2006
DOI: 10.1007/11774303_23
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Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques

Abstract: This paper presents an approach for intelligent tutoring in the field of legal argumentation. In this approach, students study transcripts of US Supreme Court oral argument and create a graphical representation of argument flow as tests offered by attorneys being challenged by hypotheticals posed by Justices. The proposed system, which is based on the collaborative modeling framework Cool Modes, is capable of detecting three types of weaknesses in arguments; when it does, it presents the student with a self ex… Show more

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Cited by 36 publications
(40 citation statements)
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“…That is why Baker (2009) argues that the point of CABLe is not necessarily changing learners' conceptions or beliefs, but rather broadening and deepening their views and making them more reasoned and reasonable, which will enable them to understand each other's point of view. This is an important distinction, because when learners perceive argumentation as competitive, it is likely that they will merely engage in what Asterhan and Schwarz (2009) call a Bdebate-type win-lose situation^as in law (see Pinkwart et al 2006Pinkwart et al , 2007, in which they try to refute their opponents' views and prove the superiority of their own arguments. This is more or less similar to a situation where argumentation merely serves as a means for persuasion or eristic argumentation (Bfighting^).…”
Section: Collaborative Argumentation-based Learningmentioning
confidence: 99%
“…That is why Baker (2009) argues that the point of CABLe is not necessarily changing learners' conceptions or beliefs, but rather broadening and deepening their views and making them more reasoned and reasonable, which will enable them to understand each other's point of view. This is an important distinction, because when learners perceive argumentation as competitive, it is likely that they will merely engage in what Asterhan and Schwarz (2009) call a Bdebate-type win-lose situation^as in law (see Pinkwart et al 2006Pinkwart et al , 2007, in which they try to refute their opponents' views and prove the superiority of their own arguments. This is more or less similar to a situation where argumentation merely serves as a means for persuasion or eristic argumentation (Bfighting^).…”
Section: Collaborative Argumentation-based Learningmentioning
confidence: 99%
“…Instead of evaluating the content of a text, students may be asked to represent their reasoning in a graphical form, and evaluation of the produced graphical representation of the text can be made. This approach was taken in several ITSs to evaluate students' scientific reasoning, such as the Belvedere system [98], and legal arguments, such as the Largo system [73]. The analysis of graphs is more amenable to machine processing than the analysis of arguments represented in textual form, which allows more reliable diagnosis of students' knowledge.…”
Section: Evaluating Students' Answers In a Textual Formmentioning
confidence: 99%
“…The student has identified two tests in the argument transcript, one a modification of the other, three hypotheticals posed, and a number of relations among them that (in this student's diagram somewhat imperfectly) reflect the role the hypotheticals play in evaluating the tests. LARGO helps students to find, diagram and relate the important elements of the text by providing hints based on small specific argument patterns [10].…”
Section: Arguing With Tests and Hypotheticalsmentioning
confidence: 99%
“…Presenting corrective feedback immediately after they make a mistake (as done by many successful ITS systems) would be problematic in the ill-defined domain of legal argumentation. As described in [10], LARGO's on-demand feedback avoids false error messages that are likely to occur in this domain, where it is often not clear whether a diagram correctly reflects an argument or not. False or inappropriate feedback would be very problematic also because the feedback LARGO gives is cognitively demanding (selfexplanation prompts).…”
Section: Engagement With the Systemmentioning
confidence: 99%
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