Writing Analytics (WA) has received considerable attention from researchers who aim to advance pedagogical practices using the application of data, tools and analytics on writing. It has evolved as a sub-domain of learning analytics supporting the study of written products and processes in educational contexts through natural language processing (NLP) and other automated text analysis methods (Buckingham Shum et al., 2016). Five previous WA workshops at the Learning Analytics and Knowledge conference have contributed to the growth in the field by defining key areas for attention. These include: An introduction to writing analytics with critical perspectives and community building in LAK (Buckingham Shum et al., 2016), building writing analytics literacy and practitioner capacity (Knight, Allen, Gibson, McNamara, & Buckingham Shum, 2017), a hands-on-training for developing this literacy by understanding technical affordances and aligning them to pedagogical feedback using a socio-technical tool (Shibani, Abel, Gibson, & Knight, 2018), a mapping of the state of the art work in writing analytics along with defining future pathways for the field (Shibani, Liu, Rapp, & Knight, 2019), and a consolidation of writing analytics practice by bringing together writing tool design, writing analytics and writing pedagogy (Rapp, Lang, Shibani, Benetos, & Anson, 2020).

There are an increasing number of evaluations of writing analytics tools and practices to support As writing analytics grows with evaluated tools and practices that help improve academic writing (Allen, Jacovina, & McNamara, 2015; Knight et al., 2020; Liu, Li, Xu, & Liu, 2017; Woods, Adamson, Miel, & Mayfield, 2017). A distinctive feature of writing analytics is a focus on formative feedback, in contrast to automated essay scoring systems. There is thus potential for these tools to be used to support equipping students with “higher-order thinking” skills such as critical thinking, decision making, and problem solving, in addition to building their knowledge capacity (Miri, David, & Uri, 2007). These skills can facilitate the transition of students’ knowledge and skills into responsible action in society (Zoller, 2000). Writing analytics can support the development of complex skills required by learners to thrive in the changing world. While these tools may draw on a range of emerging approaches, there is currently limited research in this area.

Few notable WA applications include the development of learner metacognition using reflective writing analytics (Gibson et al., 2017), a revision assistant for argumentative writing (Zhang, Hwa, Litman, & Hashemi, 2016), knowledge transformation in argumentative writing (Raković, Winne, Marzouk, & Chang, 2021) and self-regulated learning (Winne, 2001; Wollny, Schneider, Rittberger, & Drachsler). Key skills such as critical thinking, argumentation, data and information literacy along with creativity, metacognition, self-regulation, transdisciplinary thinking and lifelong learning are essential to develop among students for their participation in society and sustainable personal, civic, and professional decision making (Buckingham Shum & Crick, 2016). The current workshop will hence focus on how writing analytics can support these higher order thinking skills. Outcomes include an understanding of the current research landscape in writing analytics with respect to higher order thinking skills, and a co-created mapping of how data, tools and analytics can further support the development of these skills in the future.

The workshop will run as an interactive half day session with mini presentations and discussions on the theme. The provisional schedule is given below:

I. Introductions of workshop organizers and participants, and a background to the focus of the workshop.

II. Short Presentations from authors of accepted abstracts on the writing analytics tool or technology, the data collected by the tool, analysis of writing data and how it can build higher order thinking among learners.

III. Discussion Blocks to discuss Work-to-date, Work-in-progress and Proposed-work on Writing analytics for target constructs. Group work to identify potential writing analytics proxies, lower order natural NLP indices and pedagogical designs that support the development of selected higher-order constructs.

IV. Open discussion facilitated among all participants summarizing activities from prior discussions and building consensus using the co-creation of shared notes and resources.

V. Concluding remarks on the workshop and community engagement among the Special Interest Group on Writing Analytics members.

Antonette Shibani, University of Technology Sydney, Australia

Dr. Shibani is a Lecturer at the Transdisciplinary School (TD School) in the University of Technology Sydney, Australia. Her research focuses on automated feedback from Writing analytics tools and its integration into the classroom for pedagogic use. She uses text analytics for analyzing writing and revision behaviors, and studies the critical interaction of writers with automated feedback. She has chaired prior Writing Analytics workshops at ALASI and LAK. She is an elected member of the Society of Learning Analytics Research (SoLAR) executive committee and co-hosts the SoLAR podcast.

Andrew Gibson, Queensland University of Technology, Australia

Dr. Andrew Gibson is a Lecturer in Information Science at Queensland University of Technology (QUT), Brisbane, Australia. Andrew’s primary research interest is in transdisciplinary understandings of fields where people and technology interact, particularly in the area of Learning Analytics. He has developed this interest theoretically in Transepistemic Abduction, a specialised mode of reasoning, and practically in Reflective Writing Analytics. Andrew’s current research is focused on Reflective Writing Analytics for informal and professional learning, particularly in high-stakes environments. Andrew has initiated open source software projects to support his research work, notably GoingOK, and TAP (Text Analytics Pipeline). He has delivered workshops and talks locally and internationally on Reflective Writing Analytics, and previously held a position of Research Fellow in Writing Analytics at the Connected Intelligence Centre, University of Technology Sydney (UTS). Andrew brings diverse industry experience to his academic work. Originally a secondary school music teacher, specialising in music technology, he has also held positions in IT management, creative production management, and management of a small tech hardware startup. Andrew holds a PhD from QUT in Information Science, a Bachelor degree in Educational Studies, Postgraduate Diploma in Information Technology, and Diploma in Teaching. He is an active member of the Society of Learning Analytics Research.

Simon Knight, University of Technology Sydney, Australia

Dr. Simon Knight is a Senior Lecturer at the TD School, University of Technology Sydney. He is the Director of the Centre for Research on Education in a Digital Society (CREDS), and was a member of its predecessor the UTS STEM Education Futures Research Centre. He is also the theme lead of the Transformative Learning research theme in the Transdisciplinary School. His research focuses on the relationship of analytics to epistemology, pedagogy and assessment, discourse analytics, and epistemic cognition, particularly around information seeking. He has chaired Writing analytics workshops in LAK16, LAK17, LAK18 and LAK19.

Philip H Winne, Simon Fraser University, Canada

Dr. Phil Winne is Distinguished SFU Professor of Education and a Fellow of the Royal Society of Canada, the American Educational Research Association, the American Psychological Association, the Association for Psychological Science, and the Canadian Psychological Association. Formerly a 2-term Tier I Canada Research Chair, he researches self-regulated learning, metacognition and learning analytics; and develops software technologies to support learners and gather big data for learning science. Author of more than 195 scholarly publications, he has been honored to receive the Robbie Case Memorial Award, the Barry J. Zimmerman Award, and the Canadian Society for the Study of Education Mentorship Award.

Diane Littman, University of Pittsburgh, USA

Dr. Diane Litman is Professor of Computer Science, Senior Scientist with the Learning Research and Development Center, and Past (Co-)Director of the Graduate Program in Intelligent Systems, all at the University of Pittsburgh. Her research focuses on natural language processing for educational technology, which has led to the development of the eRevise automated writing evaluation system for upper elementary students, the ArgRewrite revising support system for secondary and college students, and the CourseMirror student reflection mobile app also for college students. Dr. Litman is a Fellow of the Association for Computational Linguistics and was twice elected Chair of the North American Chapter of the Association for Computational Linguistics.

Submit your abstracts using the form below latest by 17 Dec 2021 AOE. We encourage authors to structure their submissions based on the rough format below:

1. Target construct/ Skill
2. Writing analytic context
3. Work-to-date
4. Questions you have

See “What do we expect” for more information.

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