Analyzing Supervised Learning Models for Predicting Student Dropout and Success in Higher Education

Author:

Bhurre Shraddha,Prajapat Shaligram

Publisher

Springer Nature Switzerland

Reference15 articles.

1. Ajith, P., Sai, M., Tejaswi, B.: Evaluation of student performance: an outlier detection perspective. Int. J. Innov. Technol. Explor. Eng. 2(2), 40–44 (2013)

2. Sinthia, G., Balamurugan, M.: Analyzing student’s academic performance using multilayer perceptron model. Int. J. Recent. Technol. Eng. (IJRTE) 7(5S3), 156–160 (2019). ISSN: 2277-3878

3. Windarti, M., Prasetyaninrum, P.T.: Prediction analysis student graduate using multilayer perceptron. In: International Conference on Online and Blended Learning, pp. 53–57 (2019)

4. Zhang, X., Sun, G., Pan, Y., Sun, H., Tan, J.: Poor performance discovery of college students based on behavior pattern. In: 2017 IEEE, pp. 1–8 (Aug. 2017)

5. Ujkani, B., Minkovska, D., Stoyanova, L.: Application of logistic regression technique for predicting student dropout. In: 2022 XXXI International Scientific Conference Electronics (ET), Sozopol, Bulgaria, pp. 1–4 (2022)

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