International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 7, No. 6, 8 Nov. 2017
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Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques
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Author(s)
Mukesh Kumar
1,*
A.J. Singh
1
Disha Handa
2
Index Terms
Educational Data Mining, Prediction Techniques, Student attributes, Classification
Abstract
One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student’s performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.
Cite This Paper
Mukesh Kumar, A.J. Singh, Disha Handa,"Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques", International Journal of Education and Management Engineering(IJEME), Vol.7, No.6, pp.40-49, 2017. DOI: 10.5815/ijeme.2017.06.05
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