Using Smartphones as Experimental Tools—Effects on Interest, Curiosity, and Learning in Physics Education

Article
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Abstract

Smartphones as experimental tools (SETs) offer inspiring possibilities for science education, as their built-in sensors allow many different measurements, but until now, there has been little research that studies this approach. Due to current interest in their development, it seems necessary to provide empirical evidence about potential effects of SETs by a well-controlled study. For the present investigation, experiments were developed that use the smartphones’ acceleration sensors to investigate an important topic of classical mechanics (pendulum). A quasi-experimental repeated-measurement design, consisting of an experimental group using SETs (smartphone group, SG, NSG = 87) and a control group working with traditional experimental tools (CG, NCG = 67), was used to study the effects on interest, curiosity, and learning achievement. Moreover, various control variables were taken into account. With multiple-regression analyses and ANCOVA, we found significantly higher levels of interest in the SG (small to medium effect size). Pupils that were less interested at the beginning of the study profited most from implementing SETs. Moreover, the SG showed higher levels of topic-specific curiosity (small effect size). No differences were found for learning achievement. This means that the often-supposed cognitive disadvantage of distracting learners with technological devices did not lead to reduced learning, whereas interest and curiosity were apparently fostered. Moreover, the study contributes evidence that could reduce potential concerns related to classroom use of smartphones and similar devices (increased cognitive load, mere novelty effect). In sum, the study presents encouraging results for the under-researched topic of SET use in science classrooms.

Keywords

Smartphones Technology-based activities Secondary education Physics education Interest Curiosity 
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Notes

Acknowledgements

Generous financial support by the “Wilfried-und-Ingrid-Kuhn-Stiftung” for the doctoral thesis of Katrin Hochberg is gratefully acknowledged. The current paper is based largely on this thesis.

Compliance with Ethical Standards

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Conflict of Interest

Katrin Hochberg declares that she has no conflict of interest. Jochen Kuhn declares that he has no conflict of interest. Andreas Müller declares that he has no conflict of interest.

Supplementary material

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References

  1. Adair, J. G., Sharpe, D., & Huynh, C. L. (1989). Hawthorne control procedures in educational experiments: a reconsideration of their use and effectiveness. Review of Educational Research, 59(2), 215–228.CrossRefGoogle Scholar
  2. Ainsworth, S. (2006). DeFT: a conceptual framework for considering learning with multiple representations. Learn Instr, 16(3), 183–198.CrossRefGoogle Scholar
  3. Alrasheedi, M., Capretz, L. F., & Raza, A. (2015). A systematic review of the critical factors for success of mobile learning in higher education (university students’ perspective). J Educ Comput Res, 52(2), 257–276.CrossRefGoogle Scholar
  4. Arnone, M. P., Small, R. V., Chauncey, S. A., & McKenna, H. P. (2011). Curiosity, interest and engagement in technology-pervasive learning environments: a new research agenda. Educ Technol Res Dev, 59(2), 181–198.CrossRefGoogle Scholar
  5. Bahtaji, M. A. A. (2015). Improving transfer of learning through designed context-based instructional materials. European Journal of Science and Mathematics Education, 3(3), 265–274.Google Scholar
  6. Baker, G. L., & Blackburn, J. A. (2005). The pendulum. A case study in physics. New York: Oxford University Press.Google Scholar
  7. Barkham, P., & Moss, S. (2012). Should mobile phones be banned from schools? The Guardian. https://www.theguardian.com/education/2012/nov/27/should-mobiles-be-banned-schools. Accessed 21 March 2017.
  8. Beech, M. (2014). The pendulum paradigm. Boca Raton, Florida: Brown Waler Press.Google Scholar
  9. Beichner, R. J. (1996). The impact of video motion analysis on kinematics graph interpretation skills. Am J Phys, 64(10), 1272–1277.CrossRefGoogle Scholar
  10. Beland, L. P., & Murphy, R. (2015). Ill communication: technology, distraction & student performance. Centre for economic performance. http://cep.lse.ac.uk/pubs/download/dp1350.pdf. Accessed 21 March 2017.
  11. Bennett, J., Lubben, F., & Hogarth, S. (2007). Bringing science to life: a synthesis of the research evidence on the effects of context-based and STS approaches to science teaching. Sci Educ, 91(3), 347–370.CrossRefGoogle Scholar
  12. Berlyne, D. E. (1978). Curiosity and learning. Motiv Emot, 2(2), 97–175.CrossRefGoogle Scholar
  13. Brasell, H. (1987). The effect of real-time laboratory graphing on learning graphic representations of distance and velocity. J Res Sci Teach, 24(4), 385–395.CrossRefGoogle Scholar
  14. Castro-Palacio, J. C., & Velázquez-Abad, L. (2013). Using a mobile phone acceleration sensor in physics experiments on free and damped harmonic oscillations. Am J Phys, 81(6), 472–475.CrossRefGoogle Scholar
  15. CBC News. (2015). Smartphones in the classroom: a teacher’s dream or nightmare? CBC News. http://www.cbc.ca/news/technology/smartphones-in-the-classroom-a-teacher-s-dream-or-nightmare-1.3211652. Accessed 21 March 2017.
  16. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cogn Instr, 8(4), 293–332.CrossRefGoogle Scholar
  17. Chevrier, J., Madani, L., Ledenmat, S., & Bsiesy, A. (2013). Teaching classical mechanics using smartphones. Phys Teach, 51(6), 376–377.CrossRefGoogle Scholar
  18. Crompton, H., Burke, D., & Gregory, K. H. (2017). The use of mobile learning in PK-12 education: A systematic review. Computers & Education, 110, 51–63.Google Scholar
  19. Crompton, H., Burke, D., Gregory, K. H., & Gräbe, C. (2016). The use of mobile learning in science: a systematic review. J Sci Educ Technol, 25(2), 149–160.CrossRefGoogle Scholar
  20. DeWitt, J., & Storksdieck, M. (2008). A short review of school field trips: key findings from the past and implications for the future. Visitor Studies, 11(2), 181–197.CrossRefGoogle Scholar
  21. Ding, L., & Beichner, R. (2009). Approaches to data analysis of multiple-choice questions. Physical Review Special Topics—Physics Education Research, 5(2).Google Scholar
  22. Falk, J., & Balling, J. (1979). Setting a neglected variable in science education: investigations into outdoor field trips. Edgewater, MD: Smithsonian Institution, Chesapeake Bay Center for Environment Studies.Google Scholar
  23. Forinash, K., & Wisman, R. F. (2012). Smartphones as portable oscilloscopes for physics labs. The Physics Teacher, 50(4), 242–243.CrossRefGoogle Scholar
  24. Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50(3), 906–914.CrossRefGoogle Scholar
  25. Gilbert, J. K., Bulte, A. M. W., & Pilot, A. (2011). Concept development and transfer in context-based science education. International Journal of Science Education, 33(6), 817–837.CrossRefGoogle Scholar
  26. Greenslade Jr., T. B. (2016). Whistling tea kettles, train whistles, and organ pipes. The Physics Teacher, 54(9), 518–519.CrossRefGoogle Scholar
  27. Harackiewicz, J. M., Barron, K. E., Tauer, J. M., Carter, S. M. & Elliot, A. J. (2000). Short-term and longterm consequences of achievement goals: Predicting interest and performance over time. Journal of educational psychology, 92(2), 316.Google Scholar
  28. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (task load index): results of empirical and theoretical research. In N. Meshkati & P. A. Hancock (Eds.), Human Mental Workload (Vol. 52, pp. 139–183). Elsevier.Google Scholar
  29. Haßler, B., Major, L., & Hennessy, S. (2016). Tablet use in schools: a critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning, 32(2), 139–156.CrossRefGoogle Scholar
  30. Hattie, J. (2008). Visible learning: a synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.Google Scholar
  31. Hazel, E., Logan, P., & Gallagher, P. (2007). Equitable assessment of students in physics: importance of gender and language background. International Journal of Science Education, 19(4), 381–392.CrossRefGoogle Scholar
  32. Heller, K., & Perleth, C. (2000). Kognitiver Fähigkeitstest für 4.-12.Klassen, Revision (KFT 4-12+R). Göttingen: Hogrefe.Google Scholar
  33. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.CrossRefGoogle Scholar
  34. Hirth, M., Kuhn, J., & Müller, A. (2015). Measurement of sound velocity made easy using harmonic resonant frequencies with everyday mobile technology. The Physics Teacher, 53(2), 120–121.CrossRefGoogle Scholar
  35. Hochberg, K., Gröber, S., Kuhn, J., & Müller, A. (2014). The spinning disc: studying radial acceleration and its damping process with smartphone acceleration sensors. Physics Education, 49(2), 137–140.CrossRefGoogle Scholar
  36. Hoffmann, L. (2002). Promoting girls’ interest and achievement in physics classes for beginners. Learning and Instruction, 12(4), 447–465.CrossRefGoogle Scholar
  37. Hwang, G. J., & Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: a review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 42(4), E65–E70.CrossRefGoogle Scholar
  38. Jeffreys, B. (2015). Can a smartphone be a tool for learning? BBC News. http://www.bbc.com/news/education-34389063. Accessed 21 March 2017.
  39. Klein, P., Hirth, M., Gröber, S., Kuhn, J., & Müller, A. (2014). Classical experiments revisited: smartphones and tablet PCs as experimental tools in acoustics and optics. Physics Education, 49(4), 412–418.CrossRefGoogle Scholar
  40. 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 A. Kauertz, H. Ludwig, A. Müller, J. Pretsch, & W. Schnotz (Eds.), Multidisciplinary research on teaching and learning (pp. 270–288). Basingstoke: Palgrave Macmillan.Google Scholar
  41. Klein, P., Müller, A., & Kuhn, J. (2017). Assessment of representational competence in kinematics. Physical Review Special Topics—Physics Education Research, 13(1), 010132.CrossRefGoogle Scholar
  42. Koleza, E., & Pappas, J. (2008). The effect of motion analysis activities in a video-based laboratory in students’ understanding of position, velocity and frames of reference. International Journal of Mathematical Education in Science and Technology, 39(6), 701–723.CrossRefGoogle Scholar
  43. Kost-Smith, L. E., Pollock, S. J., & Finkelstein, N. D. (2010). Gender disparities in second-semester college physics: the incremental effects of a “smog of bias”. Physical Review Special Topics—Physics Education Research, 6(2), 020112.Google Scholar
  44. Krapp, A. (2005). Basic needs and the development of interest and intrinsic motivational orientations. Learn Instr, 15(5), 381–395.CrossRefGoogle Scholar
  45. Kuhn, J. (2010). Authentische Aufgaben im theoretischen Rahmen von Instruktions- und Lehr-Lern-Forschung: Effektivität und Optimierung von Ankermedien für eine neue Aufgabenkultur im Physikunterricht. Wiesbaden: Vieweg+Teubner.CrossRefGoogle Scholar
  46. Kuhn, J., & Müller, A. (2014). Context-based science education by newspaper story problems: a study on motivation and learning effects. Perspectives in Science, 2(1–4), 5–21.CrossRefGoogle Scholar
  47. Kuhn, J., & Vogt, P. (2012). iPhysicsLabs (series), column editors’ note. The Physics Teacher, 50, 372–373.CrossRefGoogle Scholar
  48. Kuhn, J., & Vogt, P. (2013). Smartphones as experimental tools: different methods to determine the gravitational acceleration in classroom physics by using everyday devices. European Journal of Physics Education, 4(1), 16–27.Google Scholar
  49. Kuhn, J., & Vogt, P. (2015). Smartphones & 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 (Eds.), Multidisciplinary research on teaching and learning (pp. 253–269). Basingstoke: Palgrave Macmillan UK.Google Scholar
  50. Kuhn, J., Molz, A., Gröber, S., & Frübis, J. (2014). iRadioactivity—possibilities and limitations for using smartphones and tablet PCs as radioactive counters. The Physics Teacher, 52(6), 351–356.CrossRefGoogle Scholar
  51. Lenhart, A. (2015). Teens, social media, and technology overview 2015. PewResearchCenter. http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/#. Accessed 21 March 2017.
  52. Litman, J. A., & Spielberger, C. D. (2003). Measuring epistemic curiosity and its diversive and specific components. Journal of Personality Assessment, 80(1), 75–86.CrossRefGoogle Scholar
  53. Main, S., & ORourke, J. (2011). “New directions for traditional lessons”: can handheld game consoles enhance mental mathematics skills? Australian Journal of Teacher Eduation, 36(2), 43–55.Google Scholar
  54. Mathews, J. (2015). Are smartphones dumbing down school, or are they vital learning tools? The Washington Post. https://www.washingtonpost.com/local/education/are-smartphones-dumbing-down-school-or-are-they-vital-learning-tools/2015/10/25/3e278ac8-7a27-11e5-b9c1-f03c48c96ac2_story.html?utm_term=.736cb682baa4. Accessed 21 March 2017.
  55. Matthews, M. R., Gauld, C. F., & Stinner, A. (2005). The pendulum. Dordrecht, The Nederlands: Springer Science & Business Media.CrossRefGoogle Scholar
  56. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educ Psychol, 38(1), 43–52.CrossRefGoogle Scholar
  57. Mazzella, A., & Testa, I. (2016). An investigation into the effectiveness of smartphone experiments on students’ conceptual knowledge about acceleration. Phys Educ, 51(5), 055010.CrossRefGoogle Scholar
  58. Miller, B. T., Krockover, G. H., & Doughty, T. (2013). Using iPads to teach inquiry science to students with a moderate to severe intellectual disability: a pilot study. Journal of Research in Science Teaching, 50(8), 887–911.CrossRefGoogle Scholar
  60. Mitchell, M. (1993). Situational interest: Its multifaceted structure in the secondary school mathematics classroom. Journal of Educational Psychology 85(3), 424–436.Google Scholar
  61. Molz, A. (2016). Verbindung von Schülerlabor und Schulunterricht – Auswirkungen auf Motivation und Kognition im Fach Physik. München: Verlag Dr. Hut.Google Scholar
  62. Monteiro, M., Cabeza, C., & Marti, A. C. (2014). Exploring phase space using smartphone acceleration and rotation sensors simultaneously. Eur J Phys, 35(4), 045013.CrossRefGoogle Scholar
  63. Monteiro, M., Vogt, P., Stari, C., Cabeza, C., & Marti, A. C. (2016). Exploring the atmosphere using smartphones. Phys Teach, 54(5), 308–309.CrossRefGoogle Scholar
  64. Moreno, R. (2005). Instructional technology—promise and pitfalls. In L. M. PytlikZillig, M. Bodvarsson, & R. Bruning (Eds.), Technology-based education (pp. 1–19). Greenwich, Conn.: IAP.Google Scholar
  65. Moshinsky, M., & Smirnov, Y. F. (1996). The harmonic oscillator in modern physics (Vol. 9). Amsterdam: Harwood Academic Publishers.Google Scholar
  66. Müller, R. (2006). Physik in interessanten Kontexten. Handreichung für die Unterrichtsentwicklung. https://www.tu-braunschweig.de/Medien-DB/ifdn-physik/physik-in-interessanten-kontexten-rmueller.pdf. Physik im Kontext. Accessed 21 March 2017.
  67. Müller, A., Vogt, P., Kuhn, J., & Müller, M. (2015). Cracking knuckles—a smartphone inquiry on bioacoustics. The Physics Teacher, 53(5), 307–308.CrossRefGoogle Scholar
  68. Müller, A., Hirth, M., & Kuhn, J. (2016). Tunnel pressure waves—a smartphone inquiry on rail travel. The Physics Teacher, 54(2), 118–119.CrossRefGoogle Scholar
  69. National Science Foundation. (1983). Educating Americans for the 21st century: report of the National Science Board on pre-college education in mathematics, science and technology. Washington, DC: National Science Foundation.Google Scholar
  70. Naylor, F. D. (1981). A state-trait curiosity inventory. Australian Psychologist, 16(2), 172–183.CrossRefGoogle Scholar
  71. Newhouse, P., & Rennie, L. (2001). A longitudinal study of the use of student-owned portable computers in a secondary school. Computers & Education, 36(3), 223–243.CrossRefGoogle Scholar
  72. Organisation for Economic Co-operation and Development [OECD]. (2007). PISA 2006 (Vol. 2: Data). Pisa: OECD Publishing.Google Scholar
  73. Organisation for Economic Co-operation and Development [OECD]. (2015). Students, computers and learning: making the connection. Pisa: OECD Publishing.Google Scholar
  74. Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: a review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079.CrossRefGoogle Scholar
  75. Paas, F. G. W. C., van Merrienboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79(1), 419–430.CrossRefGoogle Scholar
  76. Parolin, S. O., & Pezzi, G. (2013). Smartphone-aided measurements of the speed of sound in different gaseous mixtures. The Physics Teacher, 51(8), 508–509.CrossRefGoogle Scholar
  77. PASCO Scientific. (2015). SPARKvue (Version 2.3.2). PASCO Scientific. https://itunes.apple.com/de/app/sparkvue/id361907181?mt=8. Accessed 21 March 2017.
  78. Pimmer, C., Mateescu, M., & Gröhbiel, U. (2016). Mobile and ubiquitous learning in higher education settings. A systematic review of empirical studies. Computers in Human Behavior, 63, 490–501.CrossRefGoogle Scholar
  79. Ratcliffe, M., & Millar, R. (2009). Teaching for understanding of science in context: evidence from the pilot trials of the twenty first century science courses. Journal of Research in Science Teaching, 46(8), 945–959.CrossRefGoogle Scholar
  80. Reid, N., & Skryabina, E. A. (2002). Attitudes towards physics. Research in Science & Technological Education, 20(1), 67–81.CrossRefGoogle Scholar
  81. Sans, J. A., Manjón, F. J., Pereira, A. L. J., Gomez-Tejedor, J. A., & Monsoriu, J. A. (2013). Oscillations studied with the smartphone ambient light sensor. European Journal of Physics, 34(6), 1349–1354.CrossRefGoogle Scholar
  82. Shakur, A., & Sinatra, T. (2013). Angular momentum. The Physics Teacher, 51(9), 564–565.CrossRefGoogle Scholar
  83. Sharples, M., Taylor, J., & Vavoula, G. (2007). A theory of learning for the mobile age. In R. Andrews & C. Haythornthwaite (Eds.), The Sage handbook of eLearning research. London: Sage.Google Scholar
  84. Silva, N. (2012). Magnetic field sensor. The Physics Teacher, 50(6), 372–373.CrossRefGoogle Scholar
  85. Swarat, S., Ortony, A., & Revelle, W. (2012). Activity matters: understanding student interest in school science. Journal of Research in Science Teaching, 49(4), 515–537.CrossRefGoogle Scholar
  86. Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.CrossRefGoogle Scholar
  87. Taasoobshirazi, G., & Carr, M. (2008). A review and critique of context-based physics instruction and assessment. Educational Research Review, 3(2), 155–167.CrossRefGoogle Scholar
  88. Tabachnik, B. G., & Fidell, L. S. (1996). Using multivaraite statistics. New York: Harper Collins College Publishers.Google Scholar
  89. Tho, S. W., Chan, K. W., & Yeung, Y. Y. (2015). Technology-enhanced physics programme for community-based science learning: innovative design and programme evaluation in a theme park. Journal of Science Education and Technology, 24(5), 580–594.CrossRefGoogle Scholar
  90. Thoms, L.-J., Colicchia, G., & Girwidz, R. (2013). Color reproduction with a smartphone. The Physics Teacher, 51(7), 440–441.CrossRefGoogle Scholar
  91. Thornton, R. K., & Sokoloff, D. R. (1990). Learning motion concepts using real-time microcomputer-based laboratory tools. American Journal of Physics, 58(9), 858–867.CrossRefGoogle Scholar
  92. Tornaría, F., Monteiro, M., & Marti, A. C. (2014). Understanding coffee spills using a smartphone. The Physics Teacher, 52(8), 502–503.CrossRefGoogle Scholar
  93. Tossell, C. C., Kortum, P., Shepard, C., Rahmati, A., & Zhong, L. (2014). You can lead a horse to water but you cannot make him learn: smartphone use in higher education. British Journal of Educational Technology, 46(4), 713–724.CrossRefGoogle Scholar
  94. Trowbridge, D. E., & McDermott, L. C. (1981). Investigation of student understanding of the concept of acceleration in one dimension. American Journal of Physics, 49(3), 242–253.CrossRefGoogle Scholar
  95. Uguroglu, M. E., & Walberg, H. J. (1979). Motivation and achievement: a quantitative synthesis. American Educational Research Journal, 16(4), 375–389.CrossRefGoogle Scholar
  96. van Bruggen, J. M., Kirschner, P. A., & Jochems, W. (2002). External representation of argumentation in CSCL and the management of cognitive load. Learn Instr, 12(1), 121–138.CrossRefGoogle Scholar
  97. Vogt, P., Kasper, L., & Burde, J. (2015). The sound of church bells: tracking down the secret of a traditional arts and crafts trade. The Physics Teacher, 53(7), 438–439.CrossRefGoogle Scholar
  98. von Stumm, S., Hell, B., & Chamorro-Premuzic, T. (2011). The hungry mind: intellectual curiosity is the third pillar of academic performance. Perspectives on Psychological Science, 6(6), 574–588.CrossRefGoogle Scholar
  99. Wild, E., Hofer, M., & Pekrun, R. (2001). Psychologie des Lerners. In A. Krapp & B. Weidenmann (Eds.), Pädagogische Psychologie - Ein Lehrbuch (pp. 207–270). Weinheim: Beltz Psychologie Verlags Union.Google Scholar
  100. Williams, J. B. (2007). Assertion‐reason multiple‐choice testing as a tool for deep learning: a qualitative analysis. Assessment & Evaluation in Higher Education, 31(3), 287–301.Google Scholar
  101. Wu, W. H., Wu, Y. C. J., Chen, C. Y., Kao, H. Y., Lin, C. H., & Huang, S. H. (2012). Review of trends from mobile learning studies: a meta-analysis. Computers & Education, 59(2), 817–827.CrossRefGoogle Scholar

Copyright information

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Authors and Affiliations

  1. 1.Department of Physics/Physics Education Research GroupUniversity of KaiserslauternKaiserslauternGermany
  2. 2.Faculty of Sciences/Physics Section and Institute of Teacher Education, Pavillon d’Uni Mail (IUFE)University of GenevaGenevaSwitzerland

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