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Smartphone und Tablet-PC als mobiles Mini-Labor

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Physik ganz smart

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Zusammenfassung

Smartphone und Tablet-PC gehören mehr und mehr zum Alltag speziell der jungen Generation. Auch in Schulen hält der Tablet-PC zunehmend Einzug, wobei die Nutzung der Geräte bisher primär als Notebook-Ersatz erfolgt (z. B. als Cognitive Tool, zu Recherchezwecken, und für Standardanwendungen). Doch Smartphones bringen im Schulalltag auch einige Probleme mit sich. Bedenkt man allerdings die technischen Möglichkeiten und die große Vertrautheit der Lernenden mit den Geräten, so lässt sich erkennen, dass ein zielgerichteter Einsatz dieser Medien den Unterricht durchaus bereichern kann.

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Literatur

  1. West, M., & Vosloo, S. (2013). UNESCO policy guidelines for mobile learning. Paris: UNESCO Publications.

    Google Scholar 

  2. Kuhn, J., & Vogt, P. (2012). iPhysicsLabs. Column Editors’ note. The Physics Teacher, 50, 118.

    Article  ADS  Google Scholar 

  3. Kuhn, J., Wilhelm, T., & Lück, S. (2013). Smarte Physik: Physik mit Smartphones und Tablet-PCs. Physik in Unserer Zeit, 44(1), 44–45.

    Article  ADS  Google Scholar 

  4. Greeno, J. G., Smith, D. R., & Moore, J. L. (1993). Transfer of situated learning. In D. K. Dettermann & R. J. Sternberg (Hrsg.), Transfer on trial: Intelligence, cognition and instruction (S. 99–167). Norwood, NJ: Ablex.

    Google Scholar 

  5. Gruber, H., Law, L.-C., Mandl, H., & Renkl, A. (1995). Situated learning and transfer. In P. Reimann & H. Spada (Hrsg.), Learning in humans and machines: Towards an interdisciplinary learning science (S. 168–188). Oxford, United Kingdom: Pergamon.

    Google Scholar 

  6. Kuhn, J., Müller, A., Müller, W., & Vogt, P. (2010). Kontextorientierter Physikunterricht: Konzeptionen, Theorien und Forschung zu und Lernen. Praxis der Naturwissenschaften – Physik in der Schule, 5(59), 13–25.

    Google Scholar 

  7. Kuhn, J., & Vogt, P. (2015). Smartphone & Co. in Physics Education: Effects of learning with new media experimental tools in acoustics. In W. Schnotz, A. Kauertz, H. Ludwig, A. Müller, & J. Pretsch (Hrsg.), Multidisciplinary research on teaching and learning (S. 253–269). Basingstoke: Palgrave Macmillan.

    Google Scholar 

  8. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(2000), 68–78.

    Article  Google Scholar 

  9. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(2000), 54–67.

    Article  Google Scholar 

  10. Mayer, R. E. (2009). Multimedia learning (2. Aufl.). New York: Cambridge University Press.

    Book  Google Scholar 

  11. De Cock, M. (2012). Representation use and strategy choice in physics problem solving. Physical Review Physics Education Research, 8(2), 020117.

    Article  Google Scholar 

  12. Kohl, P., & Finkelstein, N. (2005). Students’ representational competence and self-assessment when solving physics problems. Physical Review Physics Education Research, 2005, 010104.

    Article  Google Scholar 

  13. Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. Visualization in Science Education, 1, 121–146.

    Article  Google Scholar 

  14. Rau, M. A. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4), 717–761.

    Article  Google Scholar 

  15. Klein, P., Müller, A., & Kuhn, J. (2017). KiRC inventory: Assessment of representational competence in kinematics. Physical Review Physics Education Research, 13, 010132.

    Article  ADS  Google Scholar 

  16. Tytler, R., Prain, V., Hubber, P., & Waldrip, B. (Hrsg.). (2013). Constructing representations to learn in science. Rotterdam: Sense.

    Google Scholar 

  17. Tsui, C., & Treagust, D. (Hrsg.). (2013). Multiple representations in biological education. Dordrecht: Springer.

    Google Scholar 

  18. Gilbert, J. K., & Treagust, D. (Hrsg.). (2009). Multiple representations in chemical education. The Netherlands: Springer.

    Google Scholar 

  19. Docktor, J. L., & Mestre, J. P. (2014). Synthesis of discipline-based education research in physics. Physical Review Physics Education Research, 10(2), 020119.

    Article  ADS  Google Scholar 

  20. Treagust, D., Duit, R., & Fischer, H. (Hrsg.). (2017). Multiple representations in physics education. Dordrecht: Springer.

    Google Scholar 

  21. Lesh, R., Post, T., & Behr, M. (1987). Representations and translations among representations in mathematics learning & solving. In C. Janvier (Hrsg.), Problems of representation in the teaching and learning of mathematics (S. 33–40). Hillsdale: Lawrence Erlbaum.

    Google Scholar 

  22. Even, R. (1998). Factors involved in linking representations of functions. Journal of Mathematical Behaviour, 17, 105–121.

    Article  Google Scholar 

  23. Van Heuvelen, A., & Zou, X. (2001). Multiple representations of workenergy processes. American Journal of Physics, 69, 184.

    Article  ADS  Google Scholar 

  24. Hubber, P., Tytler, R., & Haslam, F. (2010). Teaching and learning about force with a representational focus: pedagogy and teacher change. Research in Science Education, 40(1), 5–28.

    Article  ADS  Google Scholar 

  25. Opfermann, M., Schmeck, A., & Fischer, H. (2017). Multiple representations in physics and science education – Why should we use them? In D. Treagust, R. Duit, & H. Fischer (Hrsg.), Multiple representations in physics education. Dordrecht: Springer.

    Google Scholar 

  26. Van Heuvelen, A. (1991). Learning to think like a physicist: A review of research-based instructional strategies. American Journal of Physics, 59, 891–897.

    Article  ADS  Google Scholar 

  27. Plötzner, R., & Spada, H. (1998). Constructing quantitative problem representations on the basis of qualitative reasoning. Interactive Learning Environments, 5, 95–107.

    Article  Google Scholar 

  28. Verschaffel, L., De Corte, E., de Jong, T., & Elen, J. (2010). Use of external representations in reasoning and problem solving. New York: Routledge.

    Book  Google Scholar 

  29. Schnotz, W. (2010). Reanalyzing the expertise reversal effect. Instructional Science, 38(3), 315–323.

    Article  Google Scholar 

  30. diSessa, A. A. (2004). Metarepresentation: native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.

    Article  Google Scholar 

  31. Etkina, E., Van Heuvelen, A., White-Brahmia, S., Brookes, D. T., Gentile, M., Murthy, S., Rosengrant, D., & Warren, A. (2006). Scientific abilities and their assessment. Physical Review Physics Education Research, 2(2), 020103-1–020103-15.

    ADS  Google Scholar 

  32. Ainsworth, S. E., Bibby, P. A., & Wood, D. J. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11, 25–61.

    Article  Google Scholar 

  33. Schoenfeld, A., Smith, J. P., & Arcavi, A. (1993). Learning: The microgenetic analysis of one student’s evolving understanding of a complex subject matter domain. In R. Glaser (Hrsg.), Advances in instructional psychology. Hillsdale: LEA.

    Google Scholar 

  34. Scheid, J., Müller, A., Hettmansperger, R., & Kuhn, J. (2017). Erhebung von repräsentationaler Kohärenzfähigkeit von Schülerinnen und Schülern im Themenbereich Strahlenoptik. Zeitschrift für Didaktik der Naturwissenschaften, 23, 181–203.

    Article  Google Scholar 

  35. Nieminen, P., Savinainen, A., & Viiri, J. (2010). Force concept inventory-based multiple-choice test for investigating students’ representational consistency. Physical Review Physics Education Research, 6(2), 020109.

    Article  ADS  Google Scholar 

  36. Klein, P., Kuhn, J., Müller, A., & Gröber, S. (2015). Video analysis exercises in regular introductory mechanics physics courses: Effects of conventional methods and possibilities of mobile devices. In W. Schnotz, A. Kauertz, H. Ludwig, A. Müller, & J. Pretsch (Hrsg.), Multidisciplinary research on teaching and learning (S. 270–288). Basingstoke: Palgrave Macmillan.

    Google Scholar 

  37. Klein, P., Kuhn, J., & Müller, A. (2018). Förderung von und Experimentbezug in den vorlesungsbegleitenden Übungen zur Experimentalphysik – Empirische Untersuchung eines videobasierten Aufgabenformates. Zeitschrift für Didaktik der Naturwissenschaften, 24(1), 17–34.

    Article  Google Scholar 

  38. Hochberg, K., Kuhn, J., & Müller, A. (2018). Using Smartphones as experimental tools – Effects on interest, curiosity and learning in physics education. Journal of Science Education and Technology, 27(5), 385–403.

    Article  ADS  Google Scholar 

  39. Becker, S., Klein, P., Gößling, A., & Kuhn, J. (2019). Förderung von Konzeptverständnis und Repräsentationskompetenz durch Tablet-PC-gestützte Videoanalyse: Empirische Untersuchung der Lernwirksamkeit eines digitalen Lernwerkzeugs im Mechanikunterricht der Sekundarstufe 2. Zeitschrift für Didaktik der Naturwissenschaften, 25(1), 1–24.

    Article  Google Scholar 

  40. Hochberg, K., Becker, S., Louis, M., Klein, P., & Kuhn, J. (2020). Using smartphones as experimental tools – a follow-up: Cognitive effects by video analysis and reduction of cognitive load by multiple representations. Journal of Science Education and Technology, 29. https://doi.org/10.1007/s10956-020-09816-w.

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Author information

Authors and Affiliations

  1. Kaiserslautern, Deutschland

    Jochen Kuhn

  2. Mainz, Deutschland

    Patrik Vogt

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  1. Jochen Kuhn
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  2. Patrik Vogt
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Correspondence to Jochen Kuhn .

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Editors and Affiliations

  1. FB Physik, Technische Universität Kaiserslautern, Kaiserslautern, Rheinland-Pfalz, Germany

    Jochen Kuhn

  2. Institut für Lehrerfort- und -weiterbildung (ILF), Mainz, Rheinland-Pfalz, Germany

    Patrik Vogt

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Kuhn, J., Vogt, P. (2019). Smartphone und Tablet-PC als mobiles Mini-Labor. In: Kuhn, J., Vogt, P. (eds) Physik ganz smart. Springer Spektrum, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59266-3_1

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