Design Research 1

Course title

Y0250016

Design Research 1

Course description

In today’s rapidly changing environment, in addition to the traditional PDCA (Plan-Do-Check-Act) cycle, there is an increasing need for OODA (Observe-Orient-Decide-Act) loops, which enable immediacy and responsiveness in decision-making. This approach is equally essential in the field of design development, where efficient and effective execution of the OODA loop requires enhanced research and analytical skills.

This course focuses on collecting quantitative, macro-level statistical data, analyzing it, and simultaneously conducting qualitative, micro-level research. By integrating these perspectives, students will engage in comprehensive analysis and evaluation.

Through this systematic process, students will cultivate multi-perspective analytical skills, enabling them to make well-informed decisions in dynamic and complex environments.

Purpose of class

To develop practical skills for effectively and swiftly executing the OODA loop in design development environments through assignments focused on data collection, analysis, and evaluation.

Goals and objectives

To be able to identify and locate appropriate statistical data based on the specific content to be researched.
To be able to process acquired data through statistical methods and convert it into analyzable data.
To be able to integrate aggregated data with qualitative observations and analyses, and conduct comprehensive evaluations and estimations of causal relationships.

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	Reports	Data analysis	The last report	Total.
1.	30%			30%
2.		30%		30%
3.			40%	40%
Total.	30%	30%	40%	-

Language

Japanese

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	Introduction: To learn about the overall structure of the course and gain a fundamental understanding of societal systems and statistical data.	To analyze and thoughtfully complete the given assignments.	200minutes
2.	Estimation and Irrationality: To learn the necessity of estimation and the irrationality of human behavior through concepts such as Fermi estimation, the Prisoner’s Dilemma, Pareto optimality, and Prospect theory.	To analyze and thoughtfully complete the given assignments.	200minutes
3.	Correlation and Causation: To learn the fundamentals of analyzing and understanding causal relationships.	To analyze and thoughtfully complete the given assignments.	200minutes
4.	Linear and Nonlinear Data Summarization: To learn the fundamentals of data analysis and interpretation by comparing different methods—Principal Component Analysis (PCA), Kernel Principal Component Analysis (Kernel PCA), and Self-Organizing Feature Maps (SOM)—on the same dataset.	To analyze and thoughtfully complete the given assignments.	200minutes
5.	Research Practice (Macro Research): To conduct data collection and analysis on a specific theme to gain a comprehensive understanding of the situation.	To analyze and thoughtfully complete the given assignments.	200minutes
6.	Research Practice (Micro Research): To develop data collection methods for qualitative observation, building upon the previous lesson’s findings.	To analyze and thoughtfully complete the given assignments.	200minutes
7.	Comprehensive Analysis: To conduct an analysis based on collected and processed data and present the findings through a structured presentation.	To analyze and thoughtfully complete the given assignments.	200minutes
Total.	-	-	1400minutes

Evaluation method and criteria

Each assignment will be graded using the following scale: S=10 points, A=8 points, B=6 points, F=0 points.
Weighting will be applied to each assignment, and the final score will be calculated based on the percentage of points earned relative to the maximum possible score if all assignments received an S rating.
A passing grade requires a minimum score of 60% of the total possible points.
Note: The evaluation method may be revised as necessary based on circumstances. Any modifications will be explained during class.

Feedback on exams, assignments, etc.

ways of feedback	specific contents about "Other"
Feedback in the class

Textbooks and reference materials

Instructions and references will be provided as necessary during the class.

Prerequisites

Preparatory Requirements:
1. Research the terms listed in the syllabus in advance.
2. Learn the basics of R or Python, as data analysis in this course will be conducted using these programming languages.

Office hours and How to contact professors for questions