Accessible Learning Analytics

This repository contains supplementary material for "Accessible Learning Analytics", published in the Companion Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK'19). This is an analysis of Moodle course student data done on Jupyter Notebook. In this study two main sources of data are used: student activity log and students grades (assignments and final grade).

Installation - Preparing of the experiment platform

Step-by-step guide for environment setup on Windows.

Files

There are two files of code - both in Jupyter notebook format: functions.ipynb and ala.ipynb.

Citation

If you use ALA's code or you take the publication as a reference for your research, please cite our work in the following way:

Mohammed Ibrahim, Daniel McSweeney, and Geraldine Gray. Accessible learning analytics. In Companion Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), pages 202–203, 2019.

Bibtex entry:

@inProceedings{ibrahimALA19,
  title={Accessible Learning Analytics},
  author = {Mohammed Ibrahim
  and Daniel McSweeney
  and Geraldine Gray},
  booktitle={Companion Proceedings of the 9th International Conference on Learning Analytics \& Knowledge (LAK'19)},
pages={202--203},
  year={2019}
}

Disclaimer

The code is undergoing more testing and integration of other features. The future versions of this jupyter notebook will include more documentation, examples and optimizations.