TechCD: Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis
This repository contains the implementation for the paper titled Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis, which was published at SIGIR'2023
. [Paper][Presentation Video]
Authors: Weibo Gao, Hao Wang, Qi Liu et al.
Email: weibogao@mail.ustc.edu.cn
Tips: The code and documentation will be completed by April.
Install the Python dependencies:
pip install -r requirements.txt
Install the CUDA version of DGL:
- Download the DGL-Cuda package from here.
- Install the DGL-Cuda package (e.g.,
dgl-1.1.2+cu118-cp311-cp311-win_amd64.whl
) usingpip install dgl-1.1.2+cu118-cp311-cp311-win_amd64.whl
.
To train TechCD:
python train.py
- RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems (SIGIR'2021). [Paper][Code][Presentation Video]
- Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives (AAAI'2024). [Paper][Code]
Please cite this paper if you use our codes. Thanks!
@inproceedings{gao2023leveraging,
title={Leveraging transferable knowledge concept graph embedding for cold-start cognitive diagnosis},
author={Gao, Weibo and Wang, Hao and Liu, Qi and Wang, Fei and Lin, Xin and Yue, Linan and Zhang, Zheng and Lv, Rui and Wang, Shijin},
booktitle={Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages={983--992},
year={2023}
}
- March 16, 2024: Updated codes
- March 14, 2024: Added codes and data examples
- August 29, 2023: Created this project