Unpacking the ‘Learning' in Student-Facing Analytics

Abstract

Founded on the need to help university students develop a greater academic metacognitive capacity, student-facing learning analytics are considered useful tools for making students overtly aware of their own learning processes, helping students to develop control over their learning, and subsequently supporting more effective learning. However, early research on the effectiveness of student-facing analytics is giving mixed results and is casting some doubt over the usefulness of student-facing learning analytics. One factor contributing to doubt over the value of student-facing learning analytics is that their design and implementation remains firmly rooted in the technical domain, with virtually no grounding in the knowledge base of learning and teaching. If the growing investment of resources into the development of student-facing learning analytics systems is to be fruitful, then there is an obvious, urgent need to re-position student-facing learning analytics within learning and teaching frameworks. With this in mind, we use Schraw & Dennison's model of metacognition and Vygotsky's zone of proximal development to unpack the ‘learning' in student-facing analytics and work towards an understanding of student-facing analytics that is more conducive to supporting metacognition and effective learning.