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The cause-effect relation of latent variables in scientific multi-text reading comprehension: Testing the sequential mediation model
Reading & Writing Pub Date : 2020-03-30 , DOI: 10.4102/rw.v11i1.256
Hsiao-Hui Lin , Yuh-Tsuen Tzeng , Hsueh-Chih Chen , Yao-Hsuan Huang

Background: The issue of science is seldom brought into focus because of the way developing assessments of students’ multiple text reading comprehension. Objectives: This study tested the sequential mediation model of scientific multi-text reading comprehension (SMTRC) by means of structural equation modelling (SEM), and aimed to advance the scientific multi-text reading comprehension assessment (SMTRCA), with a focus on discussing the causal relationship of potential variables in SMTRC. Method: Test items included 10 closed-ended and 7 open-ended questions and were categorised into four subscales: information retrieval (IR), information generalisation (IG), information interpretation (IIP), and information integration (IIG). Results: The confirmatory factor analysis results showed that there was an acceptable goodness-of-fit among the SMTRCA, indicating that the construct validity was good. Furthermore, the Cronbach’s α of the test items was 0.88, indicating good internal consistency. In addition, using 1535 students, structural equation modelling was applied to analyse the relations of the latent variables. The findings showed that when readers are performing multi-text reading comprehension, IR will simultaneously have direct influences on IG, IIP and IIG. Moreover, through IG, IR had an indirect impact on IIP; through IIP, IG had an indirect impact on IIG; through the two intermediate mediators of IG and IIP, IR had an indirect impact on IIG. Conclusion: In our data-driven model, multi-text reading comprehension is a hierarchical and complex cognitive process. That is to say, when an individual is engaging in multi-text reading comprehension, they will not just follow a single approach, but will deal with several cognitive processing routes simultaneously. Recommendations are made for future research to explore the cognitive model of scientific multi-text reading comprehension and to determine whether there are differences among multiple groups, as well as standard setting to define the cut-off scores of the criterion-referenced model, to develop an assessment reporting system of scientific multi-text reading comprehension, and strategies for scientific multi-text reading.

中文翻译:

科学多文本阅读理解中潜在变量的因果关系:测试顺序中介模型

背景:由于发展学生对多种文本阅读理解能力的评估的方式,科学问题很少引起关注。目的:本研究通过结构方程模型(SEM)测试了科学的多文本阅读理解(SMTRC)的顺序中介模型,旨在推进科学的多文本阅读理解评估(SMTRCA),重点是讨论SMTRC中潜在变量的因果关系。方法:测试项目包括10个封闭式问题和7个开放式问题,被分为四个子量表:信息检索(IR),信息概括(IG),信息解释(IIP)和信息集成(IIG)。结果:验证性因素分析结果表明,SMTRCA之间具有良好的拟合优度,表明构建体有效性良好。此外,测试项目的克伦巴赫α为0.88,表明内部一致性良好。此外,使用1535名学生,使用结构方程模型分析潜变量的关系。研究结果表明,当读者进行多文本阅读理解时,IR将同时对IG,IIP和IIG产生直接影响。此外,通过IG,IR对IIP产生了间接影响。通过IIP,IG对IIG产生了间接影响;通过IG和IIP的两个中间中介,IR对IIG产生了间接影响。结论:在我们的数据驱动模型中,多文本阅读理解是一个分层且复杂的认知过程。也就是说,当一个人正在从事多文本阅读理解时,他们将不仅遵循单一的方法,还将同时处理多个认知处理路径。为将来的研究提供了建议,以探索科学的多文本阅读理解的认知模型,确定多个组之间是否存在差异,以及确定标准参考模型的临界值的标准设置,以发展科学的多文本阅读理解评估报告系统和科学的多文本阅读策略。
更新日期:2020-03-30
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