-

Abstract

The purpose of this study was to investigate preservice science teachers’ preferences of representational modalities when they organize knowledge with multiple representations (MR) in an argumentation-based socioscientific issue context. In order to increase knowledge in this area of research, a three session lesson unit was designed on healthy eating subject. Preservice teachers organized their knowledge about healthy eating with a web-based knowledge organization system called iKOS. iKOS incorporates three distinct representation modalities: textual (Wiki), pictorial (Event), and Concept Map. In this paper, preservice teachers’ knowledge organization with MR that contributed to the knowledge base created through MR and the reasons for chosing specific type of representations to organize knowledge were investigated. A sequential mixed methods research design was employed. Data sources included logged iKOS statistics that reported the number of entries and the links between them; and an open ended questionnaire that included questions about the preservice teachers’ preferences and their use of MR. Social network analysis was employed to investigate the knowledge network. Results indicated that Wiki was the most created, and both Wiki and Concept Maps contributed to knowledge base more than Event. Open coding was employed to understand the underlying reasons for representational modality chocies of the preservice science teachers. Qualitative analysis results showed that the reasons for preservice teachers to choose specific type of representations were (a) the ease of representation construction, (b) creating multiple representations in one space, and (c) the appropriateness of using concept maps in learning socioscientific issues

Keywords

• Knowledge organization, Multiple representations, Socioscientific issues, Computersupported learning, Social network analysis

An examination of preservice science teachers’ representational modality preferences during computer-supported knowledge organization /Fen bilgisi öğretmen adaylarının bilgisayar destekli bilgi düzenleme sürecindeki gösterim türü tercihlerinin incelenmesi

Abstract

Bu çalışmanın amacı fen bilgisi öğretmen adaylarının çoklu gösterimlerle, argümantasyon tabanlı sosyo- bilimsel bir konunun öğreniminde bilgi düzenlemeleri sırasında gösterim türü tercihlerinin tespitidir. Bu araştırmada alanındaki mevcut bilgi düzeyini arttırmak için üç dersten oluşan bir sağlıklı beslenme ünitesi tasarlanmıştır. Çalışmada öğremen adayları iKOS adlı web tabanlı bilgi düzenleme ortamını kullanarak sağlıklı beslenme konusundaki bilgilerini düzenlemişlerdir. iKOS üç tür gösterim türünü içermektedir: metinsel (Viki), resimsel (Olay) ve Kavram haritası. Bu makalede, fen bilgisi öğretmen adaylarının bilgi düzenlemeleri sonucunda çoklu gösterimlerinin oluşturulan bilgi ağına nasıl katkıda bulunduğu ve belirli türde gösterimleri bilgi düzenlemesinde tercih etmelerinin nedenleri araştırılmıştır. Araştırmada sıralı karma yöntem araştırma deseni kullanılmıştır. Araştırmada kullanılan veri toplama araçları iKOS tarafından rapor edilen ve gösterim türlerinin sayısı ve aralarındaki bağların sayısını bildiren bir istatistik sayfası ve açık uçlu sorulardan oluşan bir ankettir. Bilgi ağının araştırılması için sosyal ağ analizi yöntemi kullanılmıştır. Analiz sonuçları Vikilerin en çok oluşturulan gösterim türü olduğunu ve Viki ve Kavram haritalarının bilgi ağına Olaylara oranla daha fazla katkı sağladığı göstermiştir. Öğretmen adaylarının gösterim türü tercihleri altında yatan nedenlerin araştırılması için açık kodlama kullanılmıştır. Nitel analiz sonuçlarına göre öğretmen adaylarının belirli bir gösterim türünü tercih etme nedenlerinin: (a) gösterim türünün oluşturulma kolaylığı, (b) tek bir sayfada çoklu gösterimler oluşturabilme ve (c) kavram haritalarının sosyal bilimsel konuların öğrenimindeki uygunluğu olduğu bulunmuştur. 

Keywords

• Bilgi düzenleme, Çoklu gösterimler, Sosyo-bilimsel konular, Bilgisayar destekli işbirlikli öğrenme, Sosyal ağ analizi

References

1. Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33(2-3), 131–152. doi:10.1016/S0360-1315(99)00029-9
2. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198. doi:10.1016/j.learninstruc.2006.03.001
3. Barreto-Espino, R., Zembal-Saul, C., & Avraamidou, L. (2014). Prospective elementary teachers’ knowledge of teaching science as argument: A case study. School Science and Mathematics, 114(2), 53–64.
4. Cobb, P., Confrey, J., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.
5. Corbin, J., & Strauss, A. (2008). Basics of qualitative research: techniques and procedures for developing grounded theory. Thousand Oaks, CA: Sage Publications, Inc.
6. Corradi, D., Elen, J., & Clarebout, G. (2012). Understanding and enhancing the use of multiple external representations in chemistry education. Journal of Science Education and Technology, 21(6), 780–795. doi:10.1007/s10956-012-9366-z
7. Cress, U., & Kimmerle, J. (2008). A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3(2), 105–122. doi:10.1007/s11412-007-9035-z
8. Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. . (2003). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 209–240). Thousand Oaks, CA: Sage.

9. Demirbag, M., & Gunel, M. (2014). Integrating argument-based science inquiry with modal representations: Impact on science achievement, argumentation, and writing skills. Educational Sciences: Theory & Practice, 14(1), 386–392. doi:10.12738/estp.2014.1.1632
10. DiSessa, A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.
11. Erickson, F. (2012). Qualitative research methods for science education. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education. (pp. 1451–1469). Dordrecht, The Netherlands: Springer.
12. Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998). The measurement of textual coherence with latent semantic analysis. Discourse Processes, 25(3), 285–307.
13. Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274.
14. Hand, B., & Choi, A. (2010). Examining the impact of student use of multiple modal representations in constructing arguments in organic chemistry laboratory classes. Research in Science Education, 40(1), 29–44. doi:10.1007/s11165-009-9155-8
15. Johnstone, A. H. (1982). Macro- and micro-chemistry. School Science Review, 64, 377–379.
16. Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75–83. Journal of Computer Assisted Learning, 7, 75–83.
17. Johnstone, A. H. (1993). The development of chemistry teaching: a changing response to a changing demand. Journal of Chemical Education, 70(9), 701–705.
18. Kaptan, F. (1998). Fen öğretiminde kavram haritası yönteminin kullanılması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 14, 95–99.
19. Knoke, D., & Yang, S. (2007). Social network analysis (Quantitative applications in the social sciences). (T. Liao, Ed.) (p. 133). Sage Publications, Inc.
20. Linn, M. C., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration (p. 340). Florence, KY: Routledge, Taylor & Francis Group.
21. Linn, M. C., & Eylon, B.-S. (2011). Science Learning and Instruction: Taking Advantage of Technology to Promote Knowledge Integration. New York: Routledge.
22. Moskaliuk, J., Kimmerle, J., & Cress, U. (2009). Wiki-supported learning and knowledge building: effects of incongruity between knowledge and information. Journal of Computer Assisted Learning, 25(6), 549–561. doi:10.1111/j.1365-2729.2009.00331.x
23. National Reseach Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Committee on conceptual framework for the new K-12 science education standards. Washington, DC: The National Academies Press.
24. NGSS Leads States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.
25. Noroozi, O., Weinberger, A., Biemans, H. J. A., Mulder, M., & Chizari, M. (2012). Argumentation-Based Computer Supported Collaborative Learning (ABCSCL): A synthesis of 15 years of research. Educational Research Review, 7(2), 79–106. doi:10.1016/j.edurev.2011.11.006
26. Novak, J. D., & Cañas, A. J. (2007). Theoretical origins of concept maps, how to construct them , and uses in education. Reflecting Education, 3(1), 29–42.
27. Pallant, A., & Lee, H.-S. (2014). Constructing scientific arguments using evidence from dynamic computational climate models. Journal of Science Education and Technology. doi:10.1007/s10956-014-9499-3
28. Sadler, T. D. (2011). Situating socio-scientific issues in classrooms as a means of achieving goals of science education. In T. D. Sadler (Ed.), Socio-scientific Issues in the Classroom Teaching, Learning and Research (Vol. 39, pp. 1–9). Dordrecht: Springer Netherlands. doi:10.1007/978-94-007-1159-4
29. Sadler, T. D., & Donnelly, L. A. (2006). Socioscientific argumentation: The effects of content knowledge and morality. International Journal of Science Education, 28(12), 1463–1488. doi:10.1080/09500690600708717
30. Scardamalia, M., & Bereiter, C. (2003). Knowledge building. New York: Macmillan. In J. W. Guthrie (Ed.), Encyclopedia of Education (2nd ed., pp. 1370–1373). New York: Macmillan.
31. Scardamalia, M., & Bereiter, C. (2006). Knowledge building : Theory , pedagogy , and technology. In K. Sawyer, R. (Ed.), The Cambridge handbook of the learning sciences (pp. 97–115). New York, NY.
32. Sterelny, K. (2005). Externalism, epistemic artefacts and the extended mind. In R. Schantz (Ed.), The externalist challenge: New studies on cognition and intentionality. Berlin: de Gruyter.
33. Tang, K.-S., Delgado, C., & Moje, E. B. (2014). An integrative framework for the analysis of multiple and multimodal representations for meaning-making in science education. Science Education, 98(2), 305–326. doi:10.1002/sce.21099
34. Tsui, C., & Treagust, D. F. (2003). Genetics Reasoning with Multiple External Representations. Research in Science Education, 33, 111–135.
35. Van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199–212. doi:10.1016/j.learninstruc.2006.03.007
36. Vanides, J., Yin, Y., Tomita, M., & Ruiz-Primo, M. A. (2005). Using concept maps in the science classroom. Science Scope, 28(8), 27–31.
37. Waldrip, B., Prain, V., & Carolan, J. (2010). Using multi-modal representations to improve learning in junior secondary science. Research in Science Education, 40(1), 65–80. doi:10.1007/s11165-009-9157-6
38. Wasserman, S., Faust, K. (1994). Social Network Analysis. New York: Cambridge University Press.
39. Wu, H.-K., & Puntambekar, S. (2012). Pedagogical affordances of multiple external representations in scientific processes. Journal of Science Education and Technology, 21, 754–767. doi:10.1007/s10956-011-9363-7
40. Zeidler, D. L., & Nichols, B. H. (2009). Socioscientific issues: Theory and practice. Journal of Elementary Science Education, 21(2), 49–58. doi:10.1007/BF03173684
41. Zeidler, D. L., Walker, K. A., Ackett, W. A., & Simmons, M. L. (2002). Tangled up in views: Beliefs in the nature of science and responses to socioscientific dilemmas. Science Education, 86(3), 343–367. doi:10.1002/sce.10025