Fine-Tuning Large Language Models for Data Augmentation to Detect At-Risk Students in Online Learning Communities
Hongming Li, Anthony F. Botelho
Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning - CSCL 2024, pp. 441–442 · 2024
Abstract
We introduce a working approach that combines the method of fine-tuning large language models (LLMs) to create augmented data for the regression predictive models aimed at detecting at-risk students in online learning communities. This approach has the potential to leverage scarce data to improve urgency detection, and it can also present the role of artificial intelligence in enhancing the resilience of educational communities and ensuring timely interventions within online learning settings.
Materials
Cite
Li, H., & Botelho, A. F. (2024). Fine-Tuning Large Language Models for Data Augmentation to Detect At-Risk Students in Online Learning Communities. In Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning - CSCL 2024 (pp. 441–442). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2024.208036
@inproceedings{li2024finetuning,
author = {Li, Hongming and Botelho, Anthony F.},
title = {Fine-Tuning Large Language Models for Data Augmentation to Detect At-Risk Students in Online Learning Communities},
booktitle = {Proceedings of the 17th International Conference on Computer-Supported Collaborative Learning - CSCL 2024},
year = {2024},
pages = {441--442},
publisher = {International Society of the Learning Sciences},
doi = {10.22318/cscl2024.208036},
url = {https://doi.org/10.22318/cscl2024.208036}
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