Smart Education: Predictive Analytics of Student Academic Performance Using Machine Learning Models in Weka and Dataiku Systems-Reference-Cited by-同舟云学术

Smart Education: Predictive Analytics of Student Academic Performance Using Machine Learning Models in Weka and Dataiku Systems

Published:2021 Issue: Volume: Page:3-17
ISSN:2190-3018
Container-title:Smart Education and e-Learning 2021
language:
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Author:

Uskov Vladimir L.,Bakken Jeffrey P.,Putta Prasanthi,Krishnakumar Deepali,Ganapathi Keerthi Sree

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-2834-4_1

Reference16 articles.

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2. Pierce, D.: How predictive analytics helps improve student enrolment and retention (2020). https://www.ecampusnews.com/2020/12/17/how-predictive-analytics-helps-improve-student-enrollment-and-retention/

3. Catalano, D.: What Data Can—and Can’t Yet—Tell Us (2020). https://www.insidehighered.com/views/2020/08/06/due-covid-demand-analytics-has-risen-significantly-information-doesnt-mean-action

4. Hamsa, H., et al.: Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technol. 25, 326–332 (2016). https://www.sciencedirect.com/science/article/pii/S2212017316304613

5. Hasan, R., et al.: Student academic performance prediction by using decision tree algorithm. In: Proceedings of the 2018 4th International Conference on Computer and Information Sciences (ICCOINS), IEEE, 13–14 Aug 2018, Singapore. https://ieeexplore.ieee.org/abstract/document/8510600

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