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Forecasting outbound student mobility: A machine learning approach.

Stephanie Yang1, Hsueh-Chih Chen1, Wen-Ching Chen2

  • 1Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan.

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|September 4, 2020
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Summary
This summary is machine-generated.

Forecasting international student mobility is crucial for knowledge economies. A new hybrid model, FSDESVR, accurately predicts outbound student numbers, aiding educational planning.

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Area of Science:

  • Higher Education
  • International Education
  • Data Science

Background:

  • Knowledge economy development relies on skilled international talent.
  • Outbound student mobility is vital for acquiring global competence.
  • Taiwan's outbound student mobility trends require advanced analytical methods.

Purpose of the Study:

  • To analyze outbound student mobility in Taiwan using time series methods.
  • To propose a hybrid forecasting approach: Feature Selection-Differential Evolution-Support Vector Regression (FSDESVR).
  • To enhance the accuracy of predicting international student mobility patterns.

Main Methods:

  • Utilized time series analysis for outbound student mobility data.
  • Developed a hybrid FSDESVR model combining Feature Selection (FS), Differential Evolution (DE), and Support Vector Regression (SVR).
  • Employed DE for optimal SVR parameter selection and FS for reliable input feature identification.

Main Results:

  • The FSDESVR model demonstrated superior forecasting accuracy compared to existing models.
  • Achieved the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) across ten destination countries.
  • Validated effectiveness using Taiwanese outbound student mobility data from 1998-2018.

Conclusions:

  • The FSDESVR model offers a robust tool for predicting future student mobility.
  • Accurate forecasting enables education administrators to develop more effective curricula.
  • Understanding outbound mobility trends supports national knowledge economy strategies.