Abstract
This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.
Highlights
Imagine an intelligent entity with whom a learner can discuss definitions of core concepts in a learning domain, moving from checking back whether the learner’s memory of concepts and abstract understanding of concepts is correct, toward discussing increasingly complex application concepts
4.6.1 Claim We report on features that were used as input to the classifiers that aimed to detect the existence of a claim in user response
We find that Toulmin’s model of arguments works very well: With a comparatively small dataset, we were able to develop reasonably accurate classifiers (see How Well Can Components of Toulmin’s Model of Argument Be Identified in the Given Domain? (RQ2)) that are useful within a conditional dialogue structure to decide between branches (RQ2)
Summary
Imagine an intelligent entity with whom a learner can discuss definitions of core concepts in a learning domain, moving from checking back whether the learner’s memory of concepts and abstract understanding of concepts is correct, toward discussing increasingly complex application concepts. Bloom’s taxonomy is a hierarchical categorization of educational goals It proposes to describe in which different ways one can know and learn about a learning subject. This makes it suitable to design an intelligent tutor, in the sense of providing the intelligent tutor with a didactical structure along which to proceed In this taxonomy, remembering, understanding, and applying are proposed as the first three types of learning with respect to knowledge that should be learned. Based on Toulmin’s conceptual schema, an argument can be broken into six different components: a claim, evidence/data/observation/ fact/ground, a warrant, qualifiers, rebuttal, and backing. Toulmin’s components contain six different parts but based on the model, the main components are the claim, warrant, and fact or evidence (Toulmin, 2003). The rebuttal and backing are considered as a cover for the claim and the warrant respectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
