Abstract—Higher education management problems in
delivering 100% of graduates who can satisfy business demands.
In industry it is often difficult for qualified graduates to identify
the appropriate means to evaluate problem - solving abilities as
well as shortcomings in the evaluation of problem solving skills.
This is partially due to the lack of an adequate methodology.
The purpose of this paper is to provide the appropriate
CBR-KBS model for predicting and evaluating the
characteristics of the student's dataset so as to comply with the
parameters of selection required by the university industry.
Machine learning algorithms have been used in these study
areas under supervision, uncompleted and uncontrolled;
K-Nearest neighbor, Naïve Bayes, Decision Tree, Neural
Network, Logistic Regression and Vector Support Machines.
The proposed model would allow university management to
make easier, more professional, experienced and
industry-specific plans for the manufacturing of graduates and
graduates who passed the type I and II examinations held by the
Index Terms—Case base reasoning, decision tree, knowledge base system, neural network, WEKA.
Prashant Dixit and Harish Nagar are with Sangam University, Bhilwara, Rajasthan, India (e-mail: firstname.lastname@example.org, email@example.com).
Sarvottam Dixit is with Mewar University, Rajasthan, India (e-mail: firstname.lastname@example.org).
Cite: Prashant Dixit, Harish Nagar, and Sarvottam Dixit, "Student Performance Prediction Using Case Based Reasoning Knowledge Base System (CBR-KBS) Based Data Mining," International Journal of Information and Education Technology vol. 12, no. 1, pp. 30-35, 2022.Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).