Acquisition and internalisation of many fundamental skills rely on repeated authentic practice and teachers providing support during practice. Despite this well accepted norms in skills acquisition, much of our assumptions about learning skills, mostly from a cognitive perspective, remain nebulous. Besides splitting hairs to classify skills acquisition into a paradigm, much of findings of related research from educational science and psychology have struggled to transfer into the domain of skills acquisition. Instead, in this paper, we propose to shift our view of skills acquisition from a cognitive approach to an embodied one with the help of multimodal technologies and provide a use-case which combines deliberate practice framework, embodied cognition principles, and multimodal learning.@en

The chapter highlights the role of sensors for supporting seamless learning experiences. In the first part, the relation between sensor tracking of learning activities and research around real-time feedback in educational situations is introduced. The authors present an overview of the kinds of sensor data that have been used for educational purposes in the literature. Secondly, the authors introduce the link between sensor data and educational interventions, and especially the role of building expert models from real-world expert tracking. The third part of the paper illustrates how educational AR applications have used sensor data for different forms of learning support. The authors present 15 design patterns that have been implemented in different educational AR applications that build on our analysis of sensor tracking. For future AR applications, the authors propose that the use of sensors for building expert performance models is essential for a variety of educational interventions.

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