Author: simon

Contextualizable learning analytics for writing support

UTS doctoral researcher Shibani Antonette gave a presentation today that will be of interest to HETA readers following our WP1 stream on text analytics for writing analysis. The key question she tackles is how we scale text analytics whilst also recognising the important contextual differences of students engaged in different kinds of writing, in different disciplines.

Here she is in action, full details with slides on her blog post…

Kickoff Workshop, 6-7 Feb 2018

The project convened for its kickoff workshop at the UTS Connected Intelligence Centre this week. Our skills matrix revealed an exciting mix of expertises, with staff spanning academic researchers, cloud architects, writing pedagogy specialists, analytics developers and senior managers from institutional analytics centres.

Two packed days saw the UTS team demo current tool capabilities, and the five university groups forming into their Work Package teams, generating a rich set of ideas and functional requirements for the three text analytics applications (Writing Analytics, Survey Analytics and Curriculum Analytics).

The tech teams immersed themselves in the Text Analytics Pipeline, and return now to spin up their own local instances.

Thanks everyone for all the hard work. This is going to be exciting!…

TAP/AWA tutorial (Sydney, 5 March)

The team will be running a half-day tutorial on 5 March in Sydney, as part of the International Conference on Learning Analytics & Knowledge.

Turning the TAP  on Writing Analytics

Organisers: Antonette Shibani, Sophie Abel, Andrew Gibson and Simon Knight

Writing analytics is seen as a potentially useful technique that uses textual features to provide formative feedback on students’ writing. However, for this feedback to be effective, it is important that it is aligned to pedagogic contexts. Such efficient integration of technology in pedagogy could be supported by developing writing analytics literacy. The proposed workshop aims to build this capacity by mapping technical constructs to a broader educational sense for pragmatic applications. It provides a hands-on experience for participants to work with text analytics and discuss its implications for writing feedback. Participants will work with a set of text analysis code to extract features and map them to writing feedback. They will also be given the opportunity to develop rules based on extracted text features to write feedback for their own pedagogic contexts.

Writing Analytics R&D

The R&D program at UTS has developed and piloted a tool to provide automated formative feedback to students on their writing. The research publications below document how we’re designing this, and what we’re learning.

In addition, the team runs regular workshops and tutorials (2016/2017/2018) bringing together some of the world’s leading researchers to reflect on the state of the art and future of automated writing analysis. Continue reading “Writing Analytics R&D”