Human-centered Approaches to Data-informed Feedback

Abstract

Learning analytics seeks to support and enhance learning through data-informed feedback practices. As learning analytics emphasizes an iterative loop from learner to data, metrics, and interventions, it is imperative that both teachers and learners play active roles in this process and contribute to the design and evaluation of enabling technologies. A key question that concerns us is: How can learning analytics tools enhance learners’ agency in the feedback process? We argue that the design and deployment of learning analytics need to recognize feedback as a dialogic process. In doing so, we emphasize that effective feedback is not just about providing information relevant to learning, but also about the practices of the people who carry out evaluations and produce or interpret information based on such evaluations. A human-centered approach is thus critical to the effectiveness of data-informed feedback. In this chapter we discuss key elements of feedback, current approaches to data-informed feedback and associated challenges; and propose a human-centered approach which facilitates collaborative learning and continuous learning among a network of actors and highlights the importance of developing data-informed feedback literacy among learners.