Author : Himanshu Maniyar 1
Date of Publication :14th September 2017
Abstract: Data mining refers to the set of techniques to derive hidden patterns from the large existing data. These patterns can be useful for the analysis and prediction purpose. Education data mining refers to the set of data mining applications in education field. In today’s competitive world, it is essential for an institute to predict performance of students. Students could be informed well in advance to focus in a particular direction for the betterment of their academic performances. This research work predicts students’ performances in a course, based on their previous performances in related courses. Association rule mining is used to find out a set of related subjects. Students’ performances are predicted using various classification algorithms like decision tree, naive bayes etc. The database itself covers each and every piece of information related with student’s skills. Classification smoothing algorithm is introduced to select one of the most appropriate classified performance from set of available predictions. This research work has been tested for a database of students of Bachelor of Computer Applications.
Reference :
-
- Xingquan Zhu, Ian Davidson, “Knowledge Discovery and Data Mining: Challenges and Realities”, ISBN 978- 1-59904-252, Hershey, New York, 2007.
- Koprinska, Irena, Joshua Stretton, and Kalina Yacef. "Students at Risk: Detection and Remediation." EDM.2015.
- Pelánek, Radek. "Measuring Similarity of Educational Items Using Data on Learners’ Performance."
- Yanfei Zhou, Wanggen Wan, Junwei Liu, Long Cai, “Mining Association Rules Based on an Improved Apriori Algorithm”, 978- 1-4244-585 8- 5/10/$26.00 ©2010 IEEE.
- Huan Wu, Zhigang Lu, Lin Pan, Rongsheng Xu, Wenbao Jiang, “An Improved Apriori-based Algorithm for Association Rules Mining” Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 978-0-7695- 3735-1/09 $25.00 © 2009 IEEE DOI10.1109/FSKD.2009.193.
- Venkatadri.M and Lokanatha C. Reddy ,“A comparative study on decision tree classification algorithm in data mining” , International Journal Of Computer Applications In Engineering ,Technology And Sciences (IJCAETS), Vol.- 2 ,no.- 2 , pp. 24- 29 , Sept 2010.
- Safavian, S. Rasoul, and David Landgrebe. "A survey of decision tree classifier methodology." IEEE transactions on systems, man, and cybernetics21.3 (1991): 660-674.
- McCallum, Andrew, and Kamal Nigam. "A comparison of event models for naive bayes text classification." AAAI-98 workshop on learning for text categorization. Vol. 752. 1998.
- Team, R. Core. "R language definition." Vienna, Austria: R foundation for statistical computing (2000).