MIME-Version: 1.0
Server: CERN/3.0
Date: Tuesday, 07-Jan-97 15:56:27 GMT
Content-Type: text/html
Content-Length: 8274
Last-Modified: Monday, 26-Aug-96 15:10:15 GMT

<title>Student Modeling</title><h1>Student Modeling for ITS</h1>To view a paper, click on the open book image. <br> <br><ol><! ===========================================================================><a name="assert-aaai-96.ps.Z"</a><b><li>A Novel Application of Theory Refinement to Student Modeling<br></b>Paul Baffes and Raymond J. Mooney<br><cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,pp. 403-408, Portland, OR, August, 1996. (AAAI-96)<p><blockquote>Theory refinement systems developed in machine learning automaticallymodify a knowledge base to render it consistent with a set ofclassified training examples. We illustrate a novel application ofthese techniques to the problem of constructing a student model for anintelligent tutoring system (ITS). Our approach is implemented in anITS authoring system called Assert which uses theory refinement tointroduce errors into an initially correct knowledge base so that itmodels incorrect student behavior. The efficacy of the approach hasbeen demonstrated by evaluating a tutor developed with Assert with 75students tested on a classification task covering concepts from anintroductory course on the C++ programming language. The systemproduced reasonably accurate models and students who received feedbackbased on these models performed significantly better on a post testthan students who received simple reteaching.</blockquote><!WA0><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/assert-aaai-96.ps.Z"><!WA1><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p><! ===========================================================================><a name="assert-jaied-95.ps.Z"</a><b><li> Refinement-Based Student Modeling and Automated Bug Library Construction<br></b>Paul Baffes and Raymond Mooney<br><cite>Journal of Artificial Intelligence in Education</cite>, 7, 1(1996), pp. 75-116.<p><blockquote>A critical component of model-based intelligent tutoring sytems is amechanism for capturing the conceptual state of the student, whichenables the system to tailor its feedback to suit individual strengthsand weaknesses.  To be useful such a modeling technique must be<em>practical</em>, in the sense that models are easy to construct, and<em>effective</em>, in the sense that using the model actually impacts studentlearning.  This research presents a new student modeling techniquewhich can automatically capture novel student errors using onlycorrect domain knowledge, and can automatically compile trends acrossmultiple student models.  This approach has been implemented as acomputer program, ASSERT, using a machine learning technique called<em>theory refinement</em>, which is a method for automatically revising aknowledge base to be consistent with a set of examples.  Using aknowledge base that correctly defines a domain and examples of astudent's behavior in that domain, ASSERT models student errors bycollecting any refinements to the correct knowledege base which arenecessary to account for the student's behavior.  The efficacy of theapproach has been demonstrated by evaluating ASSERT using 100 studentstested on a classification task covering concepts from an introductorycourse on the C++ programming language.  Students who receivedfeedback based on the models automatically generated by ASSERTperformed significantly better on a post test than students whoreceived simple teaching.</blockquote><!WA2><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/assert-jaied-95.ps.Z"><!WA3><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p><! ===========================================================================><a name="assert-dissertation-94.tar.Z" </a><b> <li> Automatic Student Modeling and Bug Library Construction using Theory Refinement <br> </b>  Paul T. Baffes <br>Ph.D. Thesis, Department of Computer Sciences, University of Texas atAustin, December, 1994.<p><blockquote>The history of computers in education can be characterized by acontinuing effort to construct intelligent tutorial programswhich can adapt to the individual needs of a student in aone-on-one setting. A critical component of these intelligenttutorials is a mechanism for modeling the conceptual state of thestudent so that the system is able to tailor its feedback to suitindividual strengths and weaknesses. The primary contribution ofthis research is a new student modeling technique which canautomatically capture novel student errors using only correctdomain knowledge, and can automatically compile trends acrossmultiple student models into bug libraries. This approach hasbeen implemented as a computer program, ASSERT, using a machinelearning technique called theory refinement which is a method forautomatically revising a knowledge base to be consistent with aset of examples. Using a knowledge base that correctly defines adomain and examples of a student's behavior in that domain,ASSERT models student errors by collecting any refinements to thecorrect knowledge base which are necessary to account for thestudent's behavior. The efficacy of the approach has beendemonstrated by evaluating ASSERT using 100 students tested on aclassification task using concepts from an introductory course onthe C++ programming language. Students who received feedbackbased on the models automatically generated by ASSERT performedsignificantly better on a post test than students who receivedsimple reteaching.</blockquote><!WA4><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/assert-dissertation-94.tar.Z"><!WA5><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p><! ===========================================================================><a name="assert-proposal-93.ps.Z" </a><b> <li> Learning to Model Students: Using Theory Refinement to DetectMisconceptions </b> <br>Paul T. Baffes <br> Ph.D. proposal, Department of Computer Sciences, University of Texasat Austin, 1993. <p><blockquote>A new student modeling system called ASSERT is described which uses domainindependent learning algorithms to model unique student errors and toautomatically construct bug libraries. ASSERT consists of two learning phases.The first is an application of theory refinement techniques for constructingstudent models from a correct theory of the domain being tutored. The secondlearning cycle automatically constructs the bug library by extracting commonrefinements from multiple student models which are then used to bias futuremodeling efforts. Initial experimental data will be presented which suggeststhat ASSERT is a more effective modeling system than other induction techniquespreviously explored for student modeling, and that the automatic bug libraryconstruction significantly enhances subsequent modeling efforts.</blockquote><!WA6><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/assert-proposal-93.ps.Z"><!WA7><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p><! ===========================================================================><a name="assert-cogsci-92.ps.Z" </a><b> <li> Using Theory Revision to Model Students and Acquire Stereotypical Errors </b> <br> Paul T. Baffes and Raymond J. Mooney <br> <cite> Proceedings of the Fourteenth Annual Conference of the CognitiveScience Society</cite>, pp. 617-622, Bloomington, IN, July 1992. <p><blockquote>Student modeling has been identified as an important component to the longterm development of Intelligent Computer-Aided Instruction (ICAI) systems. Twobasic approaches have evolved to model student misconceptions. One uses astatic, predefined library of user bugs which contains the misconceptionsmodeled by the system. The other uses induction to learn studentmisconceptions from scratch. Here, we present a third approach that uses amachine learning technique called theory revision. Using theory revisionallows the system to automatically construct a bug library for use in modelingwhile retaining the flexibility to address novel errors.</blockquote><!WA8><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/assert-cogsci-92.ps.Z"><!WA9><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p><! ===========================================================================><hr><address><!WA10><a href="http://www.cs.utexas.edu/users/estlin/">estlin@cs.utexas.edu</a></address>