过程数据的潜在空间模型

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Yi Chen, Jingru Zhang, Yi Yang, Young-Sun Lee
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引用次数: 1

摘要

教育评估中人机交互项目的发展为提取解决问题的有用过程信息提供了机会。然而,过程数据的复杂性、密集性和噪声性给传统的心理测量方法建模带来了挑战。社会网络方法已被应用于可视化和分析过程数据。然而,利用社会网络方法对过程信息进行统计建模的研究仍然有限。本文探讨了潜在空间模型(LSM)在教育评价过程数据分析中的应用。基于动作序列的加权有向网络和相关辅助信息,建立动作间的相邻过渡矩阵。然后,利用LSM对相邻矩阵进行建模,识别动作的低维潜在位置。介绍了基于LSM结果的三种应用:动作聚类、误差分析和性能测量。仿真研究表明,LSM可以聚类来自相同问题解决策略的动作,并通过比较学生的动作序列与最优策略来衡量学生的表现。最后,我们分析了PISA 2012的实证数据作为一个真实的案例场景来说明如何使用LSM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latent Space Model for Process Data

The development of human-computer interactive items in educational assessments provides opportunities to extract useful process information for problem-solving. However, the complex, intensive, and noisy nature of process data makes it challenging to model with the traditional psychometric methods. Social network methods have been applied to visualize and analyze process data. Nonetheless, research about statistical modeling of process information using social network methods is still limited. This article explored the application of the latent space model (LSM) for analyzing process data in educational assessment. The adjacent matrix of transitions between actions was created based on the weighted and directed network of action sequences and related auxiliary information. Then, the adjacent matrix was modeled with LSM to identify the lower-dimensional latent positions of actions. Three applications based on the results from LSM were introduced: action clustering, error analysis, and performance measurement. The simulation study showed that LSM can cluster actions from the same problem-solving strategy and measure students’ performance by comparing their action sequences with the optimal strategy. Finally, we analyzed the empirical data from PISA 2012 as a real case scenario to illustrate how to use LSM.

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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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