Systematic Reviews and Meta Analysis
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Data Mining Studies in Education: Literature Review For The Years 2014-2020

Year 2022, Volume: 17 Issue: 33, 342 - 376, 31.03.2022
https://doi.org/10.35675/befdergi.849973

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.

References

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  • 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.
  • Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers & Education, 113, 226–242.
  • Anoopkumar, M., & Rahman, A. M. J. M. Z. (2016). A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 122–133.
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194.
  • Aydoğdu, Ş. (2020). Educational Data Mining Studies in Turkey: A Systematic Review. Turkish Online Journal of Distance Education, 21(3), 170–185.
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  • Balaman, S. (2020). A Study on the Impacts of Digital Storytelling on EFL Learners’ Self-Efficacy and Attitudes toward Education Technologies. International Online Journal of Education and Teaching, 7(1), 289–311.
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  • Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 66, 541–556.
  • Cabada, R. Z., Estrada, M. L. B., & Bustillos, R. O. (2018). Mining of educational opinions with deep learning. Journal of Universal Computer Science, 24(11), 1604–1626.
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Eğitimde Veri Madenciliği Çalışmaları: 2014-2020 Yılları Literatür Taraması

Year 2022, Volume: 17 Issue: 33, 342 - 376, 31.03.2022
https://doi.org/10.35675/befdergi.849973

Abstract

Veri madenciliği eğitimde önemli ve faydalı teknolojik gelişmelerden biridir ve öğretim faaliyetlerine olumlu yönde katkı sağlayan uygulamaları içerdiği için kullanım alanı gün geçtikçe yaygınlaşmaktadır. Veri madenciliği teknikleri kullanılarak eğitim alanındaki ham verilerin anlamlı hale getirilmesi ile öğretim etkinliklerinin daha etkin ve verimli hale getirilmesi mümkündür. Veri madenciliği yöntemleriyle 2014-2020 yılları arasında eğitim alanında yapılan çalışmalar "Science Direct" veri tabanından tarandı. Tarama çalışmalarından 60 makalenin eğitimde veri madenciliği ile doğrudan ilişkili olduğu tespit edilmiştir. Bu çalışmalar e-öğrenme sistemlerinin geliştirmesi, pedagojik destek, eğitim verilerinin kümelenmesi, öğrenci performans tahminleri gibi konuları içermektedir. Bu çalışmada eğitim alanında veri madenciliği yöntemi kullanılarak 2014 ile 2020 yılları arasında yapılmış araştırmalar “Science Direct” platformu üzerinden taranmıştır. Seçilen bu 60 adet makale, makalenin amacı, uygulama alanı ve örneklem, metot ve yöntemi, literatüre katkısı şeklinde tasnif edilerek sunulmuştur. Araştırmada; veri madenciliği yöntemi kullanılarak eğitim alanında yapılan çalışmaları belirli başlıklar altında gruplamak, yöntemlerini ve amaçlarını belirlemek ve bu alanda çalışacak olan kişilere yön göstermek amaçlanmıştır.

References

  • 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.
  • 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.
  • Ahmed, A. M., Rizaner, A., & Ulusoy, A. H. (2016). Using data mining to predict instructor performance. Procedia Computer Science, 102, 137–142.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers & Education, 113, 226–242.
  • Anoopkumar, M., & Rahman, A. M. J. M. Z. (2016). A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 122–133.
  • Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194.
  • Aydoğdu, Ş. (2020). Educational Data Mining Studies in Turkey: A Systematic Review. Turkish Online Journal of Distance Education, 21(3), 170–185.
  • Badr, G., Algobail, A., Almutairi, H., & Almutery, M. (2016). Predicting students’ performance in university courses: a case study and tool in KSU mathematics department. Procedia Computer Science, 82, 80–89.
  • Bajaj, R., & Sharma, V. (2018). Smart Education with artificial intelligence based determination of learning styles. Procedia Computer Science, 132, 834–842.
  • Baker, R. (2010). Data mining for education. International Encyclopedia of Education, 7(3), 112–118.
  • Balaman, S. (2020). A Study on the Impacts of Digital Storytelling on EFL Learners’ Self-Efficacy and Attitudes toward Education Technologies. International Online Journal of Education and Teaching, 7(1), 289–311.
  • Bhullar, M. S., & Kaur, A. (2012). Use of data mining in education sector. Proceedings of the World Congress on Engineering and Computer Science, 1, 24–26.
  • Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 66, 541–556.
  • Cabada, R. Z., Estrada, M. L. B., & Bustillos, R. O. (2018). Mining of educational opinions with deep learning. Journal of Universal Computer Science, 24(11), 1604–1626.
  • Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521.
  • Chakraborty, B., Chakma, K., & Mukherjee, A. (2016). A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory. 2016 IEEE International Conference on Engineering and Technology (ICETECH), 431–436.
  • Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., & Tsolakidis, A. (2014). Improving quality of educational processes providing new knowledge using data mining techniques. Procedia-Social and Behavioral Sciences, 147, 390–397.
  • Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16–24.
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Details

Primary Language English
Subjects Other Fields of Education
Journal Section Review
Authors

Zehra Bilici 0000-0002-5417-428X

Durmuş Özdemir 0000-0002-9543-4076

Early Pub Date March 29, 2022
Publication Date March 31, 2022
Submission Date December 30, 2020
Acceptance Date April 3, 2021
Published in Issue Year 2022 Volume: 17 Issue: 33

Cite

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