Latent Feature Extraction for Process Data via Multidimensional Scaling
Latent Feature Extraction for Process Data via Multidimensional Scaling
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DOI：
10.1007/s11336-020-09708-3
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发表时间：
2020-06-22
期刊：
影响因子：
3
通讯作者：
Ying, Zhiliang
中科院分区：
文献类型：
--
作者：
Tang, Xueying;Wang, Zhi;Ying, Zhiliang
基于计算机的交互式项目已成为流行在最近的教育评估。在这些项目中，详细的人机交互过程，称为响应过程，记录在日志文件中。记录的反应过程提供了很好的机会来了解个人的问题解决过程。然而，在分析这些数据时存在困难，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，我们认为一个探索性的分析，提取潜在的变量从过程数据通过一个多维尺度框架。描述了一种相异性度量来量化两个响应过程之间的差异。该方法应用于PIAAC 2012中14个PSTRE项目的模拟数据和真实的过程数据。一个预测过程是用来检查所提取的潜变量中包含的信息。我们发现，提取的潜变量保留了大量的信息在这个过程中，并具有合理的解释。我们还实证证明，过程数据包含更多的信息，比经典的二进制项目的反应，在许多变量的样本外预测。
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.