Early-predicting dropout of university students: an application of innovative multilevel machine learning and statistical techniques
该研究结合理论模型与数据驱动方法，利用多层级统计和机器学习技术，基于意大利某顶尖大学的行政数据开发早期预警系统，用于识别可能辍学的学生。
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university.