Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan

Mahsa Ashouri (), Kate Cai (), Furen Lin () and Galit Shmueli ()
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Mahsa Ashouri: Institute of Service Science, National Tsing Hua University, Hsinchu 30013, Taiwan
Kate Cai: Department of Quantitative Finance, National Tsing Hua University, Hsinchu 30013, Taiwan
Furen Lin: Institute of Service Science, National Tsing Hua University, Hsinchu 30013, Taiwan
Galit Shmueli: Institute of Service Science, National Tsing Hua University, Hsinchu 30013, Taiwan

Service Science, 2018, vol. 10, issue 1, 58-75

Abstract: Analytics is important for education planning. Deploying forecasting analytics requires management information systems (MISs) that collect the needed data and deliver the forecasts to stakeholders. A critical question is whether the data collected by a system is adequate for producing the analytics 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 are in need of accurate school-level one-to-five-year-ahead forecasts of the number of incoming first-grade classrooms. The Ministry of Education therefore launched a K–9 student data management system (k9sdms) that allows schools to directly update data on existing and prospective students. 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 in 2015–2019. The quality of forecasts shows that k9sdms can produce valuable results, thereby achieving its purpose.

Keywords: data collection system; education; forecasting; school-level forecasting; predictive analytics; MIS (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:10:y:2018:i:1:p:58-75

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