Advertisement

Role of Image and Cognitive Load in Anatomical Multimedia

  • Timothy D. WilsonEmail author
Chapter

Abstract

Visualizations are an important part of anatomical education and appear in all software on the market. Not all visualization methods are the methods utilized by educators can covertly and significantly impact student learning and stratify the class based on learner abilities that are not directly related to anatomical comprehension. Often, the proposed mechanism for good, bad, or ugly visualizations is on aesthetics, rather than the cognitive load imparted on the learner. The appropriate use of multimedia principles that include using pictures, images, and visualizations in general will positively influence student attention and learning. This chapter outlines components of multimedia learning as it pertains to the use of visualizations in lectures and in online scenarios. Illustrations and research demonstrating how cognitive load can be manipulated to a pedagogic advantage are presented. Approaches and suggestions on how educators might modify their current materials and practice using visualizations are proposed that will positively affect learning through cognitive load reduction.

Keywords

Cognitive Load Spatial Ability Cognitive Load Theory Temporal Contiguity Novice Learner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    McLachlan JC, Bligh J, Bradley P, Searle J. Teaching anatomy without cadavers. Med Educ. 2004;38(4):418–24.PubMedCrossRefGoogle Scholar
  2. 2.
    Drake RL. Anatomy education in a changing medical curriculum. Anat Rec. 1998;253(1):28–31.PubMedCrossRefGoogle Scholar
  3. 3.
    Drake RL, McBride JM, Lachman N, Pawlina W. Medical education in the anatomical sciences: the winds of change continue to blow. Anat Sci Educ. 2009;2(6):253–9.PubMedCrossRefGoogle Scholar
  4. 4.
    Collins TJ, Given RL, Hulsebosch CE, Miller BT. Status of gross anatomy in the US and Canada: Dilemma for the 21st century. Clin Anat. 1994;7(5):275–96.CrossRefGoogle Scholar
  5. 5.
    Stull AT, Hegarty M, Mayer RE. Getting a handle on learning anatomy with interactive three-dimensional graphics. J Educ Psychol. 2009;101(4):803–16.CrossRefGoogle Scholar
  6. 6.
    Mathewson JH. Visual-spatial thinking: an aspect of science overlooked by educators. Sci Educ. 1999;83(1):33–54.CrossRefGoogle Scholar
  7. 7.
    Mayer RE. Instruction based on visualization. In: Mayer RE, Alexander PA, editors. Handbook of research on learning and instruction. New York, NY: Routledge; 2011. p. 427–45.Google Scholar
  8. 8.
    Mayer RE. Multimedia learning. New York, NY: Cambridge University Press; 2009.CrossRefGoogle Scholar
  9. 9.
    Attardi SM, Rogers KA. Design and implementation of an online systemic human anatomy course with laboratory. Anatomical Sciences Education. 2014: First Published June 11, 2014, DOI: 10.1002/ase.1465.Google Scholar
  10. 10.
    Trelease RB, Rosset A. Transforming clinical imaging data for virtual reality learning objects. Anat Sci Educ. 2008;1(2):50–5.PubMedCrossRefGoogle Scholar
  11. 11.
    McLachlan JC, Patten D. Anatomy teaching: ghosts of the past, present and future. Med Educ. 2006;40(3):243–53.PubMedCrossRefGoogle Scholar
  12. 12.
    Aziz MA, McKenzie JC, Wilson JS, Cowie RJ, Ayeni SA, Dunn BK. The human cadaver in the age of biomedical informatics. Anat Rec. 2002;269(1):20–32.PubMedCrossRefGoogle Scholar
  13. 13.
    Carroll JB. Abilities in the domain of visual perception. In: Carroll JB, editor. Human cognitive abilities: a survey of factor-analytic studies. 1st ed. New York, NY: Cambridge University Press; 1993. p. 304–63.CrossRefGoogle Scholar
  14. 14.
    Nguyen N, Mulla A, Nelson AJ, Wilson TD. Visuospatial anatomy comprehension: the role of spatial visualization and problem solving strategies. Anat Sci Educ. 2014;7(4):280–8.PubMedCrossRefGoogle Scholar
  15. 15.
    Garg AX, Norman G, Spero L, Taylor I. Learning anatomy: do new computer models improve spatial understanding? Med Teach. 1999;21(5):519–22.CrossRefGoogle Scholar
  16. 16.
    Garg A, Norman GR, Spero L, Maheshwari P. Do virtual computer models hinder anatomy learning? Acad Med. 1999;74(10 Suppl):S87–9.PubMedCrossRefGoogle Scholar
  17. 17.
    Levinson AJ, Weaver B, Garside S, McGinn H, Norman GR. Virtual reality and brain anatomy: a randomised trial of e-learning instructional designs. Med Educ. 2007;41(5):495–501.PubMedCrossRefGoogle Scholar
  18. 18.
    Brewer DN, Wilson TD, Eagleson R, de Ribaupierre S. Evaluation of neuroanatomical training using a 3D visual reality model. Stud Health Technol Inform. 2012;173:85–91.PubMedGoogle Scholar
  19. 19.
    Nguyen N, Nelson AJ, Wilson TD. Computer visualizations: factors that influence spatial anatomy comprehension. Anat Sci Educ. 2012;5(2):98–108.PubMedCrossRefGoogle Scholar
  20. 20.
    Vorstenbosch MA, Klaassen TP, Kooloos JG, Bolhuis SM, Laan RF. Do images influence assessment in anatomy? Exploring the effect of images on item difficulty and item discrimination. Anat Sci Educ. 2013;6(1):29–41.Google Scholar
  21. 21.
    Ozcinar Z. The topic of instructional design in research journals: a citation analysis for the years 1980-2008. Australas J Educ Technol. 2009;25(4):559–80.Google Scholar
  22. 22.
    Sweller J. Cognitive load during problem-solving – effects on learning. Cognit Sci. 1988;12(2):257–85.CrossRefGoogle Scholar
  23. 23.
    Mayer RE. The science of learning: determining how multimedia learning works. In: Mayer RE, editor. Multi-media learning. 2nd ed. New York, NY: Cambridge University Press; 2009.CrossRefGoogle Scholar
  24. 24.
    Mayer RE, Heiser J, Lonn S. Cognitive constraints on multimedia learning: when presenting more material results in less understanding. J Educ Psychol. 2001;93(1):187–98.CrossRefGoogle Scholar
  25. 25.
    Mayer RE, Moreno R. Nine ways to reduce cognitive load in multimedia learning. Educ Psychol. 2003;38(1):43–52.CrossRefGoogle Scholar
  26. 26.
    Chandler P, Sweller J. Cognitive Load Theory and the Format of Instruction. Cogn Instr. 1991;8(4):293–332.CrossRefGoogle Scholar
  27. 27.
    Sweller J. Instructional design in technical areas. Camberwell, Australia: ACER Press; 1999.Google Scholar
  28. 28.
    Mayer RE. Cognitive theory of multimedia learning. In: Mayer RE, editor. Cambridge handbook of multimedia learning. New York, NY: Cambridge University Press; 2005. p. 31–48.CrossRefGoogle Scholar
  29. 29.
    DeLeeuw KE, Mayer RE. A comparison of three measures of cognitive load: evidence for separable measures of intrinsic, extraneous, and germane load. J Educ Psychol. 2008;100(1):223–34.CrossRefGoogle Scholar
  30. 30.
    Leacock TL, Nesbit JC. A framework for evaluating the quality of multimedia learning resources. Educ Technol Soc. 2007;10(2):44–59.Google Scholar
  31. 31.
    Kirschner PA. Cognitive load theory: implications of cognitive load theory on the design of learning. Learn Instr. 2002;12(1):1–10.CrossRefGoogle Scholar
  32. 32.
    Khalil MK, Paas F, Johnson TE, Payer AF. Interactive and dynamic anatomical visualizations: the implication of cognitive load theory. Anat Rec. 2005;286B(1):15–20.Google Scholar
  33. 33.
    Hegarty M, Just MA. Understanding machines from text and diagrams. In: Mandl H, Levin JR, editors. Knowledge acquisition from text and pictures. Amsterdam: Elsevier; 1989. p. 171–94.CrossRefGoogle Scholar
  34. 34.
    Lowe RK. Animation and learning: selective processing of information in dynamic graphics. Learn Instr. 2003;13(2):157–76.CrossRefGoogle Scholar
  35. 35.
    Sweller J. Implications of cognitive load theory for multimedia learning. In: Mayer RE, editor. The Cambridge handbook of multimedia learning. New York, NY: Cambridge University Press; 2005. p. 19–48.CrossRefGoogle Scholar
  36. 36.
    Lowe R. Interrogation of a dynamic visualization during learning. Learn Instr. 2004;14:257–74.CrossRefGoogle Scholar
  37. 37.
    Kalyuga S. Prior knowledge principle in multimedia learning. In: Mayer RE, editor. Cambridge handbook of multimedia learning. New York, NY: Cambridge University Press; 2005. p. 325–38.CrossRefGoogle Scholar
  38. 38.
    Kalyuga, S. Adapting Levels of Instructional Support to Optimize Learning Complex Cognitive Skills. In: S. Kalyuga editor. Managing Cognitive Load in Adaptive Multimedia Learning. Hershey, PA, USA, IGI Global:246–271.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Anatomy and Cell Biology, Schulich School of Medicine and DentistryCRIPT - Corps for Research of Instructional and Perceptual Technologies, Western UniversityLondonCanada

Personalised recommendations