Learning analytics, as defined by the Society for Learning Analytics research (SoLAR, https://solaresearch.org/), refers to "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” Developments in education and learning technologies in recent decades mean that universities are now awash in data about learners and learning. Online teaching tools such as Learning Management Systems (e.g. UBC’s ‘Connect’ system), discussion forums, messaging and homework systems, simulations, peer feedback environments and audio/video tools used in flipped or blended courses all collect rich sets of data about learner activity, behaviour, course choices, and performance. As a result, we now have wealth of e-traces about learners, courses, and programs.
In this talk we will review the kinds of questions that learning analytics research typically seeks to address. These include descriptive questions about learner behaviours; predictive questions about anticipated outcomes; course-level questions about quality of instruction; and institutional questions about overall trends and patterns. We will give concrete examples from our work, and describe the current state of learning analytics in higher education generally, and at UBC in particular, as well as the prospects for future work. We will also investigate the relationship between learning analytics and statistics.