Exploring Latent Constructs through Multimodal Data Analysis

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Shiyu Wang, Shushan Wu, Yinghan Chen, Luyang Fang, Liang Xiao, Feiming Li
{"title":"Exploring Latent Constructs through Multimodal Data Analysis","authors":"Shiyu Wang, Shushan Wu, Yinghan Chen, Luyang Fang, Liang Xiao, Feiming Li","doi":"10.1111/jedm.12412","DOIUrl":null,"url":null,"abstract":"This study presents a comprehensive analysis of three types of multimodal data‐response accuracy, response times, and eye‐tracking data‐derived from a computer‐based spatial rotation test. To tackle the complexity of high‐dimensional data analysis challenges, we have developed a methodological framework incorporating various statistical and machine learning methods. The results of our study reveal that hidden state transition probabilities, based on eye‐tracking features, may be contingent on skill mastery estimated from the fluency CDM model. The hidden state trajectory offers additional diagnostic insights into spatial rotation problem‐solving, surpassing the information provided by the fluency CDM alone. Furthermore, the distribution of participants across different hidden states reflects the intricate nature of visualizing objects in each item, adding a nuanced dimension to the characterization of item features. This complements the information obtained from item parameters in the fluency CDM model, which relies on response accuracy and response time. Our findings have the potential to pave the way for the development of new psychometric and statistical models capable of seamlessly integrating various types of multimodal data. This integrated approach promises more meaningful and interpretable results, with implications for advancing the understanding of cognitive processes involved in spatial rotation tests.","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":"69 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1111/jedm.12412","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

This study presents a comprehensive analysis of three types of multimodal data‐response accuracy, response times, and eye‐tracking data‐derived from a computer‐based spatial rotation test. To tackle the complexity of high‐dimensional data analysis challenges, we have developed a methodological framework incorporating various statistical and machine learning methods. The results of our study reveal that hidden state transition probabilities, based on eye‐tracking features, may be contingent on skill mastery estimated from the fluency CDM model. The hidden state trajectory offers additional diagnostic insights into spatial rotation problem‐solving, surpassing the information provided by the fluency CDM alone. Furthermore, the distribution of participants across different hidden states reflects the intricate nature of visualizing objects in each item, adding a nuanced dimension to the characterization of item features. This complements the information obtained from item parameters in the fluency CDM model, which relies on response accuracy and response time. Our findings have the potential to pave the way for the development of new psychometric and statistical models capable of seamlessly integrating various types of multimodal data. This integrated approach promises more meaningful and interpretable results, with implications for advancing the understanding of cognitive processes involved in spatial rotation tests.
通过多模态数据分析探索潜在结构
本研究全面分析了基于计算机的空间旋转测试中的三种多模态数据--反应准确性、反应时间和眼动数据。为了应对复杂的高维数据分析挑战,我们开发了一个方法框架,其中融合了各种统计和机器学习方法。我们的研究结果表明,基于眼动跟踪特征的隐藏状态转换概率,可能取决于根据流畅性 CDM 模型估计的技能掌握程度。隐藏状态轨迹为空间旋转问题的解决提供了额外的诊断见解,超过了仅由流畅性 CDM 提供的信息。此外,被试在不同隐藏状态下的分布也反映了每个项目中物体视觉化的复杂性,为项目特征的描述增添了一个细微的维度。这是对流畅度 CDM 模型中通过项目参数获得的信息的补充,而流畅度 CDM 模型依赖于反应准确性和反应时间。我们的研究结果有望为开发新的心理测量和统计模型铺平道路,使其能够无缝整合各种类型的多模态数据。这种整合方法有望得到更有意义、更可解释的结果,从而促进对空间旋转测试所涉及的认知过程的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信