Fine-Grained Knowledge Tracing model (FGKT).
This project is the Pytorch implementation for FGKT.
If you have more questions about our experiments, you can contact us. email: [email protected]
In 'data' folder, we have provided the processed datasets. If you would like to access the raw datasets, the raw datasets are placed in the following links:
- Statics : address
- Synthetic-5 : address
- ASSISTments2009 : address
- ASSISTments2015 : address
- ASSISTments Competition : address
Service:
- Linux operation system
Environment:
- python 3+
- sklearn 0.21.3
- tqdm 4.54.1
- torch 1.7.0
- numpy 1.19.2
Here is a example for using FGKT model (on ASSISTments Competition):
python main.py --dataset assist2017
If you do not want to use the default parameters for your experiments, you can change the model parameters in the following way:
python main.py --dataset assist2017 --gpu 0 --patience 5 --lr 0.001 --num_heads 1 --mode 3 --exercise_embed_dim 128 --batch_size 32
Explanation of parameters:
- gpu: Specify the GPU to be used, e.g '0,1,2,3'. If CPU is used then fill in -1.
- patience: Maximum number of times if validation loss does not decrease.
- lr: Learning rate
- num_heads: Number of head attentions.
- mode: Selection of integration function.
- exercise_embed_dim: Number of exercise embedding dimensions.
- batch_size: Number of batch size.