Assessing Student Collaboration with Multimodal Data and Large Language Models
Shan Zhang, 
Hongming Li, 
Seiyon Lee, , , Anthony F. Botelho
Proceedings of the 19th International Conference on Computer-Supported Collaborative Learning - CSCL 2026, pp. 767–769 · 2026
Abstract
We introduce a multimodal evaluation approach that leverages large language models to assess group collaboration quality in computer-supported collaborative learning environments. Using synchronized log data, dialogue transcripts, and human-coded annotations from undergraduate dyads working in the Graspable Math platform, we develop a theoretically grounded rubric capturing cognitive, social, and regulatory dimensions of collaboration, and examine the extent to which LLM-generated evaluations align with expert human judgments.
Materials
Cite
Zhang, S., Li, H., Lee, S., Lee, J.-E., Schroeder, N. L., & Botelho, A. F. (2026). Assessing Student Collaboration with Multimodal Data and Large Language Models. In Proceedings of the 19th International Conference on Computer-Supported Collaborative Learning - CSCL 2026 (pp. 767–769). International Society of the Learning Sciences. https://doi.org/10.22318/cscl2026.817551
@inproceedings{zhang2026assessing,
author = {Zhang, Shan and Li, Hongming and Lee, Seiyon and Lee, Ji-Eun and Schroeder, Noah L. and Botelho, Anthony F.},
title = {Assessing Student Collaboration with Multimodal Data and Large Language Models},
booktitle = {Proceedings of the 19th International Conference on Computer-Supported Collaborative Learning - CSCL 2026},
year = {2026},
pages = {767--769},
publisher = {International Society of the Learning Sciences},
doi = {10.22318/cscl2026.817551},
url = {https://repository.isls.org/handle/1/12586}
}Zotero and Mendeley save this paper from the page in one click.