Assistant Professor - Digital Assessment and Learning Analytics

Assessment is one of the most important and rapidly changing fields in digital education. Research into data driven user modelling, adaptive systems and personalized assessment build on recent developments in AI technologies. This enables future scenarios for human-machine cooperation on hybrid performance assessment and also supports educators in assessment. Applications are currently developed in fields from mathematics education, programming education, writing assessment as multi-modal behavior analysis.

Innovations in assessment driven by tracking and sensor data are becoming more and more important in the diagnosis and assessment of human behaviour and performance. From analysing learning tracking data in online systems and social media to inferring human characteristics and behaviour online, this has also extended to human interaction with sensor systems and IoT networks. This has recently been addressed in multi-modal learning analytics and performance support systems.

We want to strengthen the diversity of our team with an ambitious, enthusiastic assistant professor which will be making a significant contribution to research in digital education.

This role is affiliated and connected to the Leiden-Delft-Erasmus Centre for Education and Learning (LDE-CEL) and the 4TU.Centre for Engineering Education.These Centres are bundling efforts and partners of the TU Delft for Research, Development and Innovation in Digital Education. They bring together an interdisciplinary team of Computer Science, Learning Science, Psychology and Learning Engineering experts to conceptualize, develop, evaluate and pilot educational innovation projects in cooperation with all partners.

As assistant professor you will work with an enthusiastic team and the networks of the Dutch 4TU federation as also the Leiden-Delft-Erasmus partnership. You will come into a highly innovative interdisciplinary environment expecting high quality research and education in digital education and data science. You will work in national and international highly visible research groups and networks and enthusiastic teams in different faculties working on research for strengthening and applying latest innovation in teaching and learning for students and educators.

In this position you will research methods for using interaction and tracking data from diverse online and IoT sources for assessment, classification and diagnosis of human behaviour.

This includes

• using social media and online user traces for inference and modelling of human traits;
• using sensor data and interaction with embedded devices for skills and competence assessment;
• using statistical and machine learning methods for data integration and classification;
• using simulation and computational models for data generation;
• designing real-time feedback and interaction loops as also interactive machine learning approaches.