An NLP-informed learning analytics approach for extracting and measuring aspects of argumentation
Venue: Buchanan C105C
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.
Hackathon participants, please share your files with us! :
– Manipulated data
– Results (including failed results)
Please include a readme.txt file with the following information:
– Names of group members
– Names and descriptions of the included files
– A short description of the process – what was your approach and what have you found (or did not find)? (do not spend much time on this)
This week we watched this video from Educause which had various professionals discussing why measuring learning is difficult.
Some key ideas from the video that had us talking were descriptions of the process of learning as a “black box” or “magic”. We tried to bring the discussion of measuring and studying learning into the context of learning analytics.