We’ve all got used to the idea that computers can understand writing and speech to some degree — Google adverts that match your search queries… asking Siri simple questions… IBM Watson winning Jeopardy! But how does natural language processing fit into learning that goes beyond getting the right answer to a focused question, or matching some key concepts?
Language is clearly front and centre in the way that we learn from others, share our understanding, and narrate to ourselves. However, the idea that computers have any substantive contribution to make to the teaching and assessment of writing elicits strong reactions from educators, and understandably so (learn more from this workshop).
In recent work that we’ve been doing at University of Technology Sydney, we’re exploring to what extent text analytics could support the integrative practices of writing that many are now using to help students reflect on their learning. This is particularly germane to future-oriented pedagogy that seeks to help the learner narrate their own learning journey in relation to their identity (how does my learning change who I think I am becoming?), place them in authentic (often professional) learning contexts (on the ward; in the classroom; in a company), and so forth.
As we explain in this article, there are many pedagogical reasons for valuing deep, academic reflective writing, but it poses significant challenges to educators to teach this well, and to students for whom this is often a new genre of writing. Below is the presentation we gave earlier this year. Current the Academic Writing Analytics (AWA) web tool is available only within UTS as we pilot it, but in the future this may become available to other universities, and beyond that, to schools interested in collaborative research.
Buckingham Shum, S., Á. Sándor, R. Goldsmith, X. Wang, R. Bass and M. McWilliams (2016). Reflecting on Reflective Writing Analytics: Assessment Challenges and Iterative Evaluation of a Prototype Tool. 6th International Learning Analytics & Knowledge Conference (LAK16), Edinburgh, UK, April 25 – 29 2016, ACM, New York, NY. http://dx.doi.org/10.1145/2883851.2883955 Preprint: http://bit.ly/LAK16paper