Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework

Author:

Delen DursunORCID,Davazdahemami Behrooz,Rasouli Dezfouli Elham

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Information Systems,Theoretical Computer Science,Software

Reference79 articles.

1. Alduayj, S. S., & Rajpoot, K. (2018). Predicting employee attrition using machine learning. 2018 International Conference on Innovations in Information Technology (IIT), 93–98.

2. Alkhasawneh, R., & Hargraves, R. H. (2014). Developing a hybrid model to predict student first year retention in STEM disciplines using machine learning techniques.Journal of STEM Education: Innovations and Research, 15(3).

3. Andrianov, Y. (2017). The Impact of Macroeconomic Factors on Student Enrollment in Canadian Post-Secondary Institutions. Proceedings of the Annual Thompson Rivers University Undergraduate Research and Innovation Conference, 11(1), 5.

4. An, Q., Wen, Y., Ding, T., & Li, Y. (2019). Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method. Omega, 85, 16–25. https://doi.org/10.1016/j.omega.2018.05.008

5. An, W., Wang, H., Zhang, Y., & Dai, Q. (2017). Exponential decay sine wave learning rate for fast deep neural network training. 2017 IEEE Visual Communications and Image Processing (VCIP), 1–4. https://doi.org/10.1109/VCIP.2017.8305126

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