A two-phase machine learning approach for predicting student outcomes-Reference-Cited by-同舟云学术

A two-phase machine learning approach for predicting student outcomes

Published:2020-06-25 Issue:1 Volume:26 Page:69-88
ISSN:1360-2357
Container-title:Education and Information Technologies
language:en
Short-container-title:Educ Inf Technol

Author:

Iatrellis Omiros^ORCID,Savvas Ilias Κ.,Fitsilis Panos,Gerogiannis Vassilis C.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Education

Link

http://link.springer.com/content/pdf/10.1007/s10639-020-10260-x.pdf

Reference47 articles.

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3. 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(April 2018), 13–49. https://doi.org/10.1016/j.tele.2019.01.007.

4. Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., … Olatunji, S. (2017). Student performance prediction using support vector machine and K-nearest neighbor. Canadian Conference on Electrical and Computer Engineering, 1–4. https://doi.org/10.1109/CCECE.2017.7946847.

5. Anand, V. K., Abdul Rahiman, S. K., Ben George, E., & Huda, A. S. (2018). Recursive clustering technique for students’ performance evaluation in programming courses. Proceedings of Majan international conference: Promoting entrepreneurship and technological skills: National Needs, global trends, MIC 2018, 1–5. https://doi.org/10.1109/MINTC.2018.8363153.

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