Research in Science Education

, Volume 43, Issue 3, pp 1107–1133 | Cite as

Learning the Language of Evolution: Lexical Ambiguity and Word Meaning in Student Explanations

  • Meghan A. Rector
  • Ross H. Nehm
  • Dennis Pearl


Our study investigates the challenges introduced by students’ use of lexically ambiguous language in evolutionary explanations. Specifically, we examined students’ meaning of five key terms incorporated into their written evolutionary explanations: pressure, select, adapt, need, and must. We utilized a new technological tool known as the Assessment Cascade System (ACS) to investigate the frequency with which biology majors spontaneously used lexically ambiguous language in evolutionary explanations, as well as their definitions and explanations of what they meant when they used such terms. Three categories of language were identified and examined in this study: terms with Dual Ambiguity, Incompatible Ambiguity, and Unintended Ambiguity. In the sample of 1282 initial evolutionary explanations, 81 % of students spontaneously incorporated lexically ambiguous language at least once. Furthermore, the majority of these initial responses were judged to be inaccurate from a scientific point of view. While not significantly related to gender, age, or reading/writing ability, students’ use of contextually appropriate evolutionary language (pressure and adapt) was significantly associated with academic performance in biology. Comparisons of initial responses to follow-up responses demonstrated that the majority of student explanations were not reinterpreted after consideration of the follow-up response; nevertheless, a sizeable minority was interpreted differently. Most cases of interpretation change were a consequence of resolving initially ambiguous responses, rather than a change of accuracy, resulting in an increased understanding of students’ evolutionary explanations. We discuss a series of implications of lexical ambiguity for evolution education.


Discourse Evolution Language Lexical ambiguity Multivalent terms Biology education Undergraduates 



We thank Judy Ridgway and Minsu Ha for help with data collection and analysis, Silas Baronda and Mike Gee for helping to develop and program the ACS, and the National Science Foundation REESE program (DRL 0909999) and a TeLR grant from The Ohio State University for funding parts of this work. We also thank the anonymous reviewers for helping to improve our work, and Dr. Jennifer Kaplan for insightful discussions of lexical ambiguity. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the NSF.


  1. Abrams, E., Southerland, S., & Cummins, C. (2001). The how’s and why’s of biological change: how learners neglect physical mechanisms in their search for meaning. International Journal of Science Education, 23(12), 1271–1281.CrossRefGoogle Scholar
  2. Baker, R. R. (2009). Bird predation as a selective pressure on the immature stages of the cabbage butterflies. Pieris rapae and P. brassicae. Journal of Zoology, 162, 43–59.CrossRefGoogle Scholar
  3. Bishop, B., & Anderson, C. (1990). Student conceptions of natural selection and its role in evolution. Journal of Research in Science Teaching, 27(5), 415–427.CrossRefGoogle Scholar
  4. Brookes, D. T., & Etkina, E. (2007). Using conceptual metaphor and functional grammar to explore how language used in physics affects student learning. Physical Review Special Topics—Physics Education Research. doi: 10.1103/PhysRevSTPER.3.010105.
  5. Brumby, M. N. (1984). Misconceptions about the concept of natural selection by medical biology students. Science Education, 68, 493–503.CrossRefGoogle Scholar
  6. Campbell, N. A., & Reece, J. B. (2008). Biology (8th ed.). San Francisco: Pearson Benjamin Cummings.Google Scholar
  7. 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.CrossRefGoogle Scholar
  8. Chi, M. T. H., & Feltovich, P. J. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152.CrossRefGoogle Scholar
  9. Clerk, D., & Rutherford, M. (2000). Language as a confounding variable in the diagnosis of misconceptions. International Journal of Science Education, 22(7), 703–717.CrossRefGoogle Scholar
  10. Clough, E. E., & Wood-Robinson, C. (1985). How secondary students interpret instances of biological adaptation. Journal of Biological Education, 19, 125–130.CrossRefGoogle Scholar
  11. Dagher, Z., & Crossman, G. (1992). Verbal explanations given by science teachers: their nature and implications. Journal of Research in Science Teaching, 29(4), 361–374.CrossRefGoogle Scholar
  12. Driver, R., Squires, A., Rushworth, P., & Wood-Robinson, V. (1994). Making sense of secondary science. New York: Routledge.Google Scholar
  13. Durkin, K., & Shire, B. (1991). Lexical ambiguity in mathematical contexts. In K. Durkin & B. Shire (Eds.), Language in mathematical education: Research and practice (pp. 71–84). Philadelphia: Open University Press.Google Scholar
  14. Grimberg, B. I., & Hand, B. (2009). Cognitive pathways: analysis of students’ written texts for science understanding. International Journal of Science Education, 31(4), 503–521.CrossRefGoogle Scholar
  15. Gunkel, K. L., Covitt, B. A., & Anderson, C. W. (2009). Learning a secondary discourse: shifts from force-dynamic to model-based reasoning in understanding water in socio-ecological systems. Iowa City, IA: Paper presented at the Learning Progressions in Science Conference.Google Scholar
  16. Hand, B., & Choi, A. (2010). Examining the impact of student use of multiple representations in constructing arguments in organic chemistry laboratory classes. Research in Science Education, 40, 29–44.Google Scholar
  17. Hand, B., & Prain, V. (2006). Moving from border crossing to convergence of perspectives in language and science literacy research and practice. International Journal of Science Education, 28(2–3), 101–107.CrossRefGoogle Scholar
  18. Hayworth, R. M. (1999). Procedural and conceptual knowledge of expert and novice students for the solving of a basic problem in chemistry. International Journal of Science Education, 21(2), 195–211.CrossRefGoogle Scholar
  19. Heckler, A., Scaife, T. M., & Sayre, E. C. (2010). Response times and misconception-like responses to science questions. In S. Ohlsson, & R. Catrambone (Eds.), Proceedings of the 32 annual conference of the cognitive science society (pp. 139–144). Austin, TX: Cognitive Science Society.Google Scholar
  20. Hestenes, D., Wills, M., & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141–158.CrossRefGoogle Scholar
  21. Hewson, M. G., & Hewson, P. W. (1983). Effect of instruction using students’ prior knowledge and conceptual change strategies on science education. Journal of Research in Science Teaching, 20, 731–743.CrossRefGoogle Scholar
  22. Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: expert-novice understanding of complex systems. The Journal of the Learning Sciences, 16(3), 307–331.CrossRefGoogle Scholar
  23. Hodges, K. E. (2008). Defining the problem: terminology and progress in ecology. Frontiers in Ecology and the Environment, 6(1), 35–42.CrossRefGoogle Scholar
  24. Jungwirth, E. (1975). The problem of teleology in biology as a problem of biology-teacher education. Journal of Biological Education, 9, 243–246.CrossRefGoogle Scholar
  25. Kaplan, J., Fisher, D.G., & Rogness, N.T. (2009) Lexical ambiguity in statistics: What do students know about the words association, average, confidence, random and spread? Journal of Statistics Education (Online), 17, 3.
  26. Kaplan, J., Fisher, D.G., & Rogness, N.T. (2010). Lexical ambiguity in statistics: How students use and define the words: association, average, confidence, random, and spread. Journal of Statistics Education (Online), 18, 2.
  27. Kelmen, D., & Rosset, E. (2009). The human function compunction: teleological explanation in adults. Cognition, 111, 138–143.CrossRefGoogle Scholar
  28. Kintsch, W. (2003). Comprehension: A paradigm for cognition. Cambridge: Cambridge University Press.Google Scholar
  29. Klaassen, C. W. J. M., & Lijnse, P. L. (1996). Interpreting students’ and teachers’ discourse in science classes: An underestimated problem? Journal of Research in Science Teaching, 33(2), 115–134.Google Scholar
  30. Laland, K. N., Odling-Smee, J., & Myles, S. (2010). How culture shaped the human genome: bringing genetics and the human sciences together. Nature Reviews Genetics, 11, 137–148.CrossRefGoogle Scholar
  31. Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy: Supporting development in learning in contexts. In W. Damon, R. M. Lerner, K. A. Renninger, & I. E. Sigel (Eds.), Handbook of child psychology (6th ed., Vol. 4). Hoboken: Wiley.Google Scholar
  32. Lemke, J. L. (1990). Talking science: Language, learning, and values. New Jersey: Ablex Publishing Corporation.Google Scholar
  33. Lemke, J. L. (1998). Analysing verbal data: Principles, methods, and problems. In K. Tobin, & B. Fraser (Eds.), International handbook of science education (pp. 1175–1189). London: Kluwer Academic Publishers.Google Scholar
  34. Lemke, J. L. (2000). Articulation communities: sociocultural perspectives on science education. Journal of Research in Science Teaching, 38(3), 296–316.CrossRefGoogle Scholar
  35. Leung, C. (2005). Mathematical vocabulay: fixers of knowledge or points of exploration. Language and Education, 19(2), 127–135.CrossRefGoogle Scholar
  36. McComas, W. F. (1998). The principal elements of the nature of science: Dispelling the myths. (In W.F. Comas (Ed.), The Nature of Science in Science Education (pp.53-70). Kluwer Academic Publishers.)Google Scholar
  37. Mead, L. S., & Scott, E. C. (2010a). Problem concepts in evolution part I: purpose and design. Evolution Education and Outreach, 3, 78–81.CrossRefGoogle Scholar
  38. Mead, L. S., & Scott, E. C. (2010b). Problem concepts in evolution part II: cause and chance. Evolution Education and Outreach, 3, 261–264.CrossRefGoogle Scholar
  39. Metzgar, D., & Wills, C. (2000). Evidence for the adaptive evolution of mutation rates. Cell, 101, 581–584.CrossRefGoogle Scholar
  40. National Research Council (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academy Press.Google Scholar
  41. Nehm, R. H., & Ha, M. (2011). Item feature effects in evolution assessment. Journal of Research in Science Teaching, 48(3), 237–256.Google Scholar
  42. Nehm, R. H., & Reilly, L. (2007). Biology majors’ knowledge and misconceptions of natural selection. Bioscience, 57(3), 263–272.Google Scholar
  43. Nehm, R. H., & Schonfeld, I. (2008). Measuring knowledge of natural selection: A comparison of the CINS, an open-response instrument, and oral interview. Journal of Research in Science Teaching, 45(10), 1131–1160.Google Scholar
  44. Nehm, R. H., Ha, M., & Mayfield, E. (2011). Transforming biology assessment with machine learning: Automated scoring of written evolutionary explanations. Journal of Science Education and Technology, 21(1), 183–196.Google Scholar
  45. Nehm, R. H., Rector, M., & Ha, M. (2010). “Force talk” in evolutionary explanation: Metaphors and misconceptions. Evolution Education and Outreach, (3), 506–613.Google Scholar
  46. Nehm, R. H., Beggrow, E., Opfer, J., & Ha, M. (2012). Reasoning about natural selection: Diagnosing contextual competency using the ACORNS instrument. The American Biology Teacher, 74(2), 92–98.Google Scholar
  47. Nelson, C. E. (2008). Teaching evolution (and all biology) more effectively: strategies for engagement, critical reasoning, and confronting misconceptions. Integrative and Comparative Biology, 42(2), 213–225.CrossRefGoogle Scholar
  48. Oxford English Dictionary (OED), 2nd Edition (online version) (2010). Select, v. Retrieved January 18, 2011 from
  49. Pitombo, M. A., Almeida, A. M. R., & El-Hani, N. C. (2008). Gene concepts in higher education cell and molecular biology textbooks. Science Education International, 19(2), 219–234.Google Scholar
  50. Roth, W.-M. (2005). Talking science: Language and learning in science classrooms. Maryland: Rowman & Littlefield Publishers, Inc.Google Scholar
  51. Ryan, J. N. (1985a). The language gap: common words with technical meanings. Journal of Chemical Education, 62(12), 1098–1099.CrossRefGoogle Scholar
  52. Ryan, J. N. (1985b). The secret language of science or, radicals in the classroom. The American Biology Teacher, 91–91.Google Scholar
  53. Schramm, J., Wilke, B., Hartley, L., & Anderson, C. (2010) College Student Understanding of Carbon Transformation and Cycling Processes. (Paper presented at National Association for Research in Science Teaching, Philadelphia, PA).Google Scholar
  54. Scott, P. (1998). Teacher talk and meaning making in science classrooms: a Vygotskian analysis and review. Studies in Science Education, 32(1), 45–80.CrossRefGoogle Scholar
  55. Settlage, J., & Jensen, M. (1996). Investigating the inconsistencies in college student responses to natural selection test questions. Electronic Journal of Science Education, 1, 1. Retrieved October 05, 2010 from
  56. Snyder, J. L. (2000). An investigation of the knowledge structures of experts, intermediates, and novices in physics. International Journal of Science Education, 22(9), 979–992.CrossRefGoogle Scholar
  57. Southerland, S. A., Abrams, E., Cummins, C. L., & Anzelmo, J. (2001). Understanding students’ explanations of biological phenomena: conceptual frameworks or p-prims? Science Education, 85, 328–348.CrossRefGoogle Scholar
  58. Stahl, S. A. (2003). Words are learned incrementally over multiple exposures. American Educator, 27, 18–19.Google Scholar
  59. Talanquer, V. (2009). On cognitive constraints and learning progressions: the case of “structure of matter. International Journal of Science Education, 31(15), 2123–2136.CrossRefGoogle Scholar
  60. Treagust, D. F. (1988). Development and use of diagnostic tests to evaluate students’ misconceptions in science. International Journal of Science Education, 10(2), 159–169.CrossRefGoogle Scholar
  61. Vygotsky, L. S. (1987). Mind in society: The development of higher psychological processes. Massachusetts: Harvard University Press.Google Scholar
  62. Waldrip, B., Prain, V., & Carolan, J. (2010). Using multi-modal representations to improve learning in junior secondary science. Research in Science Education, 40, 65–80.Google Scholar
  63. Wandersee, J. H. (1988). The terminology problem in biology education: a reconnaissance. The American Biology Teacher, 50, 97–100.CrossRefGoogle Scholar
  64. Wilson, J., & McMeniman, M. (1992). The mediating role of language in effective science learning: teacher-in-action and student perceptions. The Australian Science Teachers Journal, 38(4), 14–18.Google Scholar
  65. Winslow, M.W., Staver, J.R., Scharmann, L.C. (2011). Evolution and personal religious belief: Christian university biology-related majors’ search for reconciliation. Journal of Research in Science Teaching (in press). doi: 10.1002/tea.20417
  66. Yore, L., & Hand, B. (2003). Examining the literacy component of science literacy: 25 years of language arts and science research. International Journal of Science Education, 25(6), 689–725.Google Scholar
  67. Yore, L. D., Bisanz, G. L., & Hand, B. M. (2003). Examining the literacy component of science literacy: 25 years of language arts and science research. International Journal of Science Education, 25(6), 689–725.CrossRefGoogle Scholar
  68. Zemplini, M.-Z., Renken, R., Hocks, J. C. J., Hoogduin, J. M., & Stowe, L. A. (2007). Semantic ambiguity processing in sentence context: Evidence from event-related fMRI. NeuroImage, 34, 1270–1279.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Meghan A. Rector
    • 1
  • Ross H. Nehm
    • 1
  • Dennis Pearl
    • 2
  1. 1.School of Teaching and LearningThe Ohio State UniversityColumbusUSA
  2. 2.Department of StatisticsThe Ohio State UniversityColumbusUSA

Personalised recommendations