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Code and real data for "Enhancing Human Learning via Spaced Repetition Optimization", PNAS 2019
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README.md
dataset2dict.py
hlr.duolingo.weights
memorize.py
observation_1k.csv
plot_utils.py
plots.ipynb
power.duolingo.weights
process_raw_data.py
spaced_rep_code.py

README.md

Memorize

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. To appear at the Proceedings of the National Academy of Sciences (PNAS), 2019.

An in a nutshell description of our algorithm is available in this supporting website: http://learning.mpi-sws.org/memorize/.

Pre-requisites

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.

Code structure

  • 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.

Execution

The code can by executed as follows:

python memorize.py

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


Experiments with Duolingo data

Pre-processing

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

Plots

See the notebook plots.ipynb.

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