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. The experimental results of simulated data demonstrate that the proposed algorithm can accurately restore various preset laws within a certain range of noise, in continues feedback setting. Application of proposed method to Lumosity training data demonstrates superior performance compared to traditional and latest models in terms of fitness. The results indicate the discovery of two new forms of skill acquisition laws, while some previous findings have been reaffirmed.

翻译：技能习得作为认知心理学的重要研究领域，涉及多种心理过程。现有实验范式下发现的定律存在争议且缺乏普适性。本文旨在从大规模训练日志数据中挖掘技能学习定律。针对认知状态不可观测及搜索中的算法爆炸问题，开发了两阶段算法：首先采用深度学习模型确定学习者认知状态并评估特征重要性，进而利用符号回归算法将神经网络模型解析为代数方程。模拟数据实验表明，在连续反馈设定下，所提算法能在一定噪声范围内准确恢复多种预设定律。将该方法应用于Lumosity训练数据，结果在拟合优度方面优于传统及最新模型。研究结果表明，在重申部分已有发现的同时，发现了两种新形式的技能习得定律。