LEARNING CONCEPT NETWORKS IN THE PHOTOSYNTHESIS BASED ON STUDENTS’ COGNITIVE LEVELS

Author:

Lim Soo-min1ORCID,Chun Hyunju1ORCID,Lee Hyonyong1ORCID,Kim Youngshin1ORCID

Affiliation:

1. Kyungpook National University, Republic of Korea

Abstract

An important educational goal is enabling students to learn scientific concepts. The scientific concepts learned in class are developed within students’ cognitive structures. Despite the successful application of Semantic Network Analysis (SNA) to study these cognitive structures, there has been limited examination of students' concept networks based on their individual characteristics. Therefore, this study aims to evaluate the differences in students’ characteristics based on their cognitive levels, which influence their thinking and behavior. To analyze these differences, this study compares concept and connected concept networks, focusing on photosynthesis, a challenging life sciences topic. The study’s results indicate that students could not clearly distinguish concepts by subtopic, but there were changes in the concept network after class. Although the types and number of concepts students knew were similar depending on their cognitive level, the concept network structure differed. Additionally, some students could not distinguish between similar concepts. Thus, teachers are advised to differentiate between similar concepts during instruction and address personal variables such as students' cognitive levels. Keywords: cognitive level, concept network, connected concept network, learning concept, photosynthesis

Publisher

Scientia Socialis Ltd

Reference39 articles.

1. Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564. https://doi.org/10.1016/j.socnet.2007.04.002

2. Choi, B., & Hur, M. (1987). Relationships between the cognitive levels of students and understanding of concrete and formal science content. Journal of the Korea Association for Science Education, 7(1), 19–32.

3. Choi, Y., Choi, B., & Lee, W. (1985). A study on the formation of middle and high school students' logical thinking skills Ⅰ. Journal of the Korea Association for Science Education, 5(1), 1–9.

4. Chung, D., Cho, A., & Park, K. (2018). A case study on usage of semantic network analysis for concept analysis of textbooks: Focused on mantle concept of earth science Ⅱ textbooks. The Journal of Learner-Centered Curriculum and Instruction, 18(12), 89–112.

5. Cochran-Smith, M., & Lytle, S. L. (1999). Relationships of knowledge and practice: Teacher learning on communities. Review of Research on Education, 24(1), 249–305.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3