Sistematik Derlemeler ve Meta Analiz

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

Cilt: 17 Sayı: 33 31 Mart 2022
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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. It is possible to make teaching activities more effective and efficient by transforming the raw data in the field of education into meaningful using data mining techniques. Studies carried out in the field of education between 2014-2020 with data mining methods were scanned from the "Science Direct" database. It was determined that 60 articles from the scanning studies were 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. The aim of the study is to group the work carried out in the field of education under specific headings using the data mining process, to evaluate its methods and objectives, and to direct the individuals who will work in this field.

Keywords

Kaynakça

  1. Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250.
  2. Agarwal, S., Pandey, G. N., & Tiwari, M. D. (2012). Data mining in education: data classification and decision tree approach. International Journal of E-Education, e-Business, e-Management and e-Learning, 2(2), 140.
  3. Ahmed, A. M., Rizaner, A., & Ulusoy, A. H. (2016). Using data mining to predict instructor performance. Procedia Computer Science, 102, 137–142.
  4. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.
  5. Alfiani, A. P., & Wulandari, F. A. (2015). Mapping student’s performance based on data mining approach (a case study). Agriculture and Agricultural Science Procedia, 3, 173–177.
  6. Aljobouri, H. K., Jaber, H. A., Kocak, O. M., Algin, O., & Cankaya, I. (2018). Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining. Journal of Neuroscience Methods, 299, 45–54.
  7. Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1–7.
  8. Amornsinlaphachai, P. (2015). The design of a framework for cooperative learning through web utilizing data mining technique to group learners. Procedia-Social and Behavioral Sciences, 174, 27–33.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Alan Eğitimleri

Bölüm

Sistematik Derlemeler ve Meta Analiz

Yayımlanma Tarihi

31 Mart 2022

Gönderilme Tarihi

30 Aralık 2020

Kabul Tarihi

3 Nisan 2021

Yayımlandığı Sayı

Yıl 2022 Cilt: 17 Sayı: 33

Kaynak Göster

APA
Bilici, Z., & Özdemir, D. (2022). Data Mining Studies in Education: Literature Review For The Years 2014-2020. Bayburt Eğitim Fakültesi Dergisi, 17(33), 342-376. https://doi.org/10.35675/befdergi.849973

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