Group Learning Orchestration Based on Evidence (GLOBE) is a data-driven infrastructure to support group learning with learning analytics (LA).
In the GLOBE framework, there are four phases of data-driven group learning: formation, orchestration, evaluation, and reflection.
By 2023, the GLOBE framework had been substantiated in several systems based on LEAF, including the group formation system, the group discussion visualization system, and the peer evaluation system.
With the support of the research fund for the KAKENHI project "Transforming Collaborative Learning: A Data-Driven System for Group Formation and Intervention (25K21357)", the GLOBE systems have been extended to include enhancements of a peer help system and a group work simulation system, supporting further learning platforms such as LAReflect via APIs.
Original GLOBE Framework
Liang, C., Majumdar, R., Nakamizo, Y., Flanagan, B., & Ogata, H. (2022). Algorithmic group formation and group work evaluation in a learning analytics-enhanced environment: implementation study in a Japanese junior high school. Interactive Learning Environments, 32(4), 1476–1499. https://doi.org/10.1080/10494820.2022.2121730
Liang, C., Majumdar, R., & Ogata, H. (2021). Learning log-based automatic group formation: system design and classroom implementation study. Research and Practice in Technology Enhanced Learning, 16(1), 14. https://doi.org/10.1186/s41039-021-00156-w
Iterative Team-based Learning under GLOBE Framework
Liang, C., Majumdar, R., Horikoshi, I., & Ogata, H. (2024). Data-driven support infrastructure for iterative team-based learning. IEEE Access, 12, 65967-65980. https://doi.org/10.1109/ACCESS.2024.3393421
Group formation system
Liang, C., Majumdar, R., Nakamizo, Y., Flanagan, B., & Ogata, H. (2022). Algorithmic group formation and group work evaluation in a learning analytics-enhanced environment: implementation study in a Japanese junior high school. Interactive Learning Environments, 32(4), 1476–1499. https://doi.org/10.1080/10494820.2022.2121730
Liang, C., Toyokawa, Y., & Ogata, H. (2025). Optimizing group formation with a mixed genetic algorithm: An empirical study in active reading using marker data. International Journal of Computer-Supported Collaborative Learning, accepted. https://doi.org/10.1007/s11412-025-09452-9
Peer Evaluation system
Liang, C., Horikoshi, I., Majumdar, R., & Ogata, H. (2025). Rater behaviors in peer evaluation: Patterns and early detection with learner model. Research and Practice in Technology Enhanced Learning, 20, 012. https://doi.org/10.58459/rptel.2025.20012
Data-driven peer help system
Liang, C., Jiang, P., Takii, K., & Ogata, H. (2025). Data-driven peer recommendation in higher education: A pilot study on academic reading. Australasian Journal of Educational Technology, 41(3), 84–101. https://doi.org/10.14742/ajet.10411
Liang, C., Chen Y., Jiang, P., & Ogata, H. (2025). Enabling data-driven peer help for extracurricular learning: system design and initial implementation in junior mathematics. Interactive Learning Environments, accepted. https://doi.org/10.1080/10494820.2025.2536576
Group work simulation
Yan, Y., Liang, C., & Ogata, H. (2025). Simulating Collaborative Learning with Data-Driven LLM Agents. In Collaboration Technologies and Social Computing: 31st International Conference, CollabTech 2025, Proceedings (pp. 135-143). https://doi.org/10.1007/978-3-032-10156-3_10