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.