Keywords
classification
regression
association rules
clustering
e-learning system
How to Cite
Abstract
The main tasks (classification and regression, association rules, clustering) and the basic principles of the Data Mining algorithms in the context of their use for a variety of research in the field of education which are the subject of a relatively new independent direction Educational Data Mining are considered. The findings about the most popular topics of research within this area as well as the perspectives of its development are presented. Presentation of the material is illustrated by simple examples. This article is intended for readers who are engaged in research in the field of education at various levels, especially those involved in the use of e-learning systems, but little familiar with this area of data analysis.References
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Almond R. G. Bayesian Networks in Educational Assessment. / Almond R. G., Mislevy R. J. at all. –— Springer, 2015. – 666 р. (in English).
Baker R. The State of Educational Data Mining in 2009: A Review and Future visions. / Baker R., Yacef, K. // Journal of Educational Data Mining. – 2009. – Vol 1. – No 1.— Рp. 3–17 (in English).
Korb K. B. Bayesian artificial intelligence / Kevin B. Korb, Ann E. Nicholson. p. cm. – Chapman & Hall/CRC computer science and data analysis, 2003. – 365 p. (in English).
Romero C. Handbook of Educational Data Mining. / by Cristobal Romero (Editor), Sebastian Ventura (Editor), Mykola Pechenizkiy (Editor), Ryan S.J.d. Baker (Editor). – CRC Press, 2010. – 536 р. (in English).
Romero C. Data mining in e-learning (Advances in Management Information). / Romero C., Ventura S. – Wit Press, 2006. – 328 p. (in English).
Romero C. Educational Data Mining: a Survey from 1995 to 2005. / Romero, C., Ventura, S. // Expert Systems with Applications. – 2007. – No 1. – Vol 33. – Рp. 135–146 (in English).
Romero C. Educational data mining: a review of the state of the art. / Romero, C., Ventura, S. // IEEE Transactions on Systems, Man, And Cybernetics – Part C: Applications And Reviews. – 2010. – Vol. 40. – No. 6. – Рp. 601–618 (in English).
Witten I. H. Data mining : practical machine learning tools and techniques. – 3rd ed. / Ian H. Witten, Frank Eibe, Mrk A. Hall. p. cm. – (The Morgan Kaufmann series in data management systems), 2011— 665 p. (in English).
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