Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware Approach.
Yuxiang Guo|Shuanghong Shen|Qi Liu|Zhenya Huang|Linbo Zhu|Yu Su|Enhong Chen
| Anthology ID: | DBLP:conf/cikm/GuoS0HZ0C24 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 727-736 |
| URL: | https://doi.org/10.1145/3627673.3679664 |
| DOI: | https://doi.org/10.1145/3627673.3679664 |
| DBLP: | conf/cikm/GuoS0HZ0C24 |
| BibTeX: |
@inproceedings{guo-2024-mitigating,
author = {Yuxiang Guo and
Shuanghong Shen and
Qi Liu and
Zhenya Huang and
Linbo Zhu and
Yu Su and
Enhong Chen},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware Approach}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {727--736},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679664},
doi = {https://doi.org/10.1145/3627673.3679664}
}