Interdisciplinary Journal of Environmental and Science Education

Interdisciplinary Journal of Environmental and Science Education, 2021 - Volume 17 Issue 1, Article No: e2224
https://doi.org/10.29333/ijese/9154

Published Online: 10 Nov 2020

Views: 122 | Downloads: 61

Graphics, graphs, diagrams, and visual representations of information play an integral role in science education and communication settings. The production of such graphics involves hundreds of design decisions, from content and layout to colour and illustration style, and any of these decisions has the potential to influence viewer’s experience and interpretation. While many studies have examined the influence of design on information transfer, comprehension, recall and so on, less attention has been given to affective impacts. In this study, we examined the impact of visual context in a biology life cycle diagram on viewers’ perception of relevance, sense of concern, and others. Results indicated that the presence of a contextual background in the diagram was associated with higher perception of relevance (related to personal experience). Context may also correlate with greater concern, though further research is needed to confirm this.

Affect; relevance; visual context; diagram design

• Bazire, M., & Brézillon, P. (2005). Understanding context before using it. Modeling and using context, 3554, 29–40. doi:10.1007/11508373_3
• Bennett, J., Hogarth, S., & Lubben, F. (2003). A systematic review of the effects of context-based and science-technology-society (sts) approaches in the teaching of secondary science. EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.
• Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65, 676–696. Retrieved from http://www.stat.ucla.edu/research/gpa
• Buchanan, E. (2016). R - exploratory factor analysis lecture & example. Retrieved from https://youtu.be/uPFQQtdKCAM
• Butterworth, G. (1992). Context and cognition in models of cognitive growth. In Context and cognition: Ways of learning and knowing (pp. 1-13). Hertfordshire: Harvester Wheatsheaf.
• Ceci, S. J., & Roazzi, A. (1994). The effect of context on cognition: postcards from brazil. In R. J. Sternberg & R. K. Wagner (Eds.), Mind in context: Interactionist perspectives on human intelligence (pp. 74–104). Cambridge: Cambridge University Press.
• Coakes, S. J., Steed, L., & Ong, C. (2010). SPSS: analysis without anguish: version 17.0 for windows. Milton: John Wiley & Sons.
• Cook, M. P. (2006). Visual representations in science education: the influence of prior knowledge and cognitive load theory on instructional design principles. Science Education, 90(6), 1073–1091. doi:10.1002/sce.20164
• Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88(4), 715–730. doi:10.1037/0022-0663.88.4.715
• Frymier, A. B., & Shulman, G. M. (1995). “What’s in it for me?”: increasing content relevance to enhance students’ motivation. Communication Education, 44(1), 40–50. doi:10.1080/03634529509378996
• Gilbert, J. K. (2006). On the nature of “context” in chemical education. International Journal of Science Education, 28(9), 957–976. doi:10.1080/09500690600702470
• Hegarty, M., Smallman, H. S., & Stull, A. T. (2012). Choosing and using geospatial displays: effects of design on performance and metacognition. Journal of Experimental Psychology: Applied, 18(1), 1–17. doi:10.1037/a0026625
• Heidig, S., Müller, J., & Reichelt, M. (2015). Emotional design in multimedia learning: differentiation on relevant design features and their effects on emotions and learning. Computers in Human Behavior, 44, 81–95. doi:10.1016/j.chb.2014.11.009
• Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. doi:10.1207/s15326985ep4102_4
• Hulleman, C. S., Godes, O., Hendricks, B. L., & Harackiewicz, J. M. (2010). Enhancing interest and performance with a utility value intervention. Journal of Educational Psychology, 102(4), 880–895. doi:10.1037/a0019506
• Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science (New York, N.Y.), 326(5958), 1410–2. doi:10.1126/science.1177067
• Klassen, S. (2006). A theoretical framework for contextual science teaching. Interchange, 37(1–2), 31–62. doi:10.1007/s10780-006-8399-8
• Komsta, L., & Novomestky, F. (2015). Moments: moments, cumulants, skewness, kurtosis and related tests. Retrieved from https://cran.r-project.org/package=moments
• Marton, F. (1981). Phenomenography - describing conceptions of the world around us. Instructional Science, 10, 177–200. doi:10.1007/BF00132516
• Mayer, R. E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14(1), 87–100. doi:10.1023/A:1013184611077
• Park, B., Flowerday, T., & Brünken, R. (2015). Cognitive and affective effects of seductive details in multimedia learning. Computers in Human Behavior, 44, 267–278. doi:10.1016/j.chb.2014.10.061
• Park, B., Moreno, R., Seufert, T., & Brünken, R. (2011). Does cognitive load moderate the seductive details effect? A multimedia study. Computers in Human Behavior, 27, 5–10. doi:10.1016/j.chb.2010.05.006
• Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: effects of shape and color on affect and learning. Learning and Instruction, 29, 128–140. doi:10.1016/j.learninstruc.2013.02.006
• R Core Team. (2016). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/
• Revelle, W. (2016). Psych: procedures for personality and psychological research. Evanston, Illinois, USA: Northwestern University. Retrieved from https://cran.r-project.org/package=psych
• RStudio Team. (2015). RStudio: integrated development for r. Boston, MA: RStudio Inc. Retrieved from http://www.rstudio.com/
• Schneider, S., Nebel, S., & Rey, D. (2016). Decorative pictures and emotional design in multimedia learning. Learning and Instruction, 44, 65–73. doi:10.1016/j.learninstruc.2016.03.002
• Schraw, G., & Lehman, S. (2001). Situational interest : A review of the literature and directions for future research. Educational Psychology Review, 13(1), 23–53. doi:10.1023/A:1009004801455
• ScienceAlert Pty Ltd. (n.d.). ScienceAlert media kit. Retrieved from www.sciencealert.com
• Smallman, H. S., & St John, M. (2005). Naive realism: limits of realism as a display principle. Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting. doi:10.1177/154193120504901714
• Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th Ed.). Boston: Pearson Education.
• Trumbo, J. (2000). Essay: seeing science: research opportunities in the visual communication of science. Science Communication, 21(4), 379–391. doi:10.1177/1075547000021004004
• Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with s. New York: Springer.
• Walker, G. J. (2012). Motivational features of science show. Australian National University.
• Wickham, H. (2009). Ggplot2: elegant graphics for data analysis. New York: Springer-Verlag.
• Wickham, H., & Francois, R. (2016). Dplyr: a grammar of data manipulation. Retrieved from https://cran.r-project.org/package=dplyr
• Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. doi:10.1006/ceps.1999.1015
• Wiley, J. (2019). Picture this! effects of photographs, diagrams, animations, and sketching on learning and beliefs about learning from a geoscience text. Applied Cognitive Psychology, 33(1), 9–19. doi:10.1002/acp.3495
• Wood, M., & Stocklmayer S. M. (2018). Exploring the relationship between visual context and affect in diagram interpretation. In P. Chapman, G. Stapleton, A. Moktefi, S. Perez-Kriz, & F. Bellucci (Eds.), Diagrammatic Representation and Inference. Diagrams 2018. Lecture Notes in Computer Science, vol 10871. Springer, Cham. doi:10.1007/978-3-319-91376-6_46

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