A series of books on interdisciplinary learning at university level. By Ilja Boor, Debby Gerritsen, Linda de Greef, and Jessica Rodermans (University of Amsterdam), published by Routledge.
Assessments, challenges, and the best possible practice.
A series of books on interdisciplinary learning at university level. By Ilja Boor, Debby Gerritsen, Linda de Greef, and Jessica Rodermans (University of Amsterdam), published by Routledge.
In most education and corporate training, assessment is straightforward: test the knowledge, grade the answer, log the result. But interdisciplinary capability doesn’t fit into multiple-choice questions. It’s not just “knowing” — it’s transferring methods from one field to another, adapting under constraints, and producing something that works in the real world.
That’s hard to measure — and even harder to measure at scale. Which is why, in many programs, assessment is an afterthought or reduced to vague participation scores. The result: graduates enjoy the experience but can’t demonstrate that they can actually do the thing.
The common denominator? Performance-based, authentic assessment that mirrors the messy conditions of the real world.
Interdisciplinary work lives in the grey space between disciplines, where “correct” depends on how well the learner integrates ideas and adapts them to context.
Global examples show this is solvable, but not simple. The NSF’s IGERT program assessed doctoral students through multi-dimensional rubrics — combining peer feedback, mentor evaluation, and performance on authentic research deliverables. The International Baccalaureate’s extended essay and Theory of Knowledge use reflective components and real-world scenarios to assess transfer, not just recall.
In an ideal world, every interdisciplinary program would run multi-month, team-based projects with live stakeholders, include multiple assessors from different disciplines, and track learner performance for months after the program ends. But most schools, universities, and companies face constraints of time, budget, data access, and scalability. So, the best possible practice is a balance of rigour with feasibility:
For example, in a Sustainability and Business Strategy program, a learner team might present a circular-economy product model to a panel including an environmental scientist, a supply-chain manager, and an investor. The panel’s rubric scores are combined with a short reflection from the team on how the same approach could be adapted to a different sector — a double-check for transfer capability.
Assessing interdisciplinary capability is not a perfect science. The best programs accept trade-offs: you can’t measure everything, but you can measure the right things well enough to give employers, institutions, and learners confidence in the outcome.
Interdisciplinary capability is not an accident of personality — it’s the product of deliberate design. The frameworks are well-researched, the benefits are proven, but the operational reality is complex.
School and university leaders, and employers, have to ask: are they using real-world expertise and the knowledge of multiple practitioners to build this depth and rigour — or are we content with putting some random elements together and calling it training or learning?
Part 3 of three on interdisciplinary learning. Draws on the NSF IGERT program’s assessment model, the IB’s extended essay and Theory of Knowledge, and the principles of authentic, performance-based assessment.