student performance
BULGARIAN ACADEMY OF SCIENCES
CYBERNETICS AND INFORMATION TECHNOLOGIES • Volume 13, No 1
Sofia • 2013
Print ISSN: 1311-9702; Online ISSN: 1314-4081
DOI: 10.2478/cait-2013-0006
Predicting Student Performance by Using Data Mining
Methods for Classification
Dorina Kabakchieva
Sofia University “St. Kl. Ohridski”, Sofia 1000
Email: dorina@fmi.uni-sofia.bg
Abstract: Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.
Keywords: Educational data mining, predicting student performance, data mining classification. 1. Introduction
Universities today are operating in a very complex and highly competitive environment. The main challenge for modern universities is to deeply analyze their performance, to identify their uniqueness and to build a strategy for further development and future actions. University management should focus more on the profile of admitted students, getting aware of the different types and specific students’ characteristics based on the received data. They should also consider if they have all the data needed to analyze the students at the entry point of the university or they need other data to help the managers support their decisions as how to organize the marketing campaign and approach the promising potential students. This paper is focused on the implementation of data mining techniques and methods for acquiring new knowledge from data collected by universities. The main goal of the research is to reveal the high potential of data mining applications for university management.
61
The specific objective of the proposed research work is to find out if there are any patterns in the
CYBERNETICS AND INFORMATION TECHNOLOGIES • Volume 13, No 1
Sofia • 2013
Print ISSN: 1311-9702; Online ISSN: 1314-4081
DOI: 10.2478/cait-2013-0006
Predicting Student Performance by Using Data Mining
Methods for Classification
Dorina Kabakchieva
Sofia University “St. Kl. Ohridski”, Sofia 1000
Email: dorina@fmi.uni-sofia.bg
Abstract: Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.
Keywords: Educational data mining, predicting student performance, data mining classification. 1. Introduction
Universities today are operating in a very complex and highly competitive environment. The main challenge for modern universities is to deeply analyze their performance, to identify their uniqueness and to build a strategy for further development and future actions. University management should focus more on the profile of admitted students, getting aware of the different types and specific students’ characteristics based on the received data. They should also consider if they have all the data needed to analyze the students at the entry point of the university or they need other data to help the managers support their decisions as how to organize the marketing campaign and approach the promising potential students. This paper is focused on the implementation of data mining techniques and methods for acquiring new knowledge from data collected by universities. The main goal of the research is to reveal the high potential of data mining applications for university management.
61
The specific objective of the proposed research work is to find out if there are any patterns in the
References: Directions for Institutional Research, Vol. 131, 2006, 69-81. CRISPWP-0800, 2000. EUROSIS. A. Brito and J. Teixeira, Eds. 2008, 5-12. In: Proc. of the Nat. Symposium on Student Retention, 2007. Vol. 18, 2004, No 5, 411-426. 8. K o v a č i ć, Z. Early Prediction of Student Success: Mining Students Enrolment Data. – In: Proceedings of Informing Science & IT Education Conference (InSITE’2010), 2010, Advantage in Higher Education, Vol. 2002, 2002, Issue 113, 17-36. 10. L u a n, J. Data Mining Applications in Higher Education. SPSS Executive Report. SPSS Inc., 2004. Mining. – In: Proceedings of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, 2000, 457-464. 12. N a n d e s h w a r, A., S. C h a u d h a r i. Enrollment Prediction Models Using Data Mining, 2009. and Retention Through Predictive Modeling. Noel-Levitz, Inc., 2008. 17. W i t t e n, I., E. F r a n k. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, Elsevier Inc., 2005. Data Science, Vol. 8, 2010, 307-325. International Conference on Educational Data Mining (EDM’2011), 6-8 July 2011, Eindhoven, The Netherlands, 347-348.