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KMID : 1059520230670010042
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Journal of the Korean Chemical Society
2023 Volume.67 No. 1 p.42 ~ p.53
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Analysis of the Cognitive Level of Meta-modeling Knowledge Components of Science Gifted Students Through Modeling Practice
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Kim Ki-Hyang
Paik Seoung-Hey
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Abstract
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The purpose of this study is to obtain basic data for constructing a modeling practice program integrated with meta-modeling knowledge by analyzing the cognition level for each meta-modeling knowledge components through model- ing practice in the context of the chemistry discipline content. A chemistry teacher conducted inquiry-based modeling prac- tice including anomalous phenomena for 16 students in the second year of a science gifted school, and in order to analyze the cognition level for each of the three meta-modeling knowledge components such as model variability, model multiplicity, and modeling process, the inquiry notes recorded by the students and observation note recorded by the researcher were used for analysis. The recognition level was classified from 0 to 3 levels. As a result of the analysis, it was found that the cognition level of the modeling process was the highest and the cognition level of the multiplicity of the model was the lowest. The cause of the low recognitive level of model variability is closely related to students' perception of conceptual models as objec- tive facts. The cause of the low cognitive level of model multiplicity has to do with the belief that there can only be one cor- rect model for a given phenomenon. Students elaborated conceptual models using symbolic models such as chemical symbols, but lacked recognition of the importance of data interpretation affecting the entire modeling process. It is necessary to intro- duce preliminary activities that can explicitly guide the nature of the model, and guide the importance of data interpretation through specific examples. Training to consider and verify the acceptability of the proposed model from a different point of view than mine should be done through a modeling practice program
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KEYWORD
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Meta-modeling knowledge, Anomalies, Model variability, Model multiplicity, Modeling process, Modeling practice
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