Symbolic Cognitice Diagnosis Model (SCDM)

This repository contains the code for the paper "Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems" published in proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI 2024). We also upload the pre-print main paper and its appendix, titled main.pdf and appendix.pdf respectively in the folder named paper. The published edition is available at https://ojs.aaai.org/index.php/AAAI/article/view/29413.

Requirements

Create the running environment with conda 4.10.3 with Python 3.9.0:

conda create -n scdm python==3.9
conda activate scdm

Install all necessary packages:

pip install -r requirements.txt

Reproducing

Run `example.py`

We have prepared a sample dataset FracSub to demonstrate the SCD framework. Having installed all necessary packages, you can run example.py using

python example.py

Run with other datasets

Step 1. prepare dataset

Refer to the sample dataset, you should prepare the following files:

├─dataset
│  └─Your_dataset
│          config.json
│          data.csv   
│          q.csv

Specifically, config.json records all necessary settings of dataset like the number of students, and the format of config.json is shown as following:

{
  "dataset": [String, the name of the dataset],
  "qMatrixPath": [string, the relative path of Q matrix],
  "dataPath": [string, the relative path of response logs],
  "studentNumber": [int, the number of students],
  "questionNumber": [int, the number of questions],
  "knowledgeNumber": [int, the number of knowledge attributes]
}

data.csv consists of response logs in the following format:

[int, student_id1],[int, question_id1],[0/1, response to question_id1]
[int, student_id1],[int, question_id2],[0/1, response to question_id2]
...
[int, student_idn],[int, question_idm],[0/1, response to question_idm]

q.csv contains the relevant between questions and knowledge attributes. Each entry in the $i$-th row and the $j$-th column means whether the $i$-th question involves the $j$-th knowledge attributes.

Step 2. coding

Refer to the example.py, you can change the path to different configuration file.

Step 3. run code

python example.py

File Tree

SymbolicCDM:
│  example.py
│  LICENSE
│  README.md
│  requirements.txt   
│      
├─asset
│      img.png        
│      
├─dataset
│  └─FracSub
│          config.json
│          data.csv   
│          q.csv
│
├─paper
│      appendix.pdf
│      main.pdf
│
└─SCDM
      eval.py
      interaction.py
      model.py
      operators.py
      parameter.py
      utility.py
      __init__.py

Citation

@inproceedings{shen2024symbolic,
 author = {Shen, Junhao and 
           Qian, Hong and 
           Zhang, Wei and 
           Zhou, Aimin},
 booktitle = {Proceedings of the 38th {AAAI} Conference on Artificial Intelligence},
 title = {Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems},
 year = {2024},
 address = {Vancouver, Canada},
 page = {14928-14936}
}

ECNU-ILOG/SymbolicCDM