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 (https://github.com/apprenticelearner/AL_Core), 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 (this repository), which provides additional functionality to AL_Train simulating adaptive curricula.
This repository does the following:
- Provides functionality to the altrain script to use adaptive sequencing controllers for learner simulations.
To install the AL_Outerloop library, first follow the installation instructions for the AL_Core and AL_Train Libraries. Next, 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_OuterloopNavigate to the directory where you cloned AL_Outerloop in a terminal / command line and run:
python -m pip install -e .Everything should now be fully installed and ready.
- Source code: https://github.com/apprenticelearner/AL_Outerloop
- 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 the AL_Core and AL_Train Libraries. These can be found on the examples page of the AL_Core wiki.
If you use the broader Apprentice Learner Architecture in a scientific publication, then we would appreciate a 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}
}