Knowledge Tracing Machines
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README.md

Knowledge Tracing Machines

  • Presented at the AIP-IIS-MLGT workshop at Georgia Tech, Atlanta, GA on March 8, 2018.
  • Presented at the BEA workshop at New Orleans, LA on June 5, 2018.
  • To be presented at the AAAI 2019 conference in Honolulu, Hawaii on January 27, 2019.

See article on arXiv:

@inproceedings{Vie2019,
  Author = {{Vie}, Jill-J{\^e}nn and {Kashima}, Hisashi},
  Booktitle = {To appear at the 33th AAAI Conference on Artificial Intelligence},
  Title = {{Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing}},
  Url = {http://arxiv.org/abs/1811.03388},
  Year = 2019}

Authors: Jill-Jênn Vie, Hisashi Kashima

Install

python3 -m venv venv   # Python 2 should work as well, but we suggest to you to use virtualenv
. venv/bin/activate
pip install -r requirements.txt

If you also want to get the factorization machines running, follow the pywFM (weird) suggestion:

git clone https://github.com/srendle/libfm  # In the same ktm folder, it's better
cd libfm
git reset --hard 91f8504a15120ef6815d6e10cc7dee42eebaab0f
make all

Run

make  # To get the encodings (npz)
make  # To get results (txt)

You can also download the Assistments 2009 dataset in data/assistments09 and get:

make big

Results

On the Assistments 2009 dataset:

AUC time users + items skills + wins + fails items + skills + wins + fails
LR 0.734 (IRT) 2s 0.651 (PFA) 9s 0.737 23s
FM d = 20 0.730 2min9s 0.652 43s 0.739 2min30s

Computation times are given for a i7 with 2.6 GHz, with 200 epochs of FM training.