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Here should be introductions.

I got my Ph.D. in Informatics at the Graduate School of Informatics, Kyoto University in Sep. 2023.

I am a program-specific researcher at Ogata Laboratory, Academic Center for Computing and Media Studies, Kyoto University.

My research focuses on data-driven group learning in data-driven environments with Learning Analytics and Generative AI, including algorithmic group formation, peer tutoring, and group learning design support.

For further details, please take a look at my publications.

Contact me: liang.changhao.8h [at] kyoto-u.ac.jp

Ph.D. in Informatics, Department of Informatics, Kyoto University (September 2023)

- Dissertation: GLOBE: Data-Driven Support for Group Learning

- Proposed a data-driven educational framework (GLOBE) for collaborative learning.

Master's Degree in Informatics, Department of Informatics, Kyoto University (2020)

- Dissertation: Learning Log-based Automatic Group Formation: System Design and Classroom Implementation Study

Bachelor's Degrees, Peking University (2018)

- Information management and information system, Department of Information Management (main degree)

- Psychology, Department of Psychology (double degree)

Research Fund information

JSPS early-career grant (2025/4 -)

- Transforming Collaborative Learning: A Data-Driven System for Group Formation and Intervention (25K21357)

Kyoto University Graduate Division Fellow (2021/12 - 2023/9)

- Kyoto University and Support for Pioneering Research Initiated by the Next Generation program operated by the Japan Science and Technology Agency (JST) (JPMJSP2110)

Educational Systems Developed (see GLOBE )

Group Formation System
- A system to create student groups based on diverse data sources, including learning logs, e-book annotations, and previous performance metrics.
- Implemented algorithms to support group formation in various educational contexts (e.g., primary school math, junior high English, and university-level courses).
- Exploring optimal grouping strategies and input attributes in different scenarios.
Peer Evaluation System
- Integrated peer evaluation results to refine group dynamics in classrooms.
- Analyze peer feedback reliability and detect unserious rater behaviors.
Peer Recommendation and Peer Help System
- Recommending peer tutors based on knowledge proficiency from learning logs.
- Online platform for peer tutoring and communication, with behavior sensors for learning analytics.

Service

APSCE SIG4 Technology Enhanced Learning for Mobility of Learners and Learning Experiences (TEML), Chair
IEEE ICALT 2026 Track 5. Computer Supported Collaborative Learning (CSCL), PC member
APSCE ICLEA 2026, Program co-chair
APSCE ICLEA 2025, PC-member
AIED 2025 Late Breaking Results (LBR) Track, PC-member
APSCE ICCE 2024/2025 C4: Technology Enhanced Learning for Mobility of Learners and Learning Experiences (TEML), Co-chair
SOLAR LAK24, PC-member

Students

Lara Monteagudo Tubau (PhD student, Kyoto Univ., 2025.10 - )
Yiming Zhou (Master student, Kyoto Univ., 2025.4 - )
Yu Yan (PhD student, Kyoto Univ., 2025.4 - )
Yu-Tung Chen (Master student, Kyoto Univ., 2024.10 - )
Yudai Okayama (Master's degree, Kyoto Univ., 2024.4 - 2026.3) Dissertation: Integrated Utilization of Multi-source Learning Data forSupporting Personalized Education and Learning (Distinguished Master Thesis Award)
Kensuke Takii (PhD degree, Kyoto Univ., 2024.3 - 2026.1) Dissertation: OKLM: Open Knowledge and Learner Model for Learning Analytics
Peixuan Jiang (Master's degree, Kyoto Univ., 2023.9 - 2025.3) Dissertation: Data-driven Peer Recommendation and Its Implementation

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