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Content Validity Uses Rasch Model on Computerized Testlet Instrument to Measure Chemical Literacy Capabilities

机译:内容有效性在计算机化测试仪器上使用RASCH模型来测量化学识字能力

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Content validity is useful for testing the suitability of test items with the materials and capabilities to be measured. This validity is done by testing the feasibility of the instrument through panel discussions by experts who are competent in their fields. In this study, content validity was applied to the computerized testlet instrument. Computerized testlet is a group of multiple-choice items to reveal the same information developed in a computerized system. The computerized testlet instrument in this study consists of sevens stem (the subject matter), each of with consists of three interrelated multiple-choice questions. This computerized testlet instrument was validated by nine raters consisting of three chemistry education experts, three chemistry teachers, and three chemistry education students. The validation was done by referring to the assessment of five aspects including the aspects of scientific competence, science subject knowledge, question context, spelling and language, and clarity of the question command. Assessment was done by giving a score of 1-4 in each aspect that refers to the assessment rubric. This research was a quantitative study using Rasch Model analysis. The result of the analysis shows that the exact agreement is 56.2% and the expected agreement is 58.2%. The most difficult aspects to be achieved in the sequential assessment of the questions are the aspects of spelling and language, clarity of the question command, science subject knowledge, and question context, while the easiest aspects to be achieved are the aspects of scientific competence.
机译:内容有效性对于测试测试项目的适用性是有用的,可以使用要测量的材料和能力。这种有效性是通过测试仪器通过专家们在其领域的专家讨论来完成的。在这项研究中,将内容有效性应用于计算机化的Testlet仪器。计算机化Testlet是一组多项选择项目,以显示在计算机化系统中开发的相同信息。本研究中的计算机化测试仪器由Sevens Step(主题)组成,每个都包括三个相互关联的多项选择题。这款计算机化的Testlet仪器由九名化学教育专家,三个化学教师和三个化学教育学生组成的九名评估者验证。验证是通过参考五个方面的评估来完成的,包括科学能力,科学主题知识,问题背景,拼写和语言的方面,以及问题命令的清晰度。通过在每个方面给出1-4的评分来完成评估,这是指评估量规。该研究是使用Rasch模型分析的定量研究。分析结果表明,确切的协议是56.2%,预期的协议是58.2%。在对问题的顺序评估中最困难的方面是拼写和语言的各个方面,阐明的问题命令,科学主题知识和问题背景,而待实现的最简单方面是科学能力的方面。

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