Learning Cognitive Models using Neural Networks
Proceedings of the 19th International Conference on Artificial Intelligence in Education
2018
Abstract
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive mod- els of learning are not only of scientific interest, but are also valuable in adap- tive online tutoring systems. A more accurate model yields more effective tutor- ing through better instructional decisions. Prior methods of automated cognitive model discovery have typically focused on well-structured domains, relied on student performance data or involved substantial human knowledge engineering. In this paper, we propose Cognitive Representation Learner (CogRL), a novel framework to learn accurate cognitive models in ill-structured domains with no data and little to no human knowledge engineering. Our contribution is two-fold: firstly, we show that representations learnt using CogRL can be used for accurate automatic cognitive model discovery without using any student performance data in several ill-structured domains: Rumble Blocks, Chinese Character, and Article Selection. This is especially effective and useful in domains where an accurate human-authored cognitive model is unavailable or authoring a cognitive model is difficult. Secondly, for domains where a cognitive model is available, we show that representations learned through CogRL can be used to get accurate estimates of skill difficulty and learning rate parameters without using any student perfor- mance data. These estimates are shown to highly correlate with estimates using student performance data on an Article Selection dataset.
BibTeX
@inproceedings{chaplot-aied-2018,
title = {Learning Cognitive Models using Neural Networks},
author = {Chaplot, Devendra Singh and MacLellan, Christopher J. and Salakhutdinov, Ruslan and Koedinger, Kenneth R.},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence in Education},
year = {2018},
}