475
Views
1
CrossRef citations to date
0
Altmetric
Articles

Combining Instructional Activities for Sense-Making Processes and Perceptual-Induction Processes Involved in Connection-Making Among Multiple Visual Representations

ORCID Icon &
Pages 361-395 | Received 01 Apr 2016, Accepted 16 May 2018, Published online: 10 Feb 2019

References

  • Acevedo Nistal, A., Van Dooren, W., & Verschaffel, L. (2015). Improving students’ representational flexibility in linear-function problems: An intervention. Educational Psychology, 34(6), 763–786. doi:10.1080/01443410.2013.785064
  • Ainsworth, S. (2006). Deft: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198. doi:10.1016/j.learninstruc.2006.03.001
  • Ainsworth, S. (2008). The educational value of multiple-representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & A. Nakama (Eds.), Visualization: Theory and practice in science education (pp. 191–208). Netherlands: Springer.
  • Ainsworth, S. (2014). The multiple representation principle in multimedia learning. In R. E. Mayer (Ed.), The cambridge handbook of multimedia learning (2nd ed., pp. 464–486). New York, NY: Cambridge University Press.
  • Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11(1), 25–61. doi:10.1207/S15327809JLS1101_2
  • Ainsworth, S., & Loizou, A. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science: A Multidisciplinary Journal, 27(4), 669–681.
  • Airey, J., & Linder, C. (2009). A disciplinary discourse perspective on university science learning: Achieving fluency in a critical constellation of modes. Journal of Research in Science Teaching, 46(1), 27–49. doi:10.1002/tea.20265
  • Barrett, T. J., & Hegarty, M. (2016). Effects of interface and spatial ability on manipulation of virtual models in a STEM domain. Computers in Human Behavior, 65, 220–231.
  • Berthold, K., Eysink, T. H. S., & Renkl, A. (2008). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 27(4), 345–363. doi:10.1007/s11251-008-9051-z
  • Berthold, K., & Renkl, A. (2009). Instructional aids to support a conceptual understanding of multiple representations. Journal of Educational Research, 101(1), 70–87. doi:10.1037/a0013247
  • Bodemer, D., & Faust, U. (2006). External and mental referencing of multiple representations. Computers in Human Behavior, 22(1), 27–42. doi:10.1016/j.chb.2005.01.005
  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4(1), 55–81. doi:10.1016/0010-0285(73)90004-2
  • Cheng, M., & Gilbert, J. K. (2009). Towards a better utilization of diagrams in research into the use of representative levels in chemical education. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (pp. 191–208). Berlin/Heidelberg: Springer.
  • Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. doi:10.1016/0364-0213(89)90002-5
  • Chi, M. T. H., de Leeuw, N., Chiu, M. H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. doi:10.1016/0364-0213(94)90016-7
  • Cobb, P., & McClain, K. (2006). Guiding inquiry-based math learning. In R. K. Sawyer (Ed.), The cambridge handbook of the learning sciences (1st ed., pp. 171–186). New York, NY: Cambridge University Press.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Cope, A. C., Bezemer, J., Kneebone, R., & Lingard, L. (2015). ‘You see?’ teaching and learning how to interpret visual cues during surgery. Medical education, 49(11), 1103–1116. doi:10.1111/medu.12780
  • Corbett, A. T., Koedinger, K., & Hadley, W. S. (2001). Cognitive tutors: From the research classroom to all classrooms. In P. S. Goodman (Ed.), Technology enhanced learning: Opportunities for change (pp. 235–263). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
  • de Jong, T., Ainsworth, S., Dobson, M., Van der Meij, J., Levonen, J., Reimann, P., … Swaak, J. (1998). Acquiring knowledge in science and mathematics: The use of multiple representations in technology-based learning environments. In M. W. Van Someren, W. Reimers, H. P. A. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 9–41). Bingley, UK: Emerald Group Publishing Limited.
  • diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.
  • diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: An introduction. The Journal of Mathematical Behavior, 19(4), 385–398. doi:10.1016/S0732-3123(01)00051-7
  • Dreher, A., & Kuntze, S. (2015). Teachers facing the dilemma of multiple representations being aid and obstacle for learning: Evaluations of tasks and theme-specific noticing. Journal für Mathematik-Didaktik, 36(1), 23–44. doi:10.1007/s13138-014-0068-3
  • Eastwood, M. L. (2013). Fastest fingers: A molecule-building game for teaching organic chemistry. Journal of Chemical Education, 90(8), 1038–1041. doi:10.1021/ed3004462
  • Eilam, B., & Ben-Peretz, M. (2012). Teaching, learning, and visual literacy: The dual role of visual representation. New York, NY: Cambridge University Press.
  • Ericsson, K. A., & Simon, H. A. (1987). Verbal protocols on thinking. In C. Faerch & G. Kasper (Eds.), Introspection in second language research (pp. 24–53). Clevedon: Multilingual Matters.
  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. doi:10.1207/s15516709cog0702_3
  • Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–405. doi:10.1037/0022-0663.95.2.393
  • Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45–56. doi:10.1037/0003-066X.52.1.45
  • Gibson, E. J. (2000). Perceptual learning in development: Some basic concepts. Ecological Psychology, 12(4), 295–302. doi:10.1207/S15326969ECO1204_04
  • Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In J. K. Gilbert (Ed.), Visualization: Theory and practice in science education (pp. 9–27). Dordrecht, Netherlands: Springer.
  • Gilbert, J. K. (2008). Visualization: An emergent field of practice and inquiry in science education. In J. K. Gilbert, M. Reiner, & M. B. Nakhleh (Eds.), Visualization: Theory and practice in science education (Vol. 3, pp. 3–24). Dordrecht, Netherlands: Springer.
  • Goldstone, R. (1997). Perceptual learning. San Diego, CA: Academic Press.
  • Goldstone, R. L., Schyns, P. G., & Medin, D. L. (1997). Learning to bridge between perception and cognition. Psychology of Learning and Motivation, 36, 1–14. doi:10.1016/S0079-7421(08)60279-0
  • Greeno, J. G., & Hall, R. P. (1997). Practicing representation. Phi Delta Kappan, 78(5), 361–367.
  • Hegarty, M., & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32(6), 717–742. doi:10.1006/jmla.1993.1036
  • Hegarty, M., & Waller, D. A. (2005). Individual differences in spatial abilities. In P. Shah & A. Miyake (Eds.), The cambridge handbook of visuospatial thinking (pp. 121–169). New York, NY: Cambridge University Press.
  • Holmqvist, K., Nystrom, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford: Oxford University Press.
  • Jarodzaka, H., Scheiter, K., Gerjets, P., & van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20(2), 146–154.
  • Johnson, C. I., & Mayer, R. E. (2012). An eye movement analysis of the spatial contiguity effect in multimedia learning. Journal of Experimental Psychology, 18(2), 178–191.
  • Kellman, P. J., & Garrigan, P. B. (2009). Perceptual learning and human expertise. Physics of Life Reviews, 6(2), 53–84. doi:10.1016/j.plrev.2008.12.001
  • Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and expertise. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 558, pp. 117–165). New York, NY: Elsevier Academic Press.
  • Kellman, P. J., Massey, C. M., Roth, Z., Burke, T., Zucker, J., Saw, A., … Wise, J. (2008). Perceptual learning and the technology of expertise: Studies in fraction learning and algebra. Pragmatics & Cognition, 16(2), 356–405. doi:10.1075/pc.16.2.07kel
  • Kellman, P. J., Massey, C. M., & Son, J. Y. (2009). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2(2), 285–305. doi:10.1111/j.1756-8765.2009.01053.x
  • Kintsch, W., & Van Dijk, T. A. (1978). Toward a model of text comprehension and production. Psychological Review, 85(5), 363–394. doi:10.1037/0033-295X.85.5.363
  • Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798. doi:10.1111/j.1551-6709.2012.01245.x
  • Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. Gilbert (Ed.), Visualization in science education (pp. 121–145). Dordrecht, Netherlands: Springer.
  • Langlois, J., Bellemare, C., Toulouse, J., & Wells, G. A. (2015). Spatial abilities and technical skills performance in health care: A systematic review. Medical education, 49(11), 1065–1085. doi:10.1111/medu.12786
  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.
  • Massey, C. M., Kellman, P. J., Roth, Z., & Burke, T. (2011). Perceptual learning and adaptive learning technology - developing new approaches to mathematics learning in the classroom. In N. L. Stein & S. W. Raudenbush (Eds.), Developmental cognitive science goes to school (pp. 235–249). New York, NY: Routledge.
  • Mayer, R. E. (2009). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The cambridge handbook of multimedia learning (2nd ed., pp. 31–48). New York, NY: Cambridge University Press.
  • McElhaney, K. W., Chang, H. Y., Chiu, J. L., & Linn, M. C. (2015). Evidence for effective uses of dynamic visualisations in science curriculum materials. Studies in Science Education, 51(1), 49–85. doi:10.1080/03057267.2014.984506
  • Moreira, R. F. (2013). A game for the early and rapid assimilation of organic nomenclature. Journal of Chemical Education, 90(8), 1035–1037. doi:10.1021/ed300473r
  • Nathan, M. J., Walkington, C. A., Srisurichan, R., & Alibali, M. W. (2011). Modal engagements in precollege engineering: Tracking math and science concepts across symbols, sketches, software, silicone and wood Proceedings of the 118th american society for engineering education. Vancouver, BC, Canada: American Society for Engineering Education.
  • NCTM. (2006). Curriculum focal points for prekindergarten through grade 8 mathematics: A quest for coherence. Reston, VA.
  • NGSS. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.
  • NMAP. (2008). Foundations for success: Report of the national mathematics advisory board panel. Retrieved from https://www2.ed.gov/about/bdscomm/list/mathpanel/report/final-report.pdf
  • NRC. (2006). Learning to think spatially. Washington, D.C.: National Academies Press.
  • NRC. (2012). A framework for k-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
  • Patel, Y., & Dexter, S. (2014). Using multiple representations to build conceptual understanding in science and mathematics. In M. Searson & M. Ochoa (Eds.), Proceedings of society for information technology & teacher education international conference 2014 (Vol. 2014, pp. 1304–1309). Chesapeake, VA: AACE.
  • Peters, M., Laeng, B., Latham, K., Jackson, M., Zaiyouna, R., & Richardson, C. (1995). A redrawn vandenberg & kuse mental rotations test: Different versions and factors that affect performance. Brain and Cognition, 28, 39–58. doi:10.1006/brcg.1995.1032
  • Plötzner, R., Bodemer, D., & Neudert, S. (2008). Successful and less successful use of dynamic visualizations in instructional texts. In R. K. Lowe & W. Schnotz (Eds.), Learning with animation. Research implications for design. New York: Cambridge University Press.
  • Rau, M. A. (2017a). Conditions for the effectiveness of multiple visual representations in enhancing stem learning. Educational Psychology Review, 29(4), 717–761. doi:10.1007/s10648-016-9365-3
  • Rau, M. A. (2017b). A framework for discipline-specific grounding of educational technologies with multiple visual representations. IEEE Transactions on Learning Technologies, 10(3), 290–305. doi:10.1109/TLT.2016.2623303
  • Rau, M. A., Aleven, V., & Rummel, N. (2013). Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave? Learning and Instruction, 23, 98–114. doi:10.1016/j.learninstruc.2012.07.003
  • Rau, M. A., Aleven, V., & Rummel, N. (2017a). Making connections between multiple graphical representations of fractions: Conceptual understanding facilitates perceptual fluency, but not vice versa. Instructional Science, 45(3), 331–357. doi:10.1007/s11251-017-9403-7
  • Rau, M. A., Aleven, V., & Rummel, N. (2017b). Supporting students in making sense of connections and in becoming perceptually fluent in making connections among multiple graphical representations. Journal of Educational Psychology, 109(3), 355–373. doi:10.1037/edu0000145
  • Rau, M. A., Aleven, V., Rummel, N., & Pardos, Z. (2014). How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study. International Journal of Artificial Intelligence in Education, 24(2), 125–161. doi:10.1007/s40593-013-0011-7
  • Rau, M. A., Michaelis, J. E., & Fay, N. (2015). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers and Education, 82, 460–485. doi:10.1016/j.compedu.2014.12.009
  • Reed, S. K. (2012). Learning by mapping across situations. Journal of the Learning Sciences, 21(3), 353–398.
  • Richman, H. B., Gobet, F., Staszewski, J. J., & Simon, H. A. (1996). Perceptual and memory processes in the acquisition of expert performance: The epam model. In K. A. Ericsson (Ed.), The road to excellence? The acqquisition of expert performance in the arts and sciences, sports and games (pp. 167–187). Mahwah, NJ: Erlbaum Associates.
  • Rittle-Johnson, B., Loehr, A. M., & Durkin, K. (2017). Promoting self-explanation to improve mathematics learning: A meta-analysis and instructional design principles. ZDM, 49(4), 599–611. doi:10.1007/s11858-017-0834-z
  • Savec, V. F., Sajovic, I., & Grm, K. S. W. (2009). Action research to promote the formation of linkages by chemistry students between the macro, submicro, and symbolic representational levels. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (pp. 309–331). Netherlands: Springer.
  • Schnotz, W. (2005). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), The cambridge handbook of multimedia learning (pp. 49–69). New York, NY: Cambridge University Press.
  • Schönborn, K. J., & Anderson, T. R. (2006). The importance of visual literacy in the education of biochemists. Biochemistry and Molecular Biology Education, 34(2), 94–102. doi:10.1002/bmb.2006.49403402094
  • Schooler, J. W., Ohlsson, S., & Brooks, K. (1993). Thoughts beyond words: When language overshadows insight. Journal of Experimental Psychology: General, 122(2), 166–183.
  • Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13(2), 227–237. doi:10.1016/S0959-4752(02)00022-1
  • Shanks, D. (2005). Implicit learning. In K. Lamberts & R. Goldstone (Eds.), Handbook of cognition (pp. 202–220). London: Sage.
  • Stalbovs, K., Scheiter, K., & Gerjets, P. (2015). Implementation intentions during multimedia learning: Using if-then plans to facilitate cognitive processing. Learning and Instruction, 35, 1–15. doi:10.1016/j.learninstruc.2014.09.002
  • Stern, E., Aprea, C., & Ebner, H. G. (2003). Improving cross-content transfer in text processing by means of active graphical representation. Learning and Instruction, 13(2), 191–203. doi:10.1016/S0959-4752(02)00020-8
  • Stieff, M. (2007). Mental rotation and diagrammatic reasoning in science. Learning and Instruction, 17(2), 219–234. doi:10.1016/j.learninstruc.2007.01.012
  • Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145. doi:10.1080/07370008.2010.507318
  • Stull, A. T., Hegarty, M., Dixon, B., & Stieff, M. (2012). Representational translation with concrete models in organic chemistry. Cognition and Instruction, 30(4), 404–434. doi:10.1080/07370008.2012.719956
  • Taber, K. S. (2014). The significance of implicit knowledge for learning and teaching chemistry. Chemistry Education Research and Practice, 15, 447–461.
  • Talanquer, V. (2013). Chemistry education: Ten facets to shape us. Journal of Chemical Education, 90, 832–838. doi:10.1021/ed300881v
  • Underwood, G., & Everatt, J. (1992). The role of eye movements in reading: Some limitations of the eye-mind assumption. In E. Chekaluk & K. R. Llewellyn (Eds.), The role of eye movements in perceptual processes (pp. 111–169). Amsterdam, The Netherlands: Elsevier Science Publishers B. V.
  • 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. doi:10.1037/a0028446
  • van der Meij, J., & de Jong, T. (2011). The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment. Journal of Computer Assisted Learning, 27(5), 411–423. doi:10.1111/j.1365-2729.2011.00411.x
  • Van Gog, T., Paas, F., Van Merriënboer, J. J. G., & Witte, P. (2005). Uncovering the problem-solving process: Cued retrospective reporting versus concurrent and retrospective reporting. Journal of Experimental Psychology, 11, 237–244. doi:10.1037/1076-898X.11.4.237
  • VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educational Psychologist, 46(4), 197–221. doi:10.1080/00461520.2011.611369
  • Vygotsky, L. S. (1978). Internalization of higher psychological functions. In M. W. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society (pp. 52–57). Cambridge, MA: Harvard University Press.
  • Welsh, M. J. (2003). Organic functional group playing card deck. Journal of Chemical Education, 80(4), 426–427.
  • Wertsch, J. V. (1997). Properties of mediated action. In J. V. Wertsch (Ed.), Mind as action (pp. 23–72). New York: Oxford University Press.
  • Wertsch, J. V., & Kazak, S. (2011). Saying more than you know in instructional settings. In T. Koschmann (Ed.), Theories of learning and studies of instructional practice (pp. 153–166). New York: Springer.
  • Wise, J. A., Kubose, T., Chang, N., Russell, A., & Kellman, P. J. (2000). Perceptual learning modules in mathematics and science instruction. In P. Hoffman & D. Lemke (Eds.), Teaching and learning in a network world (pp. 169–176). Amsterdam, The Netherlands: IOS Press.
  • Wu, H. K., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38(7), 821–842. doi:10.1002/tea.1033

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.