An NLP-informed learning analytics approach for extracting and measuring aspects of argumentation
Venue: Buchanan C105C
Abstract
This paper/presentation reports on a work-in-progress and shares preliminary results for an attempt to use NLP-informed learning analytics methods to extract and measure aspects of students’ argumentation while they learn how to think and argue like scientists. The approach explored in this paper caters to aspects of deep learning and detects the flow of the argumentation directly from the structure and the composition of the language that the students use in their writings. The model integrates insights from natural language processing techniques and argumentation theory in such a way that derives the metalinguistic features of argumentation directly from the linguistic units produced in students’ written language.