基于预测模型的过程数据子任务分析
Subtask Analysis of Process Data Through a Predictive Model
摘要：
从人机交互项目中收集的反应过程数据包含了丰富的关于被试行为模式和认知过程的信息。其不规则的格式和庞大的篇幅使标准的统计工具难以应用。本文开发了一种计算效率高的方法，探索性分析这样的过程数据。新的方法将一个冗长的过程分割成一系列短的子过程，以实现复杂性降低，易于聚类和有意义的解释。每个子进程都被视为一个子任务。分割是基于连续的行动的可预测性，使用简约的预测模型结合香农熵。进行模拟研究，以评估新方法的性能。我们使用PIAAC 2012的过程数据来演示如何使用新方法对过程数据进行探索性分析。
英文摘要：
Response process data collected from human-computer interactive items contain rich information about respondents' behavioral patterns and cognitive processes. Their irregular formats as well as their large sizes make standard statistical tools difficult to apply. This paper develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess performance of the new methods. We use the process data from PIAAC 2012 to demonstrate how exploratory analysis of process data can be done with the new approach.
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