辍学(神经网络)
人工智能
机器学习
水准点(测量)
深度学习
计算机科学
人工神经网络
排列(音乐)
大地测量学
声学
物理
地理
作者
Máté Baranyi,Marcell Nagy,Roland Molontay
标识
DOI:10.1145/3368308.3415382
摘要
The early identification of college students at risk of dropout is of great interest and importance all over the world, since the early leaving of higher education is associated with considerable personal and social costs. In Hungary, especially in STEM undergraduate programs, the dropout rate is particularly high, much higher than the EU average. In this work, using advanced machine learning models such as deep neural networks and gradient boosted trees, we aim to predict the final academic performance of students at the Budapest University of Technology and Economics. The dropout prediction is based on the data that are available at the time of enrollment. In addition to the predictions, we also interpret our machine learning models with the help of state-of-the-art interpretable machine learning techniques such as permutation importance and SHAP values. The accuracy and AUC of the best-performing deep learning model are 72.4% and 0.771, respectively that slightly outperforms XGBoost, the cutting-edge benchmark model for tabular data.
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