The Apprentice Learner Architecture provides a framework for modeling and simulating learners working educational technologies. There are three general GitHub repositories for the AL Project:
- AL_Core (this repository), which is the core library for learner modeling used to configure and instantiate agents and author their background knowledge.
- AL_Train (https://github.com/apprenticelearner/AL_Train), which contains code for interfacing AL agents with CTAT-HTML tutors and running training experiments.
- AL_Outerloop (https://github.com/apprenticelearner/AL_Outerloop), which provides additional functionality to AL_Train simulating adaptive curricula.
To install the AL_Core library, clone the respository to your machine using the GitHub deskptop application or by running the following command in a terminal / command line:
git clone https://github.com/apprenticelearner/AL_CoreNavigate to the directory where you cloned AL_Core in a terminal / command line and run:
python -m pip install -e .Next, go to the pytorch setup guide and follow the steps specified for your operating system and environment to install pytorch.
Finally, change directory to AL_Core/django and run the migrations for the django configuration:
cd AL_Core/django/
python manage.py migrateEverything should now be fully installed and ready.
- Source code: https://github.com/apprenticelearner/AL_Core
- Documentation: https://al-core.readthedocs.io/en/latest/
We have created a number of examples to demonstrate basic usage of the Appentice Learner that make use of this repository as well as AL_Train. These can be found on the examples page of the wiki.
If you use this software in a scientific publiction, then we would appreciate citation of the following paper:
Christopher J MacLellan, Erik Harpstead, Rony Patel, and Kenneth R Koedinger. 2016. The Apprentice Learner Architecture: Closing the loop between learning theory and educational data. In Proceedings of the 9th International Conference on Educational Data Mining - EDM ’16, 151–158. Retrieved from http://www.educationaldatamining.org/EDM2016/proceedings/paper_118.pdf
Bibtex entry:
@inproceedings{MacLellan2016a,
author = {MacLellan, Christopher J and Harpstead, Erik and Patel, Rony and Koedinger, Kenneth R},
booktitle = {Proceedings of the 9th International Conference on Educational Data Mining - EDM '16},
pages = {151--158},
title = {{The Apprentice Learner Architecture: Closing the loop between learning theory and educational data}},
url = {http://www.educationaldatamining.org/EDM2016/proceedings/paper{\_}118.pdf},
year = {2016}
}