文章基本信息

标题：Measuring Skill Growth and Evaluating Change: Unconditional and Conditional Approaches to Latent Growth Cognitive Diagnostic Models
本地全文：下载
作者：Lin, Qiao ; Xing, Kuan ; Park, Yoon Soo 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2020
卷号：11
页码：1-13
DOI：10.3389/fpsyg.2020.02205
出版社：Frontiers Media
摘要：During the past decade, cognitive diagnostic models (CDMs) have become prevalent in providing diagnostic information for learning. CDMs have generally focused on single cross-sectional time points. However, longitudinal assessments have been commonly used in education to assess students’ learning progress as well as evaluating intervention effects. Thus, it becomes natural to identify longitudinal growth in skills profiles mastery, which can yield meaningful inferences on learning. This study proposes longitudinal CDMs that incorporate latent growth curve modeling and covariate extensions, with the aim to measure the growth of skills mastery and to evaluate attribute-level intervention effects over time. Using real-world data, this study demonstrates applications of unconditional and conditional latent growth CDMs. Simulation studies show stable parameter recovery and classification of latent classes for different sample sizes. These findings suggest that building on the well-established growth modeling frameworks, applications of covariate-based longitudinal cognitive diagnostic model can help understand the effect of explanatory factors and intervention on the change of attribute mastery.
关键词：cognitive diagnostic model; covariate extension; latent growth curve; longitudinal analysis; learning progression