/dope

This reposity holds the code for paper Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network

Primary LanguagePython

This reposity holds the code for the paper Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network (Presentation)

DOPE

Instructions

  1. Make sure requirements are satisfied pip install -r requirements.txt

  2. Copy the project in a directory on your machine e.g., /home/XYZ/. Note that the dataset is in Data/data.csv

  3. To run an experiment call train.py

    Example python train.py --path /home/XYZ/ --experiment_name experiment1 --training_courses SS2,ST1 --testing_courses SS2,ST1 --training_periods 2013B,2013J --testing_periods 2014B,2014J

    Note 1. Input courses and periods are seperated by comma (if more than one course/period is intended).

    Note 2. Please refer to config.py for different parameters.

Citation

If you use the code or data in this repository, please cite the following paper

@inproceedings{karimi2020edm, title={Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network}, author={Karimi*, Hamid and Derr*, Tyler and Huang, Jiangtao and Tang, Jiliang}, booktitle={Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020)}, pages={444--460}, year={2020} }

*Equal contribution and co-first author

Contact

Web page: hamidkarimi.com

Email: hamid.karimi@usu.edu