Computational Modelling of Learning and Teaching

Prof Denis Mareschal, Birkbeck
Prof Diana Laurillard, IoE

Computational modelling provides a rigorous
tool for representing and testing theories of
learning and teaching. For example, it enables
formal comparison of learning with a teacher
setting goals or rewards (supervised learning in
modelling terms) versus discovery learning
(unsupervised learning). It also provides a
means for bridging between complex learning
data and neuroimaging.

Transdisciplinary question:

How can computational models based on
behavioural and neuroimaging evidence
inform teaching and learning practices,
including adaptive software in
technology-enhanced learning?