Automatic Generation of Fine-Grained Representations of Learner Response Semantics

  • Rodney D. Nielsen
  • Wayne Ward
  • James H. Martin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5091)


This paper presents a process for automatically extracting a fine-grained semantic representation of a learner’s response to a tutor’s question. The representation can be extracted using available natural language processing technologies and it allows a detailed assessment of the learner’s understanding and consequently will support the evaluation of tutoring pedagogy that is dependent on such a fine-grained assessment. We describe a system to assess student answers at this fine-grained level that utilizes features extracted from the automatically generated representations. The system classifies answers to indicate the student’s apparent understanding of each of the low-level facets of a known reference answer. It achieves an accuracy on these fine-grained decisions of 76% on within-domain assessment and 69% out of domain.


Automatic Generation Edit Distance Intelligent Tutoring System Learner Response 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 2008

Authors and Affiliations

  • Rodney D. Nielsen
    • 1
    • 2
  • Wayne Ward
    • 1
    • 2
  • James H. Martin
    • 1
  1. 1.Center for Computational Language and Education Research, CU Boulder 
  2. 2.Boulder Language Technologies 

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