Improving student–supervisor matching: educational gains from a data-driven approach

Dissertation supervision plays a central role in shaping student engagement, academic development and satisfaction in higher education. Yet, in many large programmes, the allocation of students to supervisors is treated as a logistical task rather than an educational design choice. Structuring this process around research topic alignment in a systematic and data-informed way is both feasible and educationally meaningful. Survey data from the 2024/25 cohort of postgraduate Economics students at the University of Glasgow (n = 102) suggest a positive association between topic alignment and supervision experience amongst students supervised by the same person. Similar patterns emerge for the perceived quality and relevance of feedback. The proposed measure of topic alignment, based on a hierarchical research classification system, together with the allocation algorithm, provides a scalable, transparent and fair matching method that can be readily implemented in large programmes. This approach offers a replicable framework that institutions can adapt to their own disciplinary and institutional contexts.