A Study On Profiling Students via Data Mining

Mehmet Ali Alan and Mustafa Temiz

Alphanumeric Journal, 2019, vol. 7, issue 2, 239-248

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 (search for similar items in EconPapers)
JEL-codes: C88 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:7:y:2019:i:2:p:239-248

DOI: 10.17093/alphanumeric.630866

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