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Use of Fuzzy Modeling Techniques in a Coached Practice Environment for Electronics Troubleshooting.

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Title: Use of Fuzzy Modeling Techniques in a Coached Practice Environment for Electronics Troubleshooting.
Language: English
Authors: Katz, Sandra, Lesgold, Alan
Peer Reviewed: N
Page Count: 27
Publication Date: 1992
Document Type: Reports - Research
Speeches/Meeting Papers
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Computer Simulation, Electronics, Knowledge Level, Models, Problem Solving, Programed Tutoring, Technical Education, Troubleshooting
Abstract: Student modeling--the task of building dynamic models of student ability--is fraught with uncertainty, caused by such factors as multiple sources of student errors, careless errors and lucky guesses, learning and forgetting. Within the context of the Sherlock intelligent tutoring systems project, we have been experimenting with various ways of making the task of modeling student knowledge more tractable. The philosophical basis underlying each approach is that student models do not need to be precise and accurate to be useful. We describe these approaches, focusing on the one we have developed furthest thus far. The approach, which is based on fuzzy set theory, aims at building imprecise, or "fuzzy" diagnostic student models (e.g., Hawkes et al., 1990). We have built upon this approach by developing techniques for representing and updating discrete student knowledge variables in our avionics troubleshooting tutor, Sherlock II. We describe these techniques and, more broadly, the student modeling component in this tutor. We frame our discussion of the "fuzzy" student modeling approach we are developing with a description of its more crude predecessor, and of our plans for future work on imprecise student modeling using Bayesian inferencing techniques. (Contains 52 references.) (Author/BBM)
Entry Date: 1993
Accession Number: ED349964
Database: ERIC
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  Data: Use of Fuzzy Modeling Techniques in a Coached Practice Environment for Electronics Troubleshooting.
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  Data: <searchLink fieldCode="AR" term="%22Katz%2C+Sandra%22">Katz, Sandra</searchLink><br /><searchLink fieldCode="AR" term="%22Lesgold%2C+Alan%22">Lesgold, Alan</searchLink>
– Name: PeerReviewed
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  Data: N
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  Label: Page Count
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  Data: 27
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  Label: Publication Date
  Group: Date
  Data: 1992
– Name: TypeDocument
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  Data: Reports - Research<br />Speeches/Meeting Papers
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Instruction%22">Computer Assisted Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Simulation%22">Computer Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Electronics%22">Electronics</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+Level%22">Knowledge Level</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Programed+Tutoring%22">Programed Tutoring</searchLink><br /><searchLink fieldCode="DE" term="%22Technical+Education%22">Technical Education</searchLink><br /><searchLink fieldCode="DE" term="%22Troubleshooting%22">Troubleshooting</searchLink>
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  Data: Student modeling--the task of building dynamic models of student ability--is fraught with uncertainty, caused by such factors as multiple sources of student errors, careless errors and lucky guesses, learning and forgetting. Within the context of the Sherlock intelligent tutoring systems project, we have been experimenting with various ways of making the task of modeling student knowledge more tractable. The philosophical basis underlying each approach is that student models do not need to be precise and accurate to be useful. We describe these approaches, focusing on the one we have developed furthest thus far. The approach, which is based on fuzzy set theory, aims at building imprecise, or "fuzzy" diagnostic student models (e.g., Hawkes et al., 1990). We have built upon this approach by developing techniques for representing and updating discrete student knowledge variables in our avionics troubleshooting tutor, Sherlock II. We describe these techniques and, more broadly, the student modeling component in this tutor. We frame our discussion of the "fuzzy" student modeling approach we are developing with a description of its more crude predecessor, and of our plans for future work on imprecise student modeling using Bayesian inferencing techniques. (Contains 52 references.) (Author/BBM)
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  Data: 1993
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      – Text: English
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      Pagination:
        PageCount: 27
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Computer Assisted Instruction
        Type: general
      – SubjectFull: Computer Simulation
        Type: general
      – SubjectFull: Electronics
        Type: general
      – SubjectFull: Knowledge Level
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Problem Solving
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      – SubjectFull: Programed Tutoring
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      – SubjectFull: Technical Education
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      – SubjectFull: Troubleshooting
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      – TitleFull: Use of Fuzzy Modeling Techniques in a Coached Practice Environment for Electronics Troubleshooting.
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            NameFull: Katz, Sandra
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            NameFull: Lesgold, Alan
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              Type: published
              Y: 1992
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