An overview of learning analytics

Abstract

Learning analytics, the analysis and representation of data about learners in order to improve learning, is a new lens through which teachers can understand education. It is rooted in the dramatic increase in the quantity of data about learners, and linked to management approaches that focus on quantitative metrics, which are sometimes antithetical to an educational sense of teaching.However, learning analytics offers new routes for teachers to understand their students, and hence to make effective use of their limited resources. This paper explores these issues, and describes a series of examples of learning analytics to illustrate the potential. It argues that teachers can and should engage with learning analytics as a way of influencing the metrics agenda towards richer conceptions of learning, and to improve their teaching. Keywords: learning analytics; analytics; metrics IntroductionThere is a tension between the framing of education as an economic activity and conceptions of education and learning that are concerned with the development of meaning and the transformation of understanding. These difficulties are far from purely theoretical concerns: they increasingly have very practical, concrete consequences for teachers and learners, notably around resource constraints, class sizes, and time pressures. Within this constrained environment, teachers are subject to accountability processes based on and enabled by the deployment of quantitative metrics of their practices.Quantitative metrics are increasingly used not only because of theoretical framings that support them, but also because of a substantial and dramatic change in their practicability over the last ten or twenty years. This change is often referred to as Big Data: the quantity, range and scale of data that can be and is gathered has increased exponentially (or close to exponentially). Accompanying this explosion of data is a 3 series of rapid advances in computational techniques for managing, processing and analysing these large volumes of data in ways that are actionable. These developments are transforming enquiry. The scale of data is greatest in science -for instance, the Large Hadron Collider at CERN produced 23 petabytes (23 million gigabytes) of information in 2011 (CERN, 2012). The effect is not restricted to science -for instance, the ability to manage and integrate textual and geographic data is changing scholarly practice in the classics (see e.g. Project HESTIA: the Herodotus Encoded Space-TextImaging Archive, http://www.open.ac.uk/Arts/hestia/index.html). New approaches become possible: for instance, rather than sampling, an entire population can be captured. The volume and scope of data can be so large that it is possible to start with a dataset and apply computational methods to produce results, and only subsequently to seek an interpretation or meaning.Big data is by no means restricted to the academy. Technology companies such as Google and Facebook make managing staggeringly large datasets their core business, but even companies...

Mixing and Matching Learning Design and Learning Analytics

“…Substantial progress has been made both in conceptual development [5,6] as well as how to design appropriate predictive learning analytics to support students [7,8]. Nonetheless, in line with [7,9], findings from LA research in the past have been rather limited to delivering actionable feedback, while ignoring the context in which the learning data is situated.…”

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

“…Clow (2013, p. 692) argues that "as a field, learning analytics is data-driven and is often atheoretical, or more precisely, is not explicit about its theoretical basis". Although several researchers have worked to link learning analytics to pedagogical theory (Clow, 2013;Dawson, 2008;Macfadyen & Dawson, 2010;Suthers, Vatrapu, Medina, Joseph, & Dwyer, 2008), this is still the exception, rather than the rule. However, Macfadyen and Dawson (2010, p. 597) note that "knowledge of actual course design and instructor intentions is critical in determining which variables can meaningfully represent student effort or activity, and which should be excluded".…”

“…The same (individual and aggregated) information is also available for the tutors in the form of visual dashboards (Clow, 2013;González-Torres et al, 2013;Verbert et al, 2012). Although the primary responsibility for directing the learning process is with the student, the tutor acts complementary to that selfsteering, especially in situations where the tutor considers that a more intense use of e-tutorials is desirable, given the position of the student concerned.…”

Perspectives for Defining Student Teacher Performance-Based Teaching Skills Indicators to Provide Formative Feedback through Learning Analytics

“…Clow, 2013;Ferguson, 2012). Applying LA calls for quantified and valid entry data on the progress of learning in order to provide analytical feedback via statistical modeling.…”