|Title||Natural Language Processing-informed learning analytics for tracking and measuring aspects of argumentation|
|Year 1: Project Year||Year 1|
|Year 1: Funding Year||2016/2017|
|Year 1: Project Type||Small TLEF|
|Year 1: Principal Investigator||Noureddine Elouazizi|
|Year 1: Funded Amount||49,984|
|Year 1: Team Members||
Gülnur Birol, Director, Science Centre for Learning and Teaching / Skylight, Faculty of Science
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.