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Learning About Measurement Uncertainties in Secondary Education: A Model of the Subject Matter.

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Title: Learning About Measurement Uncertainties in Secondary Education: A Model of the Subject Matter.
Authors: Priemer, Burkhard1 priemer@physik.hu-berlin.de, Hellwig, Julia2
Source: International Journal of Science & Mathematics Education. Jan2018, Vol. 16 Issue 1, p45-68. 24p.
Subject Terms: *Curriculum, *Physics education, *Science education, *Secondary education, Measurement
Abstract: Estimating measurement uncertainties is important for experimental scientific work. However, this is very often neglected in school curricula and teaching practice, even though experimental work is seen as a fundamental part of teaching science. In order to call attention to the relevance of measurement uncertainties, we developed a comprehensive model that structures and describes all subject matter on measurement uncertainties relevant to secondary education (age 13-19 years). It consists of ten basic concepts categorized within the following four dimensions: (a) existence of uncertainties, (b) handling of uncertainties, (c) assessment of uncertainties, and (d) conclusiveness of uncertainties. The model was developed by reviewing the subject literature, constructing a model for university level, validating this model with 6 experts in metrology (the science of measurement), adapting the model to the target group, and validating the simplified model with 108 science teachers. We present the model and its development by describing the dimensions and concepts and by giving examples. Thus, our work provides a base for developing and assessing instructions to teach the estimation of measurement uncertainties in secondary education. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Science+%26+Mathematics+Education%22">International Journal of Science & Mathematics Education</searchLink>. Jan2018, Vol. 16 Issue 1, p45-68. 24p.
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  Data: Estimating measurement uncertainties is important for experimental scientific work. However, this is very often neglected in school curricula and teaching practice, even though experimental work is seen as a fundamental part of teaching science. In order to call attention to the relevance of measurement uncertainties, we developed a comprehensive model that structures and describes all subject matter on measurement uncertainties relevant to secondary education (age 13-19 years). It consists of ten basic concepts categorized within the following four dimensions: (a) existence of uncertainties, (b) handling of uncertainties, (c) assessment of uncertainties, and (d) conclusiveness of uncertainties. The model was developed by reviewing the subject literature, constructing a model for university level, validating this model with 6 experts in metrology (the science of measurement), adapting the model to the target group, and validating the simplified model with 108 science teachers. We present the model and its development by describing the dimensions and concepts and by giving examples. Thus, our work provides a base for developing and assessing instructions to teach the estimation of measurement uncertainties in secondary education. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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