Research Article
Analysis, Prediction and Maintenance of Teaching learning process based on empathize Students’ View of attending Online/Regular Class
@ARTICLE{10.4108/eai.12-1-2021.168090, author={Vithya Ganesan and V. Govindarajan and Pachipala Yellamma and Naren. J}, title={Analysis, Prediction and Maintenance of Teaching learning process based on empathize Students’ View of attending Online/Regular Class}, journal={EAI Endorsed Transactions on e-Learning}, volume={7}, number={20}, publisher={EAI}, journal_a={EL}, year={2021}, month={1}, keywords={Adult learning, Data science applications in education, Distance education and online learning, Media in education, Pedagogical issues, Teaching/learning strategies, Student Behaviour Prediction}, doi={10.4108/eai.12-1-2021.168090} }- Vithya Ganesan
V. Govindarajan
Pachipala Yellamma
Naren. J
Year: 2021
Analysis, Prediction and Maintenance of Teaching learning process based on empathize Students’ View of attending Online/Regular Class
EL
EAI
DOI: 10.4108/eai.12-1-2021.168090
Abstract
Learning models have been widely used in predicting diseases, disorders, behaviour aspects in human beings etc. The current research gives an analytical study on predicting University students’ behaviour with various Machine Learning approaches. Research shows that Machine Learning approaches outwit the strategies especially in Student behaviour analysis. An analytical study on various learning approaches and its application in Behaviour Analysis is vividly presented in the paper. The study would give an understanding on how various learning approaches could be applied in Student Behaviour Analysis that includes academic performance, behavioural study with reference to courses, Online teaching modes etc. The paper also encompasses comparison with various Machine Learning approaches in student behavioural prediction.
Copyright © 2021 Vithya Ganesan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
