一种基于稀疏非负矩阵因子化的探索性Q矩阵估计方法
Jianhua Xiong1,2, Zhaosheng Luo3, Guanzhong Luo1
1School of Psychology, Jiangxi Nomal University, Nanchang, China.
Behavior research methods
|July 26, 2024
概括
这项研究引入了一种新的基于数据的认知诊断评估 (CDA) Q矩阵估计的新方法. 稀疏非负矩阵因子化 (SNMF) 方法可以准确地估计属性和Q矩阵元素,而无需事先的知识.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 数据科学数据科学数据科学
背景情况:
- 认知诊断评估 (CDA) 提供了详细的诊断信息.
- Q矩阵是CDA的基础,通常由专家或数据驱动方法定义.
- 现有的数据驱动的Q矩阵方法往往需要先前的知识,限制了它们的应用.
研究的目的:
- 提出一种新的数据驱动方法,同时估计属性和Q矩阵元素的数量.
- 开发一种不需要任何先前知识的方法,解决当前方法的局限性.
- 在G-DINA模型下应用稀疏非负矩阵分解 (SNMF) 方法.
主要方法:
- 开发了使用SNMF (Sparse Non-negative Matrix Factorization) 的属性数和Q矩阵元素的同时估计方法.
- 拟议的方法在G-DINA模型下运行,不需要初始的Q矩阵,q向量或属性计数.
- 采用模拟研究来评估SNMF方法的性能和准确性.
主要成果:
- 在各种模拟条件下,SNMF在准确估计属性数量和Q矩阵元素方面表现强.
- 该方法显示出良好的可扩展性和通用性,适合复杂的数据集.
- 成功的应用程序用现实世界的数据集来说明.
结论:
- 拟议的SNMF方法为CDA中数据驱动的Q矩阵估计提供了一个客观,准确和具有成本效益的方法.
- 这种方法有效地克服了对先前知识的需求,提高了CDA的实用性.
- 未来的研究应该探索SNMF在认知诊断评估中的进一步改进和应用.
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