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Supporting College Choice Among International Students through Collaborative Filtering.

Caitlin Tenison1, Guangming Ling1, Laura McCulla1

  • 1Educational Testing Service, Princeton, NJ USA.

International Journal of Artificial Intelligence in Education
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Summary

This study uses historic TOEFL scores and student data to recommend U.S. undergraduate institutions for international students. Structural Topic Modeling personalizes college suggestions based on test performance and preferences.

Keywords:
Collaborative filteringInternational educationRecommender systemsStructural topic modelingUndergraduate education

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Area of Science:

  • Educational Technology
  • Data Science
  • Higher Education Studies

Background:

  • International students face complex decisions when choosing U.S. undergraduate institutions.
  • Existing recommendation systems may not fully leverage student performance data and evolving institutional popularity.
  • Accurate guidance is crucial for successful academic placement and student outcomes.

Purpose of the Study:

  • To develop a data-driven recommendation system for international students selecting U.S. undergraduate programs.
  • To investigate the influence of TOEFL scores and application year on student preferences using Structural Topic Modeling (STM).
  • To provide personalized college recommendations by modeling latent preferences.

Main Methods:

  • Utilized historic score-reporting records and test-taker metadata.
  • Applied Structural Topic Modeling (STM), a probabilistic method, to analyze college preferences.
  • Modeled the latent space of college preferences based on test-taker selections and metadata.

Main Results:

  • TOEFL scores significantly explain variations in test-taker preference groups.
  • The recommendation system adjusts suggestions based on individual student TOEFL scores.
  • Including the test year captured minor shifts in institutional popularity, though it minimally impacted recommendations.

Conclusions:

  • The developed STM approach effectively provides personalized college recommendations for international students.
  • This model serves as a valuable baseline for future enhancements with additional data sources.
  • Data-driven insights from test scores and metadata can significantly support student decision-making in higher education.