Advertisement

How Do Secondary Science Teachers Perceive the Use of Interactive Simulations? The Affordance in Singapore Context

  • Wenjin Vikki Bo
  • Gavin W. Fulmer
  • Christine Kim-Eng Lee
  • Victor Der-Thanq Chen
Article

Abstract

Research has shown that teaching science with a modeling-oriented approach, particularly with interactive simulations, will promote student engagement and understanding. To date, many interactive simulations have been developed and adopted for classroom practices. The purpose of this study was to explore secondary school science teachers’ perceived affordance of interactive simulation as well as their practical experience with simulation implementation in class. Twelve science teachers from seven schools were interviewed individually and the data was triangulated with their teaching plans and student assignments. Their past experiences of simulation implementation revealed that most teachers adopted simulations for demonstration purpose in teacher-led instruction. Their attempts to provide students opportunities to use the simulations to explore alternative modeling by themselves did not seem to work well. There are various reasons for this, such as the shortage of facilities, Internet bandwidth, and technological knowledge. There was also a pressing need for teachers to complete the required syllabus in limited classroom time. The majority of teachers’ future intent to use simulation in class was quite weak, especially with the less proficient students who had some difficulty understanding simulations. Although interactive simulations have great potential to promote students’ understanding in abstract science concepts, overcoming the difficulties of implementation may require other alternatives such as a flipped classroom approach. Future studies can investigate how to design learning activities outside class, to engage students in exploring modeling in simulations.

Keywords

Interactive simulations Technology implementation Science education Modeling-oriented instruction 

Notes

Acknowledgements

This study was funded by the Education Research Funding Programme, National Institute of Education (NIE), Nanyang Technological University, Singapore, project number OER 10/15 GWF. The views expressed in this paper are the authors’ and do not necessarily represent the views of NIE. We thank the anonymous reviewers for their input and the support of participating teachers and project collaborator Mr. Wee Loo Kang from the Ministry of Education.

Funding

This study was funded by the NIE’s Education Research Funding Programme (project number OER 10/15 GWF).

Compliance with Ethical Standards

Ethical Approval

All procedures involving human participants were approved by and conducted in accordance with the ethical standards of the Nanyang Technological University Institutional Review Board (NTU/IRB), and consistent with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

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

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Baek, Y., Jung, J., & Kim, B. (2008). What makes teachers use technology in the classroom? Exploring the factors affecting facilitation of technology with a Korean sample. Computers & Education, 50(1), 224–234.CrossRefGoogle Scholar
  2. Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243–260.CrossRefGoogle Scholar
  3. Bang, E., & Luft, J. A. (2013). Secondary science teachers’ use of technology in the classroom during their first 5 years. Journal of Digital Learning in Teacher Education, 29(4), 118–126.CrossRefGoogle Scholar
  4. Bell, R. L., & Trundle, K. C. (2008). The use of a computer simulation to promote scientific conceptions of moon phases. Journal of Research in Science Teaching, 45(3), 346–372.CrossRefGoogle Scholar
  5. Brenner, A. M., & Brill, J. M. (2016). Investigating practices in teacher education that promote and inhibit technology integration transfer in early career teachers. TechTrends, 60(2), 136–144.  https://doi.org/10.1007/s11528-016-0025-8.CrossRefGoogle Scholar
  6. Brownell, S. E., Kloser, M. J., Fukami, T., & Shavelson, R. (2012). Undergraduate biology lab courses: Comparing the impact of traditionally based “cookbook” and authentic research-based courses on student lab experiences. Journal of College Science Teaching, 41(4), 36–45.Google Scholar
  7. Campbell, T., & Oh, P. S. (2015). Engaging students in modelling as an epistemic practice of science: An introduction to the special issue of the journal of science education and technology. Journal of Science Education and Technology, 24(2), 125–131.  https://doi.org/10.1007/s10956-014-9544-2.CrossRefGoogle Scholar
  8. Campbell, T., Longhurst, M. L., Wang, S. K., Hsu, H. Y., & Coster, D. C. (2015). Technologies and reformed-based science instruction: The examination of a professional development model focused on supporting science teaching and learning with technologies. Journal of Science Education and Technology, 24(5), 562–579.  https://doi.org/10.1007/s10956-015-9548-6.CrossRefGoogle Scholar
  9. Carlsen, D. D., & Andre, T. (1992). Use of a microcomputer simulation and conceptual change text to overcome student preconceptions about electric circuits. Journal of Computer-Based Instruction, 19(4), 105–109.Google Scholar
  10. Chang, K.-E., Chen, Y.-L., Lin, H.-Y., & Sung, Y.-T. (2008). Effects of learning support in simulation-based physics learning. Computers & Education, 51(4), 1486–1498.CrossRefGoogle Scholar
  11. Chen, Y., Wang, Y., & Chen, N.-S. (2014). Is FLIP enough? Or should we use the FLIPPED model instead? Computers & Education, 79, 16–27.CrossRefGoogle Scholar
  12. Christian, W., Esquembre, F., & Barbato, L. (2011). Open source physics. Science, 334(6059), 1077–1078.CrossRefGoogle Scholar
  13. Clement, J. (2008). Six levels of organization for curriculum design and teaching. In J. Clement & M. A. Ramirez (Eds.), Model based learning and instruction in science (pp. 255–272). Dordrecht: Springer.Google Scholar
  14. Creswell, J. W. (2007). Research design: Choosing among five approaches. London: Sage.Google Scholar
  15. Crocco, M. S., & Costigan, A. T. (2007). The narrowing of curriculum and pedagogy in the age of accountability urban educators speak out. Urban Education, 42(6), 512–535.CrossRefGoogle Scholar
  16. Curdt-Christiansen, X. L. (2010). Competing priorities: Singaporean teachers’ perspectives on critical literacy. International Journal of Educational Research, 49(6), 184–194.CrossRefGoogle Scholar
  17. DeBoer, G. E. (2002). Student-centered teaching in a standards-based world: Finding a sensible balance. Science & Education, 11(4), 405–417.CrossRefGoogle Scholar
  18. Dori, Y. J., & Barak, M. (2001). Virtual and physical molecular modelling: Fostering model perception and spatial understanding. Educational Technology & Society, 4(1), 61–74.Google Scholar
  19. Fretz, E. B., Wu, H.-K., Zhang, B., Davis, E. A., Krajcik, J. S., & Soloway, E. (2002). An investigation of software scaffolds supporting modelling practices. Research in Science Education, 32(4), 567–589.CrossRefGoogle Scholar
  20. Frigg, R., & Hartmann, S. (2017). Models in science. In: E. N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (2017 ed.). Spring.Google Scholar
  21. Fulmer, G. W., & Liang, L. L. (2013). Measuring model-based high school science instruction: Development and application of a student survey. Journal of Science Education and Technology, 22(1), 37–46.  https://doi.org/10.1007/s10956-012-9374-z.CrossRefGoogle Scholar
  22. Geban, Ö., Askar, P., & Özkan, Ï. (1992). Effects of computer simulations and problem-solving approaches on high school students. The Journal of Educational Research, 86(1), 5–10.CrossRefGoogle Scholar
  23. Gibson, H. L., & Chase, C. (2002). Longitudinal impact of an inquiry-based science program on middle school students' attitudes toward science. Science Education, 86(5), 693–705.CrossRefGoogle Scholar
  24. Goh, K. S. A., Wee, L. K., Yip, K. W., Toh, P. Y. J., & Lye, S. Y. (2013). Addressing learning difficulties in Newtons 1st and 3rd Laws through problem based inquiry using Easy Java Simulation. Paper presented at the the 5th redesign pedagogy, Singapore.Google Scholar
  25. Grossman, P., & Thompson, C. (2008). Learning from curriculum materials: Scaffolds for new teachers? Teaching and Teacher Education, 24(8), 2014–2026.CrossRefGoogle Scholar
  26. Guzey, S. S., & Roehrig, G. H. (2009). Teaching science with technology: Case studies of science teachers’ development of technology, pedagogy, and content knowledge. Contemporary Issues in Technology and Teacher Education, 9(1), 25–45.Google Scholar
  27. Hamdan, N., McKnight, P., McKnight, K., & Arfstrom, K. M. (2013). The flipped learning model: A white paper based on the literature review titled a review of flipped learning. Flipped Learning Network. Retrieved from https://flippedlearning.org/wp-content/uploads/2016/07/WhitePaper_FlippedLearning.pdf. Accessed 10 Jan 2018.
  28. Hershkovitz, A., de Baker, R. S. J., Gobert, J., Wixon, M., & Pedro, M. S. (2013). Discovery with models: A case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57(10), 1480–1499.CrossRefGoogle Scholar
  29. Hogan, D., Chan, M., Rahim, R., Kwek, D., Maung Aye, K., Loo, S. C., Sheng, Y. Z., & Luo, W. (2013). Assessment and the logic of instructional practice in secondary 3 English and mathematics classrooms in Singapore. Review of Education, 1(1), 57–106.CrossRefGoogle Scholar
  30. Hounshell, P. B., & Hill, S. R. (1989). The microcomputer and achievement and attitudes in high school biology. Journal of Research in Science Teaching, 26(6), 543–549.CrossRefGoogle Scholar
  31. Howard, S. K., Chan, A., & Caputi, P. (2015). More than beliefs: Subject areas and teachers' integration of laptops in secondary teaching. British Journal of Educational Technology, 46(2), 360–369.CrossRefGoogle Scholar
  32. Inan, F. A., & Lowther, D. L. (2010). Factors affecting technology integration in K-12 classrooms: A path model. Educational Technology Research and Development, 58(2), 137–154.CrossRefGoogle Scholar
  33. Jimoyiannis, A., & Komis, V. (2001). Computer simulations in physics teaching and learning: A case study on students' understanding of trajectory motion. Computers & Education, 36(2), 183–204.CrossRefGoogle Scholar
  34. Jones, M., & McLean, K. (2012). Personalising learning in teacher education through the use of technology. Australian Journal of Teacher Education, 37(1), 75.CrossRefGoogle Scholar
  35. Khan, S. (2008). What if scenarios for testing student models in chemistry. In J. Clement & M. A. Ramirez (Eds.), Model based learning and instruction in science (pp. 139–150). Dordrecht: Springer.Google Scholar
  36. Khan, S. (2011). New pedagogies on teaching science with computer simulations. Journal of Science Education and Technology, 20(3), 215–232.CrossRefGoogle Scholar
  37. Koh, K., & Luke, A. (2009). Authentic and conventional assessment in Singapore schools: An empirical study of teacher assignments and student work. Assessment in Education: Principles, Policy & Practice, 16(3), 291–318.CrossRefGoogle Scholar
  38. Koh, K. H., Tan, C., & Ng, P. T. (2012). Creating thinking schools through authentic assessment: The case in Singapore. Educational Assessment, Evaluation and Accountability, 24(2), 135–149.CrossRefGoogle Scholar
  39. Kopcha, T. J. (2012). Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development. Computers & Education, 59(4), 1109–1121.CrossRefGoogle Scholar
  40. Kuhn, D., Arvidsson, T. S., Lesperance, R., & Corprew, R. (2017). Can engaging in science practices promote deep understanding of them? Science Education, 101(2), 232–250.CrossRefGoogle Scholar
  41. Lehrer, R., & Schauble, L. (2006). Cultivating Model-Based Reasoning in Science Education. Cambridge: Cambridge University Press.Google Scholar
  42. Louca, L. T., Zacharia, Z. C., & Constantinou, C. P. (2011). In quest of productive modelling-based learning discourse in elementary school science. Journal of Research in Science Teaching, 48(8), 919–951.  https://doi.org/10.1002/tea.20435.CrossRefGoogle Scholar
  43. Margerum-Leys, J., & Marx, R. W. (2002). Teacher knowledge of educational technology: A case study of student/mentor teacher pairs. Journal of Educational Computing Research, 26(4), 427–462.CrossRefGoogle Scholar
  44. Marshall, J. A., & Young, E. S. (2006). Preservice teachers' theory development in physical and simulated environments. Journal of Research in Science Teaching, 43(9), 907–937.CrossRefGoogle Scholar
  45. McElhaney, K. W., & Linn, M. C. (2011). Investigations of a complex, realistic task: Intentional, unsystematic, and exhaustive experimenters. Journal of Research in Science Teaching, 48(7), 745–770.CrossRefGoogle Scholar
  46. McLoughlin, C., & Lee, M. J. (2010). Personalised and self regulated learning in the web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 1–16.  https://doi.org/10.14742/ajet.1100.
  47. Merriam, S. B. (1998). Qualitative Research and Case Study Applications in Education. Revised and Expanded from" Case Study Research in Education.": ERIC.Google Scholar
  48. Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: A sourcebook. Beverly Hills: Sage Publications.Google Scholar
  49. Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction—What is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474–496.  https://doi.org/10.1002/tea.20347.CrossRefGoogle Scholar
  50. Monaghan, J. M., & Clement, J. (1999). Use of a computer simulation to develop mental simulations for understanding relative motion concepts. International Journal of Science Education, 21(9), 921–944.CrossRefGoogle Scholar
  51. Mulder, Y. G., Bollen, L., de Jong, T., & Lazonder, A. W. (2016). Scaffolding learning by modelling: The effects of partially worked-out models. Journal of Research in Science Teaching, 53(3), 502–523.CrossRefGoogle Scholar
  52. Mulholland, J., & Wallace, J. (1996). Breaking the cycle: Preparing elementary teachers to teach science. Journal of Elementary Science Education, 8(1), 17–38.CrossRefGoogle Scholar
  53. National Research Council. (2012). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Committee on a conceptual framework for new K-12 science education standards. Board on science education, division of behavioral and social sciences and education. Washington, DC: The National Academies Press.Google Scholar
  54. NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academy Press.Google Scholar
  55. Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: Results from a worldwide educational assessment. Computers & Education, 37(2), 163–178.CrossRefGoogle Scholar
  56. Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.CrossRefGoogle Scholar
  57. Pringle, R. M., Dawson, K., & Ritzhaupt, A. D. (2015). Integrating science and technology: Using technological pedagogical content knowledge as a framework to study the practices of science teachers. Journal of Science Education and Technology, 24(5), 648–662.CrossRefGoogle Scholar
  58. Ramnarain, U. D. (2014). Teachers’ perceptions of inquiry-based learning in urban, suburban, township and rural high schools: The context-specificity of science curriculum implementation in South Africa. Teaching and Teacher Education, 38, 65–75.CrossRefGoogle Scholar
  59. Schrum, L. (1995). Educators and the internet: A case study of professional development. Computers & Education, 24(3), 221–228.CrossRefGoogle Scholar
  60. Schrum, L. (1999). Technology professional development for teachers. Educational Technology Research and Development, 47(4), 83–90.CrossRefGoogle Scholar
  61. Schwarz, C. V. (2009). Developing preservice elementary teachers' knowledge and practices through modelling-centered scientific inquiry. Science Education, 93(4), 720–744.CrossRefGoogle Scholar
  62. Schwarz, C. V., & Gwekwerere, Y. N. (2007). Using a guided inquiry and modelling instructional framework (EIMA) to support preservice K-8 science teaching. Science Education, 91(1), 158–186.CrossRefGoogle Scholar
  63. Schwarz, C. V., & White, B. Y. (2005). Metamodelling knowledge: Developing students' understanding of scientific modelling. Cognition and Instruction, 23(2), 165–205.CrossRefGoogle Scholar
  64. Schwarz, C. V., Meyer, J., & Sharma, A. (2007). Technology, pedagogy, and epistemology: Opportunities and challenges of using computer modelling and simulation tools in elementary science methods. Journal of Science Teacher Education, 18(2), 243–269.CrossRefGoogle Scholar
  65. Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modelling: Making scientific modelling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654.CrossRefGoogle Scholar
  66. Stewart, J., Cartier, J. L., & Passmore, C. M. (2005). Developing understanding through model-based inquiry. In M. S. Donovan & J. D Bransford (Eds.), How students learn: science in the classroom (pp. 515–565). Washington, DC: National Academies Press.Google Scholar
  67. Strudler, N., & Wetzel, K. (1999). Lessons from exemplary colleges of education: Factors affecting technology integration in preservice programs. Educational Technology Research and Development, 47(4), 63–81.CrossRefGoogle Scholar
  68. Tondeur, J., Van Keer, H., van Braak, J., & Valcke, M. (2008). ICT integration in the classroom: Challenging the potential of a school policy. Computers & Education, 51(1), 212–223.CrossRefGoogle Scholar
  69. Wachira, P., & Keengwe, J. (2011). Technology integration barriers: Urban school mathematics teachers perspectives. Journal of Science Education and Technology, 20(1), 17–25.CrossRefGoogle Scholar
  70. Wanner, T., & Palmer, E. (2015). Personalising learning: Exploring student and teacher perceptions about flexible learning and assessment in a flipped university course. Computers & Education, 88, 354–369.CrossRefGoogle Scholar
  71. Wee, L. K., & Mak, W. K. (2009). Leveraging on Easy Java Simulation tool and open source computer simulation library to create interactive digital media for mass customization of high school physics curriculum. Paper presented at the the 3rd redesigning pedagogy international conference, Singapore.Google Scholar
  72. Wee, L. K. L., Lim, A. P., Goh, K. S. A., LyeYE, S. Y., Lee, T. L., Xu, W., … Lim, E.-P. (2012). Computer Models Design for Teaching and Learning using Easy Java Simulation. Paper presented at the the world conference on physics education, İstanbul, Turkey.Google Scholar
  73. Wee, L. K., Lee, T. L., Chew, C., Wong, D., & Tan, S. (2015). Understanding resonance graphs using easy java simulations (EJS) and why we use EJS. Physics Education, 50(2), 189–196.CrossRefGoogle Scholar
  74. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.CrossRefGoogle Scholar
  75. White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modelling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.CrossRefGoogle Scholar
  76. Windschitl, M., & Sahl, K. (2002). Tracing teachers’ use of technology in a laptop computer school: The interplay of teacher beliefs, social dynamics, and institutional culture. American Educational Research Journal, 39(1), 165–205.CrossRefGoogle Scholar
  77. Winn, W., Stahr, F., Sarason, C., Fruland, R., Oppenheimer, P., & Lee, Y. L. (2006). Learning oceanography from a computer simulation compared with direct experience at sea. Journal of Research in Science Teaching, 43(1), 25–42.CrossRefGoogle Scholar
  78. Wozney, L., Venkatesh, V., & Abrami, P. C. (2006). Implementing computer technologies: Teachers' perceptions and practices. Journal of Technology and Teacher Education, 14(1), 173.Google Scholar
  79. Wu, H. K., & Huang, Y. L. (2007). Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments. Science Education, 91(5), 727–749.CrossRefGoogle Scholar
  80. Yaman, M., Nerdel, C., & Bayrhuber, H. (2008). The effects of instructional support and learner interests when learning using computer simulations. Computers & Education, 51(4), 1784–1794.CrossRefGoogle Scholar
  81. Yerdelen-Damar, S., Boz, Y., & Aydın-Günbatar, S. (2017). Mediated effects of technology competencies and experiences on relations among attitudes towards technology use, technology ownership, and self efficacy about technological pedagogical content knowledge. Journal of Science Education and Technology, 26(4), 394–405.CrossRefGoogle Scholar
  82. Yin, R. K. (2013). Case study research: design and methods. Thousand Oaks: Sage.Google Scholar
  83. 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(8), 792–823.CrossRefGoogle Scholar
  84. Zacharia, Z., & Anderson, O. R. (2003). The effects of an interactive computer-based simulation prior to performing a laboratory inquiry-based experiment on students’ conceptual understanding of physics. American Journal of Physics, 71(6), 618–629.CrossRefGoogle Scholar
  85. Zhang, J., Chen, Q., Sun, Y., & Reid, D. J. (2004). Triple scheme of learning support design for scientific discovery learning based on computer simulation: Experimental research. Journal of Computer Assisted Learning, 20(4), 269–282.CrossRefGoogle Scholar
  86. Zhang, B., Liu, X., & Krajcik, J. S. (2006). Expert models and modelling processes associated with a computer-modelling tool. Science Education, 90(4), 579–604.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Singapore University of Social SciencesSingaporeSingapore
  2. 2.Department of Teaching & LearningUniversity of IowaIowa CityUSA
  3. 3.National Institute of EducationSingaporeSingapore

Personalised recommendations