Technology, Culture, and Values: Implications for Enactment of Technological Tools in Precollege Science Classrooms

Chapter
Part of the Innovations in Science Education and Technology book series (ISET, volume 24)

Abstract

This chapter explores the culture of technology and the impact of technology on culture and values in the context of precollege science classrooms that implement (or aim to implement) technology-supported inquiry environments. We adopt perspectives of technology that at its core emphasize context and the artifact and its associated activity. These perspectives inform how notions of technology, culture, and values are intertwined in complex interactions of agents (designer, developer, or educational researcher and/or teacher educator, teacher, or student), and context of practice (science classroom), and mediated by associated knowledge and skills. Toward explicating how these interactions are realized in practice, first, we explore conceptualizations of technology that leverage human activity associated with design, development, implementation, use, and sustainability of technological tools. Second, we draw on Pacey’s (The culture of technology. MIT Press, Cambridge, MA, 1983) technology-practice framework to elucidate how cultural, organizational, and technical aspects of technology must be part and parcel of any analysis of the culture of technology implementation in precollege science classrooms. Third, we explicate how understandings of the nature of technology (NoT) undergird the role of context and associated culture and values of science teaching and learning. Dimensions of NoT (notions of technological progression, technology as part of systems, technological diffusion, technology as a fix and expertise) highlight how culture and values are influenced by various factors at different stages of technology adoption and implementation. Fourth, we examine empirical investigations of the enactment of technology-supported inquiry environments to identify (a) conceptualizations of technology, (b) how dimensions of NoT are manifested, and (c) how the presence or absence of these dimensions reflect key aspects of culture and values within science classrooms. We conclude that understandings of culture and values have implications for (a) conceptualizing technological tools in the context of precollege science classrooms, (b) transference of technologies across contexts (including requisite transference of habits of mind, practices, and expertise), and (c) implementing, enacting, and reappropriating technologies in science classrooms.

References

  1. Adadan, E., Trundle, K. C., & Irving, K. E. (2010). Exploring grade 11 students’ conceptual pathways of the particulate nature of matter in the context of multi representational instruction. Journal of Research in Science Teaching, 47, 1004–1035.Google Scholar
  2. Ardac, D., & Akaygun, S. (2004). Effectiveness of multimedia-based instruction that emphasizes molecular representations on students’ understanding of chemical change. Journal of Research in Science Teaching, 41, 317–337.CrossRefGoogle Scholar
  3. Arthur, W. B. (2009). The nature of technology. New York: Free Press.Google Scholar
  4. Barab, S. A., Hay, K. E., Barnett, M., & Keating, T. (2000). Virtual solar system project: Building understanding through model building. Journal of Research in Science Teaching, 37, 719–756.CrossRefGoogle Scholar
  5. Basalla, G. (1996). The evolution of technology. New York: Cambridge University Press.Google Scholar
  6. Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. Arlington: National Science Teachers Association Press.Google Scholar
  7. Carberry, A. R., & Baker, D. R. (2018). The impact of culture on engineering and engineering education. In Y. J. Dori, Z. Mevarech, & D. Baker (Eds.), Cognition, metacognition, and culture in STEM education (pp. 217–239). Springer.Google Scholar
  8. Clark, A. (2003). Natural born cyborgs: Minds, technologies, and the future of human intelligence. New York: Oxford University Press.Google Scholar
  9. Crawford, B. A., & Cullin, M. J. (2004). Supporting prospective teachers’ conceptions of modeling in science. International Journal of Science Education, 26, 1379–1401.CrossRefGoogle Scholar
  10. De Jong, O., Van Driel, J. H., & Verloop, N. (2005). Preservice teachers’ pedagogical content knowledge of using particle models in teaching chemistry. Journal of Research in Science Teaching, 42, 947–964.CrossRefGoogle Scholar
  11. Dori, Y. J., & Hameiri, M. (2003). Multidimensional analysis system for quantitative chemistry problems: Symbol, macro, micro and process aspects. Journal of Research in Science Teaching, 40, 278–302.CrossRefGoogle Scholar
  12. Dori, Y. J., & Kaberman, Z. (2012). Assessing high school chemistry students’ modeling sub-skills in a computerized molecular modeling learning environment. Instructional Science, 40, 69–91.CrossRefGoogle Scholar
  13. Eglash, R. (2004). Appropriating technology: An introduction. In R. Eglash, J. L. Croissant, G. Di Chiro, & R. Fouche (Eds.), Appropriating technology: Vernacular science and social power (pp. 1–28). Minneapolis: University of Minnesota Press.Google Scholar
  14. Ellul, J. (1964). The technological society. Toronto: Alfred A. Knopf.Google Scholar
  15. Gobert, J. D., O’Dwyer, L., Horwitz, P., Buckley, B. C., Tal Levy, S., & Wilenksy, U. (2011). Examining the relationship between students’ understanding of the nature of models and conceptual learning in biology, physics, and chemistry. International Journal of Science Education, 33, 653–684.CrossRefGoogle Scholar
  16. Heidegger, M. (1977). The question concerning technology and other essays. New York: Harper & Row, Publishers.Google Scholar
  17. Hsu, Y. (2008). Learning about seasons in a technologically enhanced environment: The impact of teacher-guided and student-centered instructional approaches on the process of students’ conceptual change. Science Education, 92, 320–344.CrossRefGoogle Scholar
  18. Illich, I. (1973). Tools for conviviality. New York: Harper & Row.Google Scholar
  19. Jaakkola, T., Nurmi, S., & Veermans, K. (2011). A comparison of students’ conceptual understanding of electric circuits in simulation only and simulations-laboratory contexts. Journal of Research in Science Teaching, 48, 71–93.CrossRefGoogle Scholar
  20. Kikas, E. (2004). Teachers’ conceptions and misconceptions concerning three natural phenomena. Journal of Research in Science Teaching, 41, 432–448.CrossRefGoogle Scholar
  21. Kim, M., Hannafin, M. J., & Bryan, L. (2007). Technology-enhanced inquiry tools in science education: An emerging pedagogical framework for classroom practice. Science Education, 96, 1010–1030.CrossRefGoogle Scholar
  22. Koehler, M. J., & Mishra, P. (2008). Introducing TPCK. In AACTE (Ed.), Handbook of technological pedagogical content knowledge (TPCK) for educators (pp. 3–29). New York: Routledge.Google Scholar
  23. Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34, 949–968.CrossRefGoogle Scholar
  24. Lead States, N. G. S. S. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.Google Scholar
  25. Liu, L., & Hmelo-Silver, C. E. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. Journal of Research in Science Teaching, 46, 1023–1040.CrossRefGoogle Scholar
  26. Liu, X., & Lesniak, K. (2006). Progression in children’s understanding of the matter concept from elementary to high school. Journal of Research in Science Teaching, 43, 320–347.CrossRefGoogle Scholar
  27. Marbach-Ad, G., Rotbain, Y., & Stavy, R. (2008). Using computer animation and illustration activities to improve high school students’ achievement in molecular genetics. Journal of Research in Science Teaching, 45, 273–292.CrossRefGoogle Scholar
  28. McDermott, J. (2009). Technology: The opiate of the intellectuals. In A. H. Teich (Ed.), Technology and the future (pp. 87–97). Boston: Wadsworth Cengage Learning.Google Scholar
  29. Mitcham, C. (1994). Thinking through technology: The path between engineering and philosophy. Chicago: The University of Chicago Press.Google Scholar
  30. National Research Council. (2011). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academy Press.Google Scholar
  31. Pacey, A. (1983). The culture of technology. Cambridge, MA: MIT Press.Google Scholar
  32. Rapp, F. (1999). The material and cultural aspects of technology. Society for Philosophy & Technology, 4, 45–57.Google Scholar
  33. Sins, P. H. M., Savelsbergh, E. R., van Joolingen, W. R., & van Hout Wolters, B. H. A. M. (2009). The relation between students’ epistemological understanding of computer models and their cognitive processing on a modeling task. International Journal of Science Education, 31, 1205–1229.CrossRefGoogle Scholar
  34. Stains, M., & Talanquer, V. (2008). Classification of chemical reactions: Stages of expertise. Journal of Research in Science Teaching, 45, 771–793.CrossRefGoogle Scholar
  35. Sullivan, F. R. (2008). Robotics and science literacy: Thinking skills, science process skills and systems understanding. Journal of Research in Science Teaching, 45, 373–394.CrossRefGoogle Scholar
  36. Tao, P., & Gunstone, R. F. (1999). The process of conceptual change in force and motion during computer-supported physics instruction. Journal of Research in Science Teaching, 36, 859–882.CrossRefGoogle Scholar
  37. Tenner, E. (1996). Why things bite back: Technology and the revenge of unintended # consequences. New York: First Vintage Books Edition.Google Scholar
  38. Urhahne, D., Nick, S., & Schanze, S. (2009). The effect of three-dimensional simulations on the understanding of chemical structures and their properties. Research in Science Education, 39, 495–513.CrossRefGoogle Scholar
  39. Verma, G., Puvirajah, A., & Webb, H. (2015). Enacting acts of authentication in a robotics competition: An interpretivist study. Journal of Research in Science Teaching, 52, 28–295.CrossRefGoogle Scholar
  40. Volti, R. (2010). Society and technological change. New York: Worth Publishers.Google Scholar
  41. 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, 154–182.CrossRefGoogle Scholar
  42. Waight, N., & Abd-El-Khalick, F. (2011). From scientific practice to high school science: Transfer of scientific technologies and realizations of authentic inquiry. Journal of Research in Science Teaching, 48, 37–70.CrossRefGoogle Scholar
  43. Waight, N., & Abd-El-Khalick, F. (2012). Nature of technology: Implications for design, development, and enactment of technological tools in school science classrooms. International Journal of Science Education, 34, 2875–2905.CrossRefGoogle Scholar
  44. Waight, N., Liu, X., Gregorius, R. M., Smith, E., & Park, M. (2014). Teacher conceptions and approaches associated with an immersive instructional implementation of computer-based models and assessment in secondary chemistry classrooms. International Journal of Science Education, 36, 467–505.CrossRefGoogle Scholar
  45. Winner, L. (2003). Social constructivism: Opening the black box and finding it empty. In R. C. Scharff & V. Dusek (Eds.), Philosophy of technology: The technological condition (pp. 232–243). Malden: Blackwell.Google Scholar
  46. Wu, H., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821–842.CrossRefGoogle Scholar
  47. Yerrick, R., Radosta, M., & Greene, K. (2018). Technology, culture and young science teachers—A promise unfulfilled and proposals for change. In Y. J. Dori, Z. Mevarech, & D. Baker (Eds.), Cognition, metacognition, and culture in STEM education (pp. 117–138). Springer.Google Scholar
  48. Zacharia, Z. (2003). Beliefs, attitudes, and intentions of science teachers regarding the educational use of computer simulations and inquiry-based experiments in physics. Journal of Research in Science Teaching, 40, 792–823.CrossRefGoogle Scholar
  49. Zhang, B., Liu, X., & Krajcik, J. S. (2006). Expert models and modeling processes associated with a computer-modeling tool. Science Education, 90, 579–604.CrossRefGoogle Scholar
  50. Zhao, Y., & Frank, K. A. (2003). Factors affecting technology uses in schools: An ecological perspective. American Educational Research Journal, 40, 807–840.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Graduate School of EducationUniversity at Buffalo, SUNYBuffaloUSA
  2. 2.School of EducationThe University of North Carolina at Chapel HillChapel HillUSA

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