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

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