Figure 1. The architecture of EAKT.
This is an implementation of the EAKT model, described in the following paper: EAKT: Embedding Cognitive Framework with Attention for Interpretable Knowledge Tracing. The datasets generated and/or analysed during the current study are available in the EAKT repository, [https://github.com/ranydb/EAKT]
- Yanjun Pu: [email protected]
- Fang Liu : [email protected]
- Tianhao Peng: [email protected]
State Key Laboratory of Software Development Environment Admire Group, School of Computer Science and Engineering, Beihang University
The csv folder has four datasets, assist2009, assist2015, assist2017, simulation. Every dataset include three files:train data, test data, and Q-matrix data.
The statistical information of the above datasets is given in following Table.
| Dataset | Student | Question | Interaction | Average length | Maximum length |
|---|---|---|---|---|---|
| ASSIST2009 | 4,151 | 110 | 325,637 | 78 | 1,261 |
| ASSIST2015 | 19,917 | 100 | 708,631 | 35 | 632 |
| ASSIST2017 | 1,709 | 102 | 942,816 | 551 | 3,057 |
| Simu | 20,000 | 30 | 1,000,000 | 50 | 50 |
Usage:
run.py (eakt) --data=<h> --questions=<h> [options]
Options:
--length=<int> max length of question sequence [default: 50]
--questions=<int> num of question [default: 124]
--lr=<float> learning rate [default: 0.001]
--bs=<int> batch size [default: 64]
--seed=<int> random seed [default: 59]
--epochs=<int> number of epochs [default: 30]
--cuda=<int> use GPU id [default: 0]
--hidden=<int> dimention of hidden state [default: 128]
--kc=<int> knowledge compoments dimention [default: 10]
--layers=<int> layers of rnn or transformer [default: 1]
--heads=<int> head number of transformer [default: 8]
--dropout=<float> dropout rate [default: 0.1]
--beta=<float> reduce rate of MyModel [default: 0.95]
--data=<string> dataset [default: assist2009]
--kernels=<int> the kernel size of CNN [default: 7]
--memory_size=<int> memory size of DKVMN model [default: 20]
--weight_decay=<float> weight_decay of optimizer [default: 0]
--sigmoida=<float> coefficient of custom sigmoid function [default: 5]
--sigmoidb=<float> constant custom sigmoid function [default: 6.9]
--save_model=<bool> whether save the KT model [default: true]
--save_epoch=<int> the epoch to save the KT model [default: 0]
--gpu=<int> select the gpu card [default: 0]
# Run EAKT model with assist2009 dataset.
python -m evaluation.run eakt --data=assist2009updated --questions=110 --kc=30 --heads=8 --hidden=128 --bs=5 --epochs=20 --save_epoch=19 --lr=0.001 --weight_decay=0.000001 --sigmoida=1 --sigmoidb=0
# Run EAKT model with assist2015 dataset.
python -m evaluation.run eakt --data=assist2015 --questions=100 --kc=10 --heads=8 --hidden=128 --bs=64 --epochs=20 --save_epoch=19 --lr=0.001 --weight_decay=0 --sigmoida=1 --sigmoidb=0
# Run EAKT model with assist2017 dataset.
python -m evaluation.run eakt --data=assist2017 --questions=102 --kc=10 --heads=8 --hidden=128 --bs=5 --epochs=20 --save_epoch=19 --lr=0.001 --weight_decay=0.000001 --sigmoida=1 --sigmoidb=0
# Run EAKT model with simu dataset.
python -m evaluation.run eakt --data=simu --questions=30 --kc=10 --heads=8 --hidden=256 --bs=32 --epochs=10 --save_epoch=9 --lr=0.00001 --weight_decay=0 --sigmoida=10 --sigmoidb=6.9