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The early stages of the internet and world wide web drew attention to the communication and connective capacities of global networks. The ability to collaborate and interact with colleagues from around the world provided academics with new models of teaching and learning. Today, online education is a fast growing segment of the education sector. A side effect, to date not well explored, of digital learning is the collection of data and analytics in order to understand and inform teaching and learning. As learners engage in online or mobile learning, data trails are created. These data trails indicate social networks, learning dispositions, and how different learners come to understand core course concepts. Aggregate and large-scale data can also provide predictive value about the types of learning patterns and activity that might indicate risk of failure or drop out.

The Society for Learning Analytics Research defines learning analytics as 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 (http://www. solaresearch. org/mission/about/). As numerous papers in this issue reference, data analytics has drawn the attention of academics and academic leaders. High expectations exist for learning analytics to provide new insights into educational practices and ways to improve teaching, learning, and decision-making. The appropriateness of these expectations is the subject of researchers in the young but rapidly growing learning analytics field.