Doctoral capstone theories as indicators of university rankings: Insights from a machine learning approach

Abstract

Although journal articles dominate visibility and recognition in scholarly output, doctoral theses or capstones represent a significant, yet often overlooked, component of university research. This study takes a learning analytics perspective to explore the relationship between university rankings and the theoretical frameworks used in doctoral capstones within the education field, an area largely underexamined in prior research. Using the 2023 Academic Ranking of World Universities (ARWU) for education, a dataset of 9770 doctoral capstone abstracts, and a curated list of 59 learning theories, we investigated theory prevalence relative to university ranking. Employing machine learning to calculate cosine similarity between capstones and learning theories, followed by multivariate ANOVA, we identified statistically significant differences in theory usage across ranking groups. These findings suggest that theoretical choices in capstones may contribute to the external evaluations underpinning university rankings, offering insights for aligning doctoral programs with ranking criteria. However, this study's limitations, mainly its correlational nature and the U.S.-exclusive dataset, suggest the need for further research on interdisciplinarity and theory clustering across global institutions. The study makes headway in the empirical investigation into how theoretical frameworks of doctoral research may be related to university rankings, and its findings pertain to the management of educational and psychological research at doctoral level by means of learning analytics.