Abstract
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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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
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