How can we best align learning analytics practices with disciplinary knowledge practices in order to support student learning? Although learning analytics itself is an interdisciplinary field, it tends to take a ˜one-size-fits-all' approach to the collection, measurement, and reporting of data, overlooking disciplinary knowledge practices. In line with a recent trend in higher education research, this paper considers the contribution of a realist sociology of education to the field of learning analytics, drawing on findings from recent student focus groups at an Australian university. It examines what learners say about their data needs with reference to organizing principles underlying knowledge practices within their disciplines. The key contribution of this paper is a framework that could be used as the basis for aligning the provision and/or use of data in relation to curriculum, pedagogy, and assessment with disciplinary knowledge practices. The framework extends recent research in Legitimation Code Theory, which understands disciplinary differences in terms of the principles that underpin knowledge-building. The preliminary analysis presented here both provides a tool for ensuring a fit between learning analytics practices and disciplinary practices and standards for achievement, and signals disciplinarity as an important consideration in learning analytics practices.
: Jen McPherson, Huong Ly Tong, Scott J. Fatt and Danny Y.T. Liu (2016). "Student perspectives on data provision and use: Starting to unpack disciplinary differences". In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York.