Learning analytics (LA) describes 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’ (Siemens & Gašević, 2012). Originally, ‘analytic’ referred to a way of using data to support decision-making and understanding a domain. Essential LA components are (1) data, (2) goals or (research) questions, optionally based on educational theory, (3) measures that give information about goal attainment or (research) construct, optionally, (4) descriptive or predictive models that use these values as variables and (5) computing models and routines that compute these measures’ values, modelling results from the given data. LA systems also comprise (6) automatic or semi-automatic ways of reporting these results to the chosen stakeholders. Optionally, (7) the results can be deployed within some application functionality. Examples of (1) and (3) are ‘clickstream’ data1 used to measure learner behaviour and knowledge, or text data underpinning domain models. The goal (2) could be to depict collaboration between learners. The descriptive or predictive models (4) may comprise learner profiles or models for predicting whether a learner is ‘at risk of dropping out’. The computing models that determine these measures (5) range from simple counts via clustering techniques to classifier learning.2 The models may be purely statistical (correlating measured variables) or refer to theory (which would, for example, explain why someone with certain behaviour is at risk of dropping out and what the behaviour and the risk have to do with learning). A typical choice for (6) is dashboards, when the results are reported to teachers or information is given as feedback to learners. An example of (7) is the use of learner models to offer users personalised learning resources that are assumed useful for an individual’s learning. For a related model of components, see Greller and Drachsler, 2012.