Bert Bredeweg
DynaLearn - An intelligent learning environment for learning conceptual knowledge
Bredeweg, Bert; Liem, Jochem; Beek, Wouter; Linnebank, Floris; Gracia, Jorge; Lozano, Esther; Wißner, Michael; Bühling, René; Salles, Paulo; Noble, Richard; Zitek, Andreas; Borisova, Petya; Mioduser, David
Authors
Jochem Liem
Wouter Beek
Floris Linnebank
Jorge Gracia
Esther Lozano
Michael Wißner
René Bühling
Paulo Salles
Dr Richard Noble R.A.Noble@hull.ac.uk
Research Associate (HIFI)
Andreas Zitek
Petya Borisova
David Mioduser
Abstract
Articulating thought in computerbased media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge- based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.
Citation
Bredeweg, B., Liem, J., Beek, W., Linnebank, F., Gracia, J., Lozano, E., Wißner, M., Bühling, R., Salles, P., Noble, R., Zitek, A., Borisova, P., & Mioduser, D. (2013). DynaLearn - An intelligent learning environment for learning conceptual knowledge. AI Magazine, 34(4), 46-65. https://doi.org/10.1609/aimag.v34i4.2489
Journal Article Type | Article |
---|---|
Online Publication Date | Jun 27, 2013 |
Publication Date | 2013 |
Deposit Date | Apr 1, 2022 |
Journal | AI Magazine |
Print ISSN | 0738-4602 |
Electronic ISSN | 0738-4602 |
Publisher | Association for the Advancement of Artificial Intelligence |
Peer Reviewed | Peer Reviewed |
Volume | 34 |
Issue | 4 |
Pages | 46-65 |
DOI | https://doi.org/10.1609/aimag.v34i4.2489 |
Public URL | https://hull-repository.worktribe.com/output/3628547 |
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