Data Mining Studies in Education: Literature Review For The Years 2014-2020

Abstract

Data mining is one of the important and beneficial technological developments in education and its usage area is becoming widespread day by day as it includes applications that contribute positively to teaching activities. By making raw data in the field of education meaningful using data mining techniques, teaching activities can be made more effective and efficient. Studies carried out in the field of education between 2014-2020 with data mining methods were scanned from the "Science Direct" database. As a result of scanning studies, 60 papers were found to be directly related to data mining in education. The studies include issues such as the development of e-learning systems, pedagogical support, clustering of educational data, and student performance predictions. These selected articles were analyzed in terms of purpose, application area, method, and contribution to the literature. This study aims to group the studies conducted in the field of education using the data mining method under certain headings, evaluate the methods and goals and present the need in this field to the researchers who will work in this field.

Understanding data-driven decision making approach in Chinese higher education through the lens of Bakers model

“…Similarly, with the rise in this trend, researchers also developed models to show how and what kind of data mining should be used in the educational environment (Baker, 2010; Bilici & Özdemir, 2022; Jones, 2019; Sagebrush Corporation, 2004). Educational data models have proven to be very effective in ensuring the better utilization of educational data for decision-making (Kazi et al, 2014).…”

“…HEIs data models also use students' assessment data and relevant background information to make informed decisions about planning instructional strategies for various groups of students (Jones, 2019). Therefore, the scope and variety of educational data have also increased over time because of these new model developments (Bilici & Özdemir, 2022; Orr et al, 2020). Among various data models for HEIs, Baker’s model is considered one of the most comprehensive models for HEIs (Chaushi et al, 2015).…”

A Prediciton Model Based on Students’s Behavior in E-Learning Environments Using Data Mining Techniques

“…That engagement and participating which are represented the student behavior will affect the online learning outcomes. [10], Xiao et al [11] suggested that Data Mining (DM) techniques are an effective approach in analyzing educational data and predicting learning outcomes. Hence, this paper applied various DM algorithms to predict the performance of students in Iraqi undergraduate educational institutes during COVID 19 pandemic.…”

Evolución de las técnicas de minería de datos para extraer datos provenientes de twitter aplicadas a la educación superior: una revisión sistemática

“…Para solventar este inconveniente, surge la necesidad de contar con técnicas que permitan gestionar este volumen de información, procesar y analizar los datos, para posteriormente descubrir el conocimiento, esta técnica es conocida como Minería de Datos «Data Mining» (DM), cuyo objetivo principal es la extracción de la información oculta de un conjunto de datos (Bhatia, 2019;Morales Carrillo et al, 2019;Wu et al, 2014). Para Morales Carrillo et al (2019) la inclusión de la Minería de Datos en la educación, más comúnmente conocida como Minería de Datos Educativos, del inglés «Educational Data Mining» (EDM) se han incrementado en los últimos años, con diferentes propósitos y aplicaciones, entre los que se pueden destacar los siguientes: sistemas recomendadores, modelos predictivos, identificación de patrones de aprendizaje (Yu y Wu, 2015); así como también, sistemas de aprendizaje electrónico, apoyo pedagógico, agrupación de datos educativos y predicción del rendimiento académico (Bilici y Özdemir, 2022).…”