Research Background
In Society 5.0, school education must cultivate students’ ability to proactively engage in lifelong learning, as well as develop their skills in inquiry, expression, and various competencies. At the societal level, it will become increasingly important to effectively utilize human resources with such diverse attributes.
To achieve this, school education must reform learning practices by recording what kinds of knowledge, skills, and competencies students acquire. In society, it is essential to implement competency management that leverages each individual’s abilities and characteristics, placing people in roles that suit them best. At the same time, we must make effective use of both physical and virtual environments to accelerate work style reform.
Meanwhile, in elementary and secondary education, the GIGA School Initiative has been implemented nationwide, providing each student with a personal device and access to high-speed internet. In higher education, the BYOD (Bring Your Own Device) approach has also become widespread. Leveraging various digital technologies such as video conferencing systems and virtual reality (VR), educators have been able to flexibly combine in-person and online instruction.
As a result, vast amounts of educational data and learning logs have been accumulated, drawing increasing attention to the analysis of such data — known as learning analytics.
Research Aims
This project aims to research and develop the Open Knowledge and Learner Model (OKLM) Digital Twin — a human-centered digital twin that supports individually optimized learning and appropriate talent placement — by leveraging vast amounts of accumulated learning log data.
By enabling teachers and learners to use the OKLM to automatically construct individually optimized teaching and learning environments — both in virtual and real-world settings — and to run simulations, this project aims to enhance the efficiency of human resource development and contribute to competency management and job-based talent management.
Research Period
August 2023 – March 2028
Research Plan
Overall Plan
