Journal Article

The UK Research Excellence Framework and the Matthew effect: Insights from machine learning

Balbuena, Lloyd D.

2018

Overview

With the high cost of the research assessment exercises in the UK, many have called for simpler and less time-consuming alternatives. In this work, we gathered publicly available REF data, combined them with library-subscribed data, and used machine learning to examine whether the overall result of the Research Excellence Framework 2014 could be replicated. A Bayesian additive regression tree model predicting university grade point average (GPA) from an initial set of 18 candidate explanatory variables was developed. One hundred and nine universities were randomly divided into a training set (n = 79) and test set (n = 30). The model \"learned\" associations between GPA and the other variables in the training set and was made to predict the GPA of universities in the test set. GPA could be predicted from just three variables: the number of Web of Science documents, entry tariff, and percentage of students coming from state schools (r-squared = .88). Implications of this finding are discussed and proposals are given.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Academic achievement / Analysis / Artificial intelligence / Author productivity / Bayes Theorem / Bayesian analysis / Bayesian statistical decision theory / Bibliometrics / Colleges & universities / Computer and Information Sciences / Educational Measurement / Grades (Scholastic marks) / Higher education / Hirsch index / Humans / Learning algorithms / Machine Learning / Models, Theoretical / People and Places / Physical Sciences / Regression analysis / Regression models / Research and Analysis Methods / Researchers / Scholarly Communication / Schools / Science Policy / Scientists / Scientometrics / Social Sciences / Students / Success / Tariffs / Training / United Kingdom / Universities - economics / Universities and colleges