Researchers have developed a new supervision model for doctoral and master's students pursuing Higher Degree by Research (HDR) programs, using design thinking methods centered on student experience.
The Cohort-based Advisory Team (CAT) model represents a shift away from traditional one-on-one advisor relationships. Instead of relying solely on individual supervisors, the prototype brings together multiple faculty members to support a cohort of research students simultaneously. This structure addresses common pain points in research supervision: isolation, inconsistent feedback, limited access to expertise, and unclear progress benchmarks.
Design thinking, a problem-solving methodology that prioritizes empathy for end users, shaped the model's development. Researchers began by studying the actual experiences of HDR candidates, identifying friction points in the supervision process. Rather than imposing solutions, they designed interventions based on what students reported needing most.
The CAT approach offers several practical advantages. Students gain exposure to multiple perspectives on their research methodology and findings. Advisors share responsibility for student progress, reducing the burden on individual supervisors and creating accountability checks. Cohort members benefit from peer learning and collective problem-solving, transforming what can otherwise be isolating research journeys into collaborative experiences.
This work-based learning strategy treats postgraduate research supervision as a learnable discipline rather than an art left to individual interpretation. By systematizing support through structured teams and design principles, institutions can improve outcomes for students pursuing research degrees.
The model reflects broader recognition that doctoral and master's education requires intentional design. As research programs increasingly serve diverse student populations with varying support needs, cohort-based advisory systems offer scalability that individual supervision alone cannot match. Early prototypes suggest that students report greater confidence, clearer milestones, and stronger networks when supported through this framework.
The approach applies to universities implementing HDR programs in any discipline, from STEM to social sciences. It prioritizes student agency while distrib