This is a repository containing code and data for the paper:

B. Tabibian, U. Upadhyay, A. De, A. Zarezade, Bernhard Schölkopf, and M. Gomez-Rodriguez. Enhancing Human Learning via Spaced Repetition Optimization. Proceedings of the National Academy of Sciences (PNAS), March, 2019.

The paper is available from PNAS website.

The supporting website also gives a description of our algorithm in a nutshell.

This code depends on the following packages:

1. numpy
2. pandas
3. matplotlib
4. seaborn
5. scipy
6. dill
7. click

Apart from this, the instructions assume that the Duolingo dataset has been downloaded, extracted, and saved at ./data/raw/duolingo.csv.

• memorize.py contains the memorize algorithm.
• preprocesed_weights.csv contains estimated model parameters for the HLR model, as described in section 8 of supplementary materials.
• observations_1k.csv contains a set of 1K user-item pairs and associated number of total/correct attempts by every user for given items. This dataset has been curated from a larger dataset released by Duolingo, available here.

The code can by executed as follows:

python memorize.py

The code will use default parameter value (q) used in the code.

Convert to Python dict by user_id, lexeme_id and pruning it for reading it:

python dataset2dict.py ./data/raw/duolingo.csv ./data/duo_dict.dill --success_prob 0.99 --max_days 30 
python process_raw_data.py ./data/raw/duolingo.csv ./data/duolingo_reduced.csv

See the notebook plots.ipynb.