比较联合与分步方法，发现不同分析策略影响对学习者技能发展的判断。
Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis
- 联合建模同时估计技能结构与学习过程，分步法先确定结构再分析学习。
- 两种方法在多数学习者已掌握技能上一致，但在部分掌握人数上分歧明显。
- 当技能结构不确定或题目池变化时，联合分析更可靠，适合教育数据研究者。
数字学习环境记录学习者对单个题目的作答，使研究特定技能的发展成为可能。从这些数据中推断学习情况需依赖将作答与潜在技能关联并追踪其随时间变化的模型。当每个题目测量的技能未知时，分析者必须决定是联合估计技能结构（Q矩阵）与学习过程，还是先建立结构再研究学习。我们发现这一选择会影响关于学习发展的实质性结论。使用动态认知诊断模型，分析了来自两个阅读游戏的数据，涵盖二年级至三年级的词汇与理解能力，利用题目文本嵌入提供未知Q矩阵的先验信息。联合分析与校正偏差的分步分析均显示大多数学习者向两项技能的掌握迈进，但在三年级仍处于部分掌握状态的学习者数量上存在分歧，影响阅读进展报告。模拟研究识别出两种分析分歧的条件，表明当项目-技能结构不确定且题目池跨年级变化时，联合分析更可靠。本文提供两种分析的R代码。
原文摘要 · Abstract (English)
Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than overall scores. Drawing conclusions about learning from these data requires a model that links responses to latent skills and tracks how mastery changes over time. When the skills measured by each item are unknown, the analyst must decide whether to estimate this structure, the Q-matrix, jointly with the learning process, or to establish it first and study learning afterwards. We show that this decision can change substantive conclusions about how learners develop. Using dynamic cognitive diagnostic models, we analyse data from two reading games measuring vocabulary and comprehension from Grade 2 to Grade 3, with item-text embeddings providing prior information for the unknown Q-matrix. A joint analysis and a bias-corrected stepwise analysis agree that most learners move toward mastering both skills, but disagree about how many remain only partially proficient at Grade 3, changing how reading progress would be reported. A simulation study identifies when the two analyses diverge and shows that joint analysis is more reliable when the item-skill structure is uncertain and the item pool changes between grades. We provide R code for both analyses.
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