Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A visualization of group cognition: semantic network analysis of a CSCL community
ICLS '10 Proceedings of the 9th International Conference of the Learning Sciences - Volume 1
Attention please!: learning analytics for visualization and recommendation
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
InterLACE: interactive learning and collaboration environment
Proceedings of the 2013 conference on Computer supported cooperative work companion
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Interactive technologies have become an important part of teaching and learning. However, the data that these systems generate is increasingly unstructured, complex, and therefore difficult of which to make sense of. Current computationally driven methods (e.g., latent semantic analysis or learning based image classifiers) for classifying student contributions don't include the ability to function on multimodal artifacts (e.g., sketches, videos, or annotated images) that new technologies enable. We have developed and implemented a classifcation algorithm based on learners' interactions with the artifacts they create. This new form of semi-automated concept classification, coined Collaborative Spatial Classification, leverages the spatial arrangement of artifacts to provide a visualization that generates summary level data about about idea distribution. This approach has two benefits. First, students learn to identify and articulate patterns and connections among classmates ideas. Second, the teacher receives a high-level view of the distribution of ideas, enabling them to decide how to shift their instructional practices in real-time.