You are here:

Visualization for Middle School Students’ Engagement in Science Learning
Article

, , Texas A&M University, United States

JCMST Volume 23, Number 2, ISSN 0731-9258 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA

Abstract

This mixed-methods study explored the effects of studentgenerated visualization on middle-schoolers' science concept learning. We compared students who visualized during study time with those who did not and found that visualization as a study strategy led to students' improved test performances (p=.02). However, middle schoolers' scores on a test of science concepts did not improve as a result of a computerbased visualization workshop (p=.03). In fact, workshop participants scored lower on the test than did those who did not receive the workshop. Qualitative analysis identified elements in the school setting that interfered with instructional effectiveness in the computer-based workshop. Findings indicated that across groups students were quite unskilled at visualization. We found that visualization is a difficult but powerful study strategy and recommend that science curriculum focus on visualization of concepts.

Citation

Cifuentes, L. & Hsieh, Y.C.J. (2004). Visualization for Middle School Students’ Engagement in Science Learning. Journal of Computers in Mathematics and Science Teaching, 23(2), 109-137. Norfolk, VA: Association for the Advancement of Computing in Education (AACE). Retrieved March 25, 2019 from .

Keywords

View References & Citations Map

References

  1. Anderson-Inman, L. (1992). Computer-supported studying for students with reading and writing difficulties. Reading and Writing Quarterly: Overcoming Learning Disabilities, 8, 317-319.
  2. Anderson-Inman, L., & Ditson, L. (1999). Computer-based concept mapping: A tool for negotiating meaning. Learning and Leading with Technology, 26(8), 6-13.
  3. Anderson-Inman, L., Redekopp, R., & Adams, V. (1992). Electronic studying: Using computer-based outlining programs as study tools. Reading and Writing Quarterly: Overcoming Learning Disabilities, 8, 337-358.
  4. Anderson-Inman, L., & Zeitz, L. (1993). Computer-based concept mapping: Active studying for active learners. The Computer Teacher, 21(1), 6-8, 10-11. Baker, L., & Brown, A. L. (1984). Matacognitive skills and reading. In P.D. Pearson (Ed.), Handbook of reading research (pp. 353-394). New York: Longman.
  5. Bliss, J., Askew, M., & Macrae, S. (1996). Effective teaching and learning: Scaffolding revisited. Oxford Review of Education, 22(1), 37-61.
  6. Bransford, J. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy of Sciences.
  7. Bruner, J. S. (1986). Actual minds, possible worlds. Cambridge: Harvard University Press.
  8. Cifuentes, L. (1992). The effects of instruction in visualization as a study strategy. Unpublished doctoral dissertation, University of North Carolina, Chapel Hill.
  9. Cifuentes, L., & Hsieh, Y.C. (2003a). Visualization for construction of meaning during study time: A qualitative analysis. International Journal of Instructional Media, 30(3).
  10. Cifuentes, L., & Hsieh, Y.C. (2003b). Visualization for construction of meaning during study time: A Quantitative Analysis. International Journal of Instructional Media, 30(4).
  11. Cohen, J. (1965). Some statistical issues in psychological research. In B. B. Wolman (Ed.), Handbook of clinical psychology (pp. 95-121). New York: McGraw-Hill.
  12. Cognition and Technology Group at Vanderbilt (1999). Technology for teaching and learning with understanding. Boston: Houghton Mifflin.
  13. Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Eds.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp.453-494). Hillsdale, NJ: Lawrence Erlbaum.
  14. Cook, L.K., & Mayer, R. E. (1988). Teaching readers about the structure of scientific text. Journal of Educational Psychology, 80, 448-456.
  15. Cox, G. C., Smith, D. L., & Rakes, T. A. (1994). Enhancing comprehension through the use of visual elaboration strategies. Reading Research and Instruction, 33(3), 157-174.
  16. Dacy, J. (1989). Fundamentals of creative thinking. Lexington, MA: D.C. Health.
  17. De Bono, E. (1995). Mind power. New York: Dorling Kindersky.
  18. Dodge, B. (1998). A taxonomy of information patterns. Retrieved January 29, 2002, from http://projects.edtech.sandi.net/staffdev/tpss98/patterns-taxonomy. Html
  19. Downes, T. (2000, April). The computer as a “playable” tool. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
  20. Driver, R. (1983). The pupil as scientist? Milton Keynes, England: Open University.
  21. Earnshaw, R. A., & Wiseman, N. (1992). An introductory guide to scientific visualization. New York: Springer-Verlag.
  22. Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing ethnographic fieldnotes. Chicago: The University of Chicago Press.
  23. Finke, R. (1990). Creative Imagery: Discoveries and inventions in visualization. Hillsdale, N.J.: Lawrence Erlbaum.
  24. Fosnot, C. T. (1996). Constructivism: Theory, perspectives, and practice. New York: Teachers College Press.
  25. Gall, M. D., Borg, W. R. & Gall, J. P. (1996). Educational research: An introduction. White Planes, NY: Longman.
  26. Gardner, H.E. (1999). Multiple approaches to understandings. In C. M. Reigeluth (Ed.), Instructional-design theories and models: Vol.2. A new paradigm of instructional theory (pp.69-89). Hillsdale, NJ: Lawrence Erlbaum. Gobert, J. D., & Clement, J. J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36(1), 39-53.
  27. Guilford, J. P., & Hoepfner, R. (1971). The analysis of intelligence. New York: Macmillan.
  28. Huck, S. W., & Sandler, H. M. (1979). Rival hypotheses: Alternative interpretations of data based conclusions. New York: Harper & Row.
  29. Jonassen, D. H. (1999). Designing constructivist learning environments. In C. M Reigeluth (Ed.), Instructional-design theories and models: Vol.2. A new paradigm of instructional theory (pp.215-239). Hillsdale, NJ: Lawrence Erlbaum.
  30. Jonassen, D. H. (2000). Computers as mindtools for schools: Engaging critical thinking. Upper Saddle River, NJ: Merrill.
  31. Julyan, C., & Duckworth, E. (1996). A constructivist perspective on teaching and learning science. In C. T. Fosnot (Ed.), Constructivism: Theory, perspectives, and practice (pp.55-72). New York: Teachers College Press.
  32. Lee, P-L. H. (1997). Integrating concept mapping and metacognitive methods in a hypermedia environment for learning science. Unpublished doctoral dissertation, Purdue University.
  33. Lesgold, A. M., Levin, J.R., Shimron, J., & Cuttmann, J. (1975). Pictures and young children’s learning from oral prose. Journal of Educational Psychology, 67(5), 636-642.
  34. Lipson, M. (1994). Effects of a mnemonic imagery strategy on the prose recall of developmental and nondevelopmental college readers. Reading Improvement, 31(1), 9-13.
  35. Mann, L. (1997). Image processing for unlimited exploration. Curriculum/Technology Quarterly, 6(2), 1.
  36. Marzano, R. J. (1988). Dimensions of thinking: A framework for curriculum and instruction. Alexandria, VA: Association for Supervision and Curriculum Development.
  37. Maton, A. (1997). Prentice hall exploring physical science. (Teacher edition) Englewood Cliffs, NJ: Prentice Hall.
  38. McKeachie, W. J. (2000). Helping students learn how to learn. (ERIC Document Reproduction Service No. ED 450 864)
  39. Papert, S., & Harel, I. (1991). Constructivism: Research reports and essays, 1985-1990. Epistemology and Learning Research Group, the Media Laboratory, Massachusetts Institute of Technology. Norwood, NJ: Ablex.
  40. Peeck, J. (1980, April). Experimenter-provided and learner-generated pictures in learning from text. Paper presented at the annual meeting of the American Educational Research Association, Boston.
  41. Peltzer, A. (1988). The intellectual factors believed by physicists to be most important to physics students. Journal of Research in Science Teaching, 25(9), 721-731.
  42. Perkins, D. N., & Unger, C. (1999). Teaching and learning to understanding. In C. M Reigeluth (Eds.), Instructional-design theories and models: Vol.2. A new paradigm of instructional theory (pp.91-114). Hillsdale, NJ: Lawrence Erlbaum.
  43. Schmid, R. F., & Telaro, G. (1990). Concept mapping as an instructional strategy for high school biology. Journal of Educational Research, 84, 78-85. Shulman, L.S. (1997). Disciplines of inquiry in education: A new overview. In R.M. Jaeger (Ed.), Complementary methods for research in education (pp. 3-29). Washington, DC: American Educational Research Association.
  44. Sinatra, R. (1981). Using visuals to help the second language learners. The Reading Teacher, 5, 539-546.
  45. Sodamann, P. (1991). Visual imagery and achievement test outcomes. International Journal of Instructional Media, 18(2), 175-182.
  46. Tenny, J. L. (1992). Computer-supported study strategies for purple people. Reading and Writing Quarterly: Overcoming Learning Disabilities, 8, 359377.
  47. Torrance, E. P., & Safter, H. T. (1999). Making the creative leap beyond… Buffalo, NY: Creative Education Foundation Press.
  48. Weller, H. G., Repman, J., Lan, W., & Rooze, G. (1995). Improving the effectiveness of learning through hypermedia-based instruction: The importance

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact info@learntechlib.org.