Proceedings of the Annual Meeting of the Cognitive Science Society

How can we explain that people are capable of performing new tasks with no or little instruction? Earlier work has proposed that new tasks can be acquired by a rapid composition of cognitive skills, and implemented this in the ACT-R and PRIMs cognitive architectures. Here, we discuss a possible application of rapid composition in building tutoring systems. The goal is to identify underlying skills through unsupervised machine learning from a dataset of arithmetic learning for students in a Dutch vocational program. The resulting skill graph is used as a basis for a tutoring system. The results show evidence for predictive power of the system and tentative evidence of a learning benefit compared to control groups.