Natural Language Processing-informed learning analytics for tracking and measuring aspects of argumentation

TitleNatural Language Processing-informed learning analytics for tracking and measuring aspects of argumentation
Faculty/College/UnitScience
StatusCompleted
Duration1 Year
Initiation04/01/2016
Completion05/31/2020
Project Summary

We propose to develop Natural Language Processing (NLP)-informed Learning Analytics methods to track and measure students’ argumentation in writing intensive courses and in ways that make it easy for faculty, students and scholars to use.

We identified the need to develop these learning analytics methods based on the evaluation of the pedagogical practices for teaching and assessing the abilities of students to argue and reason like a scientist in the context of Scie113 course. (see: Birol et al., 2013; Elouazizi et al. 2015).

The anticipated outcome of this project is a functional prototype tool and documented NLP methods to be used in Scie 113 and potentially in other courses at the Faculty of Science and UBC. We have the expertise to employ the well-established Natural Language Processing methods in novel ways to serve the pedagogy underlying the learning and the teaching of argumentation skills in the context of science education.

Funding Details
Year 1: Project YearYear 1
Year 1: Funding Year2016/2017
Year 1: Project TypeSmall TLEF
Year 1: Principal InvestigatorNoureddine Elouazizi
Year 1: Funded Amount49,984
Year 1: Team Members

Noureddine Elouazizi, Strategist, Learning Technologies Ecosystem, Science Centre for Learning and Teaching (Skylight), Faculty of Science
Gülnur Birol, Director, Science Centre for Learning and Teaching / Skylight, Faculty of Science
Gunilla Öberg, SCIE 113 Director / Professor, Institute for Resources, Environment and Sustainability, Faculty of Science

Year 1: TLEF ShowcaseYear 1: TLEF Showcase