Multimodal Analysis of Spatial Characteristics of a Real-world Learning Field

Real-world learning is important because it encourages learners to obtain knowledge through various experiences. To increase the learning effects, it is necessary to analyze the diverse learning activities that occur in real-world learning and to develop workable strategies for learning support. Our viewpoint is that a real-world learning field is the key to promoting diverse learning interactions. Using the technologies of multimodal sensing and knowledge externalization, we propose a method to capture the time-series occurrence of real-world learning and to analyze the spatial characteristics of a learning field that draws out diverse intellectual interactions. Our data analysis found that each region in a learning field draws out different real-world learning. The analysis also showed that real-world knowledge is ubiquitously but unevenly distributed. Our method contributes toward discovering knowledge useful for learning support.