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

Yıl 2022, , 177 - 183, 10.09.2022

Senad Orhani

https://doi.org/10.17278/ijesim.1122751

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

Veri madenciliği, EDM, matematik, performans, öğrenciler

Kaynakça

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Guruler, H., & Istanbullu, H. (2014). Modeling student performance in higher education using data mining. Studies in Computational Intelligence. 524,105-124.
• He, W. (2013). Examining students’ online interaction in a live video streaming environment. Computers in Human Behavior, 29(1), 90-102.
• Idil, F. H., Narli, S., & Aksoy, E. (2016). Using Data Mining Techniques Examination of the Middle School Students’ Attitude towards Mathematics in the Context of Some Variables. International Journal of Education in Mathematics, Science and Technology, 4(3), 210-228.
• Kumar, S. A., & Vijayalakshmi, M. N. (2011). Efficiency of decision trees in predicting student’s academic performance. First International Conference On Computer Science, Engineering And Applications, India.
• Pandey, U. K., & Pal, S. (2011). Data Mining: A prediction of performer or underperformer using classification. International Journal of Computer Science and Information Technologies, 2 (2), 686-690.
• Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40, 601-618.
• Saa, A. A. (2016). Educational Data Mining & Students’ Performance Prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212-220.
• Siemens, G., & Baker, R. S. (2014). Educational data mining and learning analytics. In K.Sawyer (Ed.). Cambridge Handbook of the Learning Sciences, 253-274.
• Stedman, C., & Hughes, A. (2022). Data mining. Retrieved from Teach Target: https://www.techtarget.com/searchbusinessanalytics/definition/data-mining
• Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance using Data Mining Techniques. Procedia Computer Science 72, 414-422.