Applying the Multi-Layer Perceptron Neural Network Model to Predicting School Closures: An Example of Taipei City

Abstract

In this study, we utilised multi-layer perceptron neural networks (MLPNNs) to assess the issue of school closures. Specifically, this study uses the MLPNN model to conduct learning assessments to identify schools that may suffer from poor management. The empirical findings are briefly summarised as follows: (1) The research shows that more than half of the private schools in this study will face bankruptcy. In Taipei City, out of the 23 existing private schools, only four operate normally, while the remaining 19 private vocational high schools require assistance. About 12 schools face severe problems in terms of poor management, accounting for 63% of the total, while seven schools had a prediction value below 50, indicating a severe problem. These schools have expressed an immediate need for government assistance. (2) According to the MLPNN model in this study, reducing the number of full-time teachers is a primary factor contributing to school closures. First, since full-time teachers are a fixed cost, the dismissal of teachers tends to be prioritised to bring down school management costs. This, in turn, reduces the teacher-student ratio. Other factors that contribute to school bankruptcy are dismissing staff and part-time teachers, reducing expenditure, and poor operational maintenance. When the above policies are implemented in schools with poor management, a vicious cycle is created, leading to the bankruptcy of schools.

Keywords

• multilayer perceptron neural networks
• predicting school closure; school closures
• high education
• population decline