Recent Methodologies for Improving and Evaluating Academic Performance

Ajay Varma¹ , Y. S. Chouhan²

Section:Review Paper, Product Type: Isroset-Conference
Vol.3 , Issue.2 , pp.10-16, Mar-2015

Online published on Jun 22, 2015

Copyright © Ajay Varma , Y. S. Chouhan . 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.
 

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Abstract :
In the real world, predicting the performance of the students is a challenging task. Many of the well known technical colleges are successful as they have meritorious students and faculty with them and a foolproof system working for them to grow continuously. The primary goal of Data mining in practice tends to be Prediction and Description. For educational institutions, the success of creation of human capital is the subject of a continuous analysis. To date, higher educational organizations are placed in a very high competitive environment and to remain competitive, organizations need better assessment, evaluation, planning, and decision making. As such, classification modeling for academic performance for the graduates could provide some insight to the university in order to take necessary information for improving the students’ academic performance. Hence, the aim of this study is to provide the review of different data mining techniques that have been used in educational field with regard to evaluation of students’ academic performance. Academic Data Mining used many techniques such as Decision Trees, Neural Networks, Naïve Bayes, K- Nearest neighbor, and many others. Using these techniques many kinds of knowledge can be discovered such as association rules, classifications and clustering. The discovered knowledge can be used for prediction and analysis purposes of student patterns.

Key-Words / Index Term :
Classification, Data Mining, Bayesian Network, Neural Network

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