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An Efficient Datamining Approach for Student Engagement Predictions Systems

File Size:
1.33 MB
Volume:
Volume 5, Issue 8 (August 2019)
Publication No:
IJTC201908003
Author:
Rahul Moudgil, Mandeep Singh
Downloads:
2 x

Abstract:
Over the past decade there has been a rapid growth in the higher education system. A lot of new institutions have come up both from the public and private sector offering a variety of courses for under graduating and postgraduate students. The rates of enrolments for higher education has also increased but not as much as the number of higher institutions are increasing. It is a concern for today’s education system and this gap has to be identified and properly addressed to the learning community. Hence it has become important to understand the requirement of students and their academic progression. Educational Data Mining helps in a big way to answer the issues of predictions and profiling of not only students but other stakeholders of education sectors. This paper discusses the proposed Data Mining techniques using PCA and Fuzzy approach in collaboration with neural networks which can be effectively used in answering the issues of predictions of student’s performance and their profiling.

Keywords:
Data Mining, Educational Data Mining, Prediction, Profiling

Tags Associated: Data Mining