Advertisement

Virtual Reality Simulations and Writing: a Neuroimaging Study in Science Education

  • Richard L. LambEmail author
  • Elisabeth Etopio
Article

Abstract

This study investigates the role that textbooks, virtual reality (VR), and mixed approaches (i.e., text and VR) can play in the development of the two writing types, summary and argument writing. This study uses hemodynamics as a proxy for learning. Differences in hemodynamic responses during writing tasks were measured across four different pedagogical modalities: VR alone, VR followed by textbook readings, textbook readings followed by VR, and textbook readings alone. Adult students N = 80, recruited from non-science-based higher education programs, responded to two prompts related to content found in the VR environment and discussed in the textbook. The authors hypothesized that exposure to a virtual marine environment prior to writing would enhance the two writing products, when compared with participants who only had access to textbook experiences. Of the four conditions participants exposed to the VR environment then a textbook demonstrated significantly greater hemodynamic response than those who had access to VR alone or text alone.

Keywords

Virtual reality Writing in science fNIRS Neuroimaging 

Notes

Compliance and Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

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

References

  1. Akaygun, S., & Jones, L. L. (2014). Words or pictures: A comparison of written and pictorial explanations of physical and chemical equilibria. Int J Sci Educ, 36(5), 783–807.Google Scholar
  2. Antonenko, P. D. (2019). Educational neuroscience: Exploring cognitive processes that underlie learning. In Mind, brain and technology (pp. 27–46). Cham: Springer.Google Scholar
  3. Aslin, R. N., Shukla, M., & Emberson, L. L. (2015). Hemodynamic correlates of cognition in human infants. Annu Rev Psychol, 66(1), 349–379.Google Scholar
  4. Baddeley, A. (1996). Exploring the central executive. The Quarterly Journal of Experimental Psychology Section A, 49(1), 5–28.Google Scholar
  5. Baddeley, A. D., & Hitch, G. (1974). Working memory. In Psychology of learning and motivation (Vol. 8, pp. 47-89). Academic press.Google Scholar
  6. Baddeley, A. D., & Della Sala, S. (1996). Working memory and executive control. Philos Trans R Soc Lond B Biol Sci, 351(1346), 1397–1404.Google Scholar
  7. Buehl, D. (2017). Classroom strategies for interactive learning. Portsmouth: Stenhouse Publishers.Google Scholar
  8. Campbell, N., & Reece, J. (2004). Biology (7th ed.). San Francisco, CA: Benjamin Cummings.Google Scholar
  9. Chen, Y. C., Hand, B., & Park, S. (2016). Examining elementary students’ development of oral and written argumentation practices through argument-based inquiry. Sci & Educ, 25(3–4), 277–320.Google Scholar
  10. Cikmaz, A., Bae, Y., Hand, B., & Choi, K. M. (2016). Examining the transfer of language from science to math writing: As an epistemic tool. The Eurasia proceedings of educational & social sciences, 4, 298–302.Google Scholar
  11. Cohen, J. (1969). Statistical power analysis for the behavioral sciences. New York, NY: Academic Press.Google Scholar
  12. Coleman, D., & Willis, D. S. (2015). Reflective writing: The student nurse’s perspective on reflective writing and poetry writing. Nurse Educ Today, 35(7), 906–911.Google Scholar
  13. Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on psychological science, 8(3), 223–241.Google Scholar
  14. Fang, Z. (2005). Scientific literacy: A systemic functional linguistics perspective. Sci Educ, 89(2), 335–347.Google Scholar
  15. Ferretti, R. P., & Lewis, W. E. (2018). Argumentative writing. In S. Graham, C. A. MacArthur, & J. Fitzgerald (Eds.), Best practices in writing instruction (p. 135). New York, NY: Guilford Press.Google Scholar
  16. Ferretti, R. P., Lewis, W. E., & Andrews-Weckerly, S. (2009). Do goals affect the structure of students’ argumentative writing strategies? J Educ Psychol, 101(3), 577–589.Google Scholar
  17. Frear, M. W., & Bitchener, J. (2015). The effects of cognitive task complexity on writing complexity. J Second Lang Writ, 30, 45–57.Google Scholar
  18. Gillespie Rouse, A., Graham, S., & Compton, D. (2017). Writing to learn in science: Effects on grade 4 students’ understanding of balance. J Educ Res, 110(4), 366–379.Google Scholar
  19. Gregg, L. W., & Steinberg, E. R. (Eds.). (2016). Cognitive processes in writing. Routledge.Google Scholar
  20. Guell, X., Gabrieli, J. D., & Schmahmann, J. D. (2017). Embodied cognition and the cerebellum: Perspectives from the dysmetria of thought and the universal cerebellar transform theories. Cortex. Google Scholar
  21. Hampshire, A., & Sharp, D. J. (2015). Contrasting network and modular perspectives on inhibitory control. Trends Cogn Sci, 19(8), 445–452.Google Scholar
  22. Hand, B., Shelley, M. C., Laugerman, M., Fostvedt, L., & Therrien, W. (2018). Improving critical thinking growth for disadvantaged groups within elementary school science: A randomized controlled trial using the science writing heuristic approach. Sci Educ, 102(4), 693–710.Google Scholar
  23. Jhangiani, R. S., Dastur, F. N., Le Grand, R., & Penner, K. (2018). As good or better than commercial textbooks: Students’ perceptions and outcomes from using open digital and open print textbooks. Canadian Journal for the Scholarship of Teaching and Learning, 9(1), n1, 22.Google Scholar
  24. Kosko, K. W. (2016). Making use of what’s given: Children’s detailing in mathematical argumentative writing. J Math Behav, 41, 68–86.Google Scholar
  25. Lamb, R. (2015). Video games as assessment. In M. Spector (Ed.), Encyclopedia of educational technology. Thousand Oaks, CA: Sage Publications.Google Scholar
  26. Lamb, R., Akmal, T., & Petrie, K. (2015a). Development of a cognition-priming model describing learning in a STEM classroom. J Res Sci Teach, 52(3), 410–437.Google Scholar
  27. Lamb, R., Annetta, L., & Vallet, D. (2015b). The interface of creativity, fluency, lateral thinking and technology while designing serious educational games in a science classroom.Google Scholar
  28. Lamb, R., Antonenko, P., Etopio, E., & Seccia, A. (2018). Comparison of virtual reality and hands on activities in science education via functional near infrared spectroscopy. Comput Educ, 124, 14–26.Google Scholar
  29. Lamb, R., Cavagnetto, A., & Akmal, T. (2016). Examination of the nonlinear dynamic systems associated with science student cognition while engaging in science information processing. Int J Sci Math Educ, 14(1), 187–205.Google Scholar
  30. Lamb, R., Etopio, E., & Lamb, R. (2018). Virtual reality play therapy. Play Therapy Magazine. Retrieved from www.a4pt.org. Accessed 26 Feb 2019
  31. Lamb, R., Firestone, J., Schmitter-Edgecombe, M., & Hand, B. (2018). A computational mode of student cognitive processes which solving a critical thinking problem in science. J Educ Res, 1–12.Google Scholar
  32. Lamb, R., Hand, B., & Yoon, S. (2017). Examination of cognitive processing of science writing tasks. Journal of Psychology and Brain Studies, 1(1), 1–5.Google Scholar
  33. Lamb, R. L. (2016). Examination of the effects of dimensionality on cognitive processing in science: A computational modeling experiment comparing online laboratory simulations and serious educational games. J Sci Educ Technol, 25(1), 1–15.Google Scholar
  34. Lamb, R. L., & Annetta, L. (2013). The use of online modules and the effect on student outcomes in a high school chemistry class. J Sci Educ Technol, 22(5), 603–613.Google Scholar
  35. Lamb, R. L., Annetta, L., Firestone, J., & Etopio, E. (2018). A meta-analysis with examination of moderators of student cognition, affect, and learning outcomes while using serious educational games, serious games, and simulations. Comput Hum Behav, 80, 158–167.Google Scholar
  36. Lamb, R. L., Annetta, L., Meldrum, J., & Vallett, D. (2012). Measuring science interest: Rasch validation of the science interest survey. Int J Sci Math Educ, 10(3), 643–668.Google Scholar
  37. Lamb, R. L., Annetta, L., Vallett, D. B., & Sadler, T. D. (2014). Cognitive diagnostic like approaches using neural-network analysis of serious educational videogames. Comput Educ, 70, 92–104.Google Scholar
  38. Lamb, R. L., Etopio, E., Hand, B., & Yoon, S. (2019). Virtual reality simulation: Effects on academic performance within two domains of writing in science. Journal of Science Education and Technology (in press). Google Scholar
  39. Lamb, R. L., Vallett, D., & Annetta, L. (2014). Development of a short-form measure of science and technology self-efficacy using Rasch analysis. J Sci Educ Technol, 23(5), 641–657.Google Scholar
  40. Manz, E. (2015). Representing student argumentation as functionally emergent from scientific activity. Rev Educ Res, 85(4), 553–590.Google Scholar
  41. Matsumura, L. C., Correnti, R., & Wang, E. (2015). Classroom writing tasks and students’ analytic text-based writing. Read Res Q, 50(4), 417–438.Google Scholar
  42. Mayer, R. E. (1999). Designing instruction for constructivist learning. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. 2, pp. 141–159).Google Scholar
  43. McNeill, K. L., González-Howard, M., Katsh-Singer, R., & Loper, S. (2016). Pedagogical content knowledge of argumentation: Using classroom contexts to assess high-quality PCK rather than pseudoargumentation. J Res Sci Teach, 53(2), 261–290.Google Scholar
  44. Melby-Lervåg, M., & Lervåg, A. (2014). Reading comprehension and its underlying components in second-language learners: A meta-analysis of studies comparing first-and second-language learners. Psychol Bull, 140(2), 409–433.Google Scholar
  45. National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.Google Scholar
  46. Norris, D. (2017). Short-term memory and long-term memory are still different. Psychol Bull, 143(9), 992–1009.Google Scholar
  47. Oken, B. S., Chamine, I., & Wakeland, W. (2015). A systems approach to stress, stressors and resilience in humans. Behavioural brain research, 282, 144–154.Google Scholar
  48. Perret, P. (2015). Children’s inductive reasoning: Developmental and educational perspectives. J Cogn Educ Psychol, 14(3), 389–408.Google Scholar
  49. Rispoli, M., Lang, R., Neely, L., Camargo, S., Hutchins, N., Davenport, K., & Goodwyn, F. (2013). A comparison of within-and across-activity choices for reducing challenging behavior in children with autism spectrum disorders. Journal of Behavioral Education, 22(1), 66–83.Google Scholar
  50. Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Pavia, J. M., Wolf, U., & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage, 85, 6–27.Google Scholar
  51. Shanahan, T. (2016). Relationships between reading and writing development. In Handbook of writing research (pp. 194–207).Google Scholar
  52. Shanahan, T., Fisher, D., & Frey, N. (2016). The challenge of challenging text. In On developing readers: Readings from educational leadership (EL Essentials) (p. 100).Google Scholar
  53. Shymansky, J. A., Yore, L. D., & Good, R. (1991). Elementary school teachers’ beliefs about and perceptions of elementary school science, science reading, science textbooks, and supportive instructional factors. J Res Sci Teach, 28(5), 437–454.Google Scholar
  54. Snow, C., & O’Connor, C. (2016). Close reading and far-reaching classroom discussion: Fostering a vital connection. J Educ, 196(1), 1–8.Google Scholar
  55. Souza, A. S., Rerko, L., & Oberauer, K. (2015). Refreshing memory traces: Thinking of an item improves retrieval from visual working memory. Annals of the New York Academy of Sciences, 1339(1), 20–31.Google Scholar
  56. Stephenson, N. S., & Sadler-McKnight, N. P. (2016). Developing critical thinking skills using the science writing heuristic in the chemistry laboratory. Chemistry Education Research and Practice, 17(1), 72–79.Google Scholar
  57. Stephens, L. A., Lamp, R., Riman, J., & Pearson, K. (2016). Considering Virtual Labs: A State Univeristy of New York Preliminary Report.Google Scholar
  58. Storbeck, J., Robinson, M. D., & McCourt, M. E. (2006). Semantic processing precedes affect retrieval: The neurological case for cognitive primacy in visual processing. Rev Gen Psychol, 10(1), 41–55.Google Scholar
  59. Strauss, E., Sherman, E. M., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary. American Chemical Society.Google Scholar
  60. Tai, K., & Chau, T. (2009). Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface. Journal of Neuroengineering and Rehabilitation, 6(1), 39.Google Scholar
  61. Takahashi, S., Nakamura, H., & Tsunashima, H. (2010). Multichannel temporal data classification of motor imagination using fNIRS. In ICCAS 2010 (pp. 2443–2447). IEEE.Google Scholar
  62. Vitorio, R., Stuart, S., Rochester, L., Alcock, L., & Pantall, A. (2017). Fnirs response during walking—artefact or cortical activity? A systematic review. Neurosci Biobehav Rev, 83, 160–172.Google Scholar
  63. Yaman, F. (2018). Effects of the science writing heuristic approach on the quality of prospective science teachers’ argumentative writing and their understanding of scientific argumentation. Int J Sci Math Educ, 16(3), 421–442.Google Scholar
  64. Yamamoto, Y., & Nakakoji, K. (2005). Interaction design of tools for fostering creativity in the early stages of information design. International Journal of Human-Computer Studies, 63(4-5), 513–535.Google Scholar
  65. Yoon, H. (2012). Re-writing the writing script: Teachers and children translating curriculum in everyday practice. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of EducationEast Carolina UniversityGreenvilleUSA
  2. 2.University at BuffaloBuffaloUSA

Personalised recommendations