ProcData: An R Package for Process Data Analysis.

IF 3.1 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Psychometrika Pub Date : 2021-12-01 Epub Date: 2021-08-11 DOI:10.1007/s11336-021-09798-7
Xueying Tang, Susu Zhang, Zhi Wang, Jingchen Liu, Zhiliang Ying
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引用次数: 5

Abstract

Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents' response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class 'proc' for organizing process data and extend generic methods summary and print for 'proc'. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.

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ProcData:一个过程数据分析的R包。
过程数据是指记录在计算机项目日志文件中的数据。这些数据以带有时间戳的动作序列表示,跟踪受访者的响应问题解决行为。过程数据分析旨在利用响应过程中包含的丰富信息,提高教育评估的准确性,服务于其他评估目的。本文中介绍的R包ProcData旨在提供检查、处理和分析过程数据的工具。我们定义了S3类“proc”来组织过程数据,并为“proc”扩展了通用方法summary和print。在包中实现了过程数据的特征提取方法,将不规则响应过程中的信息压缩成规则的数值向量。ProcData还提供了从基于神经网络的序列模型进行预测的功能。此外,包中还包括2012年国际学生评估项目中气候控制项目响应过程的真实数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
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