Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework
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DOI: 10.1007/s10796-023-10397-3
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- Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018.
"Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods,"
VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy
181544, Verein für Socialpolitik / German Economic Association.
- Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
- Johannes Berens & Simon Oster & Kerstin Schneider & Julian Burghoff, 2018. "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," Schumpeter Discussion Papers sdp18006, Universitätsbibliothek Wuppertal, University Library.
- An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
- Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
- Miriam Kasztura & Aude Richard & Nefti-Eboni Bempong & Dejan Loncar & Antoine Flahault, 2019. "Cost-effectiveness of precision medicine: a scoping review," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(9), pages 1261-1271, December.
- Pierre Berthon & Leyland Pitt & Michael Ewing & Christopher L. Carr, 2002. "Potential Research Space in MIS: A Framework for Envisioning and Evaluating Research Replication, Extension, and Generation," Information Systems Research, INFORMS, vol. 13(4), pages 416-427, December.
- Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
- Boyacı, Burak & Zografos, Konstantinos G. & Geroliminis, Nikolas, 2015. "An optimization framework for the development of efficient one-way car-sharing systems," European Journal of Operational Research, Elsevier, vol. 240(3), pages 718-733.
- Tang, Yao & Chen, Rachel R. & Guan, Xu, 2021. "Daily-deal market with consumer retention: Price discrimination or quality differentiation," Omega, Elsevier, vol. 102(C).
- Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
- Guo, Mengzhuo & Zhang, Qingpeng & Liao, Xiuwu & Chen, Frank Youhua & Zeng, Daniel Dajun, 2021. "A hybrid machine learning framework for analyzing human decision-making through learning preferences," Omega, Elsevier, vol. 101(C).
- Höppner, Sebastiaan & Stripling, Eugen & Baesens, Bart & Broucke, Seppe vanden & Verdonck, Tim, 2020. "Profit driven decision trees for churn prediction," European Journal of Operational Research, Elsevier, vol. 284(3), pages 920-933.
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