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Predicting freshmen enrollment based on machine learning

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Abstract

The enrollment rate of freshmen has always been a headache for colleges and universities. It is also very difficult to accurately predict the number of freshmen before they register. In recent years, deep learning and machine learning technology have made a breakthrough and are widely used in data processing, edge computing, and situation awareness. So far, no researcher has used machine learning methods to forecast the enrollment of new students, because the intuitive feeling is that the registration of a freshman is very subjective, dependent on several factors. To date, the number of freshmen in universities has always been forecasted using traditional methods, that is, phone calls and fee payment status inquiries. On the basis of the historical admission enrollment data of a university, this study used a variety of machine learning methods, including decision tree, random forest, and back propagation neural network, for forecasting. Based on the results of our research, it is possible to predict whether a freshman will register in a university, which makes this work very worthy of further study.

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Grants 61872452, 61872451, and 61702365, in part by the Macao FDCT under Grants 0098/2018/A3, 0076/2019/A2, and 0037/2020/A1. Li Feng is the corresponding author.

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  1. Macau University of Science and Technology, WeiLong road, Macau, China

    Lei Yang, Li Feng, Longqing Zhang & Liwei Tian

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  1. Lei Yang
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  2. Li Feng
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  3. Longqing Zhang
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  4. Liwei Tian
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Correspondence to Li Feng.

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Yang, L., Feng, L., Zhang, L. et al. Predicting freshmen enrollment based on machine learning. J Supercomput 77, 11853–11865 (2021). https://doi.org/10.1007/s11227-021-03763-y

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