Using analogy to cluster hand-drawn sketches for sketch-based educational software

Maria D. Chang, Kenneth D Forbus

Research output: Contribution to journalArticlepeer-review

7 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 article describes how analogical retrieval and generalization can be used to cluster automatically analyzed handdrawn 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)
Pages (from-to)76-84
Number of pages9
JournalAI Magazine
Volume35
Issue number1
DOIs
StatePublished - 2014

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Using analogy to cluster hand-drawn sketches for sketch-based educational software'. Together they form a unique fingerprint.

Cite this