First international workshop on

Personalising Feedback

Monday 5 March 2018

SMC Conference & Function Centre, Sydney, Australia as part of the LAK18 conference

User-centred analytics benefits greatly from impactful practices such as the provision of effective and timely feedback of and for learning (Brown & Knight, 1994; Hattie & Timperley, 2007; Hounsell, 2003; Sadler, 1989).

The personalisation of feedback (using LA) has become a sort of holy grail for educators aspiring to improve their students’ learning and satisfaction (Bienkowski, Feng, & Means, 2012; King, Kinash, Kordyban, & Pamenter, 2014).

Although LA have made tangible connections with critical aspects that can strongly shape learning, such as learning design and self-regulation, the provision of feedback to students has been relatively neglected (Liu, Bartimote-Aufflick, Pardo, & Bridgeman, 2017; Pardo, 2017). This is despite the affordances of LA to leverage the generation of theoretical and technical mechanisms for understanding and improving learning by "informing and empowering instructors and learners" (Siemens & Baker, 2012).

To allow this to happen, teachers need concrete tools and approaches to bridge the gap between LA research and classroom practice. LA systems are starting to support teachers with means to provide rich feedback beyond typical early warning messages (e.g. SRES, Ontask, Tempelaar, Rienties, & Giesbers, 2015), but it is clear that there is a need and appetite in the LA community of research and practice to further explore data-informed student-centred pedagogies to provide feedback at scale.

Scope of the workshop

This will be a half-day workshop with mixed participation (including selected presenters and interested delegates).

This workshop brings together scholars and practitioners to explore interesting examples of effective feedback, demonstrate the use of supporting tools, and explore what and how data can be used to improve the process and richness of feedback for both learners and educators. The workshop has three primary goals:

  • Provide a multidisciplinary theoretical foundation for practitioners and researchers in LA for the effective provision of data-informed feedback practices in HE;
  • Showcase current implementations of tools and methods which enhance feedback practices, especially around personalisation;
  • Promote reflection on both pedagogical and technological approaches to improve feedback practices targeted at the improvement of student learning and their ability to self-regulate learning.

Who is the workshop for?

Those who wish to understand and apply principles of feedback of and for learning. Given the explicit multidisciplinary nature of the workshop we expect that it will provide an opportunity to discuss and share innovations, impact on learning, and explore future directions in the application of learning analytics (LA) to personalisation of feedback. Likely interested participants are:

  • Educators/teachers and researchers
  • Technologists and educational developers
  • Learning scientists and data scientists/analysts
  • Academic managers
  • and anyone else interested in personalisation of learning and teaching

Outcomes for attending participants

We expect a range of presentations that will cover practical, evidence-based approaches to personalising data-driven feedback at scale. Participants will be able to:

  • Obtain a broad perspective of different approaches to using data for personalising feedback
  • Enhance their understanding of the forms of feedback that could improve student learning
  • Gain an appreciation of the range of contexts where feedback can be valuable, and how data can inform these
  • Discuss cases, issues, and potential solutions to implementing LA-enhanced feedback practices
  • Connect with researchers and practitioners working to provide personalised feedback, yielding opportunities for collaborating on approaches and tools across attending institutions.

After the workshop, given the commitment to further collaborations, contributors will be invited to consider more substantial submissions with the intention to collate the works into either a special issue of a journal, or CEUR proceeding, or an edited book on the topic.

Some References

Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1–57.

Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112.

Hounsell, D. (2003). Student feedback, learning and development. Higher Education and the Lifecourse, 67–78.

King, C., Kinash, S., Kordyban, R., & Pamenter, J. (2014). Personalising student learning through education. Bond University. Retrieved from

Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-Driven Personalization of Student Learning Support in Higher Education. In Learning Analytics: Fundaments, Applications, and Trends (pp. 143–169). Springer, Cham.

Pardo, A. (2017). A feedback model for data-rich learning experiences. Assessment & Evaluation in Higher Education, 0(0), 1–11.

Sadler, D. R. (2010). Beyond feedback: developing student capability in complex appraisal. Assessment & Evaluation in Higher Education, 35(5), 535–550.

Siemens, G., & Baker, R. S. J. d. (2012). Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. In Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge (pp. 252–254). New York, NY, USA: ACM.

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47(Supplement C), 157–167.