Abstract
This study investigates university students’ strategies and difficulties with graph interpretation in three different domains: mathematics, physics (kinematics), and contexts other than physics. Eight sets of parallel mathematics, physics, and other context questions were developed and administered to 385 first year students at Faculty of Science, University of Zagreb. In addition, the questions were administered to 417 first year students at the University of Vienna. Besides giving answers to the questions in the test, students were also required to provide explanations and procedures that accompanied their answers so that additional insight in the strategies that were used in different domains could be obtained. Rasch analysis of data was conducted and linear measures for item difficulties were produced. The analysis of item difficulties obtained through Rasch modeling pointed to higher difficulty of items which involved context (either physics or other context) compared to direct mathematical problems on graph. In addition, student explanations were analyzed and categorized. Student strategies of graph interpretation were found to be largely domain specific. In physics, the dominant strategy seems to be the use of formulas, especially among students at the University of Zagreb. This strategy seems to block the use of other, more productive strategies, which students possess and use in other domains. Students are generally better at interpreting graph slope than area under the graph which is difficult for students and needs more attention in physics and mathematics teaching.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Araujo, I. S., Veit, E. A., & Moreira, M. A. (2008). Physics students’ performance using computational modeling activities to improve kinematics graphs interpretation. Computers & Education, 50, 1128–1140.
Beichner, R. J. (1990). The effect of simultaneous motion presentation and graph generation in a kinematics lab. Journal of Research in Science Teaching, 27, 803–815.
Beichner, R. J. (1994). Testing student interpretation of kinematics graphs. American Journal of Physics, 62, 750–762.
Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah: Lawrence Erlbaum.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (1999). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271–315.
Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences, 14(2), 161–199.
Christensen, W. M., & Thompson, J. R. (2012). Investigating graphical representations of slope and derivative without a physics context. Physical Review Special Topics – Physics Education Research, 8, 023101.
diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2 & 3), 105–225.
Dreyfus, T., & Eisenberg, T. (1990). On difficulties with diagrams: Theoretical issues. In G. Booker, P. Cobb, & T. N. De Mendicuti (Eds.), Proceedings of the fourteenth annual conference of the International Group for the Psychology of mathematics education (Vol. 1, pp. 27–36). Oaxtepex: PME.
Erickson, T. (2006). Stealing from physics: Modeling with mathematical functions in data-rich contexts. Teaching Mathematics and its Applications: An International Journal of the IMA, 25, 23–32.
Forster, P. A. (2004). Graphing in physics: Processes and sources of error in tertiary entrance examinations in Western Australia. Research in Science Education, 34, 239–265.
Graham, T., & Sharp, J. (1999). An investigation into able students’ understanding of motion graphs. Teaching Mathematics Applications, 18, 128–135.
Hadjidemetriou, C., & Williams, J. S. (2002). Children’s graphical conceptions. Research in Mathematics Education, 4(1), 69–87.
Hammer, D., Elby, A., Scherr, R. E., & Redish, E. F. (2005). Resources, framing, and transfer. In J. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 89–120). Greenwich: Information Age Publishing.
Ivanjek, L., Susac, A., Planinic, M., Andrasevic, A., & Milin-Sipus, Z. (2016). Student reasoning about graphs in different contexts. Physical Review Special Topics – Physics Education Research, 12, 010106.
Kerslake, D. (1981). Graphs. In K. M. Hart (Ed.), Children’s understanding of mathematics: 11–16 (pp. 120–136). London: John Murray.
Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990). Functions, graphs, and graphing: Tasks, learning, and teaching. Review of Educational Research, 60(1), 1–64.
Linacre, J. M. (2006). WINSTEPS Rasch measurement computer program. Available at http://www.winsteps.com
Linn, M. C., Eylon, B., & Davis, E. A. (2004). The knowledge integration perspective on learning. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 29–46). Mahwah: Lawrence Erlbaum Associates.
McDermott, L. C., Rosenquist, M. L., & van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55, 503–513.
Michelsen, C. (2005). Expanding the domain – Variables and functions in an interdisciplinary context between mathematics and physics. In A. Beckmann, C. Michelsen, & B. Sriraman (Eds.), Proceedings of the 1st International symposium of mathematics and its connections to the arts and sciences (pp. 201–214). Schwäbisch Gmünd: The University of Education.
Nguyen, D. H., & Rebello, N. S. (2011). Students’ understanding and application of the area under the curve concept in physics problems. Physical Review Special Topics – Physics Education Research, 7, 010112.
Özdemir, G., & Clark, D. B. (2007). An overview of conceptual change theories. Eurasia Journal of Mathematics, Science & Technology Education, 3(4), 351–361.
Planinic, M., Milin-Sipus, Z., Katic, H., Susac, A., & Ivanjek, L. (2012). Comparison of student understanding of line graph slope in physics and mathematics. International Journal of Science and Mathematics Education, 10(6), 1393–1414.
Planinic, M., Ivanjek, L., Susac, A., & Milin-Sipus, Z. (2013). Comparison of university students’ understanding of graphs in different contexts. Physical Review Special Topics – Physics Education Research, 9, –020103.
Sherin, B. L. (2001). How students understand physics equations. Cognition and Instruction, 19(4), 479–541.
Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4(1), 45–69.
Wemyss, T., & van Kampen, P. (2013). Categorization of first-year university students’ interpretations of numerical linear distance-time graphs. Physical Review Special Topics – Physics Education Research, 9, 010107.
Acknowledgments
This research is part of the Lise Meitner Project M1737-G22 “Development of Graph Inventory”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Ivanjek, L., Planinic, M., Hopf, M., Susac, A. (2017). Student Difficulties with Graphs in Different Contexts. In: Hahl, K., Juuti, K., Lampiselkä, J., Uitto, A., Lavonen, J. (eds) Cognitive and Affective Aspects in Science Education Research. Contributions from Science Education Research, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-58685-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-58685-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58684-7
Online ISBN: 978-3-319-58685-4
eBook Packages: EducationEducation (R0)