This Writing Activity with Writing Analytics (WAWA) was developed as part of the doctoral research conducted by Shibani Antonette, as part of the Academic Writing Analytics project at the University of Technology Sydney’s Connected Intelligence Centre.
Academic writing is a key skill required for higher education students, which is often challenging to learn. A promising approach to help students develop this skill is the use of automated tools that provide formative feedback on writing. However, such tools are not widely adopted by students unless useful for their discipline-related writing, and embedded in the curriculum. This recognition motivates an increased emphasis in the field on aligning learning analytics applications with learning design, so that analytics-driven feedback is congruent with the pedagogy and assessment regime. The current design shows the implementation of a pedagogic intervention that was developed for law students to make use of the AcaWriter tool for improving their academic writing. By developing an effective learning analytics intervention design for writing instruction, I exemplify the integration of writing analytics technology into the classroom in authentic contexts. To augment automated feedback with human insights and improve the learning design, I’m also studying the impact of an additional peer discussion component in the design. The design can be implemented using a learning analytics tool which is developed to facilitate the intervention and provide analytic capabilities by collecting learner data. The validated design could be potentially transferrable across different writing instruction contexts with the development of standardized abstractions.
