Student performance prediction using datamining classification algorithms: Evaluating generalizability of models from geographical aspect-Reference-Cited by-同舟云学术

Student performance prediction using datamining classification algorithms: Evaluating generalizability of models from geographical aspect

Published:2023-04-03 Issue:11 Volume:28 Page:14167-14185
ISSN:1360-2357
Container-title:Education and Information Technologies
language:en
Short-container-title:Educ Inf Technol

Author:

Parhizkar Amirmohammad,Tejeddin Golnaz,Khatibi Toktam

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Education

Link

https://link.springer.com/content/pdf/10.1007/s10639-022-11560-0.pdf

Reference34 articles.

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2. Abu Zohair, L. M. (2019). ‘Prediction of Student’s performance by modelling small dataset size’, International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0160-3.

3. Ahmed, S. T., Al-Hamdani, R. S., & Croock, M. S. (2019). EDM preprocessing and hybrid feature selection for improving classification accuracy. Journal of Theoretical and Applied Information Technology, 97(1), 279–289.

4. Aldowah, H., Al-Samarraie, H. and Fauzy, W. M. (2019). ‘Educational data mining and learning analytics for 21st century higher education: A review and synthesis’, Telematics and Informatics. Elsevier, pp. 13–49. https://doi.org/10.1016/j.tele.2019.01.007.

5. Alloghani, M. Al-Jumeily, D., Hussain, A., Aljaaf, A. J., Mustafina, J., & Petrov, E. (2018). Application of Machine Learning on Student Data for the Appraisal of Academic Performance. In 2018 11th International Conference on Developments in eSystems Engineering (DeSE) (pp. 157–162). Cambridge, UK. https://doi.org/10.1109/DeSE.2018.00038

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