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Processing complex information during learning can trigger related epistemic emotions, such as confusion, frustration, or boredom. These emotions are likely to impact learning and it is crucial to provide learners with relevant support according to the emotions they experience. Being aware of epistemic emotions may help teachers design adaptive learning environments that help learners control their levels and durations. Within interactive digital learning environments, students are also likely to experience epistemic emotions but their detection by a teacher can be problematic because students and teachers are not co-located. However, the trace data that students produce while interacting with digital environments can be themselves useful indicators of some epistemic emotions. These data require digital learning environments offering sufficiently rich interactive features and technically capable of collecting interaction data, although these features must be primarily designed to foster high quality learning. Moreover, the uniqueness of each learning situation imposes that interpretations of observed interaction patterns to epistemic emotions do need to be determined individually for each situation.

Building on an emerging body of literature on the use of learning analytics by educators, this chapter will explore the Open University (OU) approach to learning design that is currently being implemented on a large-scale. We aim to explain how this approach can be used to generate, visualise and interpret learning analytics that can be used by the educator to create learning design visualisations and data sets. Through comparison and analysis these can inform future design decisions. The basis for this OU approach can be found in the work of Conole (2012), whereby collaborative design teams use the Activity Type Classification Taxonomy to answer questions such as: What will students do in this module? How much will they be reading? What practical activities will they do? And, what does a ‘good’ learning design profile look like? The use of this taxonomy helps to establish a common language with which teachers can compare their teaching and learning practice with other teachers, schools and cluster groups. Where teachers are teaching from the same curriculum this data set, when visualised and analysed, can provide insight into the student learning experience. The approach allows for the measuring of what the student is doing, giving a different and unique view from those that only measure students by their academic ability. At the end of each section there is a short discussion entitled In the Classroom which suggests ways teachers in a classroom setting could adapt the approach for their circumstances.

Building on an emerging body of literature on the use of learning analytics by educators, this chapter will explore the Open University (OU) approach to learning design that is currently being implemented on a large-scale. We aim to explain how this approach can be used to generate, visualise and interpret learning analytics that can be used by the educator to create learning design visualisations and data sets. Through comparison and analysis these can inform future design decisions. The basis for this OU approach can be found in the work of Conole (2012), whereby collaborative design teams use the Activity Type Classification Taxonomy to answer questions such as: What will students do in this module? How much will they be reading? What practical activities will they do? And, what does a ‘good’ learning design profile look like? The use of this taxonomy helps to establish a common language with which teachers can compare their teaching and learning practice with other teachers, schools and cluster groups. Where teachers are teaching from the same curriculum this data set, when visualised and analysed, can provide insight into the student learning experience. The approach allows for the measuring of what the student is doing, giving a different and unique view from those that only measure students by their academic ability. At the end of each section there is a short discussion entitled In the Classroom which suggests ways teachers in a classroom setting could adapt the approach for their circumstances.