Естественнонаучная грамотность. Минск: рикз, 2020. 168 с
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- Figure 5. PISA 2015 Procedural Knowledge Procedural Knowledge
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Procedural Knowledge 48. A fundamental goal of science is to generate explanatory accounts of the material world. Tentative explanatory accounts are first developed and then tested through empirical enquiry. Empirical enquiry is reliant on certain well-established concepts such as the notion of dependent and independent variables, the control of variables, types of measurement, forms of error, methods for minimising error, common patterns observed in data, and methods of presenting data. It is this knowledge of the concepts and procedures that are essential for scientific enquiry that underpins the collection, analysis and interpretation of scientific data. Such ideas form a body of procedural knowledge which has also been called ‘concepts of evidence’ (Gott, Duggan, & Roberts, 2008; Millar, Lubben, Gott, & Duggan, 1995). One can think of procedural knowledge as knowledge of the standard procedures scientists use to obtain reliable and valid data. Such knowledge is needed both to undertake scientific enquiry and engage in critical review of the evidence that might be used to support particular claims. It is expected, for instance, that students will know that scientific knowledge has differing degrees of certainty associated with it and can explain why, for instance, that there is a difference between the confidence associated with measurements of the speed of light (which has been measured many times with ever more accurate instrumentation) and measurements of fish stocks in the North Atlantic or the mountain lion population in California. The examples listed in Figure 5 convey the general features of procedural knowledge that may be tested. Figure 5. PISA 2015 Procedural Knowledge Procedural Knowledge The concept of variables including dependent, independent and control variables; Concepts of measurement e.g. , quantitative [measurements], qualitative [observations], the use of a scale, categorical and continuous variables; Ways of assessing and minimising uncertainty such as repeating and averaging measurements; Mechanisms to ensure the replicability (closeness of agreement between repeated measures of the same quantity) and accuracy of data (the closeness of agreement between a measured quantity and a true value of the measure); Common ways of abstracting and representing data using tables, graphs and charts and their appropriate use; The control of variables strategy and its role in experimental design or the use of randomised controlled trials to avoid confounded findings and identify possible causal mechanisms; The nature of an appropriate design for a given scientific question e.g. , experimental, field based or pattern seeking. |