Online Learning Management System and Analytics using Deep Learning

Authors

  • Ansuman Singh Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
  • Ashok Singh Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
  • Devendra Singh Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
  • Laxman Sharma Student, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA
  • Dr. N K Bansode Professor, Department of Computer Engineering, Army Institute of Technology, Pune, INDIA

DOI:

https://doi.org/10.31033/ijemr.11.2.33

Keywords:

Business Intelligence in Education, Classification and Regression, Decision Trees, Random Forest, E-Learning

Abstract

During this pandemic we have seen rise in popularity of online learning platforms. In this paper, we are going to discuss E-Learning using analytics and deep learning focusing on mainly four objectives which are login systems for teachers and students, Gamification to engage learners, AR contents to increase the involvement of learners and learning analytics to develop competency. We will use Data Mining and Buisness Intelligence to extract high level knowledge from the raw data of students. To predict engagement of students we have used several ML algorithms. This study provides an introduction to the technology of AR and E-Learning systems. The main focus of this paper is to use research on augmented reality and integrate it with Buisness Intelligence and Data Mining(DM).

Engaging student till the end of the course became really difficult for traditional E-Learning Platform. Therefore, Gamification in E-learning is good way to solve this problem.

Downloads

Published

2021-04-30

How to Cite

Ansuman Singh, Ashok Singh, Devendra Singh, Laxman Sharma, & Dr. N K Bansode. (2021). Online Learning Management System and Analytics using Deep Learning. International Journal of Engineering and Management Research, 11(2), 232–238. https://doi.org/10.31033/ijemr.11.2.33

Most read articles by the same author(s)