Clustering Hand-Drawn Sketches via Analogical Generalization

Authors

  • Maria D. Chang Northwestern University
  • Kenneth D. Forbus Northwestern University

DOI:

https://doi.org/10.1609/aaai.v27i2.18991

Abstract

One of the major challenges to building intelligent educational software is determining what kinds of feedback to give learners. Useful feedback makes use of models of domain-specific knowledge, especially models that are commonly held by potential students. To empirically determine what these models are, student data can be clustered to reveal common misconceptions or common problem-solving strategies. This paper describes how analogical retrieval and generalization can be used to cluster automatically analyzed hand-drawn sketches incorporating both spatial and conceptual information. We use this approach to cluster a corpus of hand-drawn student sketches to discover common answers. Common answer clusters can be used for the design of targeted feedback and for assessment.

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Published

2013-07-14

How to Cite

Chang, M., & Forbus, K. (2013). Clustering Hand-Drawn Sketches via Analogical Generalization. Proceedings of the AAAI Conference on Artificial Intelligence, 27(2), 1507-1512. https://doi.org/10.1609/aaai.v27i2.18991