Predicting Students Performance in Examination Using Supervised Data Mining Techniques-Reference-Cited by-同舟云学术

Predicting Students Performance in Examination Using Supervised Data Mining Techniques

Published:2022 Issue: Volume: Page:63-77
ISSN:1865-0929
Container-title:Informatics and Intelligent Applications
language:
Short-container-title:

Author:

Abiodun Kazeem Moses^ORCID,Adeniyi Emmanuel Abidemi^ORCID,Aremu Dayo Reuben^ORCID,Awotunde Joseph Bamidele^ORCID,Ogbuji Emmanuel

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95630-1_5

Reference19 articles.

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2. Abayomi-Alli, A., Misra, S., Fernández-Sanz, L., Abayomi-Alli, O., Edun, A.R.: Genetic algorithm and tabu search memory with course sandwiching (GATS_CS) for university examination timetabling. Intell. Autom. Soft Comput. 26(3), 385–396 (2020)

3. Dietz-Uhler, B., Hurn, J.E.: Using learning analytics to predict (and improve) student success: a faculty perspective. J. Interact. Online Learn. 12(1), 17–26 (2013)

4. Avella, J.T., Kebritchi, M., Nunn, S.G., Kanai, T.: Learning analytics methods, benefits, and challenges in higher education: a systematic literature review. Online Learn. 20(2), 13–29 (2016)

5. Kay, D., Korn, N., Oppenheim, C.: Legal, risk and ethical aspects of analytics in higher education. Analytics series (2012)

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