从自然发生的数据中自动发现管理技能获取的象征性规律
Sannyuya Liu1,2,3, Qing Li1,2, Xiaoxuan Shen4,5
1National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China.
Nature computational science
|May 29, 2024
概括
这项研究从大规模培训数据中发现了技能获取的新规律,使用了新的深度学习和符号回归算法. 这些发现为认知心理学和技能学习过程提供了更具概括性的见解.
科学领域:
- 认知心理学 认知心理学
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 技能获取研究传统上依赖于具有有限概括性的实验范式.
- 现有的模型与不可观察的认知状态和计算复杂性作斗争.
- 大规模的培训日志数据为发现强有力的技能学习规律提供了一个有希望的途径.
研究的目的:
- 从大规模的训练日志数据中发现技能学习的可概括规律.
- 开发一种处理不可观察的认知状态和搜索复杂性的计算方法.
- 通过对现有发现和基准数据集验证发现的规律.
主要方法:
- 一个两阶段的算法,结合了深度学习用于认知状态推断和象征回归用于定律发现.
- 深度学习模型以识别学习者状态和特征的重要性.
- 符号回归用于将神经网络模型转换为可解释的代数方程.
主要成果:
- 该算法在模拟的噪音,连续反环境中准确地恢复了预定义的规律.
- 对Lumosity培训数据的应用表明,与传统和最近的模型相比,其性能优越.
- 识别了两种新型的技能获取法规,并重申了先前的发现.
结论:
- 开发的算法有效地从大型数据集中提取可概括的技能学习规律.
- 这种方法克服了认知心理学中传统实验方法的局限性.
- 这些发现为人类技能获取和学习的机制提供了新的见解.
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