基于日志文件的行为过程数据分析的顺序储层计算

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Jiawei Xiong, Shiyu Wang, Cheng Tang, Qidi Liu, Rufei Sheng, Bowen Wang, Huan Kuang, Allan S. Cohen, Xinhui Xiong
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引用次数: 0

摘要

近年来,随着越来越多的测评由计算机进行,在测评中使用过程数据的做法越来越受关注。记录在计算机日志文件中的过程数据可以捕捉考生在测评过程中的反应活动顺序,例如,带有时间戳记的击键。传统的测量方法往往不足以处理这类数据。在本文中,我们提出了一种基于水库计算模型的顺序水库法(SRM),该模型使用回波状态网络,并以粒子群优化和奇异值分解作为优化手段。该方法旨在通过计算自学习算法对过程数据中的特征进行正则化处理,我们利用模拟数据和经验数据对该方法进行了评估。模拟结果表明,一方面,该模型能有效地将动作序列转化为标准化和有意义的特征;另一方面,这些特征有助于对潜在行为组进行分类和预测潜在信息。实证结果进一步表明,SRM 可以预测评估效率。通过相关性分析,SRM 提取的特征与动作序列长度的相关性得到了验证。所提出的方法提高了从过程数据中提取和获取有意义信息的能力,为现有的过程数据技术提供了一种替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sequential Reservoir Computing for Log File‐Based Behavior Process Data Analyses
The use of process data in assessment has gained attention in recent years as more assessments are administered by computers. Process data, recorded in computer log files, capture the sequence of examinees' response activities, for example, timestamped keystrokes, during the assessment. Traditional measurement methods are often inadequate for handling this type of data. In this paper, we proposed a sequential reservoir method (SRM) based on a reservoir computing model using the echo state network, with the particle swarm optimization and singular value decomposition as optimization. Designed to regularize features from process data through a computational self‐learning algorithm, this method has been evaluated using both simulated and empirical data. Simulation results suggested that, on one hand, the model effectively transforms action sequences into standardized and meaningful features, and on the other hand, these features are instrumental in categorizing latent behavioral groups and predicting latent information. Empirical results further indicate that SRM can predict assessment efficiency. The features extracted by SRM have been verified as related to action sequence lengths through the correlation analysis. This proposed method enhances the extraction and accessibility of meaningful information from process data, presenting an alternative to existing process data technologies.
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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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