https://github.com/maimemo/SSP-MMC-Plus
Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory
https://github.com/maimemo/SSP-MMC-Plus
paper reproducibility spaced-repetition-algorithm
Last synced: 9 months ago
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Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory
- Host: GitHub
- URL: https://github.com/maimemo/SSP-MMC-Plus
- Owner: maimemo
- License: mit
- Created: 2022-07-10T07:29:28.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-14T11:42:48.000Z (over 2 years ago)
- Last Synced: 2025-09-19T01:54:10.593Z (9 months ago)
- Topics: paper, reproducibility, spaced-repetition-algorithm
- Language: Python
- Homepage: https://doi.org/10.1109/TKDE.2023.3251721
- Size: 43.9 KB
- Stars: 58
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# SSP-MMC-Plus
Copyright (c) 2023 [MaiMemo](https://www.maimemo.com/), Inc. MIT License.
SSP-MMC-Plus is the extended version of [SSP-MMC](https://github.com/maimemo/SSP-MMC), a spaced repetition scheduling algorithm used to help learners remember more words in MaiMemo, a language learning application in China.
This repository contains a public release of the data and code used for several experiments in the following paper (which introduces SSP-MMC-Plus):
> J. Su, J. Ye, L. Nie, Y. Cao and Y. Chen, "Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2023.3251721.
The paper is a substantial extension of our previous conference paper [A Stochastic Shortest Path Algorithm for Optimizing Spaced Repetition Scheduling](https://www.maimemo.com/paper/) (free access).
When using this dataset and/or code, please cite this publication. A BibTeX record is:
```
@ARTICLE{10059206,
author={Su, Jingyong and Ye, Junyao and Nie, Liqiang and Cao, Yilong and Chen, Yongyong},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={Optimizing Spaced Repetition Schedule by Capturing the Dynamics of Memory},
year={2023},
volume={35},
number={10},
pages={10085-10097},
doi={10.1109/TKDE.2023.3251721}}
```
## Dataset and Format
The dataset is available on [Dataverse](https://doi.org/10.7910/DVN/VAGUL0) (1.6 GB). This is a 7zipped TSV file containing our experiments' 220 million MaiMemo student memory behavior logs.
The columns are as follows:
u - student user ID who reviewed the word (anonymized)
w - spelling of the word
i - total times the user has reviewed the word
d - difficulty of the word
t_history - interval sequence of the historic reviews
r_history - recall sequence of the historic reviews
delta_t - time elapsed from the last review
r - result of the review
p_recall - probability of recall
total_cnt - number of users who did the same memory behavior