Student Performance Prediction Using Case Based Reasoning Knowledge Base System (CBR-KBS) Based Data Mining

Author:

Dixit Prashant, ,Nagar Harish,Dixit Sarvottam

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 employment opportunities.

Publisher

EJournal Publishing

Subject

Computer Science Applications,Education

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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4. Recommendation System of Food Package Using Apriori and FP-Growth Data Mining Methods;Journal of Advances in Information Technology;2023

5. A Knowledge-Based Consultant Student System Using Reasoning Techniques for Selection of Courses in Smart University;Algorithms for Intelligent Systems;2023

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