Why do Plants Wilt? Investigating Students’ Understanding of Water Balance in Plants with External Representations at the Macroscopic and Submicroscopic Levels
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University of Ljubljana, Faculty of Education, Department of Biology, Chemistry and Home Economics, Ljubljana, SLOVENIA
The Educational Research Institute, Ljubljana, SLOVENIA
University of Ljubljana, Faculty of Arts, Department of Psychology, Ljubljana, SLOVENIA
Online publish date: 2018-03-21
Publish date: 2018-03-21
EURASIA J. Math., Sci Tech. Ed 2018;14(6):2265–2276
In order to understand water balance in plants, students must understand the relation between external representations at the macroscopic, microscopic, and submicroscopic levels. This study investigated how Slovenian students (N = 79) at the primary, secondary, and undergraduate tertiary levels understand water balance in plants. The science problem consisted of a text describing the setting, visualizations of the process occurring in a wilted plant stem, and five tasks. To determine students’ visual attention to the various elements of the tasks, we used eye tracking and focused on the total fixation duration in particular areas of interest. As expected, primary school students showed less knowledge and understanding of the process than the secondary school and university students did. Students with correct answers spent less time observing the biological phenomena displayed at the macroscopic and submicroscopic levels than those with incorrect answers, and more often provided responses that combined the macro-, micro-, and submicroscopic levels of thought. Learning about difficult scientific topics, such as the water balance in plants, with representations at the macroscopic and submicroscopic levels can be either helpful or confusing for learners, depending on their expertise in using multiple external representations, which is important to consider in biology and science education.
1. Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33(2), 131–152.
2. Ainsworth, S. (2008). The educational value of multiple-representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & M. Nakhleh (Eds.), Visualization: Theory and practice in science education (pp. 191–208). Dordrecht: Springer.
3. AlHarbi, N. N., Treagust, D. F., Chandrasegaran, A. L., & Won, M. (2015). Influence of particle theory conceptions on pre-service science teachers’ understanding of osmosis and diffusion. Journal of Biological Education, 49(3), 232–245.
4. Bačnik, A., Bukovec, N., Vrtačnik, M., Poberžnik, A., Križaj, M., Stefanovik, V., & Preskar, S. (2011). Program osnovna šola. Kemija. Učni načrt [Primary school programme. Chemistry. Syllabus]. Ljubljana: Zavod RS za šolstvo. Retrieved from
5. Ballard, D. H., Hayhoe, M. M., Pook, P. K., & Rao, R. P. (1997). Deictic codes for the embodiment of cognition. Behavioral and Brain Sciences, 20(4), 723–742.
6. Chen, S.-C., She, H.-C., Chuang, M.-H., Wu, J.-Y., Tsai, J.-L., & Jung, T.-P. (2014). Eye movements predict students’ computer-based assessment performance of physics concepts in different presentation modalities. Computers & Education, 74, 61-72.
7. Cook, M., Carter, G., & Wiebe, E. N. (2008). The interpretation of cellular transport graphics by students with low and high prior knowledge. International Journal of Science Education, 30(2), 239–261.
8. Cook, M., Wiebe, E. N., & Carter, G. (2008). The influence of prior knowledge on viewing and interpreting graphics with macroscopic and molecular representations. Science Education, 92(5), 848–867.
9. Datta, S., & Dutta Roy, D. (2015). Abstract reasoning and spatial visualization in formal operational stage [sic]. International Journal of Scientific and Research Publications, 5(10), 1–6.
10. Ferk Savec, V., Hrast, Š., Devetak, I., & Torkar, G. (2016). Beyond the use of an explanatory key accompanying submicroscopic representations. Acta Chimica Slovenica, 63(4), 864–873.
11. Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: a meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23, 523–552.
12. Hannus, M., & Hyönä, J. (1999). Utilization of illustrations during learning of science textbook passages among low- and high-ability children. Contemporary Educational Psychology, 24, 95–123.
13. Hasni, A., Roy, P., & Dumais, N. (2016). The teaching and learning of diffusion and osmosis. Eurasia Journal of Mathematics, Science and Technology Education, 12(6), 1507–1531.
14. Hegarty, M., Mayer, R. E., & Monk, C. A. (1995). Comprehension of arithmetic word problems: A comparison of successful and unsuccessful problem solvers. Journal of Educational Psychology, 87(1), 18–32.
15. Henderson, J. M. (2007). Regarding scenes. Current Directions in Psychological Science, 16(4), 219–222.
16. Hinze, S. R., Rapp, D. N., Williamson, V. M., Shultz, M. J., Deslongchamps, G., & Williamson, K. C. (2013). Beyond ball-and-stick: Students’ processing of novel STEM visualizations. Learning and Instruction, 26, 12–21.
17. Inhelder, B., & Piaget, J. (1958). The growth of logical thinking: From childhood to adolescence. Oxon: Routledge.
18. Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7(2), 75–83.
19. Johnstone, A. H., & Mahmoud, N. A. (1980). Isolating topics of high perceived difficulty in school biology. Journal of Biological Education, 14(2), 163–166.
20. Just, M. A., & Carpenter, P. A. (1976). Eye fixations and cognitive processes. Cognitive Psychology, 8(4), 441–480.
21. Just, M. A., & Carpenter, P. A. (1980). A theory of reading: From eye fixations to comprehension. Psychological Review, 87(4), 329-354.
22. Lai, M.-L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T.-C., Lee, S. W.-Y., ... Tsai, C.-C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.
23. Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.
24. Lin, Y.-Y., Holmquist, K., Miyoshi, K., & Ashida, H. (2017). Effects of detailed illustrations on science learning. Instructional Science, 45, 557–581.
25. Malińska, L., Rybska, E., Sobieszczuk-Nowicka, E., & Adamiec, M. (2016). Teaching about water relations in plant cells: An uneasy struggle. CBE-Life Sciences Education, 15(4), ar78.
26. Mangiafico, S. S. (2017). Package ‘rcompanion’. Retrieved from
27. Marek, E. A., Cowan, C. C., & Cavallo, A. M. (1994). Students’ misconceptions about diffusion: How can they be eliminated? The American Biology Teacher, 74–77.
28. Odom, A. L. (1995). Secondary & college biology students’ misconceptions about diffusion & osmosis. The American Biology Teacher, 409–415.
29. Odom, A. L., & Barrow, L. H. (1995). Development and application of a two‐tier diagnostic test measuring college biology students’ understanding of diffusion and osmosis after a course of instruction. Journal of Research in Science Teaching, 32(1), 45–61.
30. Odom, A. L., & Kelly, P. V. (2001). Integrating concept mapping and the learning cycle to teach diffusion and osmosis concepts to high school biology students. Science Education, 85(6), 615–635.
31. Panizzon, D. (2003). Using a cognitive structural model to provide new insights into students’ understandings of diffusion. International Journal of Science Education, 25(12), 1427–1450.
32. R Core Team. (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from
33. Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372–422.
34. Rayner, K. (2009). The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology, 62(8), 1457–1506.
35. Sanger, M. J., Brecheisen, D. M., & Hynek, B. M. (2001). Can computer animations affect college biology students’ conceptions about diffusion & osmosis? The American Biology Teacher, 63(2), 104–109.
36. She, H. C. (2004). Facilitating changes in ninth grade students’ understanding of dissolution and diffusion through DSLM instruction. Research in Science Education, 34(4), 503–525.
37. Sperelakis, N. (2012). Cell physiology source book: Essentials of membrane biophysics. London: Elsevier.
38. Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145.
39. Susac, A., Bubic, A., Kaponja, J., Planinic, M., & Palmovic, M. (2014). Eye movements reveal students’ strategies in simple equation solving. International Journal of Science and Mathematics Education, 12(3), 555–577.
40. Tai, R. H., Loehr, J. F., & Brigham, F. J. (2006). An exploration of the use of eye-gaze tracking to study problem-solving on standardized science assessments. International Journal of Research & Method in Education, 29(2), 185–208.
41. Tomažič, I., & Vidic, T. (2012). Future science teachers’ understandings of diffusion and osmosis concepts. Journal of Biological Education, 46(2), 66–71.
42. Treagust, D., F., & Tsui, C.-Y. (2013). Conclusion: Contributions of multiple representations to biological education. In D. F. Treagust & C.-Y. Tsui (Eds.), Multiple representations in biological education (pp. 349–367). Dordrecht: Springer.
43. Tsui, C. Y., & Treagust, D. F. (2013). Introduction to multiple representations: Their importance in biology and biological education. In Multiple Representations in Biological Education (pp. 3–18). Dordrecht: Springer.
44. Zuckerman, J. T. (1998). Representations of an osmosis problem. The American Biology Teacher, 27–30.