An alternative of classroom learning is situated learning by behaving in the world (e.g., environmental learning in a natural field). Among various types of human intelligence, this research is interested in understanding the process mechanism that human intelligence is formed through learner-learner and learner-environment interactions. Here, we assume that a learner’s cognition, interpretations and behavior in the world are positively or negatively affected by various levels of constraint conditions given by his/her body, cognition, and surroundings. For example, a learner may not generate a certain type of effective real-world behavior if he/she does not know basic knowledge (i.e., cognitive-level constraint). At the place where interesting objects do not exist in the world, a learner’s active inquiry will be restricted (i.e., environment-level constraint). To mine a learner’s prospective behavior for making multi-view understanding of the world, we developed technologies (1) to multidirectionally sense a learner’s behavior in the world, (2) to parameterize time-series behavior with different semantics, and (3) to extract constraint conditions hidden in the formative process of real-world learning. This research applied our analytical framework to our experiments of environmental learning with 30 participants in an experimental forest. Our initial results showed that the semantic-level data of behavior enabled us to understand the cognitive state and constraints of learners, and to find the changing points of learning situation. This illustrates our framework can be a theoretical basis to understand the mechanism of situated intelligence emerging in the world.
Koryu Nagata, Shizuoka University, Japan
Masahiro Tada, Kindai University, Japan
Masaya Okada, Kyushu University, Japan
Stream: Assessment Theories & Methodologies
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