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.

Lucas, C., Gibson, A. and Buckingham Shum, S. (In Press). Utilization of a novel online reflective learning tool for immediate formative feedback to assist pharmacy students’ reflective writing skills. American Journal of Pharmaceutical Educationhttps://doi.org/10.5688/ajpe6800 

Knight, S., Buckingham Shum, S., Ryan, P., Sándor, Á. and Wang, X. (2018). Designing Academic Writing Analytics for Civil Law Student Self-Assessment. International Journal of Artificial Intelligence in Education, 28, (1), 1-28. DOI: https://doi.org/10.1007/s40593-016-0121-0. (Part of a Special Issue on Multidisciplinary Approaches to AI and Education for Reading and Writing – Parts 1 & 2. Guest Editors: Rebecca J. Passonneau, Danielle McNamara, Smaranda Muresan, and Dolores Perin)

Shibani, A., Knight, S., Buckingham Shum S. and Ryan, P. (2017). Design and Implementation of a Pedagogic Intervention Using Writing Analytics. In Proceedings of the 25th International Conference on Computers in Education. New Zealand: Asia-Pacific Society for Computers in Education

Gibson, A., Aitken, A., Sándor, Á., Buckingham Shum, S., Tsingos-Lucas, C. and Knight, S. (2017). Reflective Writing Analytics for Actionable Feedback. Proceedings of LAK17: 7th International Conference on Learning Analytics & Knowledge, March 13-17, 2017, Vancouver, BC, Canada. (ACM Press). DOI: http://dx.doi.org/10.1145/3027385.3027436. [Preprint] [Replay]

Buckingham Shum, S., Á. Sándor, R. Goldsmith, R. Bass and M. McWilliams (2017). Towards Reflective Writing Analytics: Rationale, Methodology and Preliminary Results. Journal of Learning Analytics, 4, (1), 58–84. http://dx.doi.org/10.18608/jla.2017.41.5

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