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The cause-effect relation of latent variables in scientific multi-text reading comprehension: Testing the sequential mediation model

机译:科学多文读取理解潜在变量的原因效应关系:测试顺序调解模型

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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.
机译:背景:由于开展学生的多种文本阅读理解的评估,科学问题很少被带入焦点。目的:本研究通过结构方程建模(SEM)测试了科学多文读取理解(SMTRC)的顺序调解模型,并旨在推进科学多文阅读理解评估(SMTRCA),重点是讨论SMTRC潜在变量的因果关系。方法:测试项目包括10个封闭端和7个开放式问题,分为四个分量:信息检索(IR),信息泛化(IIP),信息解释(IIP)和信息集成(IIG)。结果:确认因素分析结果表明,SMTRCA中有可接受的良好适合,表明构建有效性良好。此外,测试项目的Cronbach的α为0.88,表明内部一致性良好。此外,使用1535名学生,应用结构方程建模分析潜在变量的关系。结果表明,当读者正在进行多文读取理解时,IR将同时对IG,IIP和IIG产生直接影响。此外,通过IG,IR对IIP进行间接影响;通过IIP,IG对IIG进行间接影响;通过IG和IIP的两个中间介质,IR对IIG进行间接影响。结论:在我们的数据驱动模型中,多文读取理解是一个分层和复杂的认知过程。也就是说,当个人正在参与多文本阅读理解时,他们不仅仅遵循一个方法,而且会同时处理多个认知处理路线。未来的研究建议探讨科学多文读取理解的认知模型,并确定多个组之间是否存在差异,以及定义标准参考模型的截止分数的标准设置。科学多文阅读理解的评估报告系统,以及科学多文读数的策略。

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