Simple and performant implementations of learner performance prediction algorithms:

• Performance Factors Analysis (PFA)
• DAS3H
• Deep Knowledge Tracing (DKT)
• Self-Attentive Knowledge Tracing (SAKT)

Create a new conda environment, install PyTorch and the remaining requirements:

conda create python==3.7 -n learner-performance-prediction
conda activate learner-performance-prediction
pip install -r requirements.txt
conda install pytorch==1.2.0 torchvision==0.4.0 -c pytorch

The code supports the following datasets:

• ASSISTments 2009-2010 (assistments09)
• ASSISTments 2012-2013 (assistments12)
• ASSISTments 2015 (assistments15)
• ASSISTments Challenge 2017 (assistments17)
• Bridge to Algebra 2006-2007 (bridge_algebra06)
• Algebra I 2005-2006 (algebra05)
• Spanish (spanish)
• Statics (statics)

For your convenience, the preprocessed data sets are in the data/ folder. You do NOT need to preprocess data sets yourself.

If you want to reproduce the preprocessing, download the data from one of the links above and:

• place the main file under data/<dataset codename>/data.csv for an ASSISTments dataset
• place the main file under data/<dataset codename>/data.txt for a KDDCup dataset
• place the two data files under data/<dataset codename>/{filename} for the Spanish dataset

python prepare_data.py --dataset <dataset codename> --remove_nan_skills

To encode a sparse feature matrix with specified features:

• Item Response Theory (IRT): -i
• PFA: -s -sc -w -a
• DAS3H: -i -s -sc -w -a -tw
• Best logistic regression features (Best-LR): -i -s -ic -sc -tc -w -a

python encode.py --dataset <dataset codename> <feature flags>

To train a logistic regression model with a sparse feature matrix encoded through encode.py:

python train_lr.py --X_file data/<dataset codename>/X-<feature suffix>.npz --dataset <dataset codename>

To train a DKT model:

python train_dkt2.py --dataset <dataset codename> 

To train a SAKT model:

python train_sakt.py --dataset <dataset codename>