A Study On Profiling Students via Data Mining

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.

Data mining approach for prediction of academic success in open and distance education

“…When the literature is examined, it is seen that the academic achievement and dropout rates of open and distance education students are the subject of many studies (Batool et al, 2023;Dabhade et al, 2021;Durairaj and Vijitha, 2014;Khasanah and Harwati, 2017;Kotsiantis et al, 2004;2005;Nahar et al, 2021;Sembiring et al, 2011). In addition, there are many studies that use data mining methods to predict or determine academic success and performance (Alan & Temiz, 2019;Dabhade et al, 2021;Elakia et al, 2014;Natek & Zwilling, 2014;Osmanbegović and Suljic, 2008;Ramesh et al, 2013;Saheed et al, 2018;Shahiri et al, 2015). Kotsiantis et al (2005) tried to predict student performance with regression models based on demographic data and grades obtained from written assignments of some courses in their research conducted in Hellenic Open University.…”