  A cognitive model of human learning provides information about skills a
learner must acquire to perform accurately in a task domain. Cognitive models
of learning are not only of scientific interest, but are also valuable in
adaptive online tutoring systems. A more accurate model yields more effective
tutoring 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 performance data. These
estimates are shown to highly correlate with estimates using student
performance data on an Article Selection dataset.
