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Research assessment exercises in the UK can be simplified. Machine learning accurately predicted university performance (GPA) using only three key variables: publications, entry tariff, and student background.

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Area of Science:

  • Bibliometrics
  • Higher Education Research
  • Data Science

Background:

  • Research assessment exercises in the UK are costly and time-consuming.
  • There is a need for simpler, more efficient evaluation alternatives.

Purpose of the Study:

  • To investigate if machine learning can replicate the UK's Research Excellence Framework (REF) 2014 results.
  • To identify key predictors of university performance.

Main Methods:

  • Collected publicly available REF data and library-subscribed data.
  • Developed a Bayesian additive regression tree model.
  • Utilized a training set (n=79) and test set (n=30) for model validation.

Main Results:

  • University Grade Point Average (GPA) was accurately predicted (r-squared = .88).
  • The model identified three significant predictors: Web of Science documents, entry tariff, and percentage of students from state schools.

Conclusions:

  • Machine learning offers a potential method for simplifying research assessment.
  • Key institutional metrics can efficiently predict overall research performance.