NDLI: A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

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A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

Content Provider	Semantic Scholar
Author	Anoopkumar, M. Rahman, A. M. J. Md. Zubair
Copyright Year	2015
Abstract	Educational Data Mining (EDM) is an interdisciplinary ingenuous research area that handles the development of methods to explore data arising in a scholastic fields. Computational approaches used by EDM is to examine scholastic data in order to study educational questions. As a result, it provides intrinsic knowledge of teaching and learning process for effective education planning. This paper conducts a comprehensive study on the recent and relevant studies put through in this field to date. The study focuses on methods of analysing educational data to develop models for improving academic performances and improving institutional effectiveness. This paper accumulates and relegates literature, identifies consequential work and mediates it to computing educators and professional bodies. We identify research that gives well-fortified advice to amend edifying and invigorate the more impuissant segment students in the institution. The results of these studies give insight into techniques for ameliorating pedagogical process, presaging student performance, compare the precision of data mining algorithms, and demonstrate the maturity of open source implements.
Starting Page	30
Ending Page	30
Page Count	1
File Format	PDF HTM / HTML
DOI	10.18797/CAASR/ICIET/2015/11/27/06
Alternate Webpage(s)	http://dl.caasr.org/admin/articles/pdf/6.%20A%20Comprehensive%20Survey%20on%20Educational%20Data%20Mining%20and%20Use%20of%20Data%20Mining%20Techniques%20for%20Improving%20Teaching%20and%20Predicting%20Student%20Performance.pdf
Alternate Webpage(s)	https://doi.org/10.18797/CAASR%2FICIET%2F2015%2F11%2F27%2F06
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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