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Behavioral Informatics Lab
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A lab that uses data to interpret learning and behavior in the real world.
AboutAbout the lab
Interpreting real-world learning through data.
The Behavioral Informatics Lab aims to understand how intellectual activities, such as the learning that people do in the real world, come about.
Centered on ubiquitous multimodal sensing technology, the lab aims to develop and put into practice data science techniques that comprehensively measure information related to people's bodies, environments, and intelligence in order to understand behavior and knowledge.
The research is pursued from interdisciplinary perspectives that include not only engineering but also cognitive psychology, the learning sciences, and social survey methods.
On the lab's website, the lab is introduced under the name "Masaya Okada Lab."
Key terms
- Real-world learning
- Learning that takes place outside classrooms and laboratories, such as in natural environments, in which learners engage with the environment through their bodies.
- Multimodal sensing
- A method of measuring information about people and the environment together, using multiple sensors for position, motion, video, audio, and so on.
- Situated intelligence
- Intelligence that produces behavior adapted to the situation a person is in. The lab investigates the mechanisms by which it emerges from a computational perspective.
- Learning analytics
- A method of analyzing data about learning in order to understand how learning comes about. It is also called learning analysis methodology.
ResearchResearch themes
The lab aims to understand how intellectual activities, such as the learning that people do in the real world, come about, and, based on that understanding, to create mechanisms that allow people to act intelligently. While developing data analysis techniques that target behavior, the lab studies real-world learning, conversational settings, scientific inquiry, and more.
*Illustrative imageUnderstanding learning in the real world
This research targets learning in the real world and aims to produce next-generation environmental education using information and communication technology (ICT), together with technologies for advanced understanding of human behavior. It is basic research for improving educational methods, considering how to nurture people who can take action to solve specific problems.
*Illustrative imageThe semantic structure of conversational settings
This research aims to understand the structure of meaning (semantics) of a conversational "setting" when people tacitly share the meaning of that setting. It is basic research on what supports human intelligence.
*Illustrative imageUnderstanding intelligence that acts according to the situation
The lab investigates "situated intelligence," in which people generate behavior suited to the situation, using an approach that treats humans as robotic systems. The lab repeats a cycle of building probabilistic and mathematical models of behavior and situations, testing them, and forming new hypotheses.
*Illustrative imageSupporting scientist-like ways of learning
The lab develops a learning analytics platform that computationally models scientists as "the highest intelligence" and teaches "scientist-like ways of learning." The lab also conducts research in which learners carry into the field a "minimal ecology laboratory" built with MEMS technology.
MembersFaculty
The members of the Behavioral Informatics Lab consist of faculty and doctoral, master's, and undergraduate students.As of 2025
- Associate ProfessorMasaya Okada
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Biology field trip Cedar Point Biological Station” Scott L. Gardner / CC BY-SA 4.0 (cropped)
- Understanding learning in the real world: “Aich-Outdoor-Education-Center-3” Evelyn-rose / CC0 (cropped)
- The semantic structure of conversational settings: “Sesión en la Escuela Libre del 11 de mayo 13” Luisalvaz / CC BY-SA 4.0 (cropped)
- Understanding intelligence that acts according to the situation: “Quori socially interactive robot platform IMG” Mary Mark Ockerbloom / CC BY-SA 4.0 (cropped)
- Supporting scientist-like ways of learning: “Young and Upcoming Scientist 02” Mistaiyke / CC BY-SA 4.0 (cropped)
- Related lab (Human Data Interaction Lab): “APRENDIZAJE SIGNIFICATIVO” SAMMYTQM / CC BY-SA 4.0 (cropped)
- Related lab (Virtual Reality Interaction Design Lab (Yutaka Arakawa)): “Huawei Smartwatch (Band 4)” TheArchivistMan / CC0 (cropped)
- Related lab (Intelligent Information Environment Lab): “Fediverse smartphone apps (photo by Elena Rossini)” Elexfedi / CC BY-SA 4.0 (cropped)
- Related lab (Machine Learning, Data Mining, and AI Lab): “Korean Robot” ApexInfinity Games / CC0 (cropped)
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