A model-building learning environment with explanatory feedback to erroneous models

Tomoya Horiguchi; Tsukasa Hirashima; Kenneth D Forbus

doi:10.1007/978-3-642-30950-2_90

A model-building learning environment with explanatory feedback to erroneous models

Tomoya Horiguchi^*, Tsukasa Hirashima, Kenneth D Forbus

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Many model-building learning environments (MBEs) have been developed to support students in acquiring the ability to build appropriate models of physical systems. However, they can't explain how the simulated behavior of an erroneous model is unnatural. Additionally, they can't create any feedback when the model is unsolvable. We introduce a MBE which overcomes these problems with two technical ideas: (1) robust simulator which analyzes the consistency of a model and relaxes some constraints if necessary, and (2) semantics of constraints which is a systematic description of physical meanings of constraints and provides heuristics for explaining the behavioral unnaturalness.

Original language	English (US)
Title of host publication	Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings
Pages	620-621
Number of pages	2
DOIs	https://doi.org/10.1007/978-3-642-30950-2_90
State	Published - 2012
Event	11th International Conference on Intelligent Tutoring Systems, ITS 2012 - Chania, Crete, Greece Duration: Jun 14 2012 → Jun 18 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7315 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Intelligent Tutoring Systems, ITS 2012
Country/Territory	Greece
City	Chania, Crete
Period	6/14/12 → 6/18/12

Keywords

errorawareness/ correction
model-building learning environment
qualitative reasoning
robust simulator
semantics of constraints

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-30950-2_90

Cite this

Horiguchi, T., Hirashima, T., & Forbus, K. D. (2012). A model-building learning environment with explanatory feedback to erroneous models. In Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings (pp. 620-621). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7315 LNCS). https://doi.org/10.1007/978-3-642-30950-2_90

Horiguchi, Tomoya ; Hirashima, Tsukasa ; Forbus, Kenneth D. / A model-building learning environment with explanatory feedback to erroneous models. Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. 2012. pp. 620-621 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{49ba274341a04579810d4f4c172b376b,

title = "A model-building learning environment with explanatory feedback to erroneous models",

abstract = "Many model-building learning environments (MBEs) have been developed to support students in acquiring the ability to build appropriate models of physical systems. However, they can't explain how the simulated behavior of an erroneous model is unnatural. Additionally, they can't create any feedback when the model is unsolvable. We introduce a MBE which overcomes these problems with two technical ideas: (1) robust simulator which analyzes the consistency of a model and relaxes some constraints if necessary, and (2) semantics of constraints which is a systematic description of physical meanings of constraints and provides heuristics for explaining the behavioral unnaturalness.",

keywords = "errorawareness/ correction, model-building learning environment, qualitative reasoning, robust simulator, semantics of constraints",

author = "Tomoya Horiguchi and Tsukasa Hirashima and Forbus, {Kenneth D}",

year = "2012",

doi = "10.1007/978-3-642-30950-2_90",

language = "English (US)",

isbn = "9783642309496",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "620--621",

booktitle = "Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings",

note = "11th International Conference on Intelligent Tutoring Systems, ITS 2012 ; Conference date: 14-06-2012 Through 18-06-2012",

}

Horiguchi, T, Hirashima, T & Forbus, KD 2012, A model-building learning environment with explanatory feedback to erroneous models. in Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7315 LNCS, pp. 620-621, 11th International Conference on Intelligent Tutoring Systems, ITS 2012, Chania, Crete, Greece, 6/14/12. https://doi.org/10.1007/978-3-642-30950-2_90

A model-building learning environment with explanatory feedback to erroneous models. / Horiguchi, Tomoya; Hirashima, Tsukasa; Forbus, Kenneth D.

Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. 2012. p. 620-621 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7315 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A model-building learning environment with explanatory feedback to erroneous models

AU - Horiguchi, Tomoya

AU - Hirashima, Tsukasa

AU - Forbus, Kenneth D

PY - 2012

Y1 - 2012

N2 - Many model-building learning environments (MBEs) have been developed to support students in acquiring the ability to build appropriate models of physical systems. However, they can't explain how the simulated behavior of an erroneous model is unnatural. Additionally, they can't create any feedback when the model is unsolvable. We introduce a MBE which overcomes these problems with two technical ideas: (1) robust simulator which analyzes the consistency of a model and relaxes some constraints if necessary, and (2) semantics of constraints which is a systematic description of physical meanings of constraints and provides heuristics for explaining the behavioral unnaturalness.

AB - Many model-building learning environments (MBEs) have been developed to support students in acquiring the ability to build appropriate models of physical systems. However, they can't explain how the simulated behavior of an erroneous model is unnatural. Additionally, they can't create any feedback when the model is unsolvable. We introduce a MBE which overcomes these problems with two technical ideas: (1) robust simulator which analyzes the consistency of a model and relaxes some constraints if necessary, and (2) semantics of constraints which is a systematic description of physical meanings of constraints and provides heuristics for explaining the behavioral unnaturalness.

KW - errorawareness/ correction

KW - model-building learning environment

KW - qualitative reasoning

KW - robust simulator

KW - semantics of constraints

UR - http://www.scopus.com/inward/record.url?scp=84862503654&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862503654&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-30950-2_90

DO - 10.1007/978-3-642-30950-2_90

M3 - Conference contribution

AN - SCOPUS:84862503654

SN - 9783642309496

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 620

EP - 621

BT - Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings

T2 - 11th International Conference on Intelligent Tutoring Systems, ITS 2012

Y2 - 14 June 2012 through 18 June 2012

ER -

Horiguchi T, Hirashima T, Forbus KD. A model-building learning environment with explanatory feedback to erroneous models. In Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. 2012. p. 620-621. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-30950-2_90

A model-building learning environment with explanatory feedback to erroneous models

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this