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A Review and Performance Prediction of Students’ Using Association Rule Mining based Approach

Sachin Kamley, Shailesh Jaloree, R. S. Thakur


For the last few decades’ education data mining has become one among foremost promising research areas. The only objective of this area is to explore data mining methods in order to analyze the student performance as well as impart the quality education for enhancing the performance of educational institutes. Data mining is the core part of the knowledge discovery process which is used to extract meaningful information from raw data. However, the various data mining techniques are proposed for achieving the most effective quality results. An Association Rule Mining (ARM) one of the well-known and popular data mining techniques which has been used extensively for educational perspective. In this study, higher education institute i.e. Government Girls College (GGC) data are considered and various attributes regarding student performance are analyzed for study purpose. Therefore, the various experiments based on support and confidence measures like 2%, 4%, 10%, 20% and 40% are conducted to generate interesting rules.  The major objective of this research study is to find the weaker students as well as those students who have bright performance in schooling level but could not be performed well on current semester exams due to certain reasons. However, teachers as well as parents can give particular attention to those students, whether they will perform better in the next semester or exams.


Apriori Algorithm, Association Rule Mining, Confidence, Education Data Mining, Preprocessing, Prediction, Support, Weka 3.7.

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N. Thai-Nghe, “Recommendation System for Predicting Student Performance”, Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced Learning, Vol. 1, pp. 2811-2819, 2010.

S. Kotsiantis, C. Pierrakeas and P. Pintelas, “Prediction of Student’s Performance in Distance Learning Using Machine Learning Techniques”, Applied Artificial Intelligence, Vol. 18, No.5, pp. 411-426, 2004.

A. K. Pujari, “Data Mining Techniques”, Universites (India) Press Private Limited, 10th Edition, Hyderabad (A.P.), 2006.

J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2nd Edition, San Francisco, CA, 2006.

B.K. Baradwaj and S. Pal, “Mining Educational Data to Analyze Student’s Performance”, International Journal of Advance Computer Science Applications (IJACSA), Vol. 2, pp. 63-69, 2011.

S. Borkar and K. Rajeshwari, “Predicting Students Academic Performance Using Education Data Mining”, International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 2, Issue (7), pp. 273-279, 2013.

V. Kumar and A. Chadha, “Mining Association Rules in Student’s Assessment Data”, International Journal of Computer Science Issues (IJCSI), Vol. 9, Issue (5), pp. 211-216, 2012.

A. Rauf and Sheeba, “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity”, Middle-East Journal of Scientific Research, Vol. 12, pp. 959-963, 2012.

M. Ramasami and R. Bhaskaran, “A CHAID based Performance Prediction Model in Educational Data Mining”, International Journal of Computer Science Issue, Vol. 7, Issue (1), pp. 10-18, 2010.

M Tiwari, R. Singh, and N. Vimal, “An Empirical Study of Applications of Data Mining Techniques for Predicting Student Performance in Higher Education”, International Journal of Computer Science and Mobile Computing, Vol. 2, pp. 53 – 57, 2014.

A. Buldua and K. Üçgün, “Data mining application on students’ data”, Procedia Social and Behavioral Sciences, Vol. 2, pp. 5251–5259, 2010.

S. Verma, R.S. Thakur and S. Jaloree, “Pattern Mining Approach to Categorization of Students Performance Using Apriori Algorithm”, International Journal of Computer Applications (IJCA), Vol. 121, No. 5, pp. 36-39, 2015.

A.M. Shahiri, W. Hussain, Nur Aini and A. Rashid, “A Review on Predicting Student’s Performance using Data Mining Techniques”, The Third Information Systems International Conference (ISICO), Procedia Computer Science, Vol. 72, pp. 414 – 422, 2015.

Education Dataset Collected from “Government Girls College”, Vidisha, MP, on Date 15/12/2015.

R. Agrawal, T. Imeielinski and A. Swami, “Mining Association Rules between Sets of Items in Large Databases”, Proceedings of the ACM SIGMOD Conference on Management of Data , Washington, D.C., pp. 207-216, 1993.

J. Ranjan and K. Malik, “Effective Educational Process: A Data Mining Approach”, Vol. 37, Issue (4), pp. 502-515, 2007.

K.Shyamala and S.P. Rajagopalan, “Data Mining Model for a better Higher Educational System”, Information Technology Journal, Vol. 5, No. 3, pp. 560-564, 2006.

R. Agarwal and R. Shrikant, “Fast Algorithms for Mining Association Rules”, Proceedings 20th International Conference on Very Large Databases, VLDB, 1994.

Aaron Ceglar and John F. Roddick, “Association Mining”, ACM Computing Surveys, Vol. 38, Issue (2), pp. 48-55, 2006.

World Wide Information Available on Weka Tool, “http://www.”.

D. Magdalene, D. Angeline and I. Samuel, “Association Rule Generation Using Apriori Algorithm for Student’s Placement”, International Journal of Emerging Sciences”, Vol. 2, No.1, pp. 78-86, 2012.

D. Magdalene and D. Angeline, “Association Rule Generation for Student Performance Analysis Using Apriori Algorithm”, The SIJ Transactions on Computer Science Engineering and its Applications (CSEA), Vol. 1, No. 1, pp. 12-16, 2013.

S. T. Jishan, R. I. Rashu, N. Haque and R. M. Rahman, “Improving Accuracy of Students Final Grade Prediction Model Using Optimal Equal Width Binning and Synthetic Minority Over-Sampling Technique”, Decision Analytics, Vol. 2, Issue (1), pp. 1–25, 2015.


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