• Dr.V.S Akshaya
• N.Mohan Karthik
• B.Kavinkumar
• B.Madhan Gokul
• Maddali Guru Venkata Lokesh

Massive Open Online Courses (MOOCs), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) are examples of online learning platforms that work with thousands or even a huge number of students to master according to their interests without regard for space or time constraints. Apart from the numerous advantages, web-based learning stages encounter a number of challenges, including understudies' disinterest, high dropout rates, low commitment, understudies' self-directed behavior, and motivating understudies to take responsibility for defining their own goals. In this paper, we present a foresight model that analyzes the challenges faced by at-risk understudies, and then collaborates with educators to find the best way to persuade understudies to increase their review commitment and improve their review execution. The vision model is created and tested using several AI (ML) and deep learning techniques (DL).

MACHINE LEARNING MODELS FOR EARLY INTERVENTION OF STUDENTS COURSE LENGTH

"MACHINE LEARNING MODELS FOR EARLY INTERVENTION OF STUDENTS COURSE LENGTH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.112-118, June-2022, Available :http://www.jetir.org/papers/JETIRFM06021.pdf

"MACHINE LEARNING MODELS FOR EARLY INTERVENTION OF STUDENTS COURSE LENGTH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pp112-118, June-2022, Available at : http://www.jetir.org/papers/JETIRFM06021.pdf