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Journal of Science Education and Technology

, Volume 23, Issue 3, pp 441–457 | Cite as

Helping Students Make Sense of Graphs: An Experimental Trial of SmartGraphs Software

  • Andrew Zucker
  • Rachel Kay
  • Carolyn Staudt
Article

Abstract

Graphs are commonly used in science, mathematics, and social sciences to convey important concepts; yet students at all ages demonstrate difficulties interpreting graphs. This paper reports on an experimental study of free, Web-based software called SmartGraphs that is specifically designed to help students overcome their misconceptions regarding graphs. SmartGraphs allows students to interact with graphs and provides hints and scaffolding to help students, if they need help. SmartGraphs activities can be authored to be useful in teaching and learning a variety of topics that use graphs (such as slope, velocity, half-life, and global warming). A 2-year experimental study in physical science classrooms was conducted with dozens of teachers and thousands of students. In the first year, teachers were randomly assigned to experimental or control conditions. Data show that students of teachers who use SmartGraphs as a supplement to normal instruction make greater gains understanding graphs than control students studying the same content using the same textbooks, but without SmartGraphs. Additionally, teachers believe that the SmartGraphs activities help students meet learning goals in the physical science course, and a great majority reported they would use the activities with students again. In the second year of the study, several specific variations of SmartGraphs were researched to help determine what makes SmartGraphs effective.

Keywords

Computers Software Physical science Motion Scaffolding Graphs 

Notes

Acknowledgments

This research was conducted under National Science Foundation Grant No. DRL-0918522, awarded to the Concord Consortium (Carolyn Staudt, PI). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References

  1. Achieve, Inc. (2013). The next generation science standards, appendix F: science and engineering practices. Retrieved from http://www.nextgenscience.org/next-generation-science-standards
  2. Ainsworth S (2008) The educational value of multiple-representations when learning complex scientific concepts. In: Gilbert JK, Reiner M, Nakhleh M (eds) Visualization: theory and practice in science education models and modeling in science education, Vol 3. Springer Science + Business, pp 191–208Google Scholar
  3. Azevedo R, Cromley JG, Seibert D (2004) Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemp Educ Psychol 29:344–370CrossRefGoogle Scholar
  4. Bowen GM, Roth W-M (1998) Lecturing graphing: what features of lectures contribute to student difficulties in learning to interpret graphs? Res Sci Educ 28:77–90CrossRefGoogle Scholar
  5. de Jong T (2006) Technological advances in inquiry learning. [Education Forum]. Science 312:532–533CrossRefGoogle Scholar
  6. Friel SN, Bright GW (1996, April) Building a theory of graphicacy: how do students read graphs? In: Paper presented at the annual meeting of the American Educational Research Association, New York (ERIC Document Reproduction Service No. ED 395 277)Google Scholar
  7. Friel SN, Curcio FR, Bright GW (2001) Making sense of graphs: critical factors influencing comprehension and instructional implications. J Res Math Educ 32(2):124–158CrossRefGoogle Scholar
  8. Kay R, Zucker A, Staudt C (2012) Being smart about SmartGraphs: findings from an experimental trial in physical classrooms. The Concord Consortium, Concord, MA. Retrieved from http://concord.org/sites/default/files/pdf/smartgraphs-research-fall-2011.pdf
  9. Lee H-S, Liu OL (2009) Assessing learning progression of energy concepts across middle school grades: the knowledge integration perspective. Sci Educ 94(4):665–688CrossRefGoogle Scholar
  10. Leinhardt G, Zaslavsky O, Stein MK (1990) Graphs and graphing: tasks, learning, and teaching. Rev Educ Res 60(1):1–64CrossRefGoogle Scholar
  11. Mokros J, Tinker R (1987) The impact of microcomputer-based labs on children’s ability to interpret graphs. J Res Sci Teach 24(4):369–383CrossRefGoogle Scholar
  12. Monk S (2003) Representation in school mathematics: learning to graph and graphing to learn. In: Kilpatrick J, Martin WG, Schifter D (eds) A research companion to principles and standards for school mathematics. National Council of Teachers of Mathematics, Reston, VA, pp 250–262Google Scholar
  13. Murray TS, Kirsch IS, Jenkins LB (1997) Adult literacy in OECD countries: a technical report for the first international adult literacy survey. National Center for Education Statistics, Washington, DCGoogle Scholar
  14. Rangel ES, Linn MC (2007) Science education that makes sense (Research Points #5:1). American Educational Research Association, Washington, DCGoogle Scholar
  15. Rethinam V, Pyke C, Lynch S (2008) Using multilevel analyses to study the effectiveness of science curriculum materials. Eval Res Educ 21(1):18–42CrossRefGoogle Scholar
  16. Robelen E (2013) Calculator use on exams to shift with the common core. Education Week 33:1, 1Google Scholar
  17. Roth W-M (1998) Unspecified things, signs, and “natural objects”: towards a phenomeno-logical hermeneutic of graphing. In: Berenson SB, Dawson KR, Blanton M, Coulombe WN, Kolb J, Norwood K, Stiff L (eds) Proceedings of the 20th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. ERIC Clearinghouse for Science, Mathematics, and Environmental Education, Columbus, OH, pp 291–297Google Scholar
  18. Roth W-M, McGinn MK (1997) Graphing: cognitive ability or practice? Sci Educ 81:91–106CrossRefGoogle Scholar
  19. Roth W-M, Bowen GM, McGinn MK (1999) Differences in graph-related practices between high school biology textbooks and scientific ecology journals. J Res Sci Teach 36:977–1019CrossRefGoogle Scholar
  20. Roth W-M, Pozzer-Ardenghi L, Han JH (2005) Critical graphicacy: understanding visual representation practices in school science. Springer-Kluwer, DordrechtGoogle Scholar
  21. van Zee E, McDermott LC (1987) Investigation of student difficulties with graphical representations in physics. In: Novak J (ed) 2nd International seminar “misconceptions and educational strategies in science and mathematics”, Ithaca, NY, 1987, Vol III. Cornell University, pp 531–539Google Scholar
  22. Woolnough J (2000) How can students learn to apply their mathematical knowledge to interpret graphs in physics? Res Sci Educ 30(3):259–268CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.The Concord ConsortiumConcordUSA

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