Forecasting outbound student mobility: A machine learning approach

Stephanie Yang, Hsueh Chih Chen, Wen Ching Chen, Cheng Hong Yang

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Background A country’s ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital. Methods To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy. Results The proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature. Conclusion With the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum.

原文英語
文章編號e0238129
期刊PloS one
15
發行號9 September
DOIs
出版狀態已發佈 - 2020 九月

ASJC Scopus subject areas

  • 生物化學、遺傳與分子生物學 (全部)
  • 農業與生物科學 (全部)
  • 多學科

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