Using Classification Methods in Higher Education
Alba Çomo1, Ilia Ninka2, Brisilda Munguli3
1MSc. Alba Çomo Department of Computer Science, Faculty of Natural Science, University of Tirana, Tirana, Albania.
2Prof. Dr. Ilia Ninka Department of Computer Science, Faculty of Natural Science, University of Tirana, Tirana, Albania.
3MSc. Brisilda Munguli Department of Computer Science, Faculty of Natural Science, University of Tirana, Tirana, Albania.
Manuscript received on August 01, 2015. | Revised Version Manuscript Received on August 14, 2015. | Manuscript published on August 20, 2015. | PP: 5-8 | Volume-1 Issue-8, August 2015.
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: currently, in Albania, there is an increased interest in data mining and educational system, making educational data misning a new growing research community. Data mining is a powerful tool for academic intervention. Through data mining, a university could, for example, predict which students will or will not graduate. The university can use this information to concentrate on those students that are most at risk. In this paper we attempt to use data mining process to help in enhancing the quality of the higher education system by evaluating student data. For this purpose we have collected data from a public faculty in Tirana from 2011 to 2014, covering 1300 students. The classification process is based on the decision tree as a classification method where the generated rules are studied. We aim to build a system that will facilitate the use of generated rules, which will allow students to predict the average grade of the next year in university.
Keywords: Student data, data mining, decision trees, higher education, classification process.