Seminar
Undergraduate and graduate seminars
I offer research exercises and supervision appropriate to both undergraduate and graduate study. Undergraduate students build research fundamentals step by step, while graduate students develop their own themes toward a master's thesis.
Undergraduate seminar
Rather than centering on completing a thesis as graduate study does, the undergraduate seminar helps students from their second through fourth years gradually build the ability to work with data, formulate questions, present, and think critically.
Although the subjects and goals differ by year, the seminar aims to connect these experiences to graduation research by fostering the habit of formulating questions, thinking between concepts and data, communicating with others, and improving through discussion.
Second year: opening the door to practice
Second-year students meet for two consecutive class periods, combining presentation exercises with hands-on work in VS Code.
In the presentation exercises, students begin with familiar topics such as self-introductions and hobbies, then move on to presentations that explain their own ideas using terminology from organizational theory. They also actively participate by giving presentations, listening to others, and exchanging questions and opinions.
In the VS Code exercises, students apply for GitHub Education and use GitHub Copilot Student to analyze data related to organizational theory and create work using generative AI. They also learn the full GitHub workflow from the basics: recording work through commits, sharing it through pushes, reviewing changes, proposing improvements for discussion through pull requests, and incorporating them through merges.
In the 2026 research exercises, the seminar will collaborate with KaleidoFuture, a company that provides software development and operational support, to conduct development exercises using generative AI.
Students set their own challenges, design and create outputs with AI, then verify and improve those outputs. Rather than simply delegating work to AI, they learn in practice to articulate purposes and requirements and to make human judgments about the quality of the resulting work.
Third year: preparing for research and external connection
Third-year students prepare to join data science competitions and joint seminars with other universities.
Data Science Competition Schedule
- Friday, November 20, 2026, periods 5-6 Preliminary round (interim presentation)
- Friday, December 11, 2026, periods 5-6 Final round (final presentation)
The emphasis is not only on analytical methods but on explaining ideas to others, engaging with questions, and presenting as a team—experience closer to research practice.
For analysis exercises, students use a sample of raw data provided by the Consumer Market Forecasting System (mif) and undertake analyses using multivariate methods and machine learning.
The goal is to learn how to handle data and structure presentations in a research setting, preparing for further research activity.
Fourth year: bringing research together for the graduation thesis
The aim is not merely to “be able to analyze” but to formulate research questions, draw insights from data, and communicate them to others.
In the fourth year—the capstone of the undergraduate seminar—students focus on writing the graduation thesis while developing research thinking and presentation skills.
In practice, students tend to write their graduation theses after completing their job searches, and I feel that seminar activities ultimately need to take job searching into account. A central aim of the fourth-year undergraduate seminar is to develop both research skills and the ability to communicate in society.
Graduate seminar
Graduate students clarify their interests as research questions and work toward completing a master's thesis through literature review, research and analysis, presentations, and discussion.
The program welcomes working professionals and international students. Prospective students are encouraged to contact me before applying. Individual research consultations, seminar presentations, and discussion help students explain and improve their research in their own words.
Many master's theses to date have used data collected through questionnaire surveys, interviews, or mixed-methods research combining both approaches.
Research connects theory with practice through topics such as organizational behavior, professional work, and the relationship between generative AI, work, and organizations.