使用贝叶斯认知建模,揭开潜在的认知,元认知,战略和情感过程,这些过程是儿童解决问题的基础
Percy K Mistry1,2,3,4, Hyesang Chang1,2,3,4, Dawlat El-Said1,2,3,4
1Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine Stanford, CA 94305, US.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
一个新的贝叶斯算术解决问题的模型 (BMAPS) 揭示了孩子们如何选择和使用数学策略. 这个模型解释了数学技能的差异以及焦虑如何影响学习.
科学领域:
- 认知心理学 认知心理学
- 发展心理学 发展心理学
- 计算神经科学是一种神经科学.
背景情况:
- 儿童的数学解决问题显示出显著的个体差异.
- 推动这些差异的认知,元认知和情感因素尚未得到充分理解.
研究的目的:
- 为了开发和验证一个新的计算模型,贝叶斯算术解决问题的模型 (BMAPS),以调查儿童算术策略中潜伏的过程.
- 阐明认知策略执行,元认知策略选择和数学能力的个体差异之间的相互作用.
主要方法:
- 开发了贝叶斯算术解决问题的模型 (BMAPS),这是一个计算框架,可以从儿童算术解决问题的数据中推断认知和元认知参数.
- 应用于BMAPS衍生参数的集群分析,以确定战略使用和元认知效率的不同配置文件.
- 研究了BMAPS参数,学业成绩和焦虑等情感因素之间的关系.
主要成果:
- BMAPS成功推断了战略执行的认知参数和战略选择的元认知参数,为自适应性问题解决提供了洞察力.
- 总体而言,BMAPS参数解释了解决问题的表现中的个体差异,并预测了算术流性和数学推理的未来收益.
- 通过集群确定了战略使用和元认知效率的不同配置文件,突出了数学问题解决的各种方法.
- 该模型表明,BMAPS参数调解了焦虑和态度对儿童数学表现的影响.
结论:
- BMAPS模型提供了一个强大的计算工具,用于量化儿童数学学习中策略选择和执行的动态.
- 这项研究促进了我们对儿童数学能力和学习轨迹的个体差异的认知和元认知基础的理解.
- 这些发现强调了在数学发展和干预策略中考虑认知和情感因素的重要性.
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