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Titlebook: Advances in Applications of Rasch Measurement in Science Education; Xiufeng Liu,William J. Boone Book 2023 The Editor(s) (if applicable) a

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發(fā)表于 2025-3-21 19:37:36 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Applications of Rasch Measurement in Science Education
影響因子2023Xiufeng Liu,William J. Boone
視頻videohttp://file.papertrans.cn/147/146671/146671.mp4
發(fā)行地址Includes principles and best practices in applications of Rasch measurement in science education.Covers the frontier of measurement and evaluation in science education.Brings together Prominent Intern
學(xué)科分類Contemporary Trends and Issues in Science Education
圖書(shū)封面Titlebook: Advances in Applications of Rasch Measurement in Science Education; Xiufeng Liu,William J. Boone Book 2023 The Editor(s) (if applicable) a
影響因子.This edited volume presents latest development in applications of Rasch measurement in science education.? It includes a conceptual introduction chapter and a set of individual chapters.? The introductory chapter reviews published studies applying Rasch measurement in the field of science education and identify important principles of Rasch measurement and best practices in applications of Rasch measurement in science education.? The individual chapters, contributed by authors from Canada, China, Germany, Philippines and the USA, cover a variety of current topics on measurement concerning science conceptual understanding, scientific argumentation, scientific reasoning, three-dimensional learning, knowledge-in-use and cross-cutting concepts of the Next Generation Science Standards, medical education learning experiences, machine-scoring bias, formative assessment, and teacher knowledge of argument. There are additional chapters on advances in Rasch analysis techniquesand technology including R, Bayesian estimation, comparison between joint maximum likelihood (JML) and marginal maximum likelihood (MML) estimations on model-data-fit, and enhancement to Rasch models by Cognitive Diagn
Pindex Book 2023
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https://doi.org/10.1007/978-1-4612-2406-8in practice, and inconsistent benchmarks for data interpretation. To mitigate these issues, recommendations are made for stricter peer-review processes and more professional development opportunities.
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Nikolaos Kamperidis,Naila Arebibal climate change epistemological knowledge. Data was provided by a module involving EzGCM (Easy Global Climate Modeling), a web-based climate modeling tool. Overall, there was not a statistically significant difference between the JML and MML estimators.
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James L. Hourrigan,Albert L. Klingspornow how to apply the PCM in an explanatory way using the Programme for International Student Assessment (PISA) dataset and illustrate how to obtain qualitatively meaningful relationships between explanatory variables and students’ scientific literacy. Bayesian Partial Credit Model and Its Applications in Science Education.
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Brigitte Collins,Elissa Bradshaw Rasch analysis and combine the LCA’s results with a unidimensional Rasch model. Our presentation is based on a concrete empirical example that investigates experimental design errors and includes the used data set and R scripts as supplemental materials.
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