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It’s Not Just the Tool but the Educational Rationale that Counts

  • Gavriel Salomon

Abstract

Massive open online courses (MOOCs) (Kop, 2011); bring your own device (BYOD) (Song, 2014); advanced learning analytics (Baker & Inventado, 2014); hybrid, blended and disruptive educational environments; networks of connected learners (Siemens, 2014); blended learning experiences (Pedaste et al., 2013); simulationbased inquiry learning (Mulder et al., 2014) with virtual manipulatives (Zacharia & de Jong, 2014); and a multitude of educational apps popping up daily (Cherner, Dix, & Lee, 2014)—these instances of technology loom so large in prevailing visions of education that the role of education as the driving force of their design and utilisation becomes lost.

Keywords

Technology Integration Educational Data Mining Educational Rationale Technological Pedagogical Content Knowledge Teacher College Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In R. S. Baker & P. S. Inventado (Eds.), Learning analytics (pp. 61–75). New York, NY: Springer.Google Scholar
  2. Ballantine, J. H., & Hammack, F. M. (2011). The sociology of education. New York, NY: Pearson.Google Scholar
  3. Bereiter, C. (2005). Education and mind in the knowledge age. New York, NY: Routledge.Google Scholar
  4. Bereiter, C., & Scardamalia, M. (2014). Knowledge building and knowledge creation: One concept, two hills to climb. In S. C. Tan, H. J. So, & J. Yeo (Eds.), Knowledge creation in education (pp. 35–52). Singapore: Springer.Google Scholar
  5. Biagi, F., & Loi, M. (2013). Measuring ICT use and learning outcomes: Evidence from recent econometric studies. European Journal of Education, 48(1), 28–42.CrossRefGoogle Scholar
  6. Biesta, G. (2009). Good education in an age of measurement: On the need to reconnect with the question of purpose in education. Educational Assessment, Evaluation and Accountability, 21(1), 33–46.CrossRefGoogle Scholar
  7. Bowen, W. G. (2015). Higher education in the digital age. New York, NY: Princeton University Press.CrossRefGoogle Scholar
  8. Brennan, K. (2015). Beyond technocentrism: Supporting constructionism in the classroom. Constructivist Foundations, 10(3), 289–296.Google Scholar
  9. Bronfenbrenner, U. (2009). The ecology of human development: Experiments by nature and design. Boston, MA: Harvard university press.Google Scholar
  10. Chen, C. M. (2008). Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2), 787–814.CrossRefGoogle Scholar
  11. Cherner, T., Dix, J., & Lee, C. (2014). Cleaning up that mess: A framework for classifying educational apps. Contemporary Issues in Technology and Teacher Education, 14(2), 158–193.Google Scholar
  12. Christensen, R. (2002). Effects of technology integration education on the attitudes of teachers and students. Journal of Research on Technology in Education, 34(4), 411–433.CrossRefGoogle Scholar
  13. Cochrane, T., Narayan, V., & Oldfield, J. (2013). iPadagogy: Appropriating the iPad within pedagogical contexts. International Journal of Mobile Learning and Organisation, 7(1), 48–65.CrossRefGoogle Scholar
  14. Cronbach, L. J. (1967). How can instruction be adapted to individual differences? In R. M. Gagne (Ed.), Learning and individual differences (pp. 23–39). Colombus, OH: Merrill.Google Scholar
  15. Cuban, L. (1993). Computers meet classroom: Classroom wins. The Teachers College Record, 95(2), 185–210.Google Scholar
  16. De Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science, 340(6130), 305–308.CrossRefGoogle Scholar
  17. Gash, H., & McCloughlin, T. (2015). Embedding technology in pedagogy. Constructivist Foundations, 10(3), 297–298.Google Scholar
  18. Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53(1), 5.CrossRefGoogle Scholar
  19. Greeno, J. G. (2011). A situative perspective on cognition and learning in interaction. In T. Koschmann (Ed.), Theories of learning and studies of instruction (pp. 41–72). New York, NY: Springer.CrossRefGoogle Scholar
  20. Greeno, J. G. (2012). Concepts in activities and discourses. Mind, Culture, and Activity, 19(3), 310–313.CrossRefGoogle Scholar
  21. Harris, B. (1987). Cities and regions in the electronic age. In J. Brotchie, P. Hall, & P. Newton (Eds.), The spatial impact of technological change (pp. 394–403). London: Croom Helm.Google Scholar
  22. Harris, J. (2005). Our agenda for technology integration: It’s time to choose. Contemporary Issues in Technology and Teacher Education, 5(2), 116–122.Google Scholar
  23. Harris, J., Mishra, P., & Koehler, M. (2009). Teachers’ technological pedagogical content knowledge and learning activity types: Curriculum-based technology integration reframed. Journal of Research on Technology in Education, 41(4), 393–416.CrossRefGoogle Scholar
  24. Keengwe, J., & Onchwari, G. (2011). Fostering meaningful student learning through constructivist pedagogy and technology integration. International Journal of Information and Communication Technology Education (IJICTE), 7(4), 1–10.CrossRefGoogle Scholar
  25. Kop, R. (2011). The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. International Review of Research in Open and Distributed Learning, 12(3), 19–38.Google Scholar
  26. Linn, M. C. (2014). Computers as learning partners: Knowledge integration. In R. Gunstone (Ed.), Encyclopedia of science education (pp. 1–6). New York, NY: Springer, Science.CrossRefGoogle Scholar
  27. Lo, J. J., Chan, Y. C., & Yeh, S. W. (2012). Designing an adaptive web-based learning system based on students’ cognitive styles identified online. Computers & Education, 58(1), 209–222.CrossRefGoogle Scholar
  28. MediaCore. (2015). Is the 95% MOOC dropout rate the big issue? Retrieved from http://www.mediacore.com/blog/is-the-95-mooc-dropout-rate-the-big-issue#sthash.Y8iwkSil.dpuf
  29. Morozov, E. (2014). To save everything, click here: The folly of technological solutionism. Philadelphia, PA: PublicAffairs.Google Scholar
  30. Newcombe, T. (2015). A cautionary tale for any government IT project: L.A.’s failed iPad program. Governing. Retrieved from http://www.governing.com/columns/tech-talk/gov-tablets-los-angeles-ipad-apple-schools.html
  31. Pajares, F., & Schunk, D. H. (2001). Self-beliefs and school success: Self-efficacy, self-concept, and school achievement. Perception, 11, 239–266.Google Scholar
  32. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.Google Scholar
  33. Papert, S. (1990). A critique of technocentrism in thinking about the school of the future. Retrieved from http://www.papert.org/articles/ACritiqueofTechnocentrism.htmlGoogle Scholar
  34. Perkins, D. N., & Salomon, G. (2012). Knowledge to go: A motivational and dispositional view of transfer. Educational Psychologist, 47(3), 248–258.CrossRefGoogle Scholar
  35. Russell, T. (1999). The no significant difference phenomenon. New York, NY: The International Distance Education Certification Center.Google Scholar
  36. Russell, T. L. (1999). The no significant difference phenomenon: A comparative research annotated bibliography on technology for distance education: As reported in 355 research reports, summaries and papers. Raleigh, NC: North Carolina State University.Google Scholar
  37. Salomon, G. (1983). The differential investment of mental effort in learning from different sources. Educational Psychologist, 18(1), 42–50.CrossRefGoogle Scholar
  38. Salomon, G. (1993). No distribution without individuals’ cognition: A dynamic interactional view. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 111–138). New York, NY: Cambridge University Press.Google Scholar
  39. Salomon, G. (1993). On the nature of pedagogic computer tools: The case of the writing partner. In S. P. Lajoie & S. J. Derry (Eds.), Computers as cognitive tools (pp. 179–196). Hillsdale, NJ: Lawrence Erlbaum Association.Google Scholar
  40. Salomon, G. (1994). Interaction of media, cognition, and learning. New York, NY: Routledge.Google Scholar
  41. Salomon, G., & Almog, T. (1998). Educational psychology and technology: A matter of reciprocal relations. Teachers College Record, 100(2), 222–241.Google Scholar
  42. Salomon, G., & Perkins, D. N. (1998). Individual and social aspects of learning. Review of Research in Education, 23(1), 1–24.CrossRefGoogle Scholar
  43. Salomon, G., Globerson, T., & Guterman, E. (1989). The computer as a zone of proximal development: Internalizing reading-related metacognitions from a reading partner. Journal of Educational Psychology, 81(4), 620–627.CrossRefGoogle Scholar
  44. Sarason, S. B. (1984). If it can be studied or developed, should it be? American Psychologist, 39(5), 477–485.CrossRefGoogle Scholar
  45. Scardamalia, M., & Bereiter, C. (2015). Education in an open informational world. Emerging trends in the social and behavioral sciences. An Interdisciplinary, Searchable, and Linkable Resource, 1–15. doi: 10.1002/9781118900772.etrds0096
  46. Siemens, G. (2014). Connectivism: A learning theory for the digital age. e-Learning Library. Retrieved from http://er.dut.ac.za/handle/123456789/69
  47. Simon, H. A. (1998). What we know about learning. Journal of Engineering Education, 87(4), 343–348.CrossRefGoogle Scholar
  48. Song, Y. (2014). ‘Bring your own device (BYOD)’ for seamless science inquiry in a primary school. Computers & Education, 74, 50–60.CrossRefGoogle Scholar
  49. Squires, D. (1999, January). Educational software and learning: Subversive use and volatile design. Systems Sciences, 1999. HICSS-32. Proceedings of the 32nd Annual Hawaii International Conference on System Sciences (Volume 1), 1–7.Google Scholar
  50. Svihla, V., & Linn, M. C. (2012). A design-based approach to fostering understanding of global climate change. International Journal of Science Education, 34(5), 651–676.CrossRefGoogle Scholar
  51. Zhang, Z. H., & Linn, M. C. (2013). Learning from chemical visualizations: Comparing generation and selection. International Journal of Science Education, 35(13), 2174–2197.CrossRefGoogle Scholar

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© Sense Publishers 2016

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  • Gavriel Salomon

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