A blog by Michele Jacobsen, Professor in Werklund School of Education and Association Dean, Supervision and Mentorship, University of Calgary

Research on graduate supervision consistently demonstrates that supervisory quality is strongly influenced by supervisor capacity, which is shaped by the number of active graduate student supervisees, discipline norms, research intensity, workload distribution across a program, and supervision and mentoring practices. Here are a few key points from the research:
  • Supervisory workload Affects Supervisor Capacity and Responsiveness:  1) Higher supervisory workload tends to be associated with reduced availability, longer feedback delays, and diminished support for students’ research development (Halse, 2011; Manathunga, 2019; Sverdlik, et al 2018); 2) Supervisors with heavy loads tend to experience greater role intensity, which can reduce the relational quality of supervision, which is a strong predictor of student satisfaction and completion.
  • Ongoing, growth-oriented feedback from a supervisor is key to student progress and success. Quality of Feedback and Relational Engagement Declines as Load Increases:  1) High-quality supervision depends on timely, individualized, developmental feedback (Becker, et al, 2025; Lee, 2008; Wisker, et al 2021); When supervisory load exceeds capacity, students report feeling “unsupported,” “isolated,” and “delayed.”
  • Supervisory workload Can Affect Time-to-Completion. 1) studies in STEM fields indicate that heavy supervisory loads correlate with longer time-to-completion and more variable progress outcomes (McAlpine & Amundsen, 2008; Sverdlik, et al 2018); on the other hand, larger supervision groups can be successful when supported by co-supervision models, research teams, and strong departmental structures (course load offsets, leadership and peer supports, access to resources).
  • Program and Institutional Context Matters. 1) optimal supervisory loads vary by discipline, but many universities use guidelines ranging from 6–10 active graduate students as the maximum expected load for a single supervisor. 2) Across departments, programs often set firm thresholds (e.g., 5 – 8) and then require additional justification from the supervisor (a rationale for the request, demonstration of supportive conditions for students) and governance oversight (GPD, ADs) for each subsequent student. Research emphasizes the importance of monitoring student progress, supervisory responsiveness, and distribution of workload across the entire faculty, given that additional students require extra workload from academic peers for committees, exams, feedback on writing, grant and scholarship applications, and so on.
  • Supervisory Load Has Equity and Sustainability Implications. 1) Female supervisors, supervisors in high-demand and highly funded areas, and those known for strong mentorship often take on disproportionate numbers of students (Amundson & McAlpine, 2009; Gordon, et al 2024; McAlpine & Amundson, 2012). 2) Without faculty and department oversight, supervisory load can inadvertently compromise supervisor well-being and student experience. A report on correlation between supervisor workload and student time to degree would be helpful here.

Summary

While the research does not (likely cannot or should not) establish a universal “maximum load,” the literature demonstrates that supervisory quality depends on the supervisor’s capacity, not just the number of students (commonly captured by supervision workload). A program’s supervision policy that requires justification and consultation for supervision workloads and is informed by data analysis is strongly aligned with established best practices.
In assessing individual supervisor requests to maintain a certain number of students as their desired workload, it is important to analyze the supervisor’s capacity to support a quality student experience for everyone under their care:
  • Student experience: Do current and past students report a good experience with this supervisor, a sense of belonging in the research group, ready access to the supervisor, and regular growth-oriented feedback on their work? Do students receive adequate funding, adequate guidance from the supervisor and engage in co-establishing reasonable expectations and accountability mechanisms to track progress over time?
  • Completion & Attrition Rates: Does this supervisor’s students tend to finish on time, and are there any regular issues, withdrawals or transfers, or other changes that should be explained.
  • Time to Degree: Does the supervisor have a good track record in being accessible, being supportive, and mentoring students through milestones while maintaining student wellness?
  • Publications & Presentations: What is the prevalence of co-authored work, conference presentation activity, and visibility of student research in this supervisor’s practice and lab group?
  • Career Trajectories: How does this supervisor support career development, employment, postdoctoral opportunities, and professional success after graduation?
Supervisor capacity can depend on: Discipline norms, Supervisor workload (supervision, teaching, service, research intensity), lab size and research group structure, Presence & engagement of co-supervisors or supervisory committees, diverse student needs and project complexity, to name a few.  Supervisory capacity cannot be evaluated solely by numbers but needs to gauge capacity using quality indicators (relational trust, accessibility, approachability, feedback timeliness, meeting frequency, progress data, and so on) that must also guide supervisory workload decisions.
To track the relationship between workload and capacity, generate a program report that compares supervision load and students’ time in program to gain data informed insight on whether current workload ranges are supported by student outcomes across supervisor and degree programs. It is important that we aim to understand whether and how supervisory load influences the supervisory capacity to provide quality and frequency of interactions and growth-oriented feedback and support to graduate students.

References

Amundsen, C., & McAlpine, L. (2009). Learning supervision: Trial by fire? Innovations in Education and Teaching International, 46(3), 331–342. https://doi.org/10.1080/14703290903068805

Becker, S., Jacobsen, M., Friesen, S. (2025). Four supervisory mentoring practices that support online doctoral students’ academic writing. Frontiers in Education: Higher Education, 10. https://doi.org/10.3389/feduc.2025.1521452

Gordon, H. R., Willink, K., & Hunter, K. (2024). Invisible labor and the associate professor: Identity and workload inequity. Journal of Diversity in Higher Education, 17(3), 285–296. https://doi.org/10.1037/dhe0000414

Halse, C. (2011). ‘Becoming a supervisor’: the impact of doctoral supervision on supervisors’ learning. Studies in Higher Education, 36(5), 557–570. https://doi.org/10.1080/03075079.2011.594593

Lee, A. (2008). How Are Doctoral Students Supervised? Concepts of Doctoral Research Supervision. Studies in Higher Education, 33(3), 267-281. https://doi.org/10.1080/03075070802049202

Manathunga, C. (2019). ‘Timescapes’ in doctoral education: the politics of temporal equity in higher education. Higher Education Research & Development, 38(6), 1227–1239. https://doi.org/10.1080/07294360.2019.1629880

McAlpine, L., & Amundsen, C. (2012) Challenging the taken- for-granted: how research analysis might inform pedagogical practices and institutional policies related to doctoral education. Studies in Higher Education, 37(6), 683-694. https://doi.org/10.1080/03075079.2010.537747

McAlpine, L., & Amundsen, C. (2009). Identity and agency: pleasures and collegiality among the challenges of the doctoral journey. Studies in Continuing Education, 31(2), 109–125. https://doi.org/10.1080/01580370902927378

Sverdlik, A., Hall, N. C., McAlpine, L., & Hubbard, K. (2018). The PhD Experience: A Review of the Factors Influencing Doctoral Students’ Completion, Achievement, and Well-Being. International Journal of Doctoral Studies, 13, 361-388. https://doi.org/10.28945/4113

Wisker, G., McGinn, M. K., Bengtsen, S. S. E., Lokhtina, I., He, F., Corner, S., Leshem, S., Inouye, K., & Lofstrom, E. (2021). Remote doctoral supervision experiences: Challenges and affordances. Innovations in Education and Teaching International, 58(6), 612-623. https://doi.org/10.1080/14703297.2021.1991427 

This Supervision Blog is part of Dr. Michele Jacobsen’s Research website.