2025 (Current Year) Faculty Courses School of Engineering Undergraduate major in Mechanical Engineering
Human-centered Informatics
- Academic unit or major
- Undergraduate major in Mechanical Engineering
- Instructor(s)
- Yoshifumi Nishida
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - unknown
- Class
- -
- Course Code
- MEC.L311
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 3Q
- Syllabus updated
- Jul 7, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This lecture will cover information processing technology for human-centered design and various examples of its implementation.
1. Data related to human social life and representative data science methods
2. How to proceed with human-centered system design using data science
3. Various examples of applying data science to human-centered system design
Course description and aims
1. To understand how to use data for human-centered design.
2. To understand representative methods of data analysis (data science) for human-centered design.
3. To have the knowledge to design an information processing system for a specific human-centered system.
Keywords
Human-centered design, data science, IoT, social impact
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This course is conducted in a face-to-face lecture. Students will be assessed based on in-class reports and exercises, as well as a final report.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | History of Human-Centered Informatics | History of Human-Centered Informatics |
| Class 2 | How to design human-centered systems using data science | How to design human-centered systems using data science |
| Class 3 | Data Science Foundations for Human-Centered Design | Data Science Foundations for Human-Centered Design |
| Class 4 | Data Science Exercises for Human-Centered Design | Data Science Exercises for Human-Centered Design |
| Class 5 | Example of a human-centered system that utilizes data science (daily life support system) | Example of a human-centered system that utilizes data science (daily life support system) |
| Class 6 | Example of a human-centered system using data science (a system based on social determinant analysis) | Example of a human-centered system using data science (a system based on social determinant analysis) |
| Class 7 | Prospects for creating human-centered systems that generate social impact | Prospects for creating human-centered systems that generate social impact |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Upload the necessary documents on LMS.
Reference books, course materials, etc.
Upload the necessary documents on LMS.
Evaluation methods and criteria
The grading will be 50% for each report and 50% for the final report.
Related courses
- MEC.B221 ： Statistical data analysis
- MEC.B334 ： Time Sequencial Data Analysis
Prerequisites
None