Educational Data Mining in Prediction of Students’ Learning Performance: A Scoping Review
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-43393-1_33
Reference32 articles.
1. Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., Tsolakidis, A.: Improving quality of educational processes providing new knowledge using data mining techniques. In: 3rd International Conference on Integrated Information (IC-ININFO), vol. 147, pp. 390–397 (2014)
2. Khanna, L., Singh, S.N., Alam, M.: Educational data mining and its role in determining factors affecting students academic performance: a systematic review. In: Proceedings of the 2016 1st India International Conference on Information Processing (IICIP), Delhi, India. IEEE (2016)
3. Moscoso-Zea, O., Saa, P., Luján-Mora, S.: Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining. Australas. J. Eng. Educ. 24(1), 4–13 (2019)
4. Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. 43(6), 162 (2019)
5. Alzafari, K., Kratzer, J.: Challenges of implementing quality in European higher education: an expert perspective. Qual. High. Educ. 25(3), 261–288 (2019)
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