Real-world learning to inquire real-world phenomena is important, but it is not known how a learner's individual or group-level of behavior activates collaborative scientific inquiries. This study proposes real-world oriented learning analytics to express and estimate the semantics of real-world behavior by modeling an observation method that measures behavior as data, and extracts the semantics of the behavior. This analysis is based on our computational model to hierarchically abstract multimodal sensing data. By the practice of our real-world jigsaw method, we found that real-world spatial behavior plays a role in learning activities. For example, we found when learners repeatedly gathered and distributed in the world, they made active scientific inquiries. We also found that the activeness of learners' scientific inquiries can be estimated by accurately sensing and estimating spatiotemporal features of learners' collaborative observation.
|Translated title of the contribution||Estimating Spatiotemporal Features of Collaborative Observation to Activate Scientific Inquiry in the World|
|Number of pages||17|
|Publication status||Published - Apr 15 2020|