These two PhD projects leverage the University of Amsterdam’s rich institutional data infrastructure to investigate learning, course design, and assessment in higher education using data science and AI. Drawing on data from online testing platforms, student information systems, learning management systems and student performance (including student assignments, feedback, examination results and student evaluations). The successful candidates will study how assessment policies, institutional practices and GenAI tools shape student success, fairness and curricular validity in university programmes.

Across the projects, you will use multiple perspectives (e.g., psychometric, sociological, economic, and educational approaches) to explore how students differ in their learning processes and outcomes, and how course and curriculum design can better respond to these differences. Key themes are how potential biases in examination and evaluation practices can obscure or distort these differences, or unevenly affect specific student groups, and how exam regulations and course design can help mitigate such biases. This includes studying patterns in student engagement and progression, examining how different instructional and organisational choices relate to performance and persistence, and identifying ways to support more personalised and inclusive learning trajectories.

Learn more about the two vacancies here: https://werkenbij.uva.nl/en/vacancies/two-fully-funded-phd-positions-in-educational-data-science-netherlands-14963