Event-Based Keyframing: Transforming Observation Data into Compact and Meaningful Form

Learning systems that adapt to learner capabilities, needs, and preferences have been shown to improve learning outcomes. However, creating systems that can interpret learner state within the context of a dynamic learning environment is costly and often tied to the specific requirements of the learning environment. We overview a new approach for monitoring and assessing system context and learner state that is not specific to a particular domain. The process is designed to transform diverse, continuous, and multichannel streams of heterogeneous system data into a consistent, discrete, and learner-centric interpretation of the situation. Key steps in the process include discretizing the data stream into “events” and then marking some events as “keyframes” that identify important steps or changes in the learning state. This keyframing process provides a compact representation for use by learner-adaptive processes (including assessment and tailoring) and simplifies the challenges of using machine learning as a mechanism for adaptation.