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Behavior Informatics Lab
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A lab that explores learning and intelligence in the real world through human behavior data.
AboutAbout the lab
Exploring human learning and intelligence through behavioral data.
The Behavior Informatics Lab studies the intelligent activities people carry out in the real world, especially how learning comes about.
The lab broadly measures information about the human body and environment with sensors, and develops data science techniques for understanding behavior and knowledge.
Using environmental learning in natural settings as its subject, the lab aims both to understand how human intelligence comes about and to realize mechanisms that let people behave intelligently.
The lab is open to students of the Graduate School of Information Science and Electrical Engineering and the School of Interdisciplinary Science and Innovation, and recruits students widely.
Key terms
- Learning analytics
- A method of analyzing data such as learners' behavior to understand how learning comes about.
- Real-world learning
- Learning carried out through the body in the real world, such as in natural environments, rather than in classrooms or laboratories.
- Situated intelligence
- The function of intelligence by which people produce behavior adapted to each of a wide variety of situations.
ResearchResearch themes
The lab aims to understand how the intelligent activities people carry out in the real world, especially learning, come about, and to design mechanisms that extend that intelligence. To do so, it collects and analyzes behavior and environment data with sensors, and builds and tests models.
*Illustrative imageAnalysis platform for learning how to learn like a scientist
The lab is developing a learning analytics platform that models scientists' intelligence computationally and lets learners learn to "learn like a scientist."
*Illustrative imageAnalyzing self-regulation in real-world learning
The lab investigates how learners autonomously gain knowledge from real-world experience. It estimates spatiotemporal clusters of places where learners stayed with concentration to segment the learning timeline, and analyzes whether learners keep or change their content and strategies.
*Illustrative imageUnderstanding intelligence that adapts to situations
In the real world, even when the number of available actions is enormous, people find constraints in the situation and decide on appropriate actions. The lab tries to understand this situated intelligence by regarding people as a kind of robotics system and repeatedly building and testing models.
MembersFaculty
The members of the Behavior Informatics Lab are faculty and students in the doctoral, master's, and undergraduate programs.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: “The scope of embodied cognition fr” John J. Madrid / CC BY 4.0 (cropped)
- Analysis platform for learning how to learn like a scientist: “Students use B-Wing Science Lab at IMSA” Tim Benson / CC BY-SA 4.0 (cropped)
- Analyzing self-regulation in real-world learning: “Self-regulated learning” Brews ohare / CC0 (cropped)
- Understanding intelligence that adapts to situations: “CU octopus suckers 0459” Betty Wills (Atsme) / CC BY-SA 4.0 (cropped)
- Related lab (Intelligent Information Environment Lab): “Big Data ( )” Learntek / CC0 (cropped)
- Related lab (Humanophilic Systems Lab): “Iot, Internet of Things Colorful Typography 01” Chiplanay / CC BY-SA 4.0 (cropped)
- Related lab (Hacking Reality Lab): “InclusiveGameLab Person-Playing-Virtual-Reality 12 CC-BY-SA” InclusiveGameLab / CC BY-SA 4.0 (cropped)
- Related lab (Intelligent Software Engineering Lab): “Programmers” Lbronn / CC BY-SA 4.0 (cropped)
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