This repository contains the implementation for the paper titled RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems, published at SIGIR'2021
. [Paper][Presentation Video]
Authors: Weibo Gao, Qi Liu et al.
Email: weibogao@mail.ustc.edu.cn
-
🔈RCD has been used in the 'Learning & Practice' business of the HUAWEI Educational Center APP, since 2022.
- Torch version: '1.7.1'
- DGL version: '0.6.1'
To run the codes using the Junyi dataset:
- Navigate to the code directory:
cd RCD/RCD
- Create two folders '/model' and '/result':
mkdir model mkdir result
- Build exercise-concept correlation local map:
python build_k_e_graph.py
- Build student-exercise interaction local map:
python build_u_e_graph.py
- Train and test RCD model:
python main.py
Note: Exercise-concept correlation local map and student-exercise interaction local map can be constructed by running build_k_e_graph.py
and build_u_e_graph.py
respectively.
log_data.json
: Student exercising records. Sourcetrain_set.json
: Data file for training.test_set.json
: Data file for testing.graph/K_Directed.txt
: Prerequisite relation from concept dependency local map, where each line is a prerequisite relation from the concept dependency local map: precursor_concept_ID\t succeed_concept_ID.graph/K_Undirected.txt
: Similarity relation from concept dependency local map, where each line is a similarity relation from concept dependency local map: concept_ID\t similar_concept_ID.
Note: Concept dependency local map construction details are provided in the paper.
log_data.json
: Student exercising records.
- Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis (SIGIR'2023). [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{gao2021rcd,
title={RCD: Relation map driven cognitive diagnosis for intelligent education systems},
author={Gao, Weibo and Liu, Qi and Huang, Zhenya and Yin, Yu and Bi, Haoyang and Wang, Mu-Chun and Ma, Jianhui and Wang, Shijin and Su, Yu},
booktitle={Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval},
pages={501--510},
year={2021}
}