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Predicting Students' Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School

Cilt: 9 Sayı: 3 10 Eylül 2022
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Predicting Students' Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School

Öz

The ability to predict students' performance tendency is very important to improve their learning skills. For this, Educational Data Mining (EDM) is a more active research field. It aims to find useful information from the educational data set using data extraction techniques. The most important EDM tasks for this study are to predict student performance. The overall goal of EDM is to understand how students learn and identify those aspects that can improve teaching and learning aspects. This paper reviews some existing research and identifies other future pathways based on EDM knowledge. Therefore, the purpose of this study is to describe how EDM techniques can help math teachers identify students who are most likely to fail and then take appropriate action, and change strategies for it to improve the performance of their students in this area.

Anahtar Kelimeler

Kaynakça

  1. Amershi, S., & Conati, C. (2009). Combining unsupervised and supervised machine learning to build user models for exploratory learning environments. Journal of Educational Data Mining, 1(1), 71-81.
  2. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics,” in Learning Analytics: From Research to Practice, eds J. A. Larusson and B. White. Springer, doi: 10.1007/978-1-4614-3305-7_4.
  3. Baradwaj, B. K., & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. International Journal of Advanced Computer Science and Applications, 2(6), 63-69.
  4. Beal, C. R., Qu, L., & Lee, H. (2006). Classifying learner engagement through integration of multiple data sources. Paper presented at the 21st National Conference on Artificial Intelligence (AAAI-2006), Boston, MA.
  5. Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, 9(4), 136-140.
  6. Fan, Y., Liu, Y., Chen, H., & Ma, J. (2019). Data mining-based design and implementation of college physical education performance management and analysis system. International Journal of Emerging Technologies in Learning, 14(6), 87-97.
  7. Guruler, H., & Istanbullu, H. (2014). Modeling student performance in higher education using data mining. Studies in Computational Intelligence. 524,105-124.
  8. He, W. (2013). Examining students’ online interaction in a live video streaming environment. Computers in Human Behavior, 29(1), 90-102.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Eğitim Üzerine Çalışmalar

Bölüm

Derleme

Yayımlanma Tarihi

10 Eylül 2022

Gönderilme Tarihi

28 Mayıs 2022

Kabul Tarihi

22 Ağustos 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 9 Sayı: 3

Kaynak Göster

APA
Orhani, S. (2022). Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School. International Journal of Educational Studies in Mathematics, 9(3), 177-183. https://doi.org/10.17278/ijesim.1122751
AMA
1.Orhani S. Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School. International Journal of Educational Studies in Mathematics. 2022;9(3):177-183. doi:10.17278/ijesim.1122751
Chicago
Orhani, Senad. 2022. “Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School”. International Journal of Educational Studies in Mathematics 9 (3): 177-83. https://doi.org/10.17278/ijesim.1122751.
EndNote
Orhani S (01 Eylül 2022) Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School. International Journal of Educational Studies in Mathematics 9 3 177–183.
IEEE
[1]S. Orhani, “Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School”, International Journal of Educational Studies in Mathematics, c. 9, sy 3, ss. 177–183, Eyl. 2022, doi: 10.17278/ijesim.1122751.
ISNAD
Orhani, Senad. “Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School”. International Journal of Educational Studies in Mathematics 9/3 (01 Eylül 2022): 177-183. https://doi.org/10.17278/ijesim.1122751.
JAMA
1.Orhani S. Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School. International Journal of Educational Studies in Mathematics. 2022;9:177–183.
MLA
Orhani, Senad. “Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School”. International Journal of Educational Studies in Mathematics, c. 9, sy 3, Eylül 2022, ss. 177-83, doi:10.17278/ijesim.1122751.
Vancouver
1.Senad Orhani. Predicting Students’ Performance in Mathematics through Educational Data Mining Techniques during the Transition from Primary to Lower Secondary School. International Journal of Educational Studies in Mathematics. 01 Eylül 2022;9(3):177-83. doi:10.17278/ijesim.1122751

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