Mining Association Rules in Student’s Assessment Data Dr. Varun Kumar 1 , Anupama Chadha 2 1 Department of Computer Science and Engineering, MVN University Palwal, Haryana, India 2 Anupama Chadha, Department of Computer Science and Engineering, ITM University Gurgaon, India Abstract Higher education, throughout the world is delivered through universities, colleges affiliated to various universities and some other recognized academic institutes. Today one of the biggest challenges, the educational institutions face, is the explosive growth of educational data and to use this data to improve the quality of managerial decisions to deliver quality education. In this paper we will perform a case study of a university that hopes to improve the quality of education by analyzing the data and discover the factors that affect the academic results so as to increase success chances of students. In this perspective we use association rules discovery techniques. Also we will show the importance of data preprocessing in data analysis which has a significant impact on the accuracy of the predicted results. Keywords: Higher education, Data mining, Knowledge discovery, Data preprocessing, Association rules 1. Introduction Education is an essential element for the betterment and progress of a country. It enables the people of a country civilized and well mannered[6]. Today the important challenge that higher education faces, is reaching a stage to facilitate the universities in having more efficient, effective and accurate educational processes. To date, higher educational organizations are placed in a very high competitive environment and are aiming to get more competitive advantages over the other competitors. To remain competitiveness among educational field, these organizations need deep and enough knowledge for a better assessment, evaluation, planning, and decision-making.[5] The required knowledge cannot be gained from the tailor made software used now a days. Data mining incorporates a multitude of techniques from a variety of fields including databases, statistics, data visualization, machine learning and others.[7] The data mining technology can discover the hidden patterns, associations, and anomalies from educational data. This knowledge can improve the decision making processes in higher educational systems. Data mining is considered as the most suited technology appropriate in giving additional insight into the lecturer, student, alumni, manager, and other educational staff behavior and acting as an active automated assistant in helping them for making better decisions on their educational activities.[1] The data mining techniques can help the institutes in extracting patterns like students having similar characteristics, Association of students’ attitude with performance, what factors will attract meritorious students and so on. The past several decades have witnessed a rapid growth in the use of data and knowledge mining as a means by which academic institutions extract useful hidden information in the student result repositories in order to improve students’ learning processes.[2] The main objective of this paper is to use data mining methodologies to study students’ performance in their courses. In this research, we will be using Association rules discovery techniques to compare the student’s performance in the subjects common at Graduation and Post Graduation level and will predict the factors which can explain their success or failure. This paper is organized as follows: Section 2: discusses about the motivations of this work and some related works. Section 3: gives the relevant information about knowledge discovery process along with the data mining and association rule for the discovery of hidden knowledge. Section 4: discusses the results of the analysis and the rules discovered from the present study. Section 5: the conclusion discussed in this section. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 3, September 2012 ISSN (Online): 1694-0814 www.IJCSI.org 211 Copyright (c) 2012 International Journal of Computer Science Issues. All Rights Reserved.