Learning analytics

Learning analytics is 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. A related field is educational data mining. For general audience introductions, see:

The definition and aims of Learning Analytics are contested. One earlier definition discussed by the community suggested that "Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning."

But this definition has been criticised:

A more holistic view than a mere definition is provided by the framework of learning analytics by Greller and Drachsler (2012). It uses a general morphological analysis (GMA) to divide the domain into six "critical dimensions".

A systematic overview on learning analytics and its key concepts is provided by Chatti et al. (2012) and Chatti et al. (2014) through a reference model for learning analytics based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?).

It has been pointed out that there is a broad awareness of analytics across educational institutions for various stakeholders, but that the way 'learning analytics' is defined and implemented may vary, including:

In that briefing paper, Powell and MacNeill go on to point out that some motivations and implementations of analytics may come into conflict with others, for example highlighting potential conflict between analytics for individual learners and organisational stakeholders.

Gašević, Dawson, and Siemens argue that computational aspects of learning analytics need to be linked with the existing educational research if the field of learning analytics is to deliver to its promise to understand and optimize learning.

Differentiating the fields of educational data mining (EDM) and learning analytics (LA) has been a concern of several researchers. George Siemens takes the position that educational data mining encompasses both learning analytics and academic analytics, the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty. Baepler and Murdoch define academic analytics as an area that "...combines select institutional data, statistical analysis, and predictive modeling to create intelligence upon which learners, instructors, or administrators can change academic behavior". They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks questions whether this distinction exists in the literature. Brooks instead proposes that a better distinction between the EDM and LA communities is in the roots of where each community originated, with authorship at the EDM community being dominated by researchers coming from intelligent tutoring paradigms, and learning anaytics researchers being more focused on enterprise learning systems (e.g. learning content management systems).

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