The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Multimedia Learning
Mapping Strategic Knowledge
Visualizing argumentation: software tools for collaborative and educational sense-making
Visualizing argumentation: software tools for collaborative and educational sense-making
Using computer supported argument visualization to teach legal argumentation
Visualizing argumentation
Enhancing deliberation through computer supported argument visualization
Visualizing argumentation
Designing Learning by Teaching Agents: The Betty's Brain System
International Journal of Artificial Intelligence in Education
A knowledge-based coach for reasoning about historical causation
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
'Tis Better to Construct than to Receive? The Effects of Diagram Tools on Causal Reasoning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Evaluating Legal Argument Instruction with Graphical Representations Using LARGO
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Will Google destroy western democracy? Bias in policy problem solving
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Will Google destroy western democracy? Bias in policy problem solving
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
International Journal of Artificial Intelligence in Education
Using tutors to improve educational games
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
The untapped promise of digital mind maps
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Policy problems like "What should we do about global warming?" are ill-defined in large part because we do not agree on a system to represent them the way we agree Algebra problems should be represented by equations. As a first step toward building a policy deliberation tutor, we investigated: (a) whether causal diagrams help students learn to evaluate policy options, (b) whether constructing diagrams promotes learning and (c) what difficulties students have constructing and interpreting causal diagrams. The first experiment tested whether providing information as text, text plus a correct diagram, or text plus a diagramming tool helped undergraduates predict the effects of policy options. A second, think-aloud study identified expert and novice errors on the same task. Results showed that constructing and receiving diagrams had different effects on performance and transfer. Students given a correct diagram on a posttest made more correct policy inferences than those given text or a diagramming tool. On a transfer test presented as text only, students who had practiced constructing diagrams made the most correct inferences, even though they did not construct diagrams during the transfer test. Qualitative results showed that background knowledge sometimes interfered with diagram interpretation but was also used normatively to augment inferences from the diagram. Taken together, the results suggest that: causal diagrams are a good representation system for a deliberation tutor, tutoring should include diagram construction, and a deliberation tutor must monitor the student's initial beliefs and how they change in response to evidence, perhaps by representing both the evidence provided and the student's synthesized causal model.