IMPLEMENTATION OFIMPROVED PREDICTIVE MODEL FOR STUDENTS GRADE PREDICTION USING RANDOM FOREST ALGORITHM

Authors

  • Dr. V. K. Manavalasundaram, C. P. Nithish Kumar, K. V. S. Prabakaran, K. Ranganathan, D. Santhosh

Keywords:

grade predictive, Forest Algorithm

Abstract

Internet learning stages like Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) work with thousands or even huge number of understudies to get the hang of as indicated by their inclinations without spatial and fleeting imperatives. Other than many benefits, web based learning stages face a few difficulties like understudies' indifference, high dropouts, low commitment, understudies' self managed conduct, and convincing understudies to take more time for settings their own objectives. In this review, we propose a prescient model that examines the issues looked by in danger understudies, therefore, working with educators for ideal intercession to convince understudies to expand their review commitment and further develop their review execution. The prescient model is prepared and tried utilizing different AI (ML) and profound learning (DL) calculations to describe the learning conduct of understudies as indicated by their review factors. The exhibition of different ML calculations is looked at by utilizing exactness, accuracy, backing, and f-score. The ML calculation that gives the best outcome regarding exactness, accuracy, review, backing, and f-score metric is eventually chosen for making the prescient model at various rates obviously length. The prescient model can help teachers in distinguishing in danger understudies right off the bat in the course for opportune mediation in this manner staying away from understudy dropouts. Our outcomes showed that understudies' appraisal scores, commitment power for example clickstream information, and time-subordinate factors are significant variables in internet learning.

Published

2022-06-28

How to Cite

Dr. V. K. Manavalasundaram, C. P. Nithish Kumar, K. V. S. Prabakaran, K. Ranganathan, D. Santhosh. (2022). IMPLEMENTATION OFIMPROVED PREDICTIVE MODEL FOR STUDENTS GRADE PREDICTION USING RANDOM FOREST ALGORITHM . International Journal of Advanced Engineering Science and Information Technology, 10(6), 46–51. Retrieved from http://ijaesit.com/index.php/home/article/view/90