Defining Learning Analytics
Learning Analytics in a Nutshell, by Yi-Shan Tsai, University of Edinburgh, for the Society for Learning Analytics Research
Over the past decade, significant growth has taken place in the use of aggregate data (sometimes called “big data”) to inform our understanding of effective learning behaviors and teaching practices. Early work in the area tended to focus on making arguments about how data could inform efforts to improve teaching and learning in disciplines across the university and to lay out a framework for understanding both benefits and potential harms associated with the use of learning analytics data (Daniel, 2014; Fournier, Kop, & Sitlia, 2011; Retalis et al., 2006; Siemens & Long, 2011; Viberga et al., 2018).
More recent work has taken up additional areas of inquiry, including ethics and privacy (Cormack, 2016; Gursoy et al., 2017; Pardo & Siemens, 2014), interventions intended to enhance student learning and success (Drachsler & Greller, 2016; Macfadyen et al., 2014), and the potential use of “nudges” and other automated communications to support self-regulated learning (Howell, Roberts, & Mancini, 2018; Pilgrim, Folkestad, & Sencindiver, 2017).
The emergence of learning analytics as a field of study has supported the exploration of learning behaviors and teaching practices that employ a wide range of data gathering and analytical methods. It has also given rise to the formation of a professional organization, the Society for Learning Analytics Research (SoLAR). On its website , SoLAR defines learning analytics as:
the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs . . . . Learning analytics is both an academic field and commercial marketplace which have taken rapid shape over the last decade. As a research and teaching field, Learning Analytics sits at the convergence of Learning (e.g. educational research, learning and assessment sciences, educational technology), Analytics (e.g. statistics, visualization, computer/data sciences, artificial intelligence), and Human-Centered Design (e.g. usability, participatory design, sociotechnical systems thinking). (https://www.solaresearch.org/about/what-is-learning-analytics/)
At CSU, efforts to explore the use of learning analytics tools and data date at least to 2012. Early discussions about learning analytics led to the formation of the Center for the Analytics of Learning and Teaching (C-ALT) in 2014 and later to the establishment of the university’s Learning Analytics Steering Committee. In 2018, a subcommittee of CSU’s Faculty Council Committee on Teaching and Learning, issued the report Ethical Principles of Learning Analytics at Colorado State University. In 2019, a task force charged by Provost Rick Miranda and VP for Information Technology and Dean of the Libraries Patrick Burns completed work on recommendations for the use of learning analytics at CSU and issued its report.