Mining Educational Data from Student’s Management System

Oshin Mundada

Abstract


Database management systems have grown exponentially since their inception. This has led to a huge amount of data, but not
knowledge. The same is the case for the educational sector, where data is plenty, but the advantages that can be inferred from this data are lean.
Thus, educational data mining is a manifesting branch of knowledge concerned with evolving methods for discovering knowledge from data
which comes from the educational domain. This paper is the outcome of a research carried out on students of a coaching class. The data includes
five years of student data to which several mining techniques have been applied. We have used the classification rules to predict the performance
of the student in competitive exams. This helps the teachers in early identification of the weaker students or students who need more attention
and allow them to act appropriately, eventually increasing the result of the class.


Keywords: Educational Data Mining (EDM), Prediction, Classification, Naïve Bayesian model


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DOI: https://doi.org/10.26483/ijarcs.v7i3.2686

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