Early Prediction of University Dropouts – A Random Forest Approach
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DOI: 10.1515/jbnst-2019-0006
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Cited by:
- Diego Opazo & Sebastián Moreno & Eduardo Álvarez-Miranda & Jordi Pereira, 2021. "Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities," Mathematics, MDPI, vol. 9(20), pages 1-27, October.
- Atanas Ivanov, 2020. "Decision Trees for Evaluation of Mathematical Competencies in the Higher Education: A Case Study," Mathematics, MDPI, vol. 8(5), pages 1-16, May.
- Snezhana Gocheva-Ilieva & Hristina Kulina & Atanas Ivanov, 2020. "Assessment of Students’ Achievements and Competencies in Mathematics Using CART and CART Ensembles and Bagging with Combined Model Improvement by MARS," Mathematics, MDPI, vol. 9(1), pages 1-17, December.
- Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
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More about this item
Keywords
student dropout; higher education; dropout prediction; educational data mining; random forest;All these keywords.
JEL classification:
- I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
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