Algorithmic Debugging for Intelligent Tutoring : How to use multiple models and improve diagnosis

Zinn, Claus

Algorithmic Debugging for Intelligent Tutoring : How to use multiple models and improve diagnosis

Dateien

Zinn_245687.pdfGröße: 133.19 KBDownloads: 344

Datum

2013

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

TIMM, Ingo J., ed., Matthias THIMM, ed.. KI 2013: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 272-283. Lecture Notes in Computer Science. 8077. ISBN 978-3-642-40941-7. Available under: doi: 10.1007/978-3-642-40942-4_24

Zusammenfassung

Intelligent tutoring systems (ITSs) are capable to intelligently diagnose learners’ problem solving behaviour only in limited and well-defined contexts. Learners are expected to solve problems by closely following a single prescribed problem solving strategy, usually in a fixed- order, step by step manner. Learners failing to match expectations are often met with incorrect diagnoses even when human teachers would judge their actions admissible. To address the issue, we extend our previous work on cognitive diagnosis, which is based on logic programming and meta-level techniques. Our novel use of Shapiro’s algorithmic debugging now analyses learner input independently against multiple models. Learners can now follow one of many possible algorithms to solve a given problem, and they can expect the tutoring system to respond with improved diagnostic quality, at negligible computational costs.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

ZINN, Claus, 2013. Algorithmic Debugging for Intelligent Tutoring : How to use multiple models and improve diagnosis. In: TIMM, Ingo J., ed., Matthias THIMM, ed.. KI 2013: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 272-283. Lecture Notes in Computer Science. 8077. ISBN 978-3-642-40941-7. Available under: doi: 10.1007/978-3-642-40942-4_24

BibTex

@inproceedings{Zinn2013Algor-24568,
  year={2013},
  doi={10.1007/978-3-642-40942-4_24},
  title={Algorithmic Debugging for Intelligent Tutoring : How to use multiple models and improve diagnosis},
  number={8077},
  isbn={978-3-642-40941-7},
  publisher={Springer Berlin Heidelberg},
  address={Berlin, Heidelberg},
  series={Lecture Notes in Computer Science},
  booktitle={KI 2013: Advances in Artificial Intelligence},
  pages={272--283},
  editor={Timm, Ingo J. and Thimm, Matthias},
  author={Zinn, Claus}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/24568">
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/24568"/>
    <dc:creator>Zinn, Claus</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/24568/2/Zinn_245687.pdf"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2013</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Zinn, Claus</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-10-16T06:28:52Z</dcterms:available>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-10-16T06:28:52Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/24568/2/Zinn_245687.pdf"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:bibliographicCitation>KI 2013: Advances in Artificial Intelligence : 36th Annual German Conference on AI, Koblenz, Germany, September 16-20, 2013 ; Proceedings / edited by Ingo J. Timm, Matthias Thimm. - Berlin : Springer, 2013. - S. 272-283. - (Lecture notes in computer science ; 8077). - ISBN 978-364-24094-2-4</dcterms:bibliographicCitation>
    <dc:language>eng</dc:language>
    <dcterms:title>Algorithmic Debugging for Intelligent Tutoring : How to use multiple models and improve diagnosis</dcterms:title>
    <dcterms:abstract xml:lang="eng">Intelligent tutoring systems (ITSs) are capable to intelligently diagnose learners’ problem solving behaviour only in limited and well-defined contexts. Learners are expected to solve problems by closely following a single prescribed problem solving strategy, usually in a fixed- order, step by step manner. Learners failing to match expectations are often met with incorrect diagnoses even when human teachers would judge their actions admissible. To address the issue, we extend our previous work on cognitive diagnosis, which is based on logic programming and meta-level techniques. Our novel use of Shapiro’s algorithmic debugging now analyses learner input independently against multiple models. Learners can now follow one of many possible algorithms to solve a given problem, and they can expect the tutoring system to respond with improved diagnostic quality, at negligible computational costs.</dcterms:abstract>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Ja