Analysis of the Cognitive Level of Meta-modeling Knowledge Components of Science Gifted Students Through Modeling Practice

Journal of the Korean Chemical Society

article

Kihyang Kim¹  Seoung-Hey Paik² 

[1] Sejong Academy of Science and Arts;Department of Chemistry Education, Korea National University of Education

关键词: Meta-modeling knowledge;    Anomalies;    Model variability;    Model multiplicity;    Modeling process;    Modeling practice;   

DOI  :  10.5012/jkcs.2023.67.1.42

学科分类：化学（综合）

来源: Korean Chemical Society

【 摘 要 】

The purpose of this study is to obtain basic data for constructing a modeling practice program integrated withmeta-modeling knowledge by analyzing the cognition level for each meta-modeling knowledge components through modeling practice in the context of the chemistry discipline content. A chemistry teacher conducted inquiry-based modeling practice including anomalous phenomena for 16 students in the second year of a science gifted school, and in order to analyze thecognition level for each of the three meta-modeling knowledge components such as model variability, model multiplicity, andmodeling process, the inquiry notes recorded by the students and observation note recorded by the researcher were used foranalysis. The recognition level was classified from 0 to 3 levels. As a result of the analysis, it was found that the cognitionlevel of the modeling process was the highest and the cognition level of the multiplicity of the model was the lowest. Thecause of the low recognitive level of model variability is closely related to students' perception of conceptual models as objective facts. The cause of the low cognitive level of model multiplicity has to do with the belief that there can only be one correct 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 introduce preliminary activities that can explicitly guide the nature of the model, and guide the importance of data interpretationthrough specific examples. Training to consider and verify the acceptability of the proposed model from a different point ofview than mine should be done through a modeling practice program.

【 授权许可】

Unknown