EDUCATIONAL, DEVELOPMENTAL COGNITIVE NEUROSCIENCE AND ARTIFICIAL INTELLIGENCE PERSPECTIVES ON LEARNING
The application of computational and neuroscience approaches in developmental science is leading to new insights into the cognitive and neurobiological mechanisms underlying developmental changes in learning. However, it is yet unclear what the possible consequences and applications of these new discoveries are for educational settings. At the same time, increased use of electronic learning materials has sparked a big data revolution in education. The emerging field of educational analytics aims to use this high-fidelity data in order to better understand and facilitate learning. Yet, there are only very few examples where analytics are informed by cognitive models of learning. The aim of this symposium is to move towards a more complementary, integrative view on how learning mechanisms change across development and what the impact of these developmental changes could be for the design of learning interventions and environments.
The aim of this symposium is to lay out the different perspectives on science of learning research in education and developmental cognitive neuroscience and to crystalize challenges and gaps in both fields where an interdisciplinary exchange would be helpful. To constrain the discussion and to maximize the potential of finding specific overlap we have selected three topics: (1) developmental changes in cognitive and motivational processes underlying learning, (2) the social context of learning, and (3) how big data may inform cognitive models of learning.
To foster the cross-disciplinary debates we aim to have roughly equal amounts of participants from the educational sciences, analytics/artificial intelligence, and developmental cognitive neuroscience.
for full program see here
Organizing committee: Julia Rodriguez Buritica (FU Berlin); Dietsje Jolles (Leiden University, UCLA); Ben Eppinger (Concordia University, TU Dresden) & Wouter van den Bos (University of Amsterdam, MPI Berlin)