Drawing for promoting learning and engagement with dynamic visualizations

Learning from Dynamic Visualization: Innovations in Research and Application

Volumn , Issue , 2017, Pages 333-356

  Stieff, Mike ^a  

^a UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85033555302     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-319-56204-9_14     Document Type: Chapter

References (76)

1. Ainsworth, S. E., & Iacovides, I. (2005). Learning by constructing self-explanation diagrams. Paper presented at the 11th EARLI Conference, Munich, Germany. Retrieved from http://www. psychology.nottingham.ac.uk/staff/sea/earli2005/ainsworth_abstract.pdf
2. Ainsworth, S., & Loizou, A. T. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27, 609-681.
3. Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333, 1096-1097.
4. Berthold, K., Eysink, T. H. S., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37, 345-363.
5. Berthold, K., Röder, H., Knörzer, D., Kessler, W., & Renkl, A. (2011). The double-edged effects of explanation prompts. Computers in Human Behavior, 27, 69-75.
6. Britton, L. A., & Wandersee, J. H. (1997). Cutting up text to make moveable, magnetic diagrams: A way of teaching and assessing biological processes. The American Biology Teacher, 59, 288-291.
7. Chang, H., Quintana, C., & Krajcik, J. S. (2009). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94, 73-94.
8. Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73-105.
9. Christian, W., & Titus, A. (1998). Developing web-based curricula using Java physlets. Computers in Physics, 12, 227-232.
10. Clegg, T.L., Bonsignore, E., Yip, J. C., Gelderblom, H., Kuhn, A., Valenstein, T. & Druin, A. (2012). Technology for promoting scientific practice and personal meaning in life-relevant learning. In Proceedings of the 11th International Conference on Interaction Design and Children (IDC'12) (pp. 152-161). New York: ACM.
11. Cooper, M. M., Groves, N. P., Pargas, R., Bryfczynski, S. P., & Gatlin, T. (2009). OrganicPad: An interactive freehand drawing application for drawing Lewis structures and the development of skills in organic chemistry. Chemistry Education Research and Practice, 10, 296-301.
12. Cromley, J. G., Bergey, B. W., Fitzhugh, S. L., Newcombe, N., Wills, T. W., Shipley, T. F., & Tanaka, J. C. (2013). Effects of three diagram instruction methods on transfer of diagram comprehension skills: The critical role of inference while learning. Learning and Instruction, 26, 45-58.
13. Dalebroux, A., Goldstein, T. R., & Winner, E. (2008). Short-term mood repair through art-making: Positive emotion is more effective than venting. Motivation and Emotion, 32, 288-295.
14. de Bock, D., Verschaffel, L., & Janssens, D. (1998). The predominance of the linear model in secondary school students' solutions of word problems involving length and area of similar plane figures. Educational Studies in Mathematics, 35, 65-83.
15. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179-201.
16. De Koning, B. B., & Jarodzka, H. (2017). Attention guidance strategies for supporting learning from dynamic visualizations. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization - Innovations in research and application. Berlin: Springer (this volume).
17. De Petrillo, L., & Winner, E. (2005). Does art improve mood? A test of a key assumption underlying art therapy. Art Therapy, 22, 205-212.
18. Dickey, M. (2005). Engaging by design: How engagement strategies in popular computer and video games can inform instructional design. Educational Technology Research and Development, 53, 67-83.
19. Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8, 391-450.
20. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59-109.
21. Gilbert, J. (2005). Visualization in science education. Dordrecht: Springer.
22. Gobert, J. (2000). A typology of models for plate tectonics: Inferential power and barriers to understanding. International Journal of Science Education, 22, 937-977.
23. Gobert, J. (2005). The effects of different learning tasks on conceptual understanding in science: Teasing out representational modality of diagramming versus explaining. Journal of Geoscience Education, 53, 444-455.
24. Gooding, D. (2004). Visualization, inference and explanation in the sciences. In G. Malcolm (Ed.), Studies in multidisciplinarity (Vol. 2, pp. 1-25). Amsterdam: Elsevier.
25. Harris, J., Mishra, P., & Koehler, M. (2009). Teachers' technological pedagogical content knowledge and learning activity types: Curriculum-based technology integration reframed. Journal of Research on Technology in Education, 41, 393-416.
26. Hayes, D., Symington, D., & Martin, M. (1994). Drawing during science activity in the primary school. International Journal of Science Education, 16, 265-277.
27. Hegedus, S., & Kaput, J. (2004). An introduction to the profound potential of connected algebra activities: Issues of representation, engagement and pedagogy. In Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education (pp. 129-136). Toronto: OISE/UT.
28. Hofstein, A., & Lunetta, V. N. (2003). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88, 28-54.
29. Jee, B. D., Gentner, D., Forbus, K., Sageman, B., & Uttal, D. H. (2009). Drawing on experience: Use of sketching to evaluate knowledge of spatial scientific concepts. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2499-2504). Amsterdam: Cognitive Science Society.
30. Johnson, J. K., & Reynolds, S. J. (2005). Concept sketches-Using student- and instructor-generated, annotated sketches for learning, teaching, and assessment in geology courses. Journal of Geoscience Education, 53, 85-95.
31. Kelly, R. M., & Jones, L. L. (2007). Exploring how different features of animations of sodium chloride dissolution affect students' explanations. Journal of Science Education and Technology, 16, 413-429.
32. Khishfe, R., & Abd-El-Khalick, F. (2002). Influence of explicit and reflective versus implicit inquiry-oriented instruction. Journal of Research in Science Teaching, 39, 551-578.
33. Kozma, R. (2003). Material and social affordances of multiple representations for science understanding. Learning and Instruction, 13, 205-226.
34. Kozma, R., Chin, E., Russell, J., & Marx, N. (2000). The role of representations and tools in the chemistry laboratory and their implications for chemistry learning. Journal of the Learning Sciences, 9, 105-143.
35. Kozma, R., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34, 949-968.
36. Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice (pp. 19-68). Cambridge, MA: MIT Press.
37. Leutner, D., Leopold, C., & Sumfleth, E. (2009). Cognitive load and science text comprehension: Effects of drawing and mentally imagining text content. Computers in Human Behavior, 25, 284-289.
38. Leutner, D., & Schmeck, A. (2014). The generative drawing principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 433-448). New York: Cambridge University Press.
39. Linn, M. C. (2010). How can selection and drawing support learning from dynamic visualizations. In K. Gomez, L. Lyons & J. Radinsky (Eds.), Proceedings of the Ninth International Conference of the Learning Sciences (ICLS) (Vol. 1, pp. 165-166). Mahwah, NJ: Erlbaum.
40. Linn, M. C., Lee, H.-S., Tinker, R., Husic, F., & Chiu, J. L. (2006). Teaching and assessing knowledge integration in science. Science, 313, 1049-1050.
41. Lowe, R. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13, 157-176.
42. Lowe, R., & Boucheix, J.-M. (2017). A composition approach to design of educational animations. In R. Lowe, & R. Ploetzner (Eds.), Learning from dynamic visualization-Innovations in research and application. Berlin: Springer (this volume).
43. Lowe, R., & Mason, L. (2017). Self-generated drawing: A help or hindrance to learning from animation? In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization-Innovations in research and application. Berlin: Springer (this volume).
44. Lowe, R., & Schnotz, W. (Eds.). (2008). Learning with animation: Research implications for design. New York: Cambridge University Press.
45. Mason, L., Lowe, R., & Tornatora, M. C. (2013). Self-generated drawings for supporting comprehension of a complex animation. Contemporary Educational Psychology, 38, 211-224.
46. Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York: Cambridge University Press.
47. McGrath, M. B., & Brown, J. R. (2005). Visual learning for science and engineering. IEEE Computer Graphics and Applications, 25, 56-63.
48. Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York: Holt, Rinehart, & Winston.
49. Newmann, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and sources of student engagement. In F. M. Newman (Ed.), Student engagement and achievement in American secondary schools (pp. 11-39). New York: Teachers College Press.
50. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255-287.
51. Plass, J. L., Milne, C., Homer, B. D., Jordan, T., Schwartz, R. N., Hayward, E. O., & Barrientos, J. (2012). Investigating the effectiveness of computer simulations for chemistry learning. Journal of Research in Science Teaching, 49, 394-419.
52. Ploetzner, R., & Breyer, B. (2017). Strategies for learning from animation with and without narration. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization-Innovations in research and application. Berlin: Springer (this volume).
53. Ploetzner, R., & Fillisch, B. (2017). Not the silver bullet: Learner-generated drawings make it difficulty to understand broader spatiotemporal structures in complex animations. Learning and Instruction, 47, 13-24.
54. Prain, V., & Tytler, R. (2012). Learning through constructing representations in science: A framework of representational construction affordances. International Journal of Science Education, 34, 2751-2773.
55. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J. S., Fretz, E., Duncan, R. G., & Soloway, E. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13, 337-386.
56. Rasco, R. W., Tennyson, R. D., & Boutwell, R. C. (1975). Imagery instructions and drawings in learning prose. Journal of Educational Psychology, 67, 188-192.
57. Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., & Leone, T. J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263-305). Mahwah, NJ: Erlbaum.
58. Roschelle, J., Schechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., & Gallagher, L. (2010). Integration of technology, curriculum, and professional development for advancing middle school mathematics: Three large-scale studies. American Educational Research Journal, 47, 833-878.
59. Roy, M., & Chi, M. T. H. (2005). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 271-286). New York: Cambridge University Press.
60. Ryan, S., Yip, J., Stieff, M., & Druin, A. (2013). Cooperative inquiry as a community of practice. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), Proceedings of the 10th International Conference on Computer-Supported Collaborative Learning (pp. 145-148). Madison, WI: International Society for the Learning Sciences.
61. Schwamborn, A., Mayer, R. E., Thillmann, H., Leopold, C., & Leutner, D. (2010). Drawing as a generative activity and drawing as a prognostic activity. Journal of Educational Psychology, 102, 872-879.
62. Snowman, J., & Cunningham, D. J. (1975). A comparison of pictorial and written adjunct aids in learning from text. Journal of Educational Psychology, 67, 307-311.
63. Stein, M., & Power, B. (1996). Putting art on the scientist's palette. In R. S. Hubbard & K. Ernst (Eds.), New entries: Learning by writing and drawing (pp. 59-68). Portsmouth, NH: Heinemann.
64. Stieff, M. (2005). Connected chemistry-A novel modeling environment for the chemistry classroom. Journal of Chemical Education, 82, 489-493.
65. Stieff, M. (2011). Fostering representational competence through argumentation with multi-representational displays. Proceedings of the 9th international conference on computer-supported collaborative learning (Vol. 1, pp. 288-295). Mahwah, NJ: Erlbaum.
66. Stieff, M. (2011). Improving representational competence using multi-representational learning environments. Journal of Research in Science Teaching, 48, 1137-1158.
67. Stieff, M., Bateman, R., & Uttal, D. H. (2005). Teaching and learning with three-dimensional representations. In J. Gilbert (Ed.), Visualization in science education (pp. 93-120). Oxford: Oxford University Press.
68. Stieff, M., & McCombs, M. (2006). Increasing representational fluency with visualization tools. In Proceedings of the Seventh International Conference of the Learning Sciences (ICLS) (Vol.1, pp. 730-736). Mahwah, NJ: Erlbaum.
69. Stieff, M., Nighelli, T., Yip, J., Ryan, S., & Berry, A. (2012). The connected chemistry curriculum (Vols. 1-9). Chicago, IL: University of Illinois.
70. Stieff, M., & Wilensky, U. (2003). Connected Chemistry-incorporating interactive simulations into the chemistry classroom. Journal of Science Education & Technology, 12, 285-302.
71. Tai, R., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312, 1143-1144.
72. Van Meter, P., & Firetto, C. M. (2013). Cognitive model of drawing construction: Learning through the construction of drawings. In G. Schraw, M. McCrudden, & D. Robinson (Eds.), Learning through visual displays (pp. 247-280). Scottsdale, AZ: Information Age Publishing.
73. Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis. Educational Psychology Review, 17, 285-325.
74. Wu, H.-K., & Huang, Y.-L. (2007). Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments. Science Education, 91, 727-749.
75. Wu, H.-K., Krajcik, J. S., & Soloway, E. (2001). Promoting conceptual understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821-842.
76. Zhang, H. Z., & Linn, M. C. (2011). Can generating representations enhance learning with dynamic visualizations? Journal of Research in Science Teaching, 48, 1177-1198.