Personalising Feedback

The provision of 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.

The provision of feedback at scale and the personalisation of 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.

However, students and educators do not hold the same perception of what constitutes quality feedback. In most cases, the idea of providing feedback is reduced to a summative, corrective and transmissive process, which gives a final judgement on students’ submitted assignments.

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 to provide feedback at scale.

While the first workshop with this topic at LAK’18 focused predominantly on tools and their applications, this second workshop shifts the attention to data-driven approaches supporting the provision of feedback, and we encourage submissions to pay more attention to the students, especially considering how they perceive the feedback and what they do with the feedback.

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