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Richard Catrambone
Researcher at Georgia Institute of Technology
Publications - 126
Citations - 5540
Richard Catrambone is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Task (project management) & Cognition. The author has an hindex of 35, co-authored 125 publications receiving 5158 citations. Previous affiliations of Richard Catrambone include Tufts University & University of Michigan.
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Overcoming contextual limitations on problem-solving transfer.
TL;DR: This article investigated transfer from multiple analogs to a superficially dissimilar target problem and found that spontaneous transfer between small numbers of dissimilar analogs is difficult to obtain, but it can be achieved by manipulations that foster abstraction of a problem schema from the training examples.
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A psychological perspective on augmented reality in the mathematics classroom
TL;DR: A framework for understanding AR learning from three perspectives: physical, cognitive, and contextual is presented, arguing that physical manipulation affords natural interactions, thus encouraging the creation of embodied representations for educational concepts.
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An evaluation of space-filling information visualizations for depicting hierarchical structures
TL;DR: Results from two empirical studies of two visualization tools for depicting hierarchies, in particular, computer file and directory structures, suggest that participants strongly preferred the Sunburst tool, citing better ability to convey structure and hierarchy.
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The subgoal learning model: Creating better examples so that students can solve novel problems.
TL;DR: The authors found that a label for a group of steps in examples helped participants to form subgoals as assessed by measures such as problem-solving performance and talk aloud protocols, and they also found that abstract labels were more likely than superficial labels to lead participants to create sub-goals with fewer ties to surface features.
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Evaluating animations as student aids in learning computer algorithms
TL;DR: The results suggest that one way animations may aid learning of procedural knowledge is by encouraging learners to predict the algorithm's behavior, but such a learning improvement was also found when learners made predictions of an algorithm'sbehavior from static diagrams.