Skip to main content

Advertisement

Log in

Using immersive and modelling environments to build scientific capacity in primary preservice teacher education

  • Published:
Journal of Computers in Education Aims and scope Submit manuscript

Abstract

Research has shown that primary school teachers often have a poor background in science and scientific concepts, and as a consequence may feel particularly under-prepared to teach science. This study examines the effect of an intervention that investigated the knowledge and understanding of science concepts for a group of 8 first-year preservice primary teachers. The intervention consisted of engaging the participants using two technology-based resources: Omosa, a 3D game-like virtual learning environment (VLE), and Omosa NetLogo, a simulation/modelling environment. A small-N study design was used in this study to determine whether or not the intervention resulted in improving preservice teachers’ science content knowledge. Data sources included semi-structured interviews and concept maps. Overall, the findings suggest that the combination of the immersive and modelling environments facilitated and provided appropriate knowledge-building opportunities for participants by supporting their cognitive engagement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Akerson, V. L. (2005). How do elementary teachers compensate for incomplete science content knowledge? Research in Science Education, 35(2), 245–268. https://doi.org/10.1007/s11165-005-3176-8.

    Article  Google Scholar 

  • Alnahdi, G. H. (2015). Single-subject designs in special education: Advantages and limitations. JRS3 Journal of Research in Special Educational Needs, 15(4), 257–265.

    Article  Google Scholar 

  • Anderson, J., & Barnett, M. (2011). Using video games to support pre-service elementary teachers learning of basic physics principles. Journal of Science Education and Technology, 20(4), 347–362.

    Article  Google Scholar 

  • Angus, M., Olney, H., & Ainley, J. G. (2007). In the balance: The future of Australia’s primary schools (Vol. 1st). Kaleen: Australian Primary Principals Association.

    Google Scholar 

  • Appleton, K. (1999). Why teach primary science? Influences on beginning teachers’ practices. International Journal of Science Education, 21(2), 155–168. https://doi.org/10.1080/095006999290769.

    Article  Google Scholar 

  • Appleton, K. (2002). Science activities that work: Perceptions of primary school teachers. Research in Science Education, 32(3), 393–410. https://doi.org/10.1023/A:1020878121184.

    Article  Google Scholar 

  • Appleton, K. (2003). How do beginning primary school teachers cope with science? Toward an understanding of science teaching practice. Research in Science Education, 33(1), 1–25. https://doi.org/10.1023/a:1023666618800.

    Article  Google Scholar 

  • Appleton, K. (2008). Developing science pedagogical content knowledge through mentoring elementary teachers. Journal of Science Teacher Education, 19(6), 523–545.

    Article  Google Scholar 

  • Appleton, K., & Kindt, I. (2002). Beginning elementary teachers’ development as teachers of science. Journal of Science Teacher Education, 13, 43–61.

    Article  Google Scholar 

  • Auditor-General, Victorian. (2012). Science and mathematics participation rates and initiatives. Melbourne: Victorian Government.

    Google Scholar 

  • Australian Science Teachers Association. (2014). Primary school science teaching survey 2014. Retrieved from ACT Australia: https://asta.edu.au/programs/assist/primary_science_teaching_survey. Accessed Oct 2018.

  • Bakouli, V., & Jimoyiannis, A. (2014). Concept mapping as cognitive tool in science education: An analysis of students’ learning using SOLO taxonomy. Paper presented at the 1st International Conference on New Developments in Science and Technology Education, Corfu, Greece.

  • Barab, S., Thomas, M., Dodge, T., Carteaux, R., & Tuzun, H. (2005). Making learning fun: Quest Atlantis, a game without guns. Educational Technology Research and Development, 53(1), 86–107. https://doi.org/10.1007/BF02504859.

    Article  Google Scholar 

  • Barker, J., & Gossman, P. (2013). The learning impact of a virtual learning environment: Students’ views. Teacher Education Advancement Network Journal (TEAN), 5(2), 19–38.

    Google Scholar 

  • Barnett, S. D., Heinemann, A. W., Libin, A., Houts, A. C., Gassaway, J., Sen-Gupta, S., et al. (2012). Small N designs for rehabilitation research. Journal of Rehabilitation Research and Development, 49(1), 175–186.

    Article  Google Scholar 

  • Baser, M. (2006). Effects of conceptual change and traditional confirmatory simulations on pre-service teachers€™ understanding of direct current circuits. Journal of Science Education and Technology, 15(5–6), 367–381.

    Article  Google Scholar 

  • Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871.

    Article  Google Scholar 

  • Biggs, J., & Collis, K. (1982). Evaluating the quality of learning: The SOLO taxonomy (structure of the observed learning outcome). Cambridge: Academic Press.

    Google Scholar 

  • Blikstein, P., & Wilensky, U. (2010). MaterialSim: A constructionist agent-based modeling approach to engineering education. In M. Jacobson & P. Reimann (Eds.), Designs for learning environments of the future: International perspectives from the learning sciences (pp. 17–60). New York: Springer.

    Chapter  Google Scholar 

  • Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals. New York: D. McKay Co.

    Google Scholar 

  • Brey, P. (2008). Virtual reality and computer simulation. In K. E. Himma & H. T. Tavani (Eds.), The handbook of information and computer ethics (pp. 361–384). Hoboken: Wiley.

    Chapter  Google Scholar 

  • Bybee, R. W. (1997). Achieving scientific literacy: From purposes to practices. Portsmouth: Heinemann.

    Google Scholar 

  • Cakiroglu, O. (2012). Single subject research: Applications to special education. BJSP British Journal of Special Education, 39(1), 21–29.

    Article  Google Scholar 

  • CBI. (2015). Tomorrow’s world: Inspiring primary scientists. Retrieved from www.cbi.org.uk/tomorrows-world/assets/download.pdf. Accessed Oct 2018.

  • Davis, E. A., Petish, D., & Smithey, J. (2006). Challenges new science teachers face. Review of Educational Research, 76(4), 607–651. https://doi.org/10.3102/00346543076004607.

    Article  Google Scholar 

  • Dede, C., Clarke, J., Ketelhut, D., Nelson, B., & Bowman, C. (2005a). Fostering motivation, learning, and transfer in multi-user virtual environments. Paper presented at the American Educational Research Association Conference, Montreal.

  • Dede, C., Clarke, J., Ketelhut, D., Nelson, B., & Bowman, C. (2005b). Students’ motivation and learning of science in a multi-user virtual environment. Paper presented at the American Educational Research Association Conference, Montreal.

  • Dede, C., Ketelhut, D., & Nelson, B. (2004). Design-based research on gender, class, race, and ethnicity in a multi-user virtual environment. Paper presented at the American Educational Research Association, San Diego.

  • Dede, C., Nelson, B., Ketelhut, D., Clarke, J., & Bowman, C. (2004). Design-based research strategies for studying situated learning in a multi-user virtual environment. Paper presented at the Proceedings of the 6th international conference on learning sciences.

  • Dickes, A. C., Sengupta, P., Farris, A. V., & Basu, S. (2016). Development of mechanistic reasoning and multilevel explanations of ecology in third grade using agent-based models. Science Education, 100(4), 734–776. https://doi.org/10.1002/sce.21217.

    Article  Google Scholar 

  • Dudley, D., & Baxter, D. (2009). Assessing levels of student understanding in pre-service teachers using a two-cycle SOLO model. Asia-Pacific Journal of Teacher Education, 37(3), 283–293. https://doi.org/10.1080/13598660903052282.

    Article  Google Scholar 

  • Dugard, P., File, P., & Todman, J. (2012). Single-case and small-n experimental designs: A practical guide to randomization tests (2nd ed.). New York: Routledge Academic.

    Book  Google Scholar 

  • Duncan, I., Miller, A., & Jiang, S. (2012). A taxonomy of virtual worlds usage in education. A taxonomy of virtual worlds usage in education. British Journal of Educational Technology, 43(6), 949–964.

    Article  Google Scholar 

  • Engel, R. J., & Schutt, R. K. (2016). The practice of research in social work. Thousand Oaks: SAGE Publications.

    Google Scholar 

  • Fetherston, T. (2007). Becoming an effective teacher. South Melbourne: Thomson.

    Google Scholar 

  • Fitzgerald, A., & Smith, K. (2016). Science that matters: Exploring science learning and teaching in primary schools. Australian Journal of Teacher Education, 41(4), 64–78.

    Article  Google Scholar 

  • Garbett, D. (2003). Science education in early childhood teacher education: Putting forward a case to enhance student teachers’ confidence and competence. Research in Science Education, 33(4), 467–481. https://doi.org/10.1023/B:RISE.0000005251.20085.62.

    Article  Google Scholar 

  • Garbett, D. (2011). Constructivism deconstructed in science teacher education. Australian Journal of Teacher Education, 36(6), 36–49.

    Article  Google Scholar 

  • Gericke, N., & Wahlberg, S. (2013). Clusters of concepts in molecular genetics: A study of Swedish upper secondary science students understanding. Journal of Biological Education, 47(2), 73–83. https://doi.org/10.1080/00219266.2012.716785.

    Article  Google Scholar 

  • Gill, L., & Dalgarno, B. (2017). A qualitative analysis of pre-service primary school teachers’ TPACK development over the four years of their teacher preparation programme. Technology, Pedagogy and Education, 26(4), 439–456. https://doi.org/10.1080/1475939X.2017.1287124.

    Article  Google Scholar 

  • Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Methods of data collection in qualitative research: Interviews and focus groups. British Dental Journal, 204(6), 291–295. https://doi.org/10.1038/bdj.2008.192.

    Article  Google Scholar 

  • Gobert, J., Horwitz, P., Tinke, B., Buckley, B., Wilensky, U., Levy, S., & Dede, C. (2004). Modeling across the curriculum: Scaling up modeling using technology. In R. Alterman & D. Kirsh (Eds.), Proceedings of the Twenty-fifth Annual Meeting of the Cognitive Science Society (pp. 1349). Boston.

  • Gouvea, J. (2017). Insights from small-N studies. CBE—Life Sciences Education, 16(3), 4. https://doi.org/10.1187/cbe.17-06-0110.

    Article  Google Scholar 

  • Graham, J. E., Karmarkar, A. M., & Ottenbacher, K. J. (2012). Small sample research designs for evidence-based rehabilitation: Issues and methods. Archives of Physical Medicine and Rehabilitation, 93(8), S111–S116. https://doi.org/10.1016/j.apmr.2011.12.017.

    Article  Google Scholar 

  • Grotzer, T. A., Powell, M. M., Derbiszewska, K. M., Courter, C. J., Kamarainen, A. M., Metcalf, S. J., et al. (2015). Turning transfer inside out: The affordances of virtual worlds and mobile devices in real world contexts for teaching about causality across time and distance in ecosystems. Technology, Knowledge and Learning, 20(1), 43. https://doi.org/10.1007/s10758-014-9241-5.

    Article  Google Scholar 

  • Grotzer, T. A., Tutwiler, M. S. K., Kamarainen, A. M., Derbiszewska, K. M., Metcalf, S. J., & Dede, C. J. (2016). Students’ reasoning tendencies about the causal dynamics of ecosystems and the impacts of MUVE vs. non-MUVE instructional contexts. Paper presented at the American Educational Research Association (AERA) Conference, Washington DC

  • Hackling, M. W., Peers, S., & Prain, V. (2007). Primary connections: Reforming science teaching in Australian primary schools. Teaching Science, 53(3), 12.

    Google Scholar 

  • Hammond, M., Fragkouli, E., Suandi, I., Crosson, S., Ingram, J., JohnstonWilder, P., et al. (2009). What happens as student teachers who made very good use of ICT during preservice training enter their first year of teaching? Teacher Development, 13(2), 93–106.

    Article  Google Scholar 

  • Harlen, W. (1997). Primary teachers’ understanding in science and its impact in the classroom. Research in Science Education, 27(3), 323–337. https://doi.org/10.1007/bf02461757.

    Article  Google Scholar 

  • Harlen, W., & Holroyd, C. (1997). Primary teachers’ understanding of concepts of science: Impact on confidence and teaching. International Journal of Science Education, 19(1), 93–105. https://doi.org/10.1080/0950069970190107.

    Article  Google Scholar 

  • Hattie, J., & Brown, G. T. L. (2004). Cognitive processes in asTTle: The SOLO taxonomy. asTTle technical report #43. University of Auckland/Ministry of Education.

  • Hoban, G., Macdonald, D., & Ferry, B. (2009). Improving preservice teachers’ science knowledge by creating, reviewing and publishing slowmations to TeacherTube. Paper presented at the SITE 2009—Society for Information Technology & Teacher Education International Conference, Chesapeake: Association for the Advancement of Computing in Education.

  • Jaber, L. Z., & Hammer, D. (2016). Learning to feel like a scientist. Science Education, 100(2), 189–220. https://doi.org/10.1002/sce.21202.

    Article  Google Scholar 

  • Jacobson, M. (2012). Omosa project: An educational MUVE in action. https://www.youtube.com/watch?v=zccXMR4gsIo&feature=youtu.be. Accessed Oct 2018.

  • Jacobson, M., & Kozma, R. (2000). Innovations in science and mathematics education: Advanced designs for technologies of learning. Mahwah: Lawrence Erlbaum Associates Inc.

    Google Scholar 

  • Jacobson, M., Taylor, C., Hu, C., Newstead, A., Wong, W.-Y., Richards, D.,… Kapur, M. (2011). Collaborative virtual worlds and productive failure: Design research with multi-disciplinary pedagogical, technical and graphics, and learning research teams. International Society of the Learning Sciences (ISLS).

  • Jacobson, M., Taylor, C., & Richards, D. (2016). Computational scientific inquiry with virtual worlds and agent-based models: new ways of doing science to learn science. Interactive Learning Environments, 24(8), 2080–2108.

    Article  Google Scholar 

  • Kallery, M., & Psillos, D. (2001). Pre-school teachers’ content knowledge in science: Their understanding of elementary science concepts and of issues raised by children’s questions Le Contenue des Connaissances des Enseignants de Maternelle en Matière de Sciences Exactes: Leur perception des concepts scientifiques de base ainsi que des interrogations soulevées par les questions des enfants El Conocimiento de Contenido de los Educadores de Preescolar en Ciencia: Su entendimiento en conceptos elementales en Ciencia y en cuestiones que surgen de las preguntas de los niños. International Journal of Early Years Education, 9(3), 165–179. https://doi.org/10.1080/09669760120086929.

    Article  Google Scholar 

  • Kamarainen, A. M., Metcalf, S., Grotzer, T., & Dede, C. (2015). Exploring ecosystems from the inside: How immersive multi-user virtual environments can support development of epistemologically grounded modeling practices in ecosystem science instruction. Journal of Science Education and Technology, 24(2–3), 148–167.

    Article  Google Scholar 

  • Kennedy-Clark, S. (2011). Pre-service teachers’ perspectives on using scenario-based virtual worlds in science education. Computers & Education, 57(4), 2224–2235. https://doi.org/10.1016/j.compedu.2011.05.015.

    Article  Google Scholar 

  • Kennedy-Jones, M., Naji, K., & Ennals, P. (2015). Using concept maps to understand student learning in a compulsory volunteering subject in occupational therapy. Focus on Health Professional Education: A Multi-disciplinary Journal, 16(4), 50–63.

    Article  Google Scholar 

  • Ketelhut, D. J. (2007). the impact of student self-efficacy on scientific inquiry skills: An exploratory investigation in “River City”, a multi-user virtual environment. Journal of Science Education and Technology, 16(1), 99–111. https://doi.org/10.1007/s10956-006-9038-y.

    Article  Google Scholar 

  • Ketelhut, D. J., Clarke, J., & Nelson, B. C. (2010). The development of River City, a multi-user virtual environment-based scientific inquiry curriculum: historical and design evolutions. In M. Jacobson & P. Reimann (Eds.), Designs for learning environments of the future: International perspectives from the learning sciences (pp. 89–110). New York: Springer.

    Chapter  Google Scholar 

  • Leden, L., Hansson, L., Redfors, A., & Ideland, M. (2013). Why, when and how to teach nature of science in compulsory school: Teachers’ views. Paper presented at the 10th Conference of the European Science Education Research Association (ESERA), Nicosia, Cyrus.

  • Lederman, N. G. (2007). Nature of science: Past, present, and future. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 831–880). Mahwah: Lawrence Erlbaum Associates Inc, Publishers.

    Google Scholar 

  • Lobo, M. A., Moeyaert, M., Baraldi Cunha, A., & Babik, I. (2017). Single-case design, analysis, and quality assessment for intervention research. Journal of Neurologic Physical Therapy, 41(3), 187–197.

    Article  Google Scholar 

  • McDougall, D., & Smith, D. (2006). Recent innovations in small-N designs for research and practice in professional school counseling. Professional School Counseling, 9(5), 392–400.

    Article  Google Scholar 

  • McPhan, G. (2008). A developmental framework for assessing concept maps. Paper presented at the Third International Conference on Concept Mapping, Estonia.

  • Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers & Education, 70, 29–40. https://doi.org/10.1016/j.compedu.2013.07.033.

    Article  Google Scholar 

  • Metcalf, S., Clarke, J., & Dede, C. (2009). Virtual worlds for education: River City and EcoMUVE. Paper presented at the MiT6 International Conference.

  • Metcalf, S., Kamarainen, A., Tutwiler, M. S., Grotzer, T., & Dede, C. (2011). Ecosystem science learning via multi-user virtual environments. International Journal of Gaming and Computer-Mediated Simulations, 3(1), 86–90.

    Article  Google Scholar 

  • Naidoo, K. (2013). Transforming beliefs and practices: Elementary teacher candidates’ development through shared authentic teaching and reflection experiences within an innovative science methods course. (PhD Dissertation), New York University.

  • National Research Council. (2001). Adding it up: Helping children learn mathematics. Washington, DC: The National Academy Press.

    Google Scholar 

  • Nelson, B., & Ketelhut, D. (2007). Scientific inquiry in educational multi-user virtual environments. Educational Psychology Review, 19(3), 265–283.

    Article  Google Scholar 

  • Novak, J. D. (2003). The promise of new ideas and new technology for improving teaching and learning. Cell Biology Education, 2(2), 122–132.

    Article  Google Scholar 

  • Nowicki, B. L., Sullivan-Watts, B., Shim, M. K., Young, B., & Pockalny, R. (2013). Factors influencing science content accuracy in elementary inquiry science lessons. Research in Science Education, 43(3), 1135–1154.

    Article  Google Scholar 

  • Nussli, N., Oh, K., & McCandless, K. (2014). Collaborative science learning in three-dimensional immersive virtual worlds: Pre-service teachers’ experiences in second life. Journal of Educational Multimedia and Hypermedia, 23(3), 253–284.

    Google Scholar 

  • OECD. (2010). PISA 2009 Results: What students know and can doStudent performance in reading, mathematics and science. Retrieved from http://dx.doi.org/10.1787/9789264091450-en. Accessed Oct 2018.

  • Oh, P. S., & Kim, K. S. (2013). Pedagogical transformations of science content knowledge in Korean elementary classrooms. International Journal of Science Education, 35(9), 1590–1624. https://doi.org/10.1080/09500693.2012.719246.

    Article  Google Scholar 

  • Oz, H. (2015). Assessing pre-service english as a foreign language teachers’ technological pedagogical content knowledge. International Education Studies, 8(5), 119–130.

    Article  Google Scholar 

  • Parr, G., Bellis, N., & Bulfin, S. (2013). Teaching English teachers for the future: Speaking back to TPACK. English in Australia, 48(1), 9–22.

    Google Scholar 

  • Patridge, N. (2003). Science out of the classroom. Journal of Biological Education, 37(2), 56–57. https://doi.org/10.1080/00219266.2003.9655851.

    Article  Google Scholar 

  • Peers, S. (2006). Making a difference: PrimaryConnections Stage 3 Project Brief. Retrieved from Canberra: Australian: www.science.org.au/primaryconnections. Accessed Oct 2018.

  • Pine, J., Aschbacher, P., Roth, E., Jones, M., McPhee, C., Martin, C.,… Foley, B. (2006). Fifth graders’ science inquiry abilities: A comparative study of students in hands-on and textbook curricula. Journal of Research in Science Teaching, 43(5), 467–484.

  • Quan, G. M., & Elby, A. (2016). Connecting self-efficacy and views about the nature of science in undergraduate research experiences. Physical Review Physics Education Research, 12(2), 020140.

    Article  Google Scholar 

  • Rassafiani, M., & Sahaf, R. (2010). Single case experimental design: An overview. International Journal of Therapy and Rehabilitation, 17(6), 285–289.

    Article  Google Scholar 

  • Reisoğlu, I., Topu, B., Yılmaz, R., Karakuş Yılmaz, T., & Göktaş, Y. (2017). 3D virtual learning environments in education: A meta-review. Asia Pacific Education Review, 18(1), 81. https://doi.org/10.1007/s12564-016-9467-0.

    Article  Google Scholar 

  • Rennie, L. J., Goodrum, D., & Hackling, M. W. (2001). Science teaching and learning in Australian schools: Results of a national study. Research in Science Education, 31(4), 455–498. https://doi.org/10.1023/A:1013171905815.

    Article  Google Scholar 

  • Rice, D. C., Ryan, J. M., & Samson, S. M. (1998). Using concept maps to assess student learning in the science classroom: Must different methods compete? Journal of Research in Science Teaching, 35(10), 1103–1127. https://doi.org/10.1002/(sici)1098-2736(199812)35:10%3c1103:aid-tea4%3e3.0.co;2-p.

    Article  Google Scholar 

  • Richards, D., Jacobson, M., Porte, J., Taylor, C. E., Taylor, M., Newstead, A., & Hanna, N. (2012). Evaluating the models, reasoning, and behaviour of 3D intelligent virtual animals in a predator-prey system. Paper presented at the Eleventh International Conference on Autonomous Agents and Multiagent Systems (AAMA, 2012), Valencia.

  • Sardone, N. B., & Devlin-Scherer, R. (2008). Teacher candidates’ views of a multi-user virtual environment (MUVE). Technology, Pedagogy and Education, 17(1), 41–51. https://doi.org/10.1080/14759390701847484.

    Article  Google Scholar 

  • Schaal, S., Bogner, F. X., & Girwidz, R. (2010). Concept mapping assessment of media assisted learning in interdisciplinary science education. Research in Science Education, 40(3), 339–352.

    Article  Google Scholar 

  • Schwarz, C. V., Meyer, J., & Sharma, A. (2007). Technology, pedagogy, and epistemology: Opportunities and challenges of using computer modeling and simulation tools in elementary science methods. Journal of Science Teacher Education, 18(2), 243–269. https://doi.org/10.1007/s10972-007-9039-6.

    Article  Google Scholar 

  • Schwendimann, B. A. (2014). Making sense of knowledge integration maps. In D. Ifenthaler & R. Hanewald (Eds.), Digital knowledge maps in education: Technology enhanced support for teachers and learners. NewYork: Springer.

    Google Scholar 

  • Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. https://doi.org/10.1007/s10639-012-9240-x.

    Article  Google Scholar 

  • Thornburg, D. (2009). Five challenges in science education. Retrieved from http://www.tcse-k12.org/pages/science.pdf. Accessed Oct 2018.

  • Timms, M. J., Moyle, K., Weldon, P. R., Mitchell, P., & Australian Council for Educational, R. (2018). Challenges in STEM learning in Australian schools: Literature and policy review.

  • Tranter, J. (2004). Biology: Dull, lifeless and boring? Journal of Biological Education, 38(3), 104–105. https://doi.org/10.1080/00219266.2004.9655914.

    Article  Google Scholar 

  • Trygstad, P. J., Smith, P. S., Banilower, E. R., & Nelson, M. M. (2013). The status of elementary science education: Are we ready for the next generation science standards? Retrieved from https://files.eric.ed.gov/fulltext/ED548249.pdf. Accessed Oct 2018.

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: learning biology through constructing and testing computational theories—An embodied modeling approach. Cognition and Instruction, 24(2), 171–209. https://doi.org/10.1207/s1532690xci2402_1.

    Article  Google Scholar 

  • Zhang, H., & Kaufman, D. (2013). Virtual environments in education: Developments, applications and challenges. International Journal of Computer Research, 20(1), 123.

    Google Scholar 

  • Zhao, Y. (2003). The use of a constructivist teaching model in environmental science at Beijing Normal University. China Papers, 2, 78–83.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr Louise Sutherland for their ongoing support in this research study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shannon Kennedy-Clark.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammed, R., Kennedy-Clark, S. & Reimann, P. Using immersive and modelling environments to build scientific capacity in primary preservice teacher education. J. Comput. Educ. 6, 451–481 (2019). https://doi.org/10.1007/s40692-019-00145-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40692-019-00145-5

Keywords

Navigation