A Sparse Latent Class Model for Cognitive Diagnosis
A Sparse Latent Class Model for Cognitive Diagnosis
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DOI：
10.1007/s11336-019-09693-2
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发表时间：
2020-03-01
期刊：
影响因子：
3
通讯作者：
Liang, Feng
中科院分区：
文献类型：
--
作者：
Chen, Yinyin;Culpepper, Steven;Liang, Feng
认知诊断模型（cognitive diagnostic models，CDM）是一种潜在变量模型，用于推断潜在技能、知识或个性，这些潜在技能、知识或个性是教育、心理和社会科学测试和测量的基础。最近的研究集中在理论和方法，使用稀疏潜在类模型（SLCM）在探索性的方式来推断潜在的过程和结构的反应。我们报告新的理论结果的SLCM参数的通用可识别性的充分条件。实践的一个重要贡献是，我们的新的通用可识别性条件更有可能满足经验的应用程序比现有的条件，确保严格的可识别性。学习潜在的潜在结构可以用公式表示为变量选择问题。我们开发了一个新的贝叶斯变量选择算法，明确执行通用的可识别性条件和单调性的项目响应函数，以确保有效的后验推理。我们目前的Monte Carlo模拟结果，以支持准确的推论，并讨论我们的研究结果对未来SLCM研究和教育测试的影响。
Cognitive diagnostic models (CDMs) are latent variable models developed to infer latent skills, knowledge, or personalities that underlie responses to educational, psychological, and social science tests and measures. Recent research focused on theory and methods for using sparse latent class models (SLCMs) in an exploratory fashion to infer the latent processes and structure underlying responses. We report new theoretical results about sufficient conditions for generic identifiability of SLCM parameters. An important contribution for practice is that our new generic identifiability conditions are more likely to be satisfied in empirical applications than existing conditions that ensure strict identifiability. Learning the underlying latent structure can be formulated as a variable selection problem. We develop a new Bayesian variable selection algorithm that explicitly enforces generic identifiability conditions and monotonicity of item response functions to ensure valid posterior inference. We present Monte Carlo simulation results to support accurate inferences and discuss the implications of our findings for future SLCM research and educational testing.