Open AccessNBC Model for Early Prediction of At-Risk Students in Course
Author(s) -
P. Sunanda,
D. Kavitha
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5440.029320
Subject(s) - computer science , field (mathematics) , drop out , machine learning , artificial intelligence , work (physics) , mathematics education , engineering , psychology , mechanical engineering , mathematics , economics , pure mathematics , demographic economics
Increase in computer usage for different purposes in different fields has made the computer important to learn things. Machine learning made systems to learn things and work accordingly on their own. Among the different fields that use machine learning, the education field is one. In the education field, machine learning has led to the advent of a digital-enabled classroom, speech recognition, adaptive learning techniques, and development of artificial instructor. Along with this, the prediction has its importance. In the education field, the main problem is students drop out. The machine learning predictive modeling approach can be used to identify the students who are at-risk and inform the instructor and students before reducing the dropouts. The main intention of this paper is to model a system that could be a solution to reduce the drop-outs and increase the education standards in students by early predicting their risk in a course.