Personalising Feedback

Effective and timely feedback of and for learning has been shown to be essential in influencing students’ achievement and promoting autonomy and self-regulation. While assessment practices have received considerable attention over the past two decades, the focus on feedback to students has remained relatively scarce.

Scaled and personalised feedback (using Learning Analytics) has become a sort of holy grail for educators aspiring to improve their students’ learning and their satisfaction with the learning experience.

In most cases there are three reasons why feedback fails:

1. students do not perceive feedback as feedback, or they don’t use it effectively (i.e. there are differences in how students and teachers understand feedback);
2. the feedback is not appropriate or good enough (this could be because of how feedback is provided or because the focus and purposes are not shared: i.e. providing feedback is reduced to a summative, corrective and transmissive process);
3. teachers fail to provide added value (i.e. too generic or superficial, providing a final judgement on students’ submitted assignments instead of an opportunity to reflect and improve).

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. 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), 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 for scaling up feedback processes.

Teachers need concrete approaches and support mechanisms to bridge the gap between LA research and classroom practice. This third workshop at LAK specifically focuses on educators and practitioners, their experiences and their stories in engaging with feedback and assessment practices supported by LA tools and approaches.

With two successful workshops (LAK18, LAK19 sites) delivered at LAK which explored the issues of tools able to support the scaling of personalized feedback and how these benefits students, this third workshop shifts the attention to educators and practitioners.

Who is the workshop for?

Those who wish to understand and apply principles of ‘good’ data-driven feedback 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

We expect a range of presentations that will cover practical, evidence-based approaches to personalising data-driven feedback at scale with a focus on teacher and practitioner perspectives. 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 seeking to personalise high-quality 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 a special issue of a journal or an edited book on the topic.

Some References