The guided discovery learning principle in multimedia learning

The Cambridge Handbook of Multimedia Learning, Second Edition

Volumn , Issue , 2014, Pages 371-390

  de Jong, Ton ^a   Lazonder, Ard W ^a  

^a UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84897378993     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9781139547369.019     Document Type: Chapter

References (65)

1. Alieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discoverybased instruction enhance learning? Journal of Educational Psychology,103, 1-18. doi: 10.1037/a0021017.
2. Bertsch, S., Pesta, B., Wiscott, R., & McDaniel, M. (2007). The generation effect: A meta-analytic review. Memory & Cognition,35, 201-210. doi: 10.3758/bf03193441.
3. Blanchard, M. R., Southerland, S. A., Osborne, J. W., Sampson, V. D., Annetta, L. A., & Granger, E. M. (2010). Is inquiry possible in light of accountability? A quantitative comparison of the relative effectiveness of guided inquiry and verification laboratory instruction. Science Education,94, 577-616. doi: 10.1002/sce.20390.
4. de Jong, T. (2006). Scaffolds for scientific discovery learning. In J. Elen & R. E. Clark (Eds.), Dealing with complexity in learning environments(pp. 107-128). London: Elsevier.
5. de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science,38, 105-134. doi: 10.1007/s11251-009-9110-0.
6. de Jong, T., & Njoo, M. (1992). Learning and instruction with computer simulations: Learning processes involved. In E. de Corte, M. Linn, H. Mandl, & L. Verschaffel (Eds.), Computer-based learning environments and problem solving(pp. 411-429). Berlin: Springer.
7. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research,68, 179-202. doi: 10.3102/00346543068002179.
8. de Jong, T., et al. (2010). Learning by creating and exchanging objects: The SCY experience. British Journal of Educational Technology,41, 909-921. doi: 10.1111/j.1467-8535.2010.01121.x.
9. Dean, D., & Kuhn, D. (2007). Direct instruction vs. discovery: The long view. Science Education,91, 384-397. doi: 10.1002/sce.20194.
10. Dunbar, K. (2001). What scientific thinking reveals about the nature of cognition. In K. Crowley, C. D. Schunn, & T. Okada (Eds.), Designing for science: Implications from everyday, classroom, and professional settings(pp. 115-140). Mahwah, NJ: Lawrence Erlbaum.
11. Eckhardt, M., Urhahne, D., Conrad, O., & Harms, U. (2013). How effective is instructional support for learning with computer simulations? Instructional Science,41, 105-124. doi: 10.1007/s11251-012-9220-y.
12. Etkina, E., Karelina, A., & Ruibal-Villasenor, M. (2008). How long does it take? A study of student acquisition of scientific abilities. Physical Review Special Topics-Physics Education Research,4, 020108. doi: 10.1103/PhysRevSTPER.4.020108.
13. Eysink, T. H. S., de Jong, T., Berthold, K., Kollöffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal,46, 1107-1149. doi: 10.3102/0002831209340235.
14. Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning,4, 304-317.
15. Fund, Z. (2007). The effects of scaffolded computerized science problem-solving on achievement outcomes: A comparative study of support programs. Journal of Computer Assisted Learning,23, 410-424. doi:10.1111/j.1365-2729.2007.00226.x.
16. Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. C. (2012). Experimental and quasiexperimental studies of inquiry-based science teaching. Review of Educational Research,82, 300-329. doi: 10.3102/0034654312457206.
17. Gijlers, H., & de Jong, T. (2009). Sharing and confronting propositions in collaborative inquiry learning. Cognition and Instruction,27, 239-268. doi: 10.1080/07370000903014352.
18. Gijlers, H., & de Jong, T. (2013). Using concept maps to facilitate collaborative simulation-based inquiry learning. Journal of the Learning Sciences,22, 340-374. doi: 10.1080/10508406.2012.748664.
19. Gillet, D., de Jong, T., Sotiriou, S., & Salzmann, C. (2013). Personalised learning spaces and federated online labs for STEM education at school. Paper presented at the IEEE EDUCON, Berlin.
20. Hagemans, M. G., van der Meij, H., & de Jong, T. (2013). The effects of a concept map-based support tool on simulation-based inquiry learning. Journal of Educational Psychology,105, 1-24. doi: 10.1037/a0029433.
21. Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist,42, 99-107. doi:10.1080/00461520701263368.
22. Horwitz, P., Gobert, J. D., Buckley, B. C., & O’Dwyer, L. M. (2010). Learning genetics from dragons: From computer-based manipulatives to hypermodels. In M. Jacobson & P. Reimann (Eds.), Designs for learning environments of the future: International perspectives from the learning sciences(pp. 61-89). Berlin: Springer.
23. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review,19, 509-539. doi: 10.1007/s10648-007-9054-3.
24. Kester, L., Kirschner, P. A., & van Merri ë nboer, J. J. G. (2004). Information presentation and troubleshooting in electrical circuits. International Journal of Science Education,26, 239-256. doi: 10.1080/69032000072809.
25. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimally guided instruction does not work. Educational Psychologist,41, 75-86.
26. Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science,12, 1-48.
27. Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science,15, 661-668. doi: 10.1111/j.0956-7976.2004.00737.x.
28. Kollöffel, B. (2012). Exploring the relation between visualizer-verbalizer cognitive styles and performance with visual or verbal learning material. Computers & Education,58, 697-706. doi: 10.1016/j.compedu.2011.09.016.
29. Kuhn, D., & Dean, D. (2005). Is developing scientific thinking all about learning to control variables? Psychological Science,16, 866-870. doi: 10.1111/j.1467-9280.2005.01628.x.
30. Kyza, E. A., Constantinou, C. P., & Spanoudis, G. (2011). Sixth graders’ coconstruction of explanations of a disturbance in an ecosystem: Exploring relationships between grouping, rel ective scaffolding, and evidence based explanations. International Journal of Science Education,33, 2489-2525. doi: 10.1080/09500693.2010.550951.
31. Lazonder, A. W. (2014). Inquiry learning. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4Thed.) (pp. 453-.464) Berlin: Springer.
32. Lazonder, A. W., Hagemans, M. G., & de Jong, T. (2010). Offering and discovering domain information in simulation-based inquiry learning. Learning and Instruction,20, 511-520. doi: 10.1016/j.learninstruc.2009.08.001.
33. Lazonder, A. W., & Kamp, E. (2012). Bit by bit or all at once? Splitting up the inquiry task to promote children’s scientific reasoning. Learning and Instruction,22, 458-464. doi: 10.1016/j.learninstruc.2012.05.005.
34. Lorch, R. F., Jr., Lorch, E. P., Calderhead, W. J., Dunlap, E. E., Hodell, E. C., & Freer, B. D. (2010). Learning the control of variables strategy in higher and lower achieving classrooms: Contributions of explicit instruction and experimentation. Journal of Educational Psychology,102, 90-101. doi: 10.1037/a0017972.
35. Mäeots, M., Pedaste, M., & Sarapuu, T. (2008). Transforming students’ inquiry skills with computer-based simulations. Paper presented at the 8th IEEE International Conference on Advanced Learning Technologies, Santander, Spain, July 1-5.
36. Manlove, S., Lazonder, A. W., & de Jong, T. (2009). Trends and issues of regulative support use during inquiry learning: Patterns from three studies. Computers in Human Behavior,25, 795-803. doi: 10.1016/j.chb.2008.07.010.
37. Marschner, J., Thillmann, H., Wirth, J., & Leutner, D. (2012). How can the use of strategies for experimentation be fostered? Zeitschrift f ü r Erziehungswissenschaft,15, 77-93. doi: 10.1007/s11618-012-0260-5.
38. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist,59, 14-19. doi: 10.1037/0003-066X.59.1.14.
39. Mayer, R. E. (2005). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning(pp. 31-49). Cambridge: Cambridge University Press.
40. McNeill, K. L., Lizotte, D. J., Krajcik, J., & Marx, R. W. (2006). Supporting students’ construction of scientific explanations by fading scaffolds in instructional materials. Journal of the Learning Sciences, 15, 153-191. doi: 10.1207/s15327809jls1502_1.
41. Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry-based science instruction-what is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching,47, 474-496. doi: 10.1002/tea.20347.
42. Mulder, Y. G., Lazonder, A. W., & de Jong, T. (2010). Finding out how they i nd it out: An empirical analysis of inquiry learners’ need for support. International Journal of Science Education,32, 2033-2053. doi: 10.1080/09500690903289993.
43. Mulder, Y. G., Lazonder, A. W., & de Jong, T. (2011). Comparing two types of model progression in an inquiry learning environment with modelling facilities. Learning and Instruction,21, 614-624. doi: 10.1016/j.learninstruc.2011.01.003.
44. National Research Council. (2000). Inquiry and the national science education standards: A guide for teaching and learning. Washington, DC: National Academy Press.
45. National Research Council. (2006). America’s lab report: Investigations in high school science. Washington, DC: National Academy Press.
46. Osborne, J., & Dilon, J. (2008). Science education in Europe: Critical rel ections. London: Nufield Foundation.
47. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
48. Plass, J. L., et al. (2012). Investigating the effectiveness of computer simulations for chemistry learning. Journal of Research in Science Teaching,49, 394-419. doi: 10.1002/tea.21008.
49. Quintana, C. et al. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13, 337-387. doi: 10.1207/s15327809jls1303_4.
50. Reid, D. J., Zhang, J., & Chen, Q. (2003). Supporting scientific discovery learning in a simulation environment. Journal of Computer Assisted Learning,19, 9-20.
51. Rocard, M., Csermely, P., Jorde, D., Lenzen, D., Walberg-Henrikson, H., & Hemmo, V. (2007). Science education now: A renewed pedagogy for the future of Europe. Brussels: European Commission, Directorate-General for Research.
52. Scalise, K., Timms, M., Moorjani, A., Clark, L., Holtermann, K., & Irvin, P. S. (2011). Student learning in science simulations: Design features that promote learning gains. Journal of Research in Science Teaching,48, 1050-1078. doi: 10.1002/tea.20437.
53. Slotta, J. (2004). The web-based inquiry science environment (WISE): Scaffolding knowledge integration in the science classroom. In M. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education(pp. 203-233). Mahwah, NJ: Lawrence Erlbaum.
54. Strand-Cary, M., & Klahr, D. (2008). Developing elementary science skills: Instructional effectiveness and path independence. Cognitive Development,23, 488-511. doi: 10.1016/j.cogdev.2008.09.005.
55. Stratford, S. J., Krajcik, J., & Soloway, E. (1998). Secondary students’ dynamic modelling processes: Analyzing, reasoning about, synthesizing and testing models of stream ecosystems. Journal of Science Education and Technology,7, 215-234.
56. Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning(pp. 19-32). Cambridge: Cambridge University Press.
57. Toth, E. E., Suthers, D. D., & Lesgold, A. M. (2002). “Mapping to know”: The effects of representational guidance and rel ective assessment on scientific inquiry. Science Education,86, 264-286.
58. Urhahne, D., Nick, S., & Schanze, S. (2009). The effect of three-dimensional simulations on the understanding of chemical structures and their properties. Research in Science Education,39, 495-513. doi: 10.1007/s11165-008-9091-z.
59. van der Meij, J., & de Jong, T. (2011). The effects of directive self-explanation prompts to support active processing of multiple representations in a simulationbased learning environment. Journal of Computer Assisted Learning,27, 411-423. doi: 10.1111/j.1365-2729.2011.00411.x
60. van Joolingen, W. R., & de Jong, T. (2003). Simquest: Authoring educational simulations. In T. Murray, S. Blessing, & S. Ainsworth (Eds.), Authoring tools for advanced technology educational software: Toward cost-effective production of adaptive, interactive, and intelligent educational software(pp. 1-31). Dordrecht: Kluwer.
61. van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsbergh, E., & Manlove, S. (2005). Co-lab: Research and development of an on-line learning environment for collaborative scientific discovery learning. Computers in Human Behavior,21, 671-688. doi: 10.1016/j.chb.2004.10.039.
62. Veermans, K. H., van Joolingen, W. R., & de Jong, T. (2006). Using heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain. International Journal of Science Education,28, 341-361. doi: 10.1080/09500690500277615.
63. Walker, D., Mickes, L., Bajic, D., Nailon, R., & Rickard, T. C. (in press). A test of two methods of arithmetic l uency training and implications for educational practice. Journal of Applied Research in Memory and Cognition. doi: dx.doi.org/10.1016/j.jarmac.2013.02.001.
64. Wichmann, A., & Leutner, D. (2009). Inquiry learning multilevel support with respect to inquiry, explanations and regulation during an inquiry cycle. Zeitschrift f ü r Pädagogische Psychologie,23, 117-127. doi: 10.1024/1010-0652.23.2.117.
65. Zhang, J., Chen, Q., Sun, Y., & Reid, D. J. (2004). Triple scheme of learning support design for scientific discovery learning based on computer simulation: Experimental research. Journal of Computer Assisted Learning,20, 269-282. doi: 10.1111/j.1365-2729.2004.00062.x.