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Towards Automated Analysis of Student Arguments

This paper presents the approach to automated analysis of student argument diagrams to be used in the Genetics Argumentation Inquiry Learning (GAIL) system. Student arguments are compared to expert arguments automatically generated using an existing argument generator developed previously for the GenIE Assistant project. A prototype argument analyzer was implemented for GAIL. Weaknesses in student arguments are identified using non-domain-specific, non-content-specific rules that recognize common error types.

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1. University of North Carolina Greensboro, Greensboro, NC, 27402, USA

   Nancy L. Green

1. Institute for Creative Technologies, University of Southern California, 12015 Waterfront Dr., 90094, Playa Vista, CA, USA

   H. Chad Lane

2. School of Information Technologies, University of Sydney, 2006, Sydney, NSW, Australia

   Kalina Yacef

3. School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA

   Jack Mostow

4. Department of Psychology, University of Memphis, 38152, Memphis, TN, USA

   Philip Pavlik

© 2013 Springer-Verlag Berlin Heidelberg

Green, N.L. (2013). Towards Automated Analysis of Student Arguments. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_66

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• DOI: https://doi.org/10.1007/978-3-642-39112-5_66

• Publisher Name: Springer, Berlin, Heidelberg

• Print ISBN: 978-3-642-39111-8

• Online ISBN: 978-3-642-39112-5

• eBook Packages: Computer ScienceComputer Science (R0)

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