Every teacher understands that different students benefit from different activities. Recent advances in data processing allow us to detect and use behavioral variability for adapting to a student. This approach allows us to optimize learning process but does not focus on understanding it. Conversely, classical findings in educational sciences allow us to understand the learner but are hard to embed in a large scale adaptive system. In this study we design and build a framework to investigate when the two approaches coincide.
@article{arxiv.1703.01377,
title = {Learning styles: Literature versus machine learning},
author = {Farah Bouassida and Łukasz Kidziński and Pierre Dillenbourg},
journal= {arXiv preprint arXiv:1703.01377},
year = {2017}
}