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Learning to Identify Students’ Relevant and IrrelevantQuestions in a Micro-blogging Supported Classroom

  • Suleyman Cetintas

    ()

    (Purdue University)

  • Luo Si

    ()

    (Purdue University)

  • Sugato Chakravarty

    ()

    (Purdue University)

  • Hans Aagard

    ()

    (Purdue University)

  • Kyle Bowen

    ()

    (Purdue University)

This paper proposes a novel application of text categorization for two types questions asked in a micro-blogging supported classroom, namely relevant and irrelevant questions. Empirical results and analysis show that utilizing the correlation between questions and available lecture materials in a lecture along with personalization and question text leads to significantly higher categorization accuracy than i) using personalization along with question text and ii) using question text alone.

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File URL: ftp://128.210.123.107/csr/wpaper/Hotseat_Research.pdf
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Paper provided by Purdue University, Department of Consumer Sciences in its series Working Papers with number 1010.

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Length: 4 pages
Date of creation: Feb 2011
Date of revision:
Publication status: Published in Lecture Notes in Computer Science, v.6095/2010
Handle: RePEc:csr:wpaper:1010
Contact details of provider: Postal: 812 West State Street, West Lafayette, IN 47906
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Web page: http://www.cfs.purdue.edu/csr/

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