Affiliation:
1. University of Illinois at Urbana-Champaign, USA
Abstract
A main opportunity provided by digital learning environments is the ability to not only examine the final products of learning activities (e.g., essays, test scores, final answers to problems), but also the detailed logs of how learners interact with the environment itself. Those logs of the learners' actions serve as breadcrumbs marking the path they take as they engage with the environment, providing fine-grained information about when and how they interact with specific components of its user interface. The emerging fields of learning analytics and educational data mining have taken a particular interest in studying how we can make sense of those fine-grained interactions to better inform us of digital learners' experiences and how we can provide new opportunities to better support learners as they engage with digital learning environments. This chapter discusses how those fine-grained logs can be analyzed to identify high-level behaviors, investigate their relationships with learning, and provide us with insights about how to adapt learning environments to learners' needs.
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