Inferences about student knowledge, skills, and attributes based on digital activity still largely come from whether students ultimately get a correct result or not. However, the ability to collect activity stream data as individuals interact with digital environments provides information about students’ processes as they progress through learning activities. These data have the potential to yield information about student cognition if methods can be developed to identify and aggregate evidence from diverse data sources. This work demonstrates how data from multiple carefully designed activities aligned to a learning progression can be used to support inferences about students’ levels of understanding of the geometric measurement of area. The article demonstrates evidence identification and aggregation of activity stream data from two different digital activities, responses to traditional assessment items, and ratings based on observation of in-person non-digital activity aligned to a common learning progression using a Bayesian Network approach.
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