NDLI: A Crucial Engineering Students Academic Performance Evaluation Data Mining Techniques

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A Crucial Engineering Students Academic Performance Evaluation Data Mining Techniques

Content Provider	Semantic Scholar
Author	Raj, M. Stephen
Copyright Year	2015
Abstract	1,2,3,4 Department of MCA 1,2,3,4 Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College, Chennai,600062, India Abstract— Data mining and knowledge engineering is to predict the analysis of data for the purpose of verdict certain informational patterns that can be used for the benefits of an educational institution. Data mining methods are often implemented at higher education engineering colleges / universities today for analyzing available data and extracting information and knowledge to support decision-making. Data mining is used to improve graduate students’ performance and overcome the problem of failures, due to the problematic subject in low grades marks and failures for engineering graduate students. The main scope of this paper are to identify the data analysis for engineering students evaluation performance based on skills in academic subject and process, the information gathered through this research can be used by the educational institution to improve its services and became top engineering college and universities in Tamil Nadu. Here, the classification, association, clustering are used to evaluate student’s performance. By using these methods to extract the knowledge description database that describes students’ performance in the end of the university examination. It also helps earlier in identifying the dropouts engineering course and students who need special attention and allow the professor to provide appropriate advising and counseling.
Starting Page	2264
Ending Page	2267
Page Count	4
File Format	PDF HTM / HTML
Volume Number	3
Alternate Webpage(s)	http://www.ijsrd.com/articles/IJSRDV3I2329.pdf
Alternate Webpage(s)	http://www.ijsrd.com/articles/IJSRDV3I2687.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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