Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning

Author:

R Jayasree1,Selvakumari Sheela1

Affiliation:

1. Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore 641008, Tamilnadu, India

Publisher

MDPI

Reference20 articles.

1. Unsupervised machine learning for networking: Techniques, applications and research challenges;Usama;IEEE Access,2019

2. A comparative study of feature selection methods for dialectal Arabic sentiment classification using support vector machine;Aljohani;Int. J. Comput. Sci. Netw. Secur.,2019

3. Anjum, N., Badugu, S., Satapathy, S., Raju, K., Shyamala, K., Krishna, D., and Favorskaya, M. (2020). Advances in Decision Sciences, Image Processing, Security and Computer Vision: International Conference on Emerging Trends in Engineering (ICETE), Springer International Publishing.

4. Aljohani, N.R., Fayoumi, A., and Hassan, S.U. (2019). Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment. Sustainability, 11.

5. Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data;Alhassan;Int. J. Adv. Comput. Sci. Appl.,2020

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