Inquiry based learning and meaning generation through modelling on geometrical optics in a constructionist environment

Constantina Kotsari 1 * , Zacharoula Smyrnaiou 1
More Detail
1 School of Philosophy, Pedagogy & Psychology, National & Kapodistrian University of Athens, Athens, Greece
* Corresponding Author
EUR J SCI MATH ED, Volume 5, Issue 1, pp. 14-27. https://doi.org/10.30935/scimath/9494
OPEN ACCESS   1664 Views   1019 Downloads
Download Full Text (PDF)

ABSTRACT

The central roles that modelling plays in the processes of scientific enquiry and that models play as the outcomes of that enquiry are well established (Gilbert & Boulter, 1998). Besides, there are considerable similarities between the processes and outcomes of science and technology (Cinar, 2016). In this study, we discuss how the use of digital tools for modelling geometrical optics supports meaning generation. More specifically, it tests: a) the role of educational software "Law of Light Reflection" in the generation of scientific meanings, b) the factors affecting this meaning generation and c) the extent to which integrated physical concepts, strategies, cooperation and verbal interaction affect scientific meaning generation. The research methodology adopted for the purpose of compliance with these three dimensions is design-based research which enabled us to investigate the effectiveness and the dynamic interaction of the tool with the specific cognitive skills. In this study, we used a specially designed of open-software for modelling on geometrical optics phenomena. The use of digital tools for modelling is studied from two perspectives: inquiry based science learning and scientific meaning generation of the interactive dialogic interaction among 6 students who were assigned to two focus groups, in a public elementary school in Athens, Greece. In this context, we focused on 5 dimensions of analysis that concern inquiry and meaning generation, based on dialogic instances during the described episodes.
Keywords: Digital Tools, Meaning generation, Modelling, Inquiry Based Learning, Microworld

CITATION

Kotsari, C., & Smyrnaiou, Z. (2017). Inquiry based learning and meaning generation through modelling on geometrical optics in a constructionist environment. European Journal of Science and Mathematics Education, 5(1), 14-27. https://doi.org/10.30935/scimath/9494

REFERENCES

  • Barrow, L. H. (2006). A brief history of inquiry: From Dewey to standards.Journal of Science Teacher Education, 17(3), 265-278.
  • Boudreaux, A., Shaffer, P. S., Heron, P. R., & McDermott, L. C. (2008). Student understanding of control of variables: Deciding whether or not a variable influences the behavior of a system. American Journal of Physics,76(2), 163-170.
  • Bybee, R. W. (2006). Enhancing science teaching and student learning: A BSCS perspective. In Proceeding of Research Conference.
  • Chinn, C. A., & Samarapungavan, A. L. A. (2009). Conceptual change—multiple routes, multiple mechanisms: A commentary on Ohlsson (2009). Educational Psychologist, 44(1), 48-57.
  • Cinar, D. (2016). Science Student Teachers’ Cognitive Structure on the Concept of “Food Pyramid”. International Education Studies, 9(7), 21.
  • Cohen, L., & Magen-Nagar, N. (2016). Self-Regulated Learning and a Sense of Achievement in MOOCs Among High School Science and Technology Students. American Journal of Distance Education, 30(2), 68-79.
  • Dimitracopoulou, A., & Komis, V. (2005). Design principles for the support of modelling and collaboration in a technology-based learning environment.International Journal of Continuing Engineering Education and Life Long Learning, 15(1-2), 30-55.
  • Dede, C. (2004). Planning for “neomillennial” learning styles: Implications for investments in technology and faculty. Harvard Graduate School of Education.
  • Evagorou, M., & Avraamidou, L. (2008). Technology in support of argument construction in school science. Educational Media International, 45(1), 33-45.
  • Gilbert, J. K., & Boulter, C. J. (1998). Learning science through models and modeling. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education, Part 1 (pp. 53–66). Dordrecht, Netherlands: Kluwer Academic Press.
  • Glezou, K., & Grigoriadou, M. (2009). Design Principles of Training Material for Introductory Courses to Programming and Logo by using preconstructed microworlds. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (Vol. 2009, No. 1, pp. 1606-1614).
  • Hestenes, D. (1987). Toward a modeling theory of physics instruction. American journal of physics, 55(5), 440-454.
  • Justi, R. S., & Gilbert, J. K. (2002). Modelling, teachers' views on the nature of modelling, and implications for the education of modellers. International Journal of Science Education, 24(4), 369-387.
  • Kariotoglou, N., Summers, S., Summers, T., Kamgarpour, M., & Lygeros, J. (2013, July). Approximate dynamic programming for stochastic reachability. InControl Conference (ECC), 2013 European (pp. 584-589). IEEE.
  • Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.). (2014). Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching. Routledge.
  • Koliopoulos, D., & Argyropoulou, M. (2012). Constructing qualitative energy concepts in a formal educational context with 6-7 year old students. Review of Science, Mathematics and ICT Education, 5(1), 63-80.
  • Kynigos, C. (2007). Half-baked logo microworlds as boundary objects in integrated design. Informatics in Education-An International Journal, (Vol 6_2), 335-359.
  • McKenney, S., & Reeves, T. (2012). Conducting Educational Design Research: What it is, How we do it, and Why. London: Routledge.
  • McKenney, S., & Reeves, T. C. (2013). Conducting educational design research. Routledge.
  • Mikropoulos, T. A., Sampson, D. G., Nikopoulos, A., & Pintelas, P. (2014). The Evolution of Educational Technology Based on a Bibliometric Study. InResearch on e-Learning and ICT in Education (pp. 15-24). Springer New York.
  • Munir, A. R., & Prem, K. D. (2016). Report on short course in educational methodology for university teachers in complementary and alternative medicine (CAM) disciplines–a pilot study conducted at Rajiv Gandhi University of Health Sciences, Karnataka, India. Journal of Complementary and Integrative Medicine, 13(1), 31-39.
  • Osborne, R. J., & Cosgrove, M. M. (1983). Children's conceptions of the changes of state of water. Journal of research in Science Teaching, 20(9), 825-838.
  • Papaevripidou, M. N., (2012). Teachers as learners and curriculum designers in the context of modeling-centered scientific inquiry.
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
  • Papert, S., & Harel, I. (1991). Situating constructionism. Constructionism, 36, 1-11.
  • Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism . Norwood, NJ: Ablex Publishing.
  • Psillos, S. (2011). Living with the abstract: realism and models. Synthese,180(1), 3-17.
  • Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International journal of educational research, 31(6), 459-470.
  • Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Acher, A., Fortus, D., et al. (2009). Developing a learning progression for scientific modeling: Making scienti fi c modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46 (6), 632–654.
  • Shen, J., Lei, J., Chang, H. Y., & Namdar, B. (2014). Technology-enhanced, modeling-based instruction (TMBI) in science education. In Handbook of Research on Educational Communications and Technology (pp. 529-540). Springer New York.
  • Smyrnaiou, Z., & Dimitracopoulou, A. (2007). Inquiry-based activities using a variety of Pedagogical tools. Computer based learning.
  • Smyrnaiou, Z., & Evripidou, R. (2012). The Metafora Platform Tools and Learning to Learn Science Together. In CSEDU (1) (pp. 200-205).
  • Smyrnaiou, Z., Otrel-Cass, K., Petropoulou, E., & Spinou, E. (2014) Tracing Computer Assisted Assessment for learning Capability in Greek Teachers. In Proceedings of the ‘NDSTE’ – International Conference on New Developments in Science and Technology Education; Corfu.
  • Smyrnaiou, Z. G., & Kynigos, C. (2012). Interactive movement and talk in generating meanings from science. Bulletin of the IEEE Technical Committee on Learning Technology, 14(4), 17.
  • Smyrnaiou, Z., Moustaki, F., Yiannoutsou, N., & Kynigos, C. (2013). Interweaving meaning generation in science with learning to learn together processes using Web 2.0 tools. Themes in Science and Technology Education, 5(1-2), pp-27.
  • Solomon, M. R. (1983). The role of products as social stimuli: A symbolic interactionism perspective. Journal of Consumer research, 319-329.
  • Vergnaud, G. (1994). Multiplicative conceptual field: What and why. The development of multiplicative reasoning in the learning of mathematics, 41-59.
  • Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in childhood. Cognitive psychology, 24(4), 535-585.
  • Vosniadou, S. (Ed.). (2009). International handbook of research on conceptual change. Routledge.
  • Yiannoutsou, N., & Kynigos, C. (2013). Boundary Objects in Educational Design Research: designing an intervention for learning how to learn in collectives with technologies that support collaboration and exploratory learning. In T. Plomp, N. Nieveen (Eds) Educational Design Research: Introduction and Illustrative Cases. SLO, Netherlands Institute for Curriculum Development, Enschede, The Netherlands, pp 357 – 379.
  • Waight, N., & Abd‐El‐Khalick, F. (2007). The impact of technology on the enactment of “inquiry” in a technology enthusiast's sixth grade science classroom. Journal of Research in Science Teaching, 44(1), 154-182.
  • Weil-Barais, A. (2001). Constructivist approaches and the teaching of science.Prospects, 31(2), 187-196.
  • Wellington, J. (2015). Educational research: Contemporary issues and practical approaches. Bloomsbury Publishing.
  • Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: Model‐based inquiry as a new paradigm of preference for school science investigations. Science education, 92(5), 941-967.