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". It has emerged to understand learning data, and how we could do interventions to improve the learning systems (i.e. MOOCs, LMS,...etc.)
Since learning analytics covers a wide range of analytics, Simon Buckingham Shum discussed the three levels of micro, meso, and macro of learning analytics as follows:
1. Macro-Level: enables analytics within the cross-institutional level. Macro-analytics can become more increasingly real-time and involves more fine data from the meso and micro levels. It is also important to note that at the macro level, learning analytics can be incorporated in non-educational sectors such as the government sectors.
2. Meso-Level: enables analytics at the institutional level. At the meso level, partners from the institution are involved like the faculty and the support staff. Simon Buckingham Shum stated that the business intelligence imperative to optimize processes to build better meso levels of analytics (ie. academic analytics).
3. Micro-Level: Might be the most popular level in learning analytics. it operates at the interpretation and tracking of individual/learner data. At this level, student success is strongly related where click-stream data are collected, analyzed, and translated to improve learning and increase the success of students.
According to Shum, the breadth and depth at the macro and meso levels add power to micro-analytics. Aggregation of millions of learners' interaction data creates a solid meso and macro levels. Therefore, effective learning analytics demands mutual enrichments between the three layers of analytics.