Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread

In this research, we aimed to demonstrate the importance of Management Information systems (MISs) in education planning by collecting data and delivering forecast results to stakeholders. A critical question is whether the data collected by a system is adequate for producing the analytics necessary for decision-making. We describe the case of a new education MIS in Taiwan, where the population of preschool children in different school districts is constantly changing. These changes challenge school resource planning, especially in terms of teacher hiring. The bureaus of education in charge of resource allocation require accurate school-level one-to-five-year-ahead forecasts of the number of incoming first-grade classrooms. Therefore, the Ministry of Education launched a K-9 student data management system (k9sdms) that allows schools to update data on existing and prospective students directly. We evaluate whether using this system supports the goal of generating one-to-five-year-ahead forecasts, thereby assessing the value of the MIS for its intended usage. Using data until 2014, we developed a forecasting model for the number of first-grade classrooms at each school in Taiwan from 2015-2019. The quality of forecasts shows that k9sdms can produce valuable results, thereby achieving its purpose. Because the time series for each school was very short (six years for a number of first-grade students and five years for five-year-old children), we did not fit separate models for each school. Instead, we attempted to capture the change in school-level population sizes by using the information from three consecutive years. We used a linear regression model that can use suitable predictors (input variables) to capture the trend and/or seasonality and other patterns. The model, estimated from the training period, could produce forecasts on future data by inserting the relevant predictor information into the estimated regression equation. We explored different linear regression configurations. The output variable was always the number of first-grade students in year t; the predictors were the number of first-grade students in prior years and the five-year-old population in previous years.

Journal article