Supplementary Materials: Evaluating the Impact of Data Augmentation on Predictive Model Performance

This repository contains supplementary materials for the following conference paper:

Valdemar Švábenský, Conrad Borchers, Elizabeth B. Cloude, and Atsushi Shimada. 2025.
Evaluating the Impact of Data Augmentation on Predictive Model Performance.
In Proceedings of the 15th Learning Analytics and Knowledge Conference (LAK '25).
Association for Computing Machinery, New York, NY, USA.
DOI: 10.1145/3706468.3706485

Preprint: https://arxiv.org/pdf/2412.02108 

@inproceedings{Svabensky2025evaluating,
    author    = {\v{S}v\'{a}bensk\'{y}, Valdemar and Borchers, Conrad and Cloude, Elizabeth B. and Shimada, Atsushi},
    title     = {{Evaluating the Impact of Data Augmentation on Predictive Model Performance}},
    booktitle = {Proceedings of the 15th International Learning Analytics and Knowledge Conference},
    series    = {LAK '25},
    location  = {Dublin, Ireland},
    publisher = {Association for Computing Machinery},
    address   = {New York, NY, USA},
    month     = {03},
    year      = {2025},
    pages     = {126--136},
    numpages  = {11},
    isbn      = {979-8-4007-0701-8},
    url       = {https://doi.org/10.1145/3706468.3706485},
    doi       = {10.1145/3706468.3706485},
}

Repository content:

Full code and results for the paper. Please see the README.md file in the attached ZIP archive for details.

If you use or build upon the materials, please use the BibTeX entry above to cite the original paper.