Skip to main content
SpringerLink
Log in

Exploring the Relationship Between Attentional Capture and Prior Knowledge in a Science-Based Multi-user Virtual Environment: an Individual Growth Model Analysis

  • Published:
Journal of Science Education and Technology Aims and scope Submit manuscript

Abstract

In this study, we investigate trends in the relationship between what students know and the types of data that capture their attention over time in a science-based multi-user virtual environment. Longitudinal analyses of the patterns of data collected by 143 middle school students (nested within 5 teachers) showed that student prior knowledge was marginally (p < .10) related to variation in the attentional value (visibility and location) of data they collected over time, explaining about 2% of said variation. By contrast, accounting for the clustering of students by teacher was statistically significantly (p < .05) related to variation in trends in attentional values and explained 36% of said variation.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Allen, R. (2006). Priorities in practice: the essentials of science, grades K–6. Alexandria: ASCD.

    Google Scholar 

  • Baker, R. S. J. d., Ocumpaugh, J., Gowda, S. M., Kamarainen, A. M., Metcalf, S. J. (2014). Extending log-based affect detection to a multi-user virtual environment for science. In V. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog, & G. Houben (Eds.), Lecture Notes in Computer Science: Vol. 8538. User Modeling, Adaptation, and Personalization (pp. 290-300). Springer International Publishing.

  • Börner, K., & Penumarthy, S. (2003). Social diffusion patterns in three-dimensional virtual worlds. Inf Vis, 2(3), 182–198.

    Article  Google Scholar 

  • Chen, J. A., Tutwiler, M. S., Metcalf, S. J., Kamarainen, A., Grotzer, T. A., & Dede, C. J. (2016). A multi-user virtual environment to support students’ self-efficacy and interest in science: a latent growth model analysis. Learn Instr, 41, 11–22.

    Article  Google Scholar 

  • Clarke, J. (2009). Exploring the complexity of inquiry learning in an open-ended problem space. Unpublished doctoral dissertation, Harvard Graduate School of Education.

  • Clarke, J., Dede, C., & Dieterle, E. (2008). Immersive learning environments in K-12 education. In International handbook of Information Technology in Education. New York: Springer.

    Google Scholar 

  • Colosi, J. C., & Zales, C. R. (1998). Jigsaw cooperative learning improves biology lab courses. Bioscience, 48(2), 118–124.

    Article  Google Scholar 

  • Courter, C. J., Grotzer, T. A., Derbiszewska, K. M., Powell, M., Tutwiler, M. S., Kamrarainen, A., Metcalf, S., Dede, C. (2014). A case study contrasting students’ explorations behaviors of a complex causal scenario in a virtual world: mapping proximity and goal-directed behaviors in EcoMUVE. National Association for the Research of Science Teaching (NARST) Conference, Pittsburgh, PA, Spring 2014.

  • De Baz, T. (2001). The effectiveness of the jigsaw cooperative learning on students' achievement and attitudes toward science. Sci Educ Int, 12(4), 6–11.

    Google Scholar 

  • Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66–69.

    Article  Google Scholar 

  • Dochy, F., Segers, M., Van den Bossche, P., & Gijbels, D. (2003). Effects of problem-based learning: a meta-analysis. Learn Instr, 13(5), 533–568.

    Article  Google Scholar 

  • Dukas, G. (2009). Characterizing student navigation in educational multiuser virtual environments: a case study using data from the River City project. Unpublished Doctoral Dissertation, Harvard Graduate School of Education.

  • Grotzer, T. A. (2012). Learning causality in a complex world: Understandings of consequence. Lanham, MD: Rowman & Littlefield.

  • Grotzer, T. A., & Derbiszewska, K. (2011). Causal dynamics assessment. Cambridge, MA: President and Fellows of Harvard College.

    Google Scholar 

  • Grotzer, T. A., & Tutwiler, M. S. (2014). Simplifying causal complexity: how interactions between modes of causal induction and information availability lead to heuristic-driven reasoning. Mind Brain Educ, 8(3), 97–114.

    Article  Google Scholar 

  • Grotzer, T. A., Tutwiler, M. S., Kamarainen, A. M., Derbiszewska, K., 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. Presented at the annual meeting for the American education research association (AERA), Washington, DC.

  • Hagmayer, Y. (2016). Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research. Synthese, 193(4), 1107–1126.

    Article  Google Scholar 

  • Harrington, M. C. R. (2011). Empirical evidence of priming, transfer, reinforcement, and learning in the real and virtual trillium trails. IEEE Trans Learn Technol, 2(2), 175–186.

    Article  Google Scholar 

  • Haruehansawasin, S., & Kiattikomol, P. (2018). Scaffolding in problem-based learning for low-achieving learners. J Educ Res, 111(3), 363–370.

    Article  Google Scholar 

  • Hume, D. (1777). Enquiries concerning the human understanding, and concerning the principles of morals. L.A.Selby-Bigge (Ed) 1902.

  • Kafai, Y., Queintero, M., & Feldon, D. (2010). Investigating the ‘why’ in Whypox: causal and systematic explorations of a virtual epidemic. Games & Culture, 5(1), 116–135.

    Article  Google Scholar 

  • Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Strauss & Giroux.

    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.

    Article  Google Scholar 

  • Ketelhut, D. J., Nelson, B. C., Clarke, J., & Dede, C. (2010). A multi‐user virtual environment for building and assessing higher order inquiry skills in science. British Journal of Educational Technology, 41(1), 56–68.

  • Ketelhut, D. J., Nelson, B., Schifter, C. C., & Kim, Y. (2013). Improving science assessments by situating them in a virtual environment. Education Sciences, 3(2), 172–192.

    Article  Google Scholar 

  • Melloni, L., van Leeuwen, S., Alink, A., & Muller, N. G. (2012). Interaction between bottom-up saliency and top-down control: how saliency maps are created in the human brain. Cereb Cortex, 22(12), 2943–2952.

    Article  Google Scholar 

  • Metcalf, S., & Kamarainen, A. M. (2011). EcoMVE content knowledge assessment. Cambridge, MA: President and Fellows of Harvard College.

    Google Scholar 

  • 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 

  • Metcalf, S. J., Tutwiler, M. S, Kamarainen, A., Grotzer, T. A., & Dede, C. J. (2013). Multi-User virtual environments to promote middle school ecosystem science learning and attitudes about science. San Francisco, CA American Educational Research Association.

  • National Research Council. (2005). How students learn: history, mathematics, and science in the classroom. Washington, DC: National Academies Press.

    Google Scholar 

  • Nelson, B. (2007). Exploring the use of individualized, reflective guidance in an educational multi-user virtual environment. The Journal of Science Education and Technology, 16(1), 83–97.

    Article  Google Scholar 

  • NGSS Lead States. (2013). Next generation science standards: for states, by states. Washington, DC: The National Academies Press.

    Google Scholar 

  • Penumarthy, S. and Börner, K. (2006). Analysis and visualization of social diffusion patterns in three-dimensional virtual worlds. In R. Schroeder & A.-S. Axelsson (Eds.), Computer supported cooperative work: Avatars at work and play: Collaboration and interaction in shared virtual environments (Vol. 34, chapter 3, pp. 39–61). Springer-Verlag.

  • Schmidt, H. G., Loyens, S. M., Van Gog, T., & Paas, F. (2007). Problem-based learning is compatible with human cognitive architecture: commentary on Kirschner, Sweller, and Clark (2006). Educ Psychol, 42(2), 91–97.

    Article  Google Scholar 

  • Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press.

    Book  Google Scholar 

  • Snow, J. C., Allen, H. A., Rafal, R. D., & Humphreys, G. W. (March 2009). Impaired attentional selection following lesions to human pulvinar: evidence for homology between human and monkey. Proc Natl Acad Sci U S A, 106(10), 4054–4059.

    Article  Google Scholar 

  • Walther, D., Itti, L., Reisenhuber, M., Poggio, T., & Koch, C. (2002). Attentional selection for object recognition––a gentle way. Proc., 2nd workshop on biologically motivated computer vision BMCV200: 472–479.

  • Wickens, C. D., & McCarley, J. (2008). Applied attention theory. BocaRaton, FL: Taylor & Francis.

    Google Scholar 

  • Wickens, C., McCarley, J., & Steelman-Allen, K. (2009). NT-SEEV: a model of attention capture and noticing on the flight deck. Proceedings of the human factors and ergonomics society annual meeting (Vol. 53, no. 12, pp. 769–773). SAGE Publications.

Download references

Funding

This study was funded in part by National Science Foundation grants 1118530 0845632 and by IES grant R305A080514.

Author information

Authors and Affiliations

  1. School of Education, University of Rhode Island, Chafee Hall, 10 Chafee Rd, Kingston, RI, 02881, USA

    M. Shane Tutwiler

Authors
  1. M. Shane Tutwiler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Shane Tutwiler.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

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

Informed consent was obtained from all individual participants included in the study (as outlined in the manuscript).

Additional information

Publisher’s Note

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

M. Shane Tutwiler is an assistant professor of Learning Foundations at the University of Rhode Island.

Electronic supplementary material

ESM 1

(DOCX 16 kb)

ESM 2

(DOCX 15 kb)

ESM 3

(DOCX 319 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tutwiler, M.S. Exploring the Relationship Between Attentional Capture and Prior Knowledge in a Science-Based Multi-user Virtual Environment: an Individual Growth Model Analysis. J Sci Educ Technol 28, 299–309 (2019). https://doi.org/10.1007/s10956-019-9766-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10956-019-9766-4

Keywords

Search

Navigation