论文标题
通过预测模型对过程数据进行子任务分析
Subtask Analysis of Process Data Through a Predictive Model
论文作者
论文摘要
从人类计算机互动项目中收集的响应过程数据包含有关受访者行为模式和认知过程的丰富信息。他们的不规则格式及其大小使标准统计工具难以应用。本文开发了一种计算有效的方法，用于对此类过程数据进行探索性分析。新方法将漫长的个人过程分为一系列简短的子过程，以降低复杂性，易于聚类和有意义的解释。每个子过程都被视为子任务。该分割基于使用简约的预测模型与香农熵结合的顺序动作可预测性。进行了模拟研究以评估新方法的性能。我们使用PIAAC 2012的过程数据来证明如何使用新方法对过程数据进行探索性分析。
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.
