Episodic Learner Modeling
Identifieur interne : 001581 ( Istex/Corpus ); précédent : 001580; suivant : 001582Episodic Learner Modeling
Auteurs : Gerhard WeberSource :
- Cognitive Science [ 0364-0213 ] ; 1996.
Abstract
Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rulebased and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.
Url:
DOI: 10.1016/S0364-0213(99)80006-8
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ISTEX:E828C8FB80F412B78FDA4040F8ABB70F105F8E77Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title>Episodic Learner Modeling</title><author><name sortKey="Weber, Gerhard" sort="Weber, Gerhard" uniqKey="Weber G" first="Gerhard" last="Weber">Gerhard Weber</name><affiliation><mods:affiliation>University of Trier Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: weber@cogpsy.uni-trier.de</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:E828C8FB80F412B78FDA4040F8ABB70F105F8E77</idno><date when="1996" year="1996">1996</date><idno type="doi">10.1016/S0364-0213(99)80006-8</idno><idno type="url">https://api.istex.fr/document/E828C8FB80F412B78FDA4040F8ABB70F105F8E77/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001581</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001581</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Episodic Learner Modeling</title><author><name sortKey="Weber, Gerhard" sort="Weber, Gerhard" uniqKey="Weber G" first="Gerhard" last="Weber">Gerhard Weber</name><affiliation><mods:affiliation>University of Trier Germany</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: weber@cogpsy.uni-trier.de</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Cognitive Science</title><title level="j" type="abbrev">COGSCI</title><idno type="ISSN">0364-0213</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1996">1996</date><biblScope unit="volume">20</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="195">195</biblScope><biblScope unit="page" to="236">236</biblScope></imprint><idno type="ISSN">0364-0213</idno></series><idno type="istex">E828C8FB80F412B78FDA4040F8ABB70F105F8E77</idno><idno type="DOI">10.1016/S0364-0213(99)80006-8</idno><idno type="PII">S0364-0213(99)80006-8</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0364-0213</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rulebased and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Gerhard Weber</name><affiliations><json:string>University of Trier Germany</json:string><json:string>E-mail: weber@cogpsy.uni-trier.de</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rulebased and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.</abstract><qualityIndicators><score>7.004</score><pdfVersion>1.2</pdfVersion><pdfPageSize>432 x 648 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>1142</abstractCharCount><pdfWordCount>15438</pdfWordCount><pdfCharCount>95944</pdfCharCount><pdfPageCount>42</pdfPageCount><abstractWordCount>167</abstractWordCount></qualityIndicators><title>Episodic Learner Modeling</title><pii><json:string>S0364-0213(99)80006-8</json:string></pii><refBibs><json:item><host><author></author><title>The architecture of cognition</title></host></json:item><json:item><author><json:item><name>J.R. Anderson</name></json:item></author><host><author></author><title>Proceedings of the Sixth Annual Conference of the Cognitive Science Society</title></host><title>Cognitive psychology and intelligent tutoring</title></json:item><json:item><author><json:item><name>J.R. 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Wolstencroft</name></json:item></author><host><volume>2</volume><pages><last>71</last><first>58</first></pages><author></author><title>AI Communications</title></host><title>Restructuring, reminding, repair: What's missing from models of analogy</title></json:item></refBibs><genre><json:string>research-article</json:string></genre><serie><language><json:string>unknown</json:string></language><title>AI Memo 406</title></serie><host><volume>20</volume><pii><json:string>S0364-0213(00)X0002-X</json:string></pii><pages><last>236</last><first>195</first></pages><issn><json:string>0364-0213</json:string></issn><issue>2</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Cognitive Science</title><publicationDate>1996</publicationDate></host><categories><wos><json:string>social science</json:string><json:string>psychology, experimental</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>psychology & cognitive sciences</json:string><json:string>experimental psychology</json:string></scienceMetrix></categories><publicationDate>1996</publicationDate><copyrightDate>1996</copyrightDate><doi><json:string>10.1016/S0364-0213(99)80006-8</json:string></doi><id>E828C8FB80F412B78FDA4040F8ABB70F105F8E77</id><score>1.4299291</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/E828C8FB80F412B78FDA4040F8ABB70F105F8E77/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/E828C8FB80F412B78FDA4040F8ABB70F105F8E77/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/E828C8FB80F412B78FDA4040F8ABB70F105F8E77/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Episodic Learner Modeling</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>ELSEVIER</p></availability><date>1996</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Episodic Learner Modeling</title><author xml:id="author-1"><persName><forename type="first">Gerhard</forename><surname>Weber</surname></persName><email>weber@cogpsy.uni-trier.de</email><note type="correspondence"><p>Correspondence and requests for reprints should be sent to Gerhard Weber, Department of Psychology, University of Trier, D-54286 Trier, Germany.</p></note><affiliation>University of Trier Germany</affiliation></author></analytic><monogr><title level="j">Cognitive Science</title><title level="j" type="abbrev">COGSCI</title><idno type="pISSN">0364-0213</idno><idno type="PII">S0364-0213(00)X0002-X</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1996"></date><biblScope unit="volume">20</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="195">195</biblScope><biblScope unit="page" to="236">236</biblScope></imprint></monogr><idno type="istex">E828C8FB80F412B78FDA4040F8ABB70F105F8E77</idno><idno type="DOI">10.1016/S0364-0213(99)80006-8</idno><idno type="PII">S0364-0213(99)80006-8</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1996</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rulebased and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.</p></abstract></profileDesc><revisionDesc><change when="1996">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/E828C8FB80F412B78FDA4040F8ABB70F105F8E77/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>COGSCI</jid><aid>99800068</aid><ce:pii>S0364-0213(99)80006-8</ce:pii><ce:doi>10.1016/S0364-0213(99)80006-8</ce:doi><ce:copyright type="unknown" year="1996"></ce:copyright></item-info><head><ce:title>Episodic Learner Modeling</ce:title><ce:author-group><ce:author><ce:given-name>Gerhard</ce:given-name><ce:surname>Weber</ce:surname><ce:cross-ref refid="COR1"><ce:sup>∗</ce:sup></ce:cross-ref><ce:e-address>weber@cogpsy.uni-trier.de</ce:e-address></ce:author><ce:affiliation><ce:textfn>University of Trier Germany</ce:textfn></ce:affiliation><ce:correspondence id="COR1"><ce:label>∗</ce:label><ce:text>Correspondence and requests for reprints should be sent to Gerhard Weber, Department of Psychology, University of Trier, D-54286 Trier, Germany.</ce:text></ce:correspondence></ce:author-group><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rulebased and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.</ce:simple-para></ce:abstract-sec></ce:abstract></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Episodic Learner Modeling</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Episodic Learner Modeling</title></titleInfo><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weber</namePart><affiliation>University of Trier Germany</affiliation><affiliation>E-mail: weber@cogpsy.uni-trier.de</affiliation><description>Correspondence and requests for reprints should be sent to Gerhard Weber, Department of Psychology, University of Trier, D-54286 Trier, Germany.</description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1996</dateIssued><copyrightDate encoding="w3cdtf">1996</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Modeling the learner is a central aspect of intelligent tutoring systems and knowledge-based help systems that support learners in complex problem-solving domains. In this article, the episodic learner model ELM is introduced as a hybrid system that analyses novices' solutions to programming tasks based on both rulebased and case-based reasoning. ELM behaves like to a human tutor. Initially, ELM is able to analyze problem solutions based only on its domain knowledge. With increasing knowledge about a particular learner captured in a dynamic episodic case base, it adapts to the learner's individual problem-solving behavior. Two simulation studies were performed to validate the system. The first study shows that the system can learn which rules are applied successfully to diagnose code produced by programmers and that using this information reduces the computational effort of diagnoses. Using information from the episodic learner model additionally speeds up the diagnostic process. The second study shows that ELM is able to predict individual solutions. Finally, correspondences and differences to related systems are discussed.</abstract><relatedItem type="host"><titleInfo><title>Cognitive Science</title></titleInfo><titleInfo type="abbreviated"><title>COGSCI</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199604</dateIssued></originInfo><identifier type="ISSN">0364-0213</identifier><identifier type="PII">S0364-0213(00)X0002-X</identifier><part><date>199604</date><detail type="volume"><number>20</number><caption>vol.</caption></detail><detail type="issue"><number>2</number><caption>no.</caption></detail><extent unit="issue pages"><start>137</start><end>299</end></extent><extent unit="pages"><start>195</start><end>236</end></extent></part></relatedItem><identifier type="istex">E828C8FB80F412B78FDA4040F8ABB70F105F8E77</identifier><identifier type="DOI">10.1016/S0364-0213(99)80006-8</identifier><identifier type="PII">S0364-0213(99)80006-8</identifier><recordInfo><recordContentSource>ELSEVIER</recordContentSource></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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