AI 中文总结
本文提出表示学习IRT框架，结合PIAAC过程数据识别行为重要动作，评估动作与表现的关联，明确不同项目行为信息的差异。
AI 中文摘要
基于计算机的评估产生的问题解决日志过程数据，提供了受访者处理和完成任务的详细信息。然而，生成的动作序列复杂且存在噪声，难以识别与成功表现相关的特定行为。本文提出了一种表示学习项目反应模型（IRT）框架，用于识别行为重要的动作，同时考虑受访者能力和项目层面的差异。原始日志序列和时间信息首先被转换为动作表示，这些表示整合了动作标签的层级结构以及每个动作发生的序列和时间上下文。这些特定于受访者的表示随后作为协变量输入到扩展的IRT模型中，采用尖峰-板条先验来识别与回答准确性相关的动作-项目组合。因此，该框架从上下文角度评估动作，而非将其作为简单的出现指标，并提供这些动作与表现关联的后验不确定性。我们将该方法应用于经合组织国际成人能力评估计划（PIAAC）的问题解决过程数据，分析识别出一组与成功和不成功表现相关的稀疏动作，并揭示了不同项目在问题解决过程中行为信息出现位置的差异。
英文摘要
Problem-solving log process data from computer-based assessments provide detailed information about how respondents approach and complete tasks. However, the resulting action sequences are complex and noisy, making it difficult to identify specific behaviors associated with successful performance. This paper proposes a representation-learning item response modeling (IRT) framework for identifying behaviorally important actions while accounting for respondent proficiency and item-level differences. Raw log sequences and timing information are first transformed into action representations that incorporate the hierarchical structure of action labels and the sequential and temporal context in which each action occurs. These respondent-specific representations are then entered as covariates in an extended IRT model, with spike-and-slab priors used to identify action-item combinations associated with response accuracy. The framework therefore evaluates actions contextually rather than as simple occurrence indicators and provides posterior uncertainty for their associations with performance. We apply the approach to problem-solving process data from the OECD Programme for the International Assessment of Adult Competencies (PIAAC). The analysis identifies a sparse set of actions associated with successful and unsuccessful performance and reveals differences across items in where behavioral information occurs within the problem-solving process.
