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Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

A Study On Profiling Students via Data Mining

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Mehmet Ali Alan, Ph.D.

Mustafa Temiz


Abstract

Data mining is a significant method which is utilized in order to reveal the hidden patterns and connections within big data. The method is used at various fields such as financial transactions, banking, education, health sector, logistics and security. Even though analysis towards the consumption habits of the customers is carried out via association rules mining more often, which is one of the basic methods of data mining, the method is also utilized in order to profile patients and students. As well as the customization of a customer is of high significance, so is distinguishing and customizing a student. Within this study, students were tried to be profiled via data mining of the student data of a high school. A set of qualities, that can directly affect the performance of students such as health conditions, financial resources, life standards and education level of the families, were taken into consideration. For that purpose, upon the analysis of data of 443 students in the database, a data warehouse was established. The Apriori algorithm, which is one of the popular algorithms of association rules mining, is utilized for the data analysis. Apriori algorithm was able to produce 72 rules which are accurate above 90%. It is thought that the produced rules can be of help in profiling the students, and they can contribute to work of school management, teachers, parents and students.

Keywords: Association Rules, Data Mining, Data Warehouse, Student Profile

Jel Classification: C88

Suggested citation

Alan, M. A. & Temiz, M. (). A Study On Profiling Students via Data Mining. Alphanumeric Journal, 7(2), 239-248. http://dx.doi.org/10.17093/alphanumeric.630866

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Volume 7, Issue 2, 2019

2019.07.02.MIS.01

alphanumeric journal

Volume 7, Issue 2, 2019

Pages 239-248

Received: Aug. 8, 2019

Accepted: Dec. 22, 2019

Published: Dec. 31, 2019

Full Text [624.0 KB]

2019 Alan, MA., Temiz, M.

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

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