Computer-based interactive items have become prevalent in recent educational assessments. In such items, detailed human–computer interactive process, known as response process, is recorded in a log file. The recorded response processes provide great opportunities to understand individuals’ problem solving processes. However, difficulties exist in analyzing these data as they are high-dimensional sequences in a nonstandard format. This paper aims at extracting useful information from response processes. In particular, we consider an exploratory analysis that extracts latent variables from process data through a multidimensional scaling framework. A dissimilarity measure is described to quantify the discrepancy between two response processes. The proposed method is applied to both simulated data and real process data from 14 PSTRE items in PIAAC 2012. A prediction procedure is used to examine the information contained in the extracted latent variables. We find that the extracted latent variables preserve a substantial amount of information in the process and have reasonable interpretability. We also empirically prove that process data contains more information than classic binary item responses in terms of out-of-sample prediction of many variables.

中文翻译：

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通过多维缩放对过程数据进行潜在特征提取

基于计算机的交互式项目在最近的教育评估中变得普遍。在此类项目中，详细的人机交互过程，称为响应过程，记录在日志文件中。记录的响应过程为了解个人解决问题的过程提供了很好的机会。然而，分析这些数据存在困难，因为它们是非标准格式的高维序列。本文旨在从响应过程中提取有用的信息。特别是，我们考虑了一种探索性分析，该分析通过多维缩放框架从过程数据中提取潜在变量。描述了一种差异度量来量化两个响应过程之间的差异。所提出的方法应用于来自 PIAAC 2012 中 14 个 PSTRE 项目的模拟数据和实际过程数据。预测程序用于检查提取的潜在变量中包含的信息。我们发现提取的潜在变量在过程中保留了大量信息并且具有合理的可解释性。我们还凭经验证明，在许多变量的样本外预测方面，过程数据比经典的二元项目响应包含更多的信息。