Using Analogy to Cluster Hand‐Drawn Sketches for Sketch‐Based Educational Software

Chang, Maria Hsia; Forbus, Kenneth D.

doi:10.1609/aimag.v35i1.2505

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…This predicted advantage for same-category comparison also follows from computational models of relational category learning based on structure mapping (Barbella & Forbus, 2013; Chang & Forbus, 2013, 2014; Corral & Jones, 2014; McLure et al, 2010; McLure, Friedman, Lovett, & Forbus, 2011; Taylor, Friedman, Forbus, Goldwater, & Gentner, 2011; Tomlinson & Love, 2006). For example, SEQL (Kuehne et al, 2000; Skorstad, Gentner, & Medin, 1988) compares new items to previously stored exemplars or schemas and attempts to align their structures.…”

mentioning

confidence: 74%

Learning relational concepts from within- versus between-category comparisons.

Corral¹,

Kurtz²,

Jones³

2018

Journal of Experimental Psychology: General

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This article examines relational category learning in light of 2 influential theories of concept acquisition: the structure-mapping theory of analogy and theories of feature-based category learning. According to current theories of analogy, comparing 2 instances of a relational concept enables alignment of their elements and reveals their shared relational structure. Therefore, learning relationally defined categories should be faster when comparing items of the same category than when comparing items of different categories. By contrast, feature-based theories predict a benefit of between-category comparisons, because such comparisons direct attention to the features that discriminate the categories. The present experiments tested these predictions using a 2-category classification-learning task in which 2 items are presented on every trial: either in the same category (match condition) or in different categories (contrast condition). Subjects in the contrast condition outperformed those in the match condition for feature-based categories (Experiment 1) and across 4 different types of relational categories (Experiments 1–4). Although theorists have posited that structure-mapping theory is directly applicable to relational category learning, the present findings pose a distinct challenge to this assertion. We propose that many relational categories are learnable based solely on which relations are present in the stimulus rather than requiring explicitly compositional representations based on role-filler binding. This process would be akin to feature processing and would not require structural alignment. This theoretical proposal, together with the empirical results, may lead to a better understanding of when people do and do not engage in the cognitively demanding process of structural alignment.

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confidence: 74%

Learning relational concepts from within- versus between-category comparisons.

Corral¹,

Kurtz²,

Jones³

2018

Journal of Experimental Psychology: General

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“…Finally, many of the lessons concerning feedback in cognitive tutors may be applicable to sketch worksheets, but again, this is a subject for future experimentation. CogSketch's visual analysis capabilities provide the prospect of using sketches as a medium for educational data mining, and using analogical generalization to help instructors identify common patterns of misconceptions (Chang and Forbus 2014). We hope that these deployments are just the next step of helping spread sketching more broadly through STEM education.…”

Section: Discussionmentioning

confidence: 99%

Sketch Worksheets in Science, Technology, Engineering, and Mathematics Classrooms: Two Deployments

Forbus¹,

Garnier

Tikoff

et al. 2020

AI Magazine

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Sketching is a valuable but underutilized tool for science education. Sketch worksheets were developed to help change this, by using artificial intelligence technology to give students immediate feedback and to give instructors assistance in grading. Sketch worksheets use automatically computed visual representations combined with conceptual information to give feedback to students, by computing analogies between students’ sketches and an instructor’s solution sketch. This enables domain experts to develop sketch worksheets, to facilitate dissemination. We describe our experiences in deploying them in geoscience and artificial intelligence classes. The geoscience worksheets, authored by geoscientists at University of Wisconsin–Madison, were used at both Wisconsin and Northwestern University. The artificial intelligence worksheets were developed and used at Northwestern. Our experience indicates that sketch worksheets can provide helpful on-the-spot feedback to students, and significantly improve grading efficiency, to the point where sketching assignments can be more practical to use broadly in science, technology, engineering, and mathematics education.

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“…Finally, many of the lessons concerning feedback in cognitive tutors may be applicable to sketch worksheets, but again, this is a subject for future experimentation. Cog-Sketch's visual analysis capabilities provides the prospect of using sketches as a medium for educational data mining, and using analogical generalization to help instructors identify common patterns of misconceptions (Chang and Forbus, 2014). We hope that these deployments are just the next step of helping spread sketching more broadly through STEM education.…”

Section: Discussionmentioning

confidence: 99%

Sketch Worksheets in STEM Classrooms: Two Deployments

Forbus¹,

Garnier

Tikoff

et al. 2018

AAAI

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Sketching can be a valuable tool for science education, but it is currently underutilized. Sketch worksheets were developed to help change this, by using AI technology to give students immediate feedback and to give instructors assistance in grading. Sketch worksheets use visual representations automatically computed by CogSketch, which are combined with conceptual information from the OpenCyc ontology. Feedback is provided to students by comparing an instructor’s sketch to a student’s sketch, using the Structure-Mapping Engine. This paper describes our experiences in deploying sketch worksheets in two types of classes: Geoscience and AI. Sketch worksheets for introductory geoscience classes were developed by geoscientists at University of Wisconsin-Madison, authored using CogSketch and used in classes at both Wisconsin and Northwestern University. Sketch worksheets were also developed and deployed for a knowledge representation and reasoning course at Northwestern. Our experience indicates that sketch worksheets can provide helpful on-the-spot feedback to students, and significantly improve grading efficiency, to the point where sketching assignments can be more practical to use broadly in STEM education.

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Using Analogy to Cluster Hand‐Drawn Sketches for Sketch‐Based Educational Software

Cited by 5 publications

References 22 publications

Learning relational concepts from within- versus between-category comparisons.

Learning relational concepts from within- versus between-category comparisons.

Sketch Worksheets in Science, Technology, Engineering, and Mathematics Classrooms: Two Deployments

Sketch Worksheets in STEM Classrooms: Two Deployments

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