Clustering Hand-Drawn Sketches via Analogical Generalization

Maria D. Chang, Kenneth D. Forbus

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages1507-1512
Number of pages6
StatePublished - 2013
Event25th Innovative Applications of Artificial Intelligence Conference, IAAI 2013 - Bellevue, United States
Duration: Jul 14 2013Jul 18 2013

Conference

Conference25th Innovative Applications of Artificial Intelligence Conference, IAAI 2013
Country/TerritoryUnited States
CityBellevue
Period7/14/137/18/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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