Rethinking Education Policy Through Models

There are relationships in education that remain unseen.

By reconstructing them as models, we create new ways of understanding.

Education policy is shaped by many interwoven factors.

Yet real-world data alone cannot fully reveal:

By reconstructing educational phenomena as models

and uncovering the structures behind them,

we aim to build a foundation for deeper understanding

of how policies work and what they truly mean.

Keywords

Unraveling the complexities of education through models

We use mathematical models that estimate differences in exam difficulty,

and agent-based simulations that recreate educational systems

within a virtual environment—

allowing us to examine questions that are difficult to test in reality.

By exploring these “what-if worlds” through models,

we provide new perspectives that support more informed policy decisions.

Our goal is to carefully reveal the structures underlying educational systems and interventions,

so that policies can move toward more thoughtful and effective forms.

業績リスト

S. Takahashi and A. Yoshikawa, "Data Science in an Agent-Based Simulation World," 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Auckland, New Zealand, 2023, pp. 1-6, doi: 10.1109/TALE56641.2023.10398326.
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科研費（若手研究）22K13785（2022〜2025）「ショートケースを利用したシチュエーションアウェアネス・スキルの評価手法の開発」研究代表者：高橋聡（関東学院大学）
科研費（若手研究）18K13243（2018〜2022）「ショートケースライティングによる知識活用学習法の開発」研究代表者：高橋聡
科研費（研究活動スタート支援）16H07225（2016〜2017）「PBL導入教材としてのマンガケース教材の課題設計手法の開発」研究代表者：高橋聡

主研究者

Satoshi Takahashi

Associate Professor
Graduate School of Economics, Nagoya University

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