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Combining Computational Models of Short Essay Grading for Conceptual Physics Problems

  • M. J. Ventura
  • D. R. Franchescetti
  • P. Pennumatsa
  • A. C. Graesser
  • G. T. Jackson X. Hu
  • Z. Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3220)

Abstract

The difficulties of grading essays with natural language processing tools are addressed. The present project investigated the effectiveness of combining multiple measures of text similarity to grade essays on conceptual physics problems. Latent semantic analysis (LSA) and a new text similarity metric called Union of Word Neighbors (UWN) were used with other measures to predict expert grades. It appears that the best strategy for grading essays is to use student derived ideal answers and statistical models that accommodate inferences. LSA and the UWN gave near equivalent performance in predicting expert grades when student derived ideal answers served as a comparison for student answers. However, if ideal expert answers are used, explicit symbolic models involving word matching are more suitable to predict expert grades. This study identified some computational constraints on models of natural language processing in intelligent tutoring systems.

Keywords

Target Word Latent Semantic Analysis Intelligent Tutoring System Cognitive Tutor Student Answer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • M. J. Ventura
    • 1
  • D. R. Franchescetti
    • 1
  • P. Pennumatsa
    • 1
  • A. C. Graesser
    • 1
  • G. T. Jackson X. Hu
    • 1
  • Z. Cai
    • 1
  1. 1.Institute for Intelligent SystemsMemphis

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