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http://dx.doi.org/10.14697/jkase.2020.40.1.41

Exploring Teaching Method for Productive Knowledge of Scientific Concept Words through Science Textbook Quantitative Analysis  

Yun, Eunjeong (Science Education Research Institute of Kyungpook National University)
Publication Information
Journal of The Korean Association For Science Education / v.40, no.1, 2020 , pp. 41-50 More about this Journal
Abstract
Looking at the understanding of scientific concepts from a linguistic perspective, it is very important for students to develop a deep and sophisticated understanding of words used in scientific concept as well as the ability to use them correctly. This study intends to provide the basis for productive knowledge education of scientific words by noting that the foundation of productive knowledge teaching on scientific words is not well established, and by exploring ways to teach the relationship among words that constitute scientific concept in a productive and effective manner. To this end, we extracted the relationship among the words that make up the scientific concept from the text of science textbook by using quantitative text analysis methods, second, qualitatively examined the meaning of the word relationship extracted as a result of each method, and third, we proposed a writing activity method to help improve the productive knowledge of scientific concept words. We analyzed the text of the "Force and motion" unit on first grade science textbook by using four methods of quantitative linguistic analysis: word cluster, co-occurrence, text network analysis, and word-embedding. As results, this study suggests four writing activities, completing sentence activity by using the result of word cluster analysis, filling the blanks activity by using the result of co-occurrence analysis, material-oriented writing activities by using the result of text network analysis, and finally we made a list of important words by using the result of word embedding.
Keywords
scientific concept; scientific concept word; word cluster; co-occurrence; text network analysis; word embedding;
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