The research reported in this abstract investigates the ‘readiness’ of learning analytics to assist academics in addressing uncertainty and complexity by analysing the contributions to the Journal of Learning Analytics (JLA), founded by SOLAR, the Society for Learning Analytics Research. The first two JLA editions were published in 2014, comprising of twelve research articles plus editorials. Looking at research reported in JLA as state of the art, readiness is examined from the perspective of an academic who wants to use learning analytics to support student learning.
Three of the articles in JLA examine the links of learning analytics to theories around personality traits and student-centred factors (Gray, 2014), privacy (Heath, 2014) and epistemology, assessment and pedagogy (Knight & Buckingham Schum, 2014). These articles make strong contributions towards grounding learning analytics in theory, but do not intend to provide approaches the academic could apply directly.
Eight articles report on concrete studies that have used and evaluated learning analytics, predominantly in higher education settings (one article focuses on academic analytics and is not considered here). All of these studies were judged to have limited conceptual readiness. Problems identified were: analysis of only a narrow stream of data without taking course context into consideration; focus on behavioural aspects while neglecting learning quality; small or unrepresentative samples; weak insights on how the analytics collected could be used to assist student learning. Several of the studies applied sophisticated statistical or data mining techniques for analysis and visualisation. While these techniques appear to be ready, they are far too complicated for the non-specialist academic. Other studies relied on specialist teaching tools, e.g. discussion tools that are not part of standard learning management systems.
The conclusion drawn from this review is that learning analytics as a field is not yet ready to make a real impact on learning in higher education. Strong work on theoretical foundations is emerging and sophisticated analysis techniques are being applied to learning analytics data. Yet, at this stage there do not seem to be approaches and tools an academic could readily use in facilitating their students’ learning.
Addressing the theme/s of the Conference
Learning analytics is one of the current hot topics in higher education. Learning analytics attempts to find solutions to challenges posed by uncertainties and complexities in higher education, related for example to student diversity and budget pressures. As stated by prominent learning analytics researchers (Long & Siemens, 2011, p 40):
“Finally, administrators and decision-makers are today confronted with tremendous uncertainty in the face of budget cuts and global competition in higher education. Learning analytics can penetrate the fog of uncertainty around how to allocate resources, develop competitive advantages, and most important, improve the quality and value of the learning experience.”
The research reported in this abstract makes a contribution to the field of learning analytics by examining how ready the field is to address the uncertainties and complexities inherent in higher education.