  Skill acquisition is a key area of research in cognitive psychology as it
encompasses multiple psychological processes. The laws discovered under
experimental paradigms are controversial and lack generalizability. This paper
aims to unearth the laws of skill learning from large-scale training log data.
A two-stage algorithm was developed to tackle the issues of unobservable
cognitive states and algorithmic explosion in searching. Initially a deep
learning model is employed to determine the learner's cognitive state and
assess the feature importance. Subsequently, symbolic regression algorithms are
utilized to parse the neural network model into algebraic equations.
Experimental results show the algorithm can accurately restore preset laws
within a noise range in continuous feedback settings. When applied to Lumosity
training data, the method outperforms traditional and recent models in fitness
terms. The study reveals two new forms of skill acquisition laws and reaffirms
some previous findings.
