Argumentation Scheme-Based Argument Generation to Support Feedback in Educational Argument Modeling Systems
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Argumentation Scheme-Based Argument Generation to Support Feedback in Educational Argument Modeling Systems Nancy L. Green 1
# International Artificial Intelligence in Education Society 2016
Abstract This paper describes an educational argument modeling system, GAIL (Genetics Argumentation Inquiry Learning). Using GAIL’s graphical interface, learners can select from possible argument content elements (hypotheses, data, etc.) displayed on the screen with which to construct argument diagrams. Unlike previous systems, GAIL uses domain-independent argumentation schemes to generate expert arguments as a knowledge source. By comparing the learner’s argument diagram to a generated argument, GAIL can provide problem-specific feedback on both the structure and meaning of the learner’s argument, e.g., that the learner’s argument contains an irrelevant premise. To generate arguments, the argumentation schemes are instantiated from causal domain models specified by lesson authors. Thus, this approach to generating expert arguments has the potential to be used in other domains. In this paper we describe use of GAIL’s Authoring Tool to create the domain model and content elements to be provided for a specific lesson, how expert arguments are generated in GAIL, and how the feedback is produced. As GAIL is a work-in-progress, the paper also describes plans for the next design iteration. Keywords Educational argument modeling systems . Formative feedback . Argumentation schemes . Critical questions
Introduction There has been significant interest within the field of science education in argumentation (e.g., Bell and Linn 2000; Bricker and Bell 2008; Jiminéz-Aleixandre et al. 2000;
* Nancy L. Green [email protected]
1
Department of Computer Science, University of North Carolina Greensboro, Greensboro, NC 27402, USA
Int J Artif Intell Educ
Sampson and Clark 2008; Sandoval and Reiser 2004; Toth et al. 2002; Zohar and Nemet 2002). According to Bricker and Bell (2008), argumentation should be a Bcore component of school science^; it can be used to help students learn science content and to help them better understand the nature of the scientific enterprise, scientific discourse, and scientific knowledge. Chinn (2006) contends that Blearning to argue well^ may enhance content learning, interest and motivation, and problem-solving and writing. However, learning argumentation skills poses significant challenges. Without guidance in how to Bargue well,^ student-produced arguments have been shown to be deficient in a number of ways, e.g., lacking support for claims (Bell and Linn 2000; Jiminéz-Aleixandre et al. 2000), failing to provide alternative explanations (Lawson 2003; Schwarz et al. 2003), and using inaccurate or irrelevant support (Zohar and Nemet 2002). To begin to address this problem we have implemented a prototype Genetics Argumentation Inquiry Learning (GAIL) system. GAIL supports learning to argue about cases in human genetics, a field that applies findings from genetics research to biomedical reasoning. GAIL is designe
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