Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks

Mohadese Eskandari; Youngshin Kim

doi:10.29333/ejmste/12266

Full Text (PDF)

Mohadese Eskandari ¹, Youngshin Kim ¹ ^*

More Detail

¹ Department of Biology Education, College of Education, Kyungpook National University, Daegu, REPUBLIC OF KOREA^* Corresponding Author

Abstract

Textbooks can have a fundamental and positive effect on both learning and teaching. As such, in order to understand how students structure concepts in their minds, it is necessary to analyze the structure in which those concepts presented in textbooks are described. This study examined a network of concepts used in the domain of biotechnology in the Iranian 12^th grade biology textbook and investigated the alignment of science concepts used in its sub-domains. The findings of this study suggest that too many concepts, as well as unconnected ones, were used in biotechnology domain, which led to a complicated conceptual network. Therefore, this paper recommends that the number of biology concepts described in the later text books in the biotechnology domain at Iranian high schools should be reduced in order to facilitate a better formation of connections between concepts and their descriptions.

Keywords

semantic network analysis
Iranian biology textbook
biotechnology domain
biotechnology concept’s articulation

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2022, Volume 18, Issue 8, Article No: em2144

https://doi.org/10.29333/ejmste/12266

Publication date: 26 Jul 2022

Article Views: 240

Article Downloads: 147

Open Access References How to cite this article

References

Al-Muhammad, A., Ebrahimi M., Ansari, M., Behzadi, K., Salmanian, A., Alavi, E., Gholami, A., & Fakhrian, B. (2020). Iran high school biology textbook 12th grade. Iran Textbook Publishing Company.
Angus, C. H. (2004). Is textbook obsolete in new education? A critical analysis textbook in an inquiry curriculum, with special reference to the new primary general studies curriculum in Hong Kong. ERIC #ED 490764. https://eric.ed.gov/?id=ED490764
Brophy, J. (1992). Probing the subtleties of subject matter teaching. Educational Leadership, 49(7), 4-8.
Chen, S. Y., & Raffan, J. (1999). Biotechnology: student’s knowledge and attitudes in the LJK and Taiwan. Journal of Biological Education, 34(1), 17-23. https://doi.org/10.1080/00219266.1999.9655678
Choi, Y., Lim, Y., & Son, D. (2017). A semantic network analysis on the recognition of STEAM by middle school students in South Korea. EURASIA Journal of Mathematics, Science and Technology Education, 13(10), 6457-6469. https://doi.org/10.12973/ejmste/77950
Diesner, J., & Carley, K. M. (2005). Revealing social structure from texts: Meta-matrix text analysis as a novel method for network text analysis. In V. K. Narayanan, & D. J. Armstrong (Eds.), Causal mapping for research in information technology (pp. 81-108). Idea Group Publishing. https://doi.org/10.4018/978-1-59140-396-8.ch004
George, A., & Ravindran, S. (2010). Protein templates in hard tissue engineering. Nano Today, 5(4), 254-266. https://doi.org/10.1016/j.nantod.2010.05.005
Ginsburg, H. P., Klein, A., & Strkey, P (1998). The development of children’s mathematical thinking: Connecting research with practice. In W. Damon, I. E. Sigel, & K. A. Renninger (Eds.), Handbook of child psychology: Child psychology in practice (pp. 401-476). John Willey & Sons Inc.
Hutchinson, T., & Torres, E. (1994). The textbook as agent of change. English Language Teaching Journal, 48(4), 315-328. https://doi.org/10.1093/elt/48.4.315
Hwang, H., & Lu, H. (2013). Microfluidic tools for developmental studies of small model organisms -nematodes, fruit flies, and zebrafish. Biotechnology Journal, 8(2), 192-205. https://doi.org/10.1002/biot.201200129
Jang, H., & Barnett, G. (1994). Cultural differences in organizational communication: A semantic network analysis. Bulletin of Sociological Methodology, 44(1), 31-59. https://doi.org/10.1177/075910639404400104
Jeong, S., & Jang, H. (2021). Network analysis of 2015 revised life science II textbooks using TEXTOM– Focusing on the fields of genes and biotechnology. Field Science Education, 15(2), 99-120.
Kim, J., Park, S., Choi, J., & Lee, H. (2013). International comparison of curriculum relevance in primary and secondary schools. Korea Curriculum and Evaluation Institute Research Report # RRC 2013-3. Seoul.
Kim, K. (2013). A study on the connection between the life science Ⅱ of high school and the general biology of college according to the educational curriculum revision 2009-based on the chapter ‘gene and biotechnology’ [Master’s thesis, Inha University].
Kim, Y. (2016). Analysis of comparison of visual materials in Korean high school ‘life sciences Ⅱ’ four kinds of textbooks based on the 2009 revised national curriculum and two kinds of general biology textbooks [Master’s thesis, Donguk University].
Kim, Y., & Kwon, H. (2016). An comparative study of articulation on science textbook concepts and extracted concepts in learning objectives using semantic network analysis – Focus on life science domain. Journal of Korean Elementary Science Education, 35(3), 377-387. https://doi.org/10.15267/keses.2016.35.3.377
Kim, Y., Lee, Y., Lee, H., & Lim, S. (2022). Alignment of concepts of meiosis among curriculum, textbooks, classroom teaching and assessment in upper secondary school in Republic of Korea. Journal of Baltic Science Education, 21(2), 232-244. https://doi.org/10.33225/jbse/22.21.232
Lebrun, J., Lenori, Y., Laforest, M, Larose, F., Roy, G. R, Spallanzani, C., & Pearson, M (2002). Past and current trends in the analysis of textbooks in a Quebec context. Curriculum Inquiry, 32(1), 51-83. https://doi.org/10.1111/1467-873X.00215
Lee, J., & Ha, M. (2012). Semantic network analysis of science gifted middle school students’ understanding of fact, hypothesis, theory, law, and scientificness. Journal of the Korean Association for Science Education, 32(5), 823-840. https://doi.org/10.14697/jkase.2012.32.5.823
Lee, S. (2012). Network analysis methods. Nonhyung. https://doi.org/10.1007/978-3-8349-7140-1_6
Lim, S., Park, S., Yoon, H., & Kim. Y. (2021). Analysis of the network of plant-related concepts in secondary school science textbooks based on the 2015 revised curriculum. Brain, Digital & learning, 11(3), 469-481. https://doi.org/10.31216/BDL.20210030
Martínez-Gracia, M. V., Gil-Quýlez, M. J., & Osada, J. (2003). Genetic engineering: A matter that requires further refinement in Spanish secondary school textbooks. International Journal of Science Education, 25(9), 1148-1168. https://doi.org/10.1080/0950069022000038222
Niknafs, S., & Aliabadi, K. (2014). The role of content analysis in the education process. Electronic Scientific-Research Journal in the Field of Communication and Media, 8(2), 124-150.
Ormandy, E., Dale, J., & Griffin, G. (2011). Genetic engineering of animals: Ethical issues, including welfare concerns. The Canadian Veterinary Journal, 52(5), 544-550.
Paranyushkin, D. (2011). Identifying the pathways for meaning circulation using text network analysis. Nodus Labs. https://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis/
Park, H., & Leydesdorff, L. (2004). Understanding and application of the KrKwic program for content analysis in Korean: Targeting news on local innovations provided by Daum.net. Journal of the Korean Data Analysis Society, 6(5), 1377-1387.
Popping, R. (2000). Computer assisted text analysis. SAGE. https://doi.org/10.4135/9781849208741
Pyo, J. (2018). A semantic network analysis of “life science II” textbook (2015 revision): Focused on biotechnological concepts [Master’s thesis, Gangneung-Wonju National University].
Riahi, F. E., & Rezaeian, L. (2020). Content analysis of the biology textbook of high school 12^th grade based on Meril’s educational goals. Quarterly Journal of Education in Basic Sciences, 6(91), 48-58.
Staver, J. R., & Bay, M. (1989). Analysis of the conceptual structure and reasoning demands of elementary science texts at the primary (K-3) level. Journal of Researching Science Teaching, 26(4), 329-349. https://doi.org/10.1002/tea.3660260406
Weinburgh, M. (2003). Confronting and changing middle school teachers’ perceptions of scientific methodology. School Science and Mathematics, 103(5), 222-232. https://doi.org/10.1111/j.1949-8594.2003.tb18203.x
Zare, F., & Rezaean, L. (2020). Content analyze of 10th grade biology book by Flesch, Mcloughlin and close methods. Research in Biology Education, 4(4), 1-14.
Zeller, M. F. (1994). Biotechnology in the high school biology curriculum: The future is here! The American Biology Teacher, 56(8), 460-464. https://doi.org/10.2307/4449889

How to cite this article

APA

Eskandari, M., & Kim, Y. (2022). Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks. Eurasia Journal of Mathematics, Science and Technology Education, 18(8), em2144. https://doi.org/10.29333/ejmste/12266

Vancouver

Eskandari M, Kim Y. Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks. EURASIA J Math Sci Tech Ed. 2022;18(8):em2144. https://doi.org/10.29333/ejmste/12266

AMA

Eskandari M, Kim Y. Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks. EURASIA J Math Sci Tech Ed. 2022;18(8), em2144. https://doi.org/10.29333/ejmste/12266

Chicago

Eskandari, Mohadese, and Youngshin Kim. "Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks". Eurasia Journal of Mathematics, Science and Technology Education 2022 18 no. 8 (2022): em2144. https://doi.org/10.29333/ejmste/12266

Harvard

Eskandari, M., and Kim, Y. (2022). Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks. Eurasia Journal of Mathematics, Science and Technology Education, 18(8), em2144. https://doi.org/10.29333/ejmste/12266

MLA

Eskandari, Mohadese et al. "Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks". Eurasia Journal of Mathematics, Science and Technology Education, vol. 18, no. 8, 2022, em2144. https://doi.org/10.29333/ejmste/12266