The UvA’s executive board has granted funding for a new Research Priority Area (RPA) in ‘Building Interdisciplinary Team Science for Future-proof Higher Education’. This RPA includes two fully funded four-year PhD projects in Educational Data Science.

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.
Study projects will focus on mapping individual differences in student performance, studying how these are related to student background variables, assessing intervention programmes aimed at mitigating inequalities, and assessment and biases therein. Across projects, we will study the effects of GenAI in teaching, learning, and assessment. Some examples of projects we are currently preparing include research into the efficacy of extra-time policies, the effects of different testing regimes (such as compensatory testing), as well as the evaluation of AI-generated grading and feedback. Candidates will be involved in further shaping and executing these studies as well as developing research projects beyond the first year of their PhD trajectory.
Researchers from five UvA faculties are developing a team science infrastructure to facilitate collaboration, ensure the use of interdisciplinary perspectives, and enable secure access to the UvA’s institutional data. Within this collaborative framework, you will be guided by two core supervisors (associate professors Dr. Niels Smits and Dr. Ingmar Visser) and project-specific collaborators from across the UvA.