FLoRA is a research project aimed at facilitating self-regulation in learning. "Learning to learn" – the ability to monitor and adapt one's learning process productively – is a key competence formulated by the European Parliament (2006) and increasingly a central focus of education. Prior research has shown that self-regulated learning (SRL) leads to better learning performance.
This research collaboration aims to enhance the support provided to students by: i) improving unobtrusive trace data collection and machine learning techniques to gain a better understanding and measurement of SRL processes, and ii) using these new insights to facilitate students' SRL by providing personalized support empowered by large language models (LLMs) such as ChatGPT.
FLoRA has been applied to many learning settings, including reflective writing tasks, academic writing studies, and role-play scenarios using LLMs to support apprenticeship training.
For more details and the latest updates, please visit the FLoRA website.
https://www.floraengine.org/home
If you have any questions or suggestions, please contact us at xinyu.li1@monash.edu
Please cite our work if you use our open source code.
@article{li2025floraengine,
title={The FLoRA Engine: Using Analytics to Measure and Facilitate Learners' own Regulation Activities},
author={Li, Xinyu and Fan, Yizhou and Li, Tongguang and Raković, Mladen and Singh, Shaveen and van der Graaf,
Joep and Lim, Lyn and Moore, Johanna and Molenaar, Inge and Bannert, Maria and Ga\v{s}evi'{c}, Dragan},
journal={Journal of Learning Analytics},
year={2025}
}
@article{li2025flora,
title={FLoRA: An Advanced AI-powered Engine to Facilitate Hybrid Human-AI Regulated Learning},
author={Li, Xinyu and Li, Tongguang and Yan, Lixiang and Li, Yuheng and Zhao, Linxuan and Raković, Mladen and
Molenaar, Inge and Ga\v{s}evi'{c}, Dragan and Fan, Yizhou},
journal = {Computers & Education},
volume = {243},
pages = {105527},
issn = {0360-1315},
doi = {10.1016/j.compedu.2025.105527},
year={2026}
}