Multimodal Method to Understand Real-world Learning Driven by Internal Strategies

ID: 49424 Type: Full Paper: Conceptual & Empirical Study
  1. Masaya Okada, College of Informatics, Academic Institute, Shizuoka University, Japan
  2. Yasutaka Kuroki, Faculty of Informatics, Shizuoka University, Japan
  3. Masahiro Tada, Faculty of Science and Engineering, Kindai University, Japan

Thursday, June 30 10:00-10:30 AM

Presider:
Kurt Ackermann, Hokusei Gakuen University Junior College, Japan

Education is traditionally designed and practiced in classrooms, but recent research has shown the importance of real-world learning for autonomously searching and obtaining knowledge in and from the world. This paper considers the characteristics of knowledge space that are self-organized inside a learner through bottom-up real-world behavior, and then models a process in which a learner's strategies activate and drive inquiry behavior to achieve knowledge in and from the world. By improving our technologies for multimodal knowledge sensing, this research proposes analytics to code and understand the time-series of learning strategies occurring through learner-environment interactions. As an initial and important step in the research, we analyze learners' possible strategies in the world to find strategies related to the production of inquiry behavior.

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