Predicting the Performance of Students in Higher Education Using Data Mining Classification Algorithms - A Case Study
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 11
Abstract
In recent years, Educational Data Mining has put on a mammoth recognition within the research realm and it has become a vital need for the academic institutions to improve the quality of education. Higher education does categorize the students by their academic performance. In higher education institutions a substantial amount of knowledge is hidden and need to be extracted using Knowledge Discovery process. Data mining helps to extract the knowledge from available dataset and should be created as knowledge intelligence for the benefit of the institution. Many factors influence the academic performance of the students. The factors that describe student performance can be used for predicting students performance by using a number of well - known data mining classification algorithms such as ID3, REPTree, Simplecart, J48, NB Tree, BFTree, Decision Table, MLP and Bayesnet. The study model is mainly focused on analyzing the prediction of the academic performance of the students by all the classification algorithms. The algorithms are analyzed based on the precision of predicting the result.
Authors and Affiliations
Jai Ruby, Dr. K. David
Keywords
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