# Knowledge_Tracing

**Repository Path**: youngnowhere/Knowledge_Tracing

## Basic Information

- **Project Name**: Knowledge_Tracing
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-11-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Knowledge_Tracing
- Paper ： 知识追踪相关论文
    - [Deep Knowledge Tracing](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/deep%20Knowledge%20Tracing.pdf):
        - 首次提出将RNN用于知识追踪，并能够基于复杂的知识联系进行建模（如构建知识图谱）
    -  [How Deep is Knowledge Tracing](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/How%20Deep%20is%20Knowledge%20Tracing%3F.pdf)
        - 探究DKT利用到的统计规律并拓展BKT，从而使BKT拥有能够与DKT相匹配的能力
    - [Going Deeper with Deep Knowledge Tracing](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/Going%20Deeper%20with%20Deep%20Knowledge%20Tracing%20.pdf)
        - 对DKT和PFA，BKT进行了模型比较，对DKT模型能碾压其他两种模型的结果进行了怀疑并加以论证，进一步讨论了原论文能够得出上述结果的原因，对进一步使用DKT模型提供了参考。
    - [Incorporating Rich Features Into Deep Knowledge Tracing](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/Incorporating%20rich%20features%20into%20Deep%20knowledge%20tracing.pdf)
        - 对DKT使用上进行数据层扩展，扩展学生和问题层的数据输入，包括结合自动编码器对输入进行转换
    - [Addressing Two Problems in Deep Knowledge Tracing viaPrediction-Consistent Regularization](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/Addressing%20Two%20Problems%20in%20Deep%20Knowledge%20Tracing%20via%20Prediction-Consistent%20Regularization.pdf)
        - 指出DKT模型现存缺点：对输入序列存在重构问题和预测结果的波动性，进而对上述问题提出了改善方法
    - [Exercise-Enhanced Sequential Modeling for Student Performance Prediction](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/Exercise-Enhanced%20Sequential%20Modeling%20for%20Student%20Performance%20Prediction.pdf)
        - 将题面信息引入，不仅作为输入送入模型，而且将题目编码后的向量计算cosine相似度作为atention的socre
    - [A Self-Attentive model for Knowledge Tracing](https://arxiv.org/pdf/1907.06837.pdf)
        - 使用Transformer应用于知识追踪
## Method
| model | paper |
| ----  | ----  |
| DKT   |  [Deep Knowledge Tracing](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/deep%20Knowledge%20Tracing.pdf) | 
| DKT+  |  [Addressing Two Problems in Deep Knowledge Tracing viaPrediction-Consistent Regularization](https://github.com/ZoeYuhan/Knowledge_Tracing/blob/master/Paper/Addressing%20Two%20Problems%20in%20Deep%20Knowledge%20Tracing%20via%20Prediction-Consistent%20Regularization.pdf)    |
| TCN-KT|  None |
| Transformer-KT | [A Self-Attentive model for Knowledge Tracing](https://arxiv.org/pdf/1907.06837.pdf) | 



##  Usage  ： 

```bash
python DKT/run_dkt.py 
```

----
### Acknowledgement
- Blog:
    - [深度知识追踪](https://sulingling123.github.io/2019/08/06/%E6%B7%B1%E5%BA%A6%E7%9F%A5%E8%AF%86%E8%BF%BD%E8%B8%AA/)
    - [论文导读：Exercise-Enhanced Sequential Modeling for Student Performance Prediction](https://blog.csdn.net/Zoe_Su/article/details/84566409)