tutoring System for Personalized Learning," IEEE Access, vol. 8, pp. 123934-123947, 2020, doi: 10.1109/ACCESS.2020.3005982.
. J. An, Y. Zhao, S. Lv, and J. Hou, "Teaching Evaluation System Based on Machine Learning and Data Mining," in 2019 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2019, pp. 1-6, doi: 10.1109/ICCCI47125.2019.9031956.
. Ozturk, C., & Bozkurt, A. (2019). Review of the literature on artificial intelligence, analytics, and adaptive learning in relation to learning and teaching in higher education. Journal of Educational Technology Development and Exchange, 12(1), 1-18. doi: 10.18785/jetde.1201.0