NBC Model for Early Prediction of At-Risk Students in Course
P. Sunanda1, D. Kavitha2

1P. Sunanda, Assistant Professor, Department of Computer Science & Engineering, G. Pulla Reddy Engineering College, Kurnool India.
2Dr. D. Kavitha, Professor, Department of Computer Science & Engineering at G. Pulla Reddy Engineering College, Kurnool India.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3466-3473 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C5440029320/2020©BEIESP | DOI: 10.35940/ijeat.C5440.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Machine Learning, Prediction, at-risk, Naive-Bayes.