Gender Imbalance in Instructional Dynamic Versus Static Visualizations: a Meta-analysis

Abstract

Studies comparing the instructional effectiveness of dynamic versus static visualizations have produced mixed results. In this work, we investigated whether gender imbalance in the participant samples of these studies may have contributed to the mixed results. We conducted a meta-analysis of randomized experiments in which groups of students learning through dynamic visualizations were compared to groups receiving static visualizations. Our sample focused on tasks that could be categorized as either biologically secondary tasks (science, technology, engineering, and mathematics: STEM) or biologically primary tasks (manipulative–procedural). The meta-analysis of 46 studies (82 effect sizes and 5474 participants) revealed an overall small-sized effect (g+ = 0.23) showing that dynamic visualizations were more effective than static visualizations. Regarding potential moderators, we observed that gender was influential: the dynamic visualizations were more effective on samples with less females and more males (g+ = 0.36). We also observed that educational level, learning domain, media compared, and reporting reliability measures moderated the results. We concluded that because many visualization studies have used samples with a gender imbalance, this may be a significant factor in explaining why instructional dynamic and static visualizations seem to vary in their effectiveness. Our findings also support considering the gender variable in research about cognitive load theory and instructional visualizations.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

*References marked with an asterisk indicate studies included in the meta-analysis.

  1. *Adesope, O. O., & Nesbit, J. C. (2013). Animated and static concept maps enhance learning from spoken narration. Learning and Instruction, 27, 1–10. https://doi.org/10.1016/j.learninstruc.2013.02.002.

    Google Scholar 

  2. Adesope, O. O., Trevisan, D. A., & Sundararajan, N. (2017). Rethinking the use of tests: a meta-analysis of practice testing. Review of Educational Research, 87(3), 659–701. https://doi.org/10.3102/0034654316689306.

    Google Scholar 

  3. Ardac, D., & Akaygun, S. (2005). Using static and dynamic visuals to represent chemical change at molecular level. International Journal of Science Education, 27(11), 1269–1298. https://doi.org/10.1080/09500690500102284.

    Google Scholar 

  4. *Arguel, A., & Jamet, E. (2009). Using video and static pictures to improve learning of procedural contents. Computers in Human Behavior, 25(2), 354–359. https://doi.org/10.1016/j.chb.2008.12.014.

    Google Scholar 

  5. *Ayres, P., Marcus, N., Chan, C., & Qian, N. (2009). Learning hand manipulative tasks: when instructional animations are superior to equivalent static representations. Computers in Human Behavior, 25(2), 348–353. https://doi.org/10.1016/j.chb.2008.12.013.

    Google Scholar 

  6. Ayres, P., & Paas, F. (2007). Making instructional animations more effective: a cognitive load approach. Applied Cognitive Psychology, 21(6), 695–700. https://doi.org/10.1002/acp.1343.

    Google Scholar 

  7. Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., & Bethel, E. C. (2009). A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research, 79(3), 1243–1289. https://doi.org/10.3102/0034654309333844.

    Google Scholar 

  8. Berney, S., & Bétrancourt, M. (2016). Does animation enhance learning? A meta-analysis. Computers & Education, 101, 150–167. https://doi.org/10.1016/j.compedu.2016.06.005.

    Google Scholar 

  9. *Berney, S., Bétrancourt, M., Molinari, G., & Hoyek, N. (2015). How spatial abilities and dynamic visualizations interplay when learning functional anatomy with 3D anatomical models. Anatomical Sciences Education, 8(5), 452–462. https://doi.org/10.1002/ase.1524.

    Google Scholar 

  10. Bétrancourt, M., & Chassot, A. (2008). Making sense of animation: how do children explore multimedia instruction? In R. K. Lowe & W. Schnotz (Eds.), Learning with animation: research implications for design (pp. 141–164). New York, NY: Cambridge University Press.

    Google Scholar 

  11. Bevilacqua, A. (2017). Commentary: Should gender differences be included in the evolutionary upgrade to cognitive load theory? Educational Psychology Review, 29(1), 189–194. https://doi.org/10.1007/s10648-016-9362-6.

    Google Scholar 

  12. Bonin, P., Gelin, M., & Bugaiska, A. (2014). Animates are better remembered than inanimates: further evidence from word and picture stimuli. Memory & Cognition, 42(3), 370–382. https://doi.org/10.3758/s13421-013-0368-8.

    Google Scholar 

  13. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2008). Comprehensive meta-analysis (version 2. 2.048) [computer software]. Englewood, NJ: Biostat.

    Google Scholar 

  14. *Boucheix, J.-M., & Schneider, E. (2009). Static and animated presentations in learning dynamic mechanical systems. Learning and Instruction, 19(2), 112–127. https://doi.org/10.1016/j.learninstruc.2008.03.004.

    Google Scholar 

  15. *Castro-Alonso, J. C., Ayres, P., & Paas, F. (2015). Animations showing Lego manipulative tasks: three potential moderators of effectiveness. Computers & Education, 85, 1–13. https://doi.org/10.1016/j.compedu.2014.12.022.

    Google Scholar 

  16. Castro-Alonso, J. C., Ayres, P., & Paas, F. (2016). Comparing apples and oranges? A critical look at research on learning from statics versus animations. Computers & Education, 102, 234–243. https://doi.org/10.1016/j.compedu.2016.09.004.

    Google Scholar 

  17. Castro-Alonso, J. C., Ayres, P., & Paas, F. (2018a). Computerized and adaptable tests to measure visuospatial abilities in STEM students. In T. Andre (Ed.), Advances in human factors in training, education, and learning sciences: proceedings of the AHFE 2017 international conference on human factors in training, education, and learning sciences (pp. 337–349). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-60018-5_33.

    Google Scholar 

  18. Castro-Alonso, J. C., Ayres, P., Wong, M., & Paas, F. (2018b). Learning symbols from permanent and transient visual presentations: don’t overplay the hand. Computers & Education, 116, 1–13. https://doi.org/10.1016/j.compedu.2017.08.011.

    Google Scholar 

  19. Chanlin, L.-J. (2001). Formats and prior knowledge on learning in a computer-based lesson. Journal of Computer Assisted Learning, 17(4), 409–419. https://doi.org/10.1046/j.0266-4909.2001.00197.x.

    Google Scholar 

  20. Chen, S.-C., Hsiao, M.-S., & She, H.-C. (2015). The effects of static versus dynamic 3D representations on 10th grade students’ atomic orbital mental model construction: evidence from eye movement behaviors. Computers in Human Behavior, 53, 169–180. https://doi.org/10.1016/j.chb.2015.07.003.

    Google Scholar 

  21. *Chien, Y.-T., & Chang, C.-Y. (2012). Comparison of different instructional multimedia designs for improving student science-process skill learning. Journal of Science Education and Technology, 21(1), 106–113. https://doi.org/10.1007/s10956-011-9286-3.

    Google Scholar 

  22. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  23. Cracco, E., Bardi, L., Desmet, C., Genschow, O., Rigoni, D., De Coster, L., et al. (2018). Automatic imitation: a meta-analysis. Psychological Bulletin, 144(5), 453–500. https://doi.org/10.1037/bul0000143.

    Google Scholar 

  24. Delgado, P., Vargas, C., Ackerman, R., & Salmerón, L. (2018). Don’t throw away your printed books: a meta-analysis on the effects of reading media on reading comprehension. Educational Research Review, 25, 23–38. https://doi.org/10.1016/j.edurev.2018.09.003.

    Google Scholar 

  25. Duval, S., & Tweedie, R. (2000). Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x.

    Google Scholar 

  26. Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629.

    Google Scholar 

  27. *Fiorella, L., & Mayer, R. E. (2016). Effects of observing the instructor draw diagrams on learning from multimedia messages. Journal of Educational Psychology, 108(4), 528–546. https://doi.org/10.1037/edu0000065.

    Google Scholar 

  28. Garland, T. B., & Sanchez, C. A. (2013). Rotational perspective and learning procedural tasks from dynamic media. Computers & Education, 69, 31–37. https://doi.org/10.1016/j.compedu.2013.06.014.

    Google Scholar 

  29. Geary, D. C. (1995). Reflections of evolution and culture in children’s cognition: implications for mathematical development and instruction. American Psychologist, 50(1), 24–37. https://doi.org/10.1037/0003-066X.50.1.24.

    Google Scholar 

  30. Geary, D. C. (2007). Educating the evolved mind: conceptual foundations for an evolutionary educational psychology. In J. S. Carlson & J. R. Levin (Eds.), Psychological perspectives on contemporary educational issues (pp. 1–99). Charlotte, NC: Information Age Publishing.

    Google Scholar 

  31. *Goff, E. E., Reindl, K. M., Johnson, C., McClean, P., Offerdahl, E. G., Schroeder, N. L., & White, A. R. (2017). Variation in external representations as part of the classroom lecture: an investigation of virtual cell animations in introductory photosynthesis instruction. Biochemistry and Molecular Biology Education, 45(3), 226–234. https://doi.org/10.1002/bmb.21032.

    Google Scholar 

  32. Hays, T. A. (1996). Spatial abilities and the effects of computer animation on short-term and long-term comprehension. Journal of Educational Computing Research, 14(2), 139–155. https://doi.org/10.2190/60y9-bqg9-80hx-ueml.

    Google Scholar 

  33. Hedges, L. V., & Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science, 269(5220), 41–45. https://doi.org/10.1126/science.7604277.

    Google Scholar 

  34. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. San Diego, CA: Academic.

    Google Scholar 

  35. Hegarty, M. (1992). Mental animation: inferring motion from static displays of mechanical systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(5), 1084–1102. https://doi.org/10.1037/0278-7393.18.5.1084.

    Google Scholar 

  36. Hegarty, M., Montello, D. R., Richardson, A. E., Ishikawa, T., & Lovelace, K. (2006). Spatial abilities at different scales: individual differences in aptitude-test performance and spatial-layout learning. Intelligence, 34(2), 151–176. https://doi.org/10.1016/j.intell.2005.09.005.

    Google Scholar 

  37. Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186.

    Google Scholar 

  38. Höffler, T. N. (2010). Spatial ability: its influence on learning with visualizations—a meta-analytic review. Educational Psychology Review, 22(3), 245–269. https://doi.org/10.1007/s10648-010-9126-7.

    Google Scholar 

  39. Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: a meta-analysis. Learning and Instruction, 17(6), 722–738. https://doi.org/10.1016/j.learninstruc.2007.09.013.

    Google Scholar 

  40. *Höffler, T. N., & Leutner, D. (2011). The role of spatial ability in learning from instructional animations: evidence for an ability-as-compensator hypothesis. Computers in Human Behavior, 27(1), 209–216. https://doi.org/10.1016/j.chb.2010.07.042.

    Google Scholar 

  41. *Höffler, T. N., Prechtl, H., & Nerdel, C. (2010). The influence of visual cognitive style when learning from instructional animations and static pictures. Learning and Individual Differences, 20(5), 479–483. https://doi.org/10.1016/j.lindif.2010.03.001.

    Google Scholar 

  42. *Höffler, T. N., & Schwartz, R. N. (2011). Effects of pacing and cognitive style across dynamic and non-dynamic representations. Computers & Education, 57(2), 1716–1726. https://doi.org/10.1016/j.compedu.2011.03.012.

    Google Scholar 

  43. Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I 2 index? Psychological Methods, 11(2), 193–206. https://doi.org/10.1037/1082-989X.11.2.193.

    Google Scholar 

  44. *Imhof, B., Scheiter, K., Edelmann, J., & Gerjets, P. (2012). How temporal and spatial aspects of presenting visualizations affect learning about locomotion patterns. Learning and Instruction, 22(3), 193–205. https://doi.org/10.1016/j.learninstruc.2011.10.006.

    Google Scholar 

  45. *Imhof, B., Scheiter, K., & Gerjets, P. (2011). Learning about locomotion patterns from visualizations: effects of presentation format and realism. Computers & Education, 57(3), 1961–1970. https://doi.org/10.1016/j.compedu.2011.05.004.

    Google Scholar 

  46. Isacco, A., Hammer, J. H., & Shen-Miller, D. S. (2016). Outnumbered, but meaningful: the experience of male doctoral students in professional psychology training programs. Training and Education in Professional Psychology, 10(1), 45–53. https://doi.org/10.1037/tep0000107.

    Google Scholar 

  47. Jaffar, A. A. (2012). YouTube: an emerging tool in anatomy education. Anatomical Sciences Education, 5(3), 158–164. https://doi.org/10.1002/ase.1268.

    Google Scholar 

  48. Jirout, J. J., & Newcombe, N. S. (2015). Building blocks for developing spatial skills: evidence from a large, representative U.S. sample. Psychological Science, 26(3), 302–310. https://doi.org/10.1177/0956797614563338.

    Google Scholar 

  49. *Kalyuga, S. (2008). Relative effectiveness of animated and static diagrams: an effect of learner prior knowledge. Computers in Human Behavior, 24(3), 852–861. https://doi.org/10.1016/j.chb.2007.02.018.

    Google Scholar 

  50. *Lewalter, D. (2003). Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction, 13(2), 177–189. https://doi.org/10.1016/s0959-4752(02)00019-1.

    Google Scholar 

  51. *Lin, H. (2011). Facilitating learning from animated instruction: effectiveness of questions and feedback as attention-directing strategies. Educational Technology & Society, 14(2), 31–42.

    Google Scholar 

  52. *Lin, H., & Dwyer, F. M. (2010). The effect of static and animated visualization: a perspective of instructional effectiveness and efficiency. Educational Technology Research and Development, 58(2), 155–174. https://doi.org/10.1007/s11423-009-9133-x.

    Google Scholar 

  53. *Lin, L., & Atkinson, R. K. (2011). Using animations and visual cueing to support learning of scientific concepts and processes. Computers & Education, 56(3), 650–658. https://doi.org/10.1016/j.compedu.2010.10.007.

    Google Scholar 

  54. Linn, M. C., & Petersen, A. C. (1985). Emergence and characterization of sex differences in spatial ability: a meta-analysis. Child Development, 56(6), 1479–1498. https://doi.org/10.2307/1130467.

    Google Scholar 

  55. Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.

    Google Scholar 

  56. Lowe, R. K. (2003). Animation and learning: selective processing of information in dynamic graphics. Learning and Instruction, 13(2), 157–176. https://doi.org/10.1016/S0959-4752(02)00018-X.

    Google Scholar 

  57. *Lowe, R. K., Schnotz, W., & Rasch, T. (2011). Aligning affordances of graphics with learning task requirements. Applied Cognitive Psychology, 25(3), 452–459. https://doi.org/10.1002/acp.1712.

    Google Scholar 

  58. *Lusk, M. M., & Atkinson, R. K. (2007). Animated pedagogical agents: does their degree of embodiment impact learning from static or animated worked examples? Applied Cognitive Psychology, 21(6), 747–764. https://doi.org/10.1002/acp.1347.

    Google Scholar 

  59. Mahmud, W., Hyder, O., Butt, J., & Aftab, A. (2011). Dissection videos do not improve anatomy examination scores. Anatomical Sciences Education, 4(1), 16–21. https://doi.org/10.1002/ase.194.

    Google Scholar 

  60. *Marcus, N., Cleary, B., Wong, A., & Ayres, P. (2013). Should hand actions be observed when learning hand motor skills from instructional animations? Computers in Human Behavior, 29(6), 2172–2178. https://doi.org/10.1016/j.chb.2013.04.035.

    Google Scholar 

  61. Matthews, W. J., Benjamin, C., & Osborne, C. (2007). Memory for moving and static images. Psychonomic Bulletin & Review, 14(5), 989–993. https://doi.org/10.3758/bf03194133.

    Google Scholar 

  62. Mayer, R. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403–423. https://doi.org/10.1111/jcal.12197.

    Google Scholar 

  63. *Mayer, R. E., DeLeeuw, K. E., & Ayres, P. (2007). Creating retroactive and proactive interference in multimedia learning. Applied Cognitive Psychology, 21(6), 795–809. https://doi.org/10.1002/acp.1350.

    Google Scholar 

  64. *Mayer, R. E., Hegarty, M., Mayer, S., & Campbell, J. (2005). When static media promote active learning: annotated illustrations versus narrated animations in multimedia instruction. Journal of Experimental Psychology: Applied, 11(4), 256–265. https://doi.org/10.1037/1076-898x.11.4.256.

    Google Scholar 

  65. McCrudden, M. T., & Rapp, D. N. (2017). How visual displays affect cognitive processing. Educational Psychology Review, 29(3), 623–639. https://doi.org/10.1007/s10648-015-9342-2.

    Google Scholar 

  66. *Michas, I. C., & Berry, D. C. (2000). Learning a procedural task: effectiveness of multimedia presentations. Applied Cognitive Psychology, 14(6), 555–575. https://doi.org/10.1002/1099-0720(200011/12)14:6<555::aid-acp677>3.0.co;2-4.

    Google Scholar 

  67. *Münzer, S., Seufert, T., & Brünken, R. (2009). Learning from multimedia presentations: facilitation function of animations and spatial abilities. Learning and Individual Differences, 19(4), 481–485. https://doi.org/10.1016/j.lindif.2009.05.001.

    Google Scholar 

  68. Newcombe, N. S., Bandura, M. M., & Taylor, D. G. (1983). Sex differences in spatial ability and spatial activities. Sex Roles, 9(3), 377–386. https://doi.org/10.1007/bf00289672.

    Google Scholar 

  69. Nikou, S. A., & Economides, A. A. (2016). The impact of paper-based, computer-based and mobile-based self-assessment on students’ science motivation and achievement. Computers in Human Behavior, 55, 1241–1248. https://doi.org/10.1016/j.chb.2015.09.025.

    Google Scholar 

  70. Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24(1), 27–45. https://doi.org/10.1007/s10648-011-9179-2.

    Google Scholar 

  71. *Paik, E. S., & Schraw, G. (2013). Learning with animation and illusions of understanding. Journal of Educational Psychology, 105(2), 278–289. https://doi.org/10.1037/a0030281.

    Google Scholar 

  72. *Park, O.-C. (1998). Visual displays and contextual presentations in computer-based instruction. Educational Technology Research and Development, 46(3), 37–50. https://doi.org/10.1007/bf02299760.

    Google Scholar 

  73. *Park, O.-C., & Gittelman, S. S. (1992). Selective use of animation and feedback in computer-based instruction. Educational Technology Research and Development, 40(4), 27–38. https://doi.org/10.1007/BF02296897.

    Google Scholar 

  74. *Patwardhan, M., & Murthy, S. (2015). When does higher degree of interaction lead to higher learning in visualizations? Exploring the role of ‘interactivity enriching features’. Computers & Education, 82, 292–305. https://doi.org/10.1016/j.compedu.2014.11.018.

    Google Scholar 

  75. Press, C., Bird, G., Flach, R., & Heyes, C. (2005). Robotic movement elicits automatic imitation. Cognitive Brain Research, 25(3), 632–640. https://doi.org/10.1016/j.cogbrainres.2005.08.020.

    Google Scholar 

  76. *Rieber, L. P. (1990). Using computer animated graphics in science instruction with children. Journal of Educational Psychology, 82(1), 135–140. https://doi.org/10.1037/0022-0663.82.1.135.

    Google Scholar 

  77. *Rieber, L. P. (1991). Animation, incidental learning, and continuing motivation. Journal of Educational Psychology, 83(3), 318–328. https://doi.org/10.1037/0022-0663.83.3.318.

    Google Scholar 

  78. Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638.

    Google Scholar 

  79. *Sanchez, C. A., & Wiley, J. (2014). The role of dynamic spatial ability in geoscience text comprehension. Learning and Instruction, 31, 33–45. https://doi.org/10.1016/j.learninstruc.2013.12.007.

    Google Scholar 

  80. *Scheiter, K., Gerjets, P., & Catrambone, R. (2006). Making the abstract concrete: visualizing mathematical solution procedures. Computers in Human Behavior, 22(1), 9–25. https://doi.org/10.1016/j.chb.2005.01.009.

    Google Scholar 

  81. *Schmidt-Weigand, F. (2011). Does animation amplify the modality effect—or is there any modality effect at all?. Zeitschrift für Pädagogische Psychologie, 25(4), 245–256. https://doi.org/10.1024/1010-0652/a000048.

    Google Scholar 

  82. *Schmidt-Weigand, F., & Scheiter, K. (2011). The role of spatial descriptions in learning from multimedia. Computers in Human Behavior, 27(1), 22–28. https://doi.org/10.1016/j.chb.2010.05.007.

    Google Scholar 

  83. Schnotz, W., Böckheler, J., & Grzondziel, H. (1999). Individual and co-operative learning with interactive animated pictures. European Journal of Psychology of Education, 14(2), 245–265. https://doi.org/10.1007/bf03172968.

    Google Scholar 

  84. Schwartz, R. N., & Plass, J. L. (2014). Click versus drag: user-performed tasks and the enactment effect in an interactive multimedia environment. Computers in Human Behavior, 33, 242–255. https://doi.org/10.1016/j.chb.2014.01.012.

    Google Scholar 

  85. Shimada, S., & Oki, K. (2012). Modulation of motor area activity during observation of unnatural body movements. Brain and Cognition, 80(1), 1–6. https://doi.org/10.1016/j.bandc.2012.04.006.

    Google Scholar 

  86. *Soemer, A., & Schwan, S. (2016). Task-appropriate visualizations: can the very same visualization format either promote or hinder learning depending on the task requirements? Journal of Educational Psychology, 108(7), 960–968. https://doi.org/10.1037/edu0000093.

    Google Scholar 

  87. *Stebner, F., Kühl, T., Höffler, T. N., Wirth, J., & Ayres, P. (2017). The role of process information in narrations while learning with animations and static pictures. Computers & Education, 104, 34–48. https://doi.org/10.1016/j.compedu.2016.11.001.

    Google Scholar 

  88. Stephenson, C. L., & Halpern, D. F. (2013). Improved matrix reasoning is limited to training on tasks with a visuospatial component. Intelligence, 41(5), 341–357. https://doi.org/10.1016/j.intell.2013.05.006.

    Google Scholar 

  89. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York, NY: Springer.

    Google Scholar 

  90. *Swezey, R. W., Perez, R. S., & Allen, J. A. (1991). Effects of instructional strategy and motion presentation conditions on the acquisition and transfer of electromechanical troubleshooting skill. Human Factors, 33(3), 309–323. https://doi.org/10.1177/001872089103300306.

    Google Scholar 

  91. Tabachnick, B. G., & Fidell, L. S. (2018). Using multivariate statistics (7th ed.). New York, NY: Pearson.

    Google Scholar 

  92. *Tekdal, M. (2013). The effect of an example-based dynamic program visualization environment on students’ programming skills. Educational Technology & Society, 16(3), 400–410.

    Google Scholar 

  93. *Thompson, S. V., & Riding, R. J. (1990). The effect of animated diagrams on the understanding of a mathematical demonstration in 11- to 14-year-old pupils. British Journal of Educational Psychology, 60(1), 93–98. https://doi.org/10.1111/j.2044-8279.1990.tb00925.x.

    Google Scholar 

  94. Türkay, S. (2016). The effects of whiteboard animations on retention and subjective experiences when learning advanced physics topics. Computers & Education, 98, 102–114. https://doi.org/10.1016/j.compedu.2016.03.004.

    Google Scholar 

  95. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: can it facilitate? International Journal of Human-Computer Studies, 57(4), 247–262. https://doi.org/10.1006/ijhc.2002.1017.

    Google Scholar 

  96. Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcombe, N. S. (2013). The malleability of spatial skills: a meta-analysis of training studies. Psychological Bulletin, 139(2), 352–402. https://doi.org/10.1037/a0028446.

    Google Scholar 

  97. VanArsdall, J. E., Nairne, J. S., Pandeirada, J. N. S., & Cogdill, M. (2015). Adaptive memory: animacy effects persist in paired-associate learning. Memory, 23(5), 657–663. https://doi.org/10.1080/09658211.2014.916304.

    Google Scholar 

  98. Voyer, D., & Jansen, P. (2017). Motor expertise and performance in spatial tasks: a meta-analysis. Human Movement Science, 54, 110–124. https://doi.org/10.1016/j.humov.2017.04.004.

    Google Scholar 

  99. Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: a meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250–270. https://doi.org/10.1037/0033-2909.117.2.250.

    Google Scholar 

  100. Wang, P.-Y., Vaughn, B. K., & Liu, M. (2011). The impact of animation interactivity on novices’ learning of introductory statistics. Computers & Education, 56(1), 300–311. https://doi.org/10.1016/j.compedu.2010.07.011.

    Google Scholar 

  101. Williamson, V. M., & Abraham, M. R. (1995). The effects of computer animation on the particulate mental models of college chemistry students. Journal of Research in Science Teaching, 32(5), 521–534. https://doi.org/10.1002/tea.3660320508.

    Google Scholar 

  102. *Wong, A., Leahy, W., Marcus, N., & Sweller, J. (2012). Cognitive load theory, the transient information effect and e-learning. Learning and Instruction, 22(6), 449–457. https://doi.org/10.1016/j.learninstruc.2012.05.004.

    Google Scholar 

  103. *Wong, A., Marcus, N., Ayres, P., Smith, L., Cooper, G. A., Paas, F., & Sweller, J. (2009). Instructional animations can be superior to statics when learning human motor skills. Computers in Human Behavior, 25(2), 339–347. https://doi.org/10.1016/j.chb.2008.12.012.

    Google Scholar 

  104. *Wong, M., Castro-Alonso, J. C., Ayres, P., & Paas, F. (2015). Gender effects when learning manipulative tasks from instructional animations and static presentations. Educational Technology & Society, 18(4), 37–52.

    Google Scholar 

  105. Wong, M., Castro-Alonso, J. C., Ayres, P., & Paas, F. (2018). Investigating gender and spatial measurements in instructional animation research. Computers in Human Behavior, 89, 446–456. https://doi.org/10.1016/j.chb.2018.02.017.

    Google Scholar 

  106. *Wu, C.-F., & Chiang, M.-C. (2013). Effectiveness of applying 2D static depictions and 3D animations to orthographic views learning in graphical course. Computers & Education, 63, 28–42. https://doi.org/10.1016/j.compedu.2012.11.012.

    Google Scholar 

  107. *Zacks, J. M., & Tversky, B. (2003). Structuring information interfaces for procedural learning. Journal of Experimental Psychology: Applied, 9(2), 88–100. https://doi.org/10.1037/1076-898X.9.2.88.

    Google Scholar 

  108. Zell, E., Krizan, Z., & Teeter, S. R. (2015). Evaluating gender similarities and differences using metasynthesis. American Psychologist, 70(1), 10–20. https://doi.org/10.1037/a0038208.

    Google Scholar 

Download references

Acknowledgements

We are thankful to Mariana Poblete and Monserratt Ibáñez for their assistance.

Funding

Funding from PIA-CONICYT Basal Funds for Centers of Excellence Project FB0003 is gratefully acknowledged.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Juan C. Castro-Alonso.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

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

Verify currency and authenticity via CrossMark

Cite this article

Castro-Alonso, J.C., Wong, M., Adesope, O.O. et al. Gender Imbalance in Instructional Dynamic Versus Static Visualizations: a Meta-analysis. Educ Psychol Rev 31, 361–387 (2019). https://doi.org/10.1007/s10648-019-09469-1

Download citation

Keywords

  • Dynamic and static visualization
  • Gender and spatial ability
  • STEM and manipulative–procedural tasks
  • Cognitive load theory
  • Meta-analysis