- Download the Eedi Dataset and put train.csv and misconception_mapping.csv in /eedi_data folder.
- Put credentials in .env file
- Setup a python 3.11 environment with the packages in requirements.txt
- Afterwards you can run the notebooks in ascending order. Note, find further details in the notebooks and our paper, because many of them do require manual input/labeling (e.g., preprocessing requires you to label solvability).
This project uses the EEDI Mining Misconceptions dataset, licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Attribution:
- Dataset: Eedi - Mining Misconceptions in Mathematics
- License: CC BY-NC 4.0
- Link: Kaggle - Eedi - Mining Misconceptions in Mathematics
TBD
Feel free to open an issue or send an email to yanick.zengaffinen@inf.ethz.ch.