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Data Mining Techniques in EDM for Predicting the Performance of Students
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pal, Ajay Kumar Pal, Saurabh |
| Copyright Year | 2013 |
| Abstract | In recent, growth of higher education has increased rapidly. Many new institutions, colleges and universities are being established by both the private and government sectors for the growth of education and welfare of the students. Each institution aims at producing higher and exemplary education rates by employing various teaching and grooming methods. But still there are cases of unemployment that exists among the medium and low risk students. This paper describes the use of data mining techniques to improve the efficiency of academic performance in the educational institutions. Various data mining techniques such as decision tree, association rule, nearest neighbors, neural networks, genetic algorithms, exploratory factor analysis and stepwise regression can be applied to the higher education process, which in turn helps to improve student’s performance. This type of approach gives high confidence to students in their studies. This method helps to identify the students who need special advising or counseling by the teacher which gives high quality of education. Keywords-component; Data Mining; KDD; EDM; Association Rule |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ijcit.com/archives/volume2/issue6/Paper020616.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
