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All content in this area was uploaded by Antonette Shibani on Apr 23, 2018
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Content uploaded by Antonette Shibani
Author content
All content in this area was uploaded by Antonette Shibani on Apr 23, 2018
Content may be subject to copyright.
Content uploaded by Antonette Shibani
Author content
All content in this area was uploaded by Antonette Shibani on Apr 23, 2018
Content may be subject to copyright.
Content uploaded by Antonette Shibani
Author content
All content in this area was uploaded by Antonette Shibani on Apr 23, 2018
Content may be subject to copyright.
Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18)
Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0)
1
AWA-Tutor: A Platform to Ground Automated Writing
Feedback in Robust Learning Design
Antonette Shibani
University of Technology Sydney, Australia
antonette.shibani@gmail.com
Increasingly, the importance of aligning learning analytics with learning design is being understood as
a way to uphold its core aim of improving educational practices, while also collecting meaningful data
about learner’s activities that can be interpreted in context (Lockyer, Heathcote, & Dawson, 2013). In
light of this, a writing analytics tool “AWA-Tutor” has been developed that integrates analytics with
pedagogy. AWA-Tutor is a web-based tool developed as an extension of the Academic Writing
Analytics (AWA) tool that provides automated feedback on students’ writing based on rhetorical
structures in the text (Shibani, Knight, Buckingham Shum, & Ryan, 2017).
AWA-Tutor extends AWA, by scaffolding an entire writing improvement activity. Students are guided
through a series of tasks, such as understanding the instructor’s rubric, improving a sample text,
reviewing exemplar improvements, self-assessing their work, and reflecting on the quality of the
automated feedback. The tool is designed in a modular fashion to support the learning design of an
instructor, who can select the task components to be included, and personalize the feedback
experience for different students. AWA-Tutor captures detailed activity traces: the time taken by
students to complete certain tasks of the activity, snapshots of drafts at customizable time intervals,
students’ requests for automated feedback and the feedback received, and feedback survey
responses. Thus, the process of drafting and revising, which were previously hard to study, are now
reconstructable for subsequent analysis in other tools such as R, for which a suite of analyses have
been developed.
AWA-Tutor has been evaluated with undergraduate law students in authentic classroom settings,
tackling tasks co-designed with the Law academic, performing the activity individually or in pairs. Both
student and instructor feedback have been positive regarding the usage of this tool over the two
semesters the intervention was run, although there is certainly scope for improvement.
Demonstration movie: https://www.youtube.com/watch?v=K212XabCL5w&feature=youtu.be
Keywords: AWA-Tutor, writing analytics, tool, learning design, pedagogy integration
REFERENCES
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning
analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459.
Shibani, A., Knight, S., Buckingham Shum, S., & Ryan, P. (2017). Design and Implementation of a
Pedagogic Intervention Using Writing Analytics. Paper presented at the 25th International
Conference on Computers in Education, New Zealand.