Exploring the High Potential Factors that Affects Students’ Academic Performance

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R. Kaviyarasi 1,* T. Balasubramanian 1

1. Department of Computer Science, Periyar University, Salem-636011, Tamilnadu, India.

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2018.06.02

Received: 18 Apr. 2018 / Revised: 24 May 2018 / Accepted: 13 Jun. 2018 / Published: 8 Nov. 2018

Index Terms

Educational Data Mining, Feature Selection, Ensemble methods, Extra Tree Classifier


The rapid increase in student population has resulted in expansion of educational facilities at all level. Nowadays, responsibilities of teachers are many. It is the responsibilities of teachers to guide the students to choose their carrier field according to their abilities and aptitudes. The Data Mining field mines the educational data from large volumes of data to improve the quality of educational processes. Today’s need of educational system is to develop the individual to enhance problem solving and decision making skills in addition to build their social skills. Educational Data Mining is one of the applications of Data Mining to find out the hidden patterns and knowledge in Educational Institutions. There are three important groups of students have been identified: Fast Learners, Average Learners, and Slow Learners. In fact, students are probably struggles in many factors. This work focuses on finding the high potential factors that affects the performance of college students. This finding will improve the students’ academic performance positively.

Cite This Paper

R. Kaviyarasi, T. Balasubramanian,"Exploring the High Potential Factors that Affects Students’ Academic Performance", International Journal of Education and Management Engineering(IJEME), Vol.8, No.6, pp.15-23, 2018. DOI: 10.5815/ijeme.2018.06.02


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