Understanding Ancient Greek Civilizations: A STEAM Teaching Perspective

Keywords: STEAM, Minoan culture, Cycladic culture, Learning and Innovation

Abstract

The aim of this research is to suggest a didactic approach as to how students comprehend the growth and the activities of the two most well-known Ancient Greek civilizations (i.e. the Mycenaean and the Cycladic). The teaching approach is based on STEM perspective, the use of several digital technologies, as well as several learning theories. Digital technologies help students delve into the process of scientific discovery. The degree of new technology and STEM – based didactic approach appealing to students is evaluated through questionnaires. In particular, in our survey, 115 students participated and the questionnaires distributed to four schools of Volos and Veria Greece region. The Research was carried out with the consensus of their parents. According to our results, students expressed great interest in STEM activities that they were exposed to. Moreover, they expressed high interest in the integration of a classic history lesson with new technologies and they developed the ability to create simulations of ancient civilization activities. Furthermore, our work is focusing on issues regarding the process and efficiency, through the use, of an interactive time – line robotic car, which will be used to categorize historical events into periods on a timeline.

References

Akerson, V. L., & Abd-El-Khalick, F. (2003). Teaching elements of nature of science: A yearlong case study of a fourth grade teacher. Journal of Research in Science Teaching, 40(10), 1025 – 1049.

Barrows, H. S. (1998). The essentials of problem-based learning. Journal of Dental Education, 62(9), 630–633.

Bereiter, C. (2002). Education and mind in the knowledge age. Mahwah, NJ: Lawrence Erlbaum Associates.

Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.) (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.

Burgess, L., & Street, D., (2004). Optimal designs for asymmetric choice experiments. CenSoC Working Paper, No. 04-004. University of Technology, Sydney, Australia.

Bybee, R.W., Trowbridge, L.W., & Powell, J.C. (2008).Teaching Secondary School Science: Strategies for Developing Scientific Literacy. New Jersey: Merrill.

Carnegie Foundation (2013). 50-State Scan of Course Credit Policies. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.

Chalufour, I., & Worth, K. (2004). Building structures with young children. St. Paul, Minnesota: Redleaf Press.

Clements, D. H., Sarama, J., & DiBiase, A.-M. (Eds.). (2004). Engaging young children in mathematics: Standards for early childhood mathematics education. Mahwah, NJ: Lawrence Erlbaum Associates.

Clements, D. H., Sarama, J., & Germeroth, C. (2016). Learning executive function and early mathematics: Directions of causal relations. Early Childhood Research Quarterly, 36, 79–90. https://doi.org/10.1016/j.ecresq.2015.12.009

Donohue, J.W. Jr. (2015) - books.google.com

Ehrlich, H. L. (1998). Geomicrobiology: its significance for geology. Earth Science Reviews, 45(1-2), 45-60. https://doi.org/10.1016/S0012-8252(98)00034-8

Engeström, Y., & Sannino, A. (2012). Whatever happened to process theories of learning? Learning. Culture and Social

Interaction, 1(1), 45–56.

Frykholm, J., & Glasson, G. (2005). Connecting science and mathematics instruction: Pedagogical context knowledge for teachers. School Science and Mathematics, 105(3), 127-141.

Gelman, R., Brenneman, K., Macdonald, G., & Roman, M. (2010). Preschool pathways to science: Facilitating scientific ways of doing, thinking, communicating and knowing about science. Baltimore, MD: Brookes Publishing.

Ghanbari, S. (2015). Learning across disciplines: A collective case study of two university programs that integrate the arts with STEM. International Journal of Education & the Arts, 16(7). Retrieved from http://www.ijea.org/v16n7/.

Hestenes, D. (1999). The scientific method. American Journal of Physics, 67, 273-276.

Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.

Ho, A. V. (2012). Computation and computational thinking. Computer Journal, 55(7), 832 – 835.

Hußmann, S., Schacht, F., & Schindler, M. (2019). Tracing conceptual development in mathematics: epistemology of webs of reasons. Mathematics Education Research Journal, 31(2), 133-149.

Hung, W. (2009).The 9-step process for designing PBL problems: Application of the 3C3R model. Educational Research Review, 4(2), 118–141.

Hung, W., Jonassen, D. H., & Liu, R. (2008). Problem-Based Learning. In J. M. Spector, J. G. van Merrienboer, M. D., Merrill, & M. Driscoll (Eds.), Handbook of Research on Educational Communications and Technology (3 ed., pp. 485-506). Mahwah, NJ: Erlbaum.

Jona, K., Wilensky, U., Trouille, L., Horn, M. S., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding computational thinking in science, technology, engineering, and math (CT-STEM). In future directions in computer science education summit meeting, Orlando, FL.

Jonnasen, D. (2000). Toward a Design Theory of Problem Solving. Educational Technology Design and Development, 48(4), 63–85.

Jonassen, D. H., & Hung, W. (2008). All Problems Are Not Equal: Implications for Problem-Based Learning. Interdisciplinary Journal of Problem-Based Learning, 2, 6-28. https://doi.org/10.7771/1541-5015.1080

Justi, R.S. & Gilbert J. K., (2002). Models and modelling in chemical education, In J. Gilbert, O. De Jong, R. Justi, D. F. Treagust and J. H. van Driel (Eds.), Chemical education: towards research-based practice (pp. 213 - 234), Dordrecht: Kluwer Academic Publishers.

Kalovrektis, K., Lykas, Ch., Fountas, I., Gkotsinas, A., & Lekakis, I. (2013). Development and application embedded systems and wireless network of sensors to control of hydroponic greenhouses. International Journal of Agriculture and Forestry, 3(5), 198-202.

Kozma, R. B. (Ed.) (2003). Technology, innovation, and educational change: A global perspective. Eugene, OR: International Society for Technology in Education.

National Academy of Engineering (NAE) & National Research Council (NRC) (2014). STEM integration in K-12 education: status, prospects, and an agenda for research. The National Academies Press, Washington

National Academy of Engineering and National Research Council [NAE & NRC] (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research. Washington: National Academies Press.

National Research Council (2012). Discipline-based education research: understanding and improving learning in undergraduate science and engineering. National Academies Press, Washington, DC.

National Science Teachers Association (2014). NSTA position statement: Early childhood science education. Retrieved from http://www.nsta.org/about/positions/earlychildhood.aspx

Nersessian, N. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. Giere (Ed.), Cognitive models of science (pp. 3-44). Minneapolis: University of Minnesota Press.

Ortiz, A.M. (2008). Engineering design as a contextual learning and teaching framework: How elementary students learn math and technological literacy. Paper presented at the PATT – 19, Salt Lake City UT.

Piaget, J. (1970). Genetic epistemology. New York: Columbia University Press.

Psycharis, S. (2016). The impact of computational experiment and formative assessment in inquiry based teaching and learning approach in STEM education. Journal of Science Education and Technology, 25(2), 316-326. https://doi.org/10.1007/s10956-015-9595-z

Psycharis, S. (2018). STEAM in Education: A Literature review on the role of Computational Thinking, Engineering Epistemology and Computational Science. Computational STEAM Pedagogy (CSP). Scientific Culture, 4(2), 51-72. https://doi.org/10.5281/zenodo.1214565

Roberts, C. (2012). Information structure in discourse: Towards an integrated formal theory of pragmatics. The Ohio State University.

Sarama, J., & Clements, D. H. (2009). Early Childhood Mathematics Education Research: Learning trajectories for young children. New York: Routledge

Savin-Baden, M. (200). Problem-based Learning in Higher Education: Untold Stories. Buckingham: The Society for Research into Higher Education/Open University Press.

Schultz-Ross, R. A., & Kline, A. E. (1999).Using problem-based learning to teach forensic psychiatry. Academic Psychiatry, 23, 37-41.

Schunn, C. (2011). Design principles for high school engineering design challenges: Experiences from high school science classrooms. Retrieved from. http://ncete.org/flash/pdfs/Design_Principles_Schunn.pdf

Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351-380.

Sentance, S., & Csizmadia, A. (2015).Teachers’ perspectives on successful strategies for teaching Computing in school. Paper presented at IFIP TCS, 2015. http://community.computingatschool.org.uk/files/6769/original.pdf

Shirey, K. (2017). Teacher Productive Resources for Engineering Design Integration in High School Physics Instruction (Fundamental). In Proceedings of the 2017 ASEE Annual Conference, Columbus, OH, June 2017.

Skinner, B. F. (1954/1968). The science of learning and the art of teaching. In B. F. Skinner (Ed.), The technology of teaching (pp. 9–28). New York: Appleton Century-Crofts. (Original work published in 1954 in the Harvard Educational Review, 24(2), 86–97).

Springer, L., Stanne, M. E. & Donovan, S. S. (1999). Effects of Small-Group Learning on Undergraduates in Science, Mathematics, Engineering and Technology: A Meta-Analysis. Review of Educational Research, 69(1), 21–51.

Wicklein, R. C., & Schell, J. W. (1995). Case studies of multidisciplinary approaches to integrating mathematics, science and technology education. Journal of Technology Education, 6(2), 59-76

Weintrop, D., Beheshti, E., Horn, M. et al. (2016). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127-147. https://doi.org/10.1007/s10956-015-9581-5

White, B., Frederiksen, J., & Spoehr, K. (1993). Conceptual models for understanding the behavior of electrical circuits. In M. Caillot (Ed.), Learning electricity and electronics with advanced educational technology (pp. 77-95). New York: Springer-Verlag.

Xenakis, A., Kalovreketis, K., Papastergiou, G. (2019). STEM Contribution of Educational Robotics Scripts to Physics and Mathematics to Enhance Computational Thinking. Journal of Education and Sciences, University of Thessaly.

Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S. & Korb, J. T. (2011). Introducing computational thinking in education courses. In: Proceedings of the 42nd ACM technical symposium on Computer science education, ACM, pp. 465–470.

Yaşar, O. (2013). Teaching Science through Computation. International Journal of Science, Technology and Society, 1(1), 9-18. https://doi.org/10.11648/j.ijsts.20130101.12

Published
2020-12-16
How to Cite
Plageras, A., Kourtis, S., Xenakis, A. C., Kalovrektis, K., Psycharis, S., & Vavougios, D. (2020). Understanding Ancient Greek Civilizations: A STEAM Teaching Perspective. Hellenic Journal of STEM Education, 1(2), 45-57. https://doi.org/10.51724/hjstemed.v1i2.9