Causal model progressions as a foundation for intelligent learning environments
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Journal of Artificial Intelligence in Education
Distributed cognition: toward a new foundation for human-computer interaction research
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 2
A cognitive framework for cooperative problem solving with argument visualization
Visualizing argumentation
Cognition and learning in the digital age: Promising research and practice
Computers in Human Behavior
Graphical argumentation and design cognition
Human-Computer Interaction
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This study investigated the effects of a tool designed for supporting the online collaborative performance of learners carrying out complex learning tasks. Appropriate collaborative cognitive activities may be evoked by structuring the whole learning task into phases and providing congruent external representations for each stage (i.e., representational scripting). It was hypothesized that this combination would lead to increased individual learning and better results for the collaborative task. In groups, 47 secondary education students worked on a complex business-economics problem in four experimental conditions, namely one where groups received task-congruent representations for all stages and three where they received one of the representations for all three phases (task-incongruent). The results indicate that groups that received task-congruent representations in a phased order scored higher on the collaborative task, though this did not result in increased individual learning.