Skip to main content
SpringerLink
Log in

Population Growth Modelling Simulations: Do They Affect the Scientific Reasoning Abilities of Students?

  • Conference paper
  • First Online:
Computer Supported Education (CSEDU 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1220))

Included in the following conference series:

Abstract

Students need to be able to be able to develop their scientific reasoning skills in secondary schools by collecting data and developing science models. Internationally, a greater number of countries are developing nationwide standards that require the use of hands-on approaches, the development and use of science models by students and include final assessments of their scientific reasoning skills. However, in biology classes this can be difficult due to the nature of the subjects. This paper discusses the use of spreadsheet-based simulations within the context a modelling-based pedagogical unit focused on population growth in introductory secondary level biology classes. The effect of the implementation on students scientific reasoning skills were assessed in terms of scientific reasoning sub-skills as well as Piagetian reasoning levels within the context of a quasi-experimental design study. The findings suggest that the implementation was successful with the treatment cohort usually outperforming the comparison cohort.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mullis, I.V.S., Martin, M.O., Goh, S., Cotter, K. (eds.): TIMSS 2015 Encyclopaedia: Education Policy and Curriculum in Mathematics and Science. Retrieved from Boston College, TIMSS & PIRLS International Study Canter website (2016). http://timssandpirls.bc.edu/timss2015/encyclopedia/

  2. Organisation of Economic Co-operation and Development (OECD, 2016). Low-Performing Students: Why they fall behind and how to help them survive, PISA, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264250246-en

  3. Heaps, A.J., Dawson, T.D., Briggs, J.C., Hansen, M.A., Jensen, J.L.: Deriving population growth models by growing fruit fly colonies. Am. Biol. Teacher 78(3), 221–225 (2016)

    Article  Google Scholar 

  4. Oswald, C., Kwiatkowski, S.: Population growth in Euglena: a student-designed investigation combining ecology, cell biology, and quantitative analysis. Am. Biol. Teacher 73(8), 469–473 (2011)

    Article  Google Scholar 

  5. Huppert, J., Lomask, S.M., Lazarowitz, R.: Computer simulations in the high school: Students’ cognitive stages, science process skills and academic achievement in microbiology. Int. J. Sci. Educ. 24(8), 803–821 (2002)

    Article  Google Scholar 

  6. Passmore, C., Gouvea, J.S., Giere, R.: Models in science and in learning science: focusing scientific practice on sense-making. In: Matthews, M.R. (ed.) International Handbook of Research in History, Philosophy and Science Teaching, pp. 1171–1202. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7654-8_36

    Chapter  Google Scholar 

  7. Coletta, V.P., Phillips, J.A., Steinert, J.J.: Why you should measure your students’ reasoning ability. Phys. Teacher 45, 235–238 (2007)

    Article  Google Scholar 

  8. Moore, J.C., Rubbo, L.J.: Scientific reasoning abilities of nonscience majors in physics-based courses. Phys. Rev. Spec. Topics – Phys. Educ. Res. 8(1), 10106 (2012)

    Article  Google Scholar 

  9. Malone, K.L., Schuchardt, A.M.: Improving students’ performance through the use of simulations and modelling: the case of population growth. In: Lane, H., Zvacek, S., Uhomobhi, J. (eds.) Proceedings of the 11th International Conference on Computer Supported Education – vol. 1, pp. 220–230. Crete, Greece, May 2019

    Google Scholar 

  10. Brody, M.J., Koch, H.: An assessment of 4th-, 8th-, and 11th-grade students’ knowledge related to marine science and natural resource issues. J. Environ. Educ. 21(2), 16–26 (1990)

    Article  Google Scholar 

  11. Munson, B.H.: Ecological misconceptions. J. Environ. Educ. 25(4), 30–34 (1994)

    Article  MathSciNet  Google Scholar 

  12. Griffiths, A.K., Grant, B.A.C.: High school students’ understanding of food webs: identification of learning hierarchy and related misconceptions. J. Res. Sci. Teach. 22(5), 421–436 (1985)

    Article  Google Scholar 

  13. Stammen, A.: The development and validation of the Middle School Life Science Concept Inventory (MS-LSCI) using Rasch analysis. (Doctoral dissertation, Ohio State University) (2018)

    Google Scholar 

  14. KMKSekretariat der Ständigen Konferenz der Kultusminister der Länder in der BRD (Ed.). Bildungsstandards im Fach Biologie für den Mittleren SchulabschlussBiology education standards for the Mittlere Schulabschluss]. München & Neuwied: Wolters Kluwer (2005)

    Google Scholar 

  15. NGSS Lead States: Next Generation Science Standards: For States, By States. The National Academies Press, Washington, DC (2013)

    Google Scholar 

  16. Berber, N.C., Guzel, H.: Fen ve matematik öğretmen adaylarının modellerin bilim ve fendeki rolüne ve amacına ilişkin algıları. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 21, 87–97 (2009)

    Google Scholar 

  17. Henze, I., Van Driel, J., Verloop, N.: The change of science teachers’ personal knowledge about teaching models and modeling in he context of science education reform. Int. J. Sci. Educ. 15(3), 819–1846 (2007)

    Google Scholar 

  18. Justi, R., Gilbert, J.: Teachers’ views on the nature of models. Int. J. Sci. Educ. 25(11), 1369–1386 (2003)

    Article  Google Scholar 

  19. Krell, M., Krüger, D.: Testing models: a key aspect to promote teaching activities related to models and modelling in biology lessons? J. Biol. Educ. 50(2), 160–173 (2016)

    Article  Google Scholar 

  20. Ware, T., Malone, K.L., Irving, K., Mollohan, K.: Models and modeling: an evaluation of teacher knowledge. In: Proceedings from HICE 2017: The 15th Annual Hawaii International Conference on Education. Honolulu, HI, pp. 1834–1842, January 2017

    Google Scholar 

  21. Giere, R.N.: How models are used to represent reality. Philos. Sci. 71, 742–752 (2004)

    Article  Google Scholar 

  22. Svoboda, J., Passmore, C.: The strategies of modeling in biology education. Sci. Educ. 22(1), 119–142 (2013)

    Article  Google Scholar 

  23. Dori, Y.J., Belcher, J.: Learning electromagnetism with visualizations and active learning. In: Gilbert, J. (ed.) Visualization in Science Education, pp. 198–216. Springer, Dordrecht (2005). https://doi.org/10.1007/1-4020-3613-2_11

    Chapter  Google Scholar 

  24. Won, M., Yoon, H., Treagust, D.F.: Students’ learning strategies with multiple representations: explanations of the human breathing mechanism. Sci. Educ. 98(5), 840–866 (2014)

    Article  Google Scholar 

  25. Harrison, A.G., Treagust, D.F.: Learning about atoms, molecules, and chemical bonds: a case study of multiple-model use in grade 11 chemistry. Sci. Educ. 84, 352–381 (2000)

    Article  Google Scholar 

  26. Chang, H., Chang, H.: Scaffolding students’ online critiquing of expert- and peer-generated molecular models of chemical reactions. Int. J. Sci. Educ. 35(12), 2028–2056 (2013). https://doi.org/10.1080/09500693.2012.733978

    Article  Google Scholar 

  27. Dauer, J.T., Momsen, J.L., Speth, E.B., Makohon-Moore, S.C., Long, T.M.: Analyzing change in students’ gene-to-evolution models in college-level introductory biology. J. Res. Sci. Teach. 50(6), 639–659 (2013)

    Article  Google Scholar 

  28. Jackson, J., Dukerich, L., Hestenes, D.: Modeling instruction: an effective model for science education. Sci. Educator 17(1), 10–17 (2008)

    Google Scholar 

  29. Malone, K.L.: Correlations among knowledge structures, force concept inventory, and problem-solving behaviors. Phys. Rev. – Spec. Topics Phys. Educ. Res. 4(2), 20107 (2008)

    Article  Google Scholar 

  30. Malone, K.L., Schunn, C.D., Schuchardt, A.M.: Improving conceptual understanding and representation skills through Excel-based modeling. J. Sci. Educ. Technol. 27(1), 30–44 (2018)

    Article  Google Scholar 

  31. Passmore, C., Stewart, J.: A modeling approach to teaching evolutionary biology in high schools. J. Res. Sci. Teach. 39(3), 185–204 (2002)

    Article  Google Scholar 

  32. Schwarz, C.V., White, B.Y.: Metamodeling knowledge: developing students’ understanding of scientific modeling. Cogn. Instruc. 23(2), 165–205 (2005)

    Article  Google Scholar 

  33. Wynne, C., Stewart, J., Passmore, C.: High school students’ use of meiosis when solving genetics problems. Int. J. Sci. Educ. 23(5), 501–515 (2001)

    Article  Google Scholar 

  34. Lehrer, R., Schauble, L.: Seeding evolutionary thinking by engaging children in modeling its foundations. Sci. Educ. 96(4), 701–724 (2012)

    Article  Google Scholar 

  35. Wells, M., Hestenes, D., Swackhamer, G.: A modeling method for high school physics instruction. Am. J. Phys. 63(7), 606–619 (1995)

    Article  Google Scholar 

  36. Jenkins, J.L., Howard, E.M.: Implementation of Modelling Instruction in a high school chemistry unit on energy and states of matter. Sci. Educ. Int. 30(2), 97–104 (2019)

    Google Scholar 

  37. Malone, K., Reiland, R.: Exploring Newton’s third law. Phys. Teacher 33(6), 410–411 (1995)

    Article  Google Scholar 

  38. Malone, K.L., Schuchardt, A.M.: The efficacy of modelling instruction in chemistry: a case study. In: Proceedings form HICE 2016: The 14th Annual Hawaii International Conference on Education, pp. 1513–1518. Honolulu, HI (2016)

    Google Scholar 

  39. Malone, K.L., Schuchardt, A.M., Sabree, Z.: Models and modeling in evolution. In: Harms, U., Reiss, M. (eds.) Evolution Education Re-considered, pp. 207–226. Springer, UK (2019). https://doi.org/10.1007/978-3-030-14698-6_12

    Chapter  Google Scholar 

  40. D’Angelo, C., Rutstein, D., Harris, C., Bernard, R., Borokhovski, E., Haertel, G.: Simulations for STEM Learning: Systematic Review and Meta-analysis. SRI International, Menlo Park (2014)

    Google Scholar 

  41. Smetana, L.K., Bell, R.L.: Computer simulations to support science instruction and learning: a critical review of the literature. Int. J. Sci. Educ. 34(9), 1337–1370 (2012)

    Article  Google Scholar 

  42. Wilensky, U., Reisman, K.: Thinking like a wolf, a sheep, or a firefly: learning biology through constructing and testing computational theories - an embodied modeling approach. Cogn. Instruct. 24(2), 171–209 (2006)

    Article  Google Scholar 

  43. Donnelly, D.F., Namdar, B., Vitale, J.M., Lai, K., Linn, M.C.: Enhancing student explanations of evolution: comparing elaborating and competing theory prompts. J. Res. Sci. Teach. 53(9), 1341–1363 (2016)

    Article  Google Scholar 

  44. Kuhn, D.: Children and adults as intuitive scientists. Psychol. Rev. 96, 674–689 (1989)

    Article  Google Scholar 

  45. Lawson, A.E.: The nature and development of scientific reasoning. Int. J. Sci. Math. Educ. 2(3), 307–338 (2004)

    Article  Google Scholar 

  46. Kuhn, D., Dean Jr., D.: Connecting scientific reasoning and causal inference. J. Cogn. Dev. 5(2), 261–288 (2004)

    Article  Google Scholar 

  47. Russ, R.S., Coffey, J.E., Hammer, D., Hutchison, P.: Making classroom assessment more accountable to scientific reasoning: a case for attending to mechanistic thinking. Sci. Educ. 93(5), 875–891 (2009)

    Article  Google Scholar 

  48. Lawson, A.E.: Developing Scientific Reasoning Patterns in College Biology. NSTA Press. Virginia (2006)

    Google Scholar 

  49. Klahr, D.: Exploring Science: The Cognition and Development of Discovery Processes. MIT Press, Cambridge (2002)

    Google Scholar 

  50. Zimmerman, C.: The development of scientific reasoning skills. Dev. Rev. 20(1), 99–149 (2000)

    Article  Google Scholar 

  51. Lawson, A.E., Banks, D.L., Logvin, M.: Self-efficacy, reasoning ability, and achievement in college biology. J. Res. Sci. Teach.: Official J. Natl. Assoc. Res. Sci. Teach. 44(5), 706–724 (2007)

    Article  Google Scholar 

  52. Coletta, V.P., Phillips, J.A.: Interpreting FCI scores: normalized gain, preinstruction scores, and scientific reasoning ability. Am. J. Phys. 73(12), 1172–1182 (2005)

    Article  Google Scholar 

  53. Ding, L.: Verification of causal influences of reasoning skills and epistemology on physics conceptual learning. Phys. Rev. Spec. Topics-Phys. Educ. Res. 10(2), 023101 (2014)

    Article  Google Scholar 

  54. Ding, L., Wei, Z., Mollohan, K.: Does higher education improve student scientific reasoning skills? Int. J. Sci. Math. Educ. 14, 619–634 (2016)

    Article  Google Scholar 

  55. Lawson, A.E.: The development and validation of a classroom test of formal reasoning. J. Res. Sci. Teach. 15, 11–24 (1978)

    Article  Google Scholar 

  56. Stammen, A., Malone, K.L., Irving, K.E.: Effects of Modeling Instruction professional development on biology teachers’ scientific reasoning skills. Educ. Sci. 8(3) (2018). https://doi.org/10.3390/educsci8030119

  57. Ben-Chaim, D., Fey, J.T., Fitzgerald, W.M., Benedetto, C., Miller, J.: Proportional reasoning among 7th grade students with different curricular experiences. Educ. Stud. Math. 36(3), 247–273 (1998)

    Article  Google Scholar 

  58. Klahr, D., Li, J.: Cognitive research and elementary science instruction: from the laboratory, to the classroom, and back. J. Sci. Educ. Technol. 14(2), 217–238 (2005)

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially funded by a grant under the federally funded Math Science Partnership State Grants Program, under Grant number OH160505 and OH160511. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.

Author information

Authors and Affiliations

  1. Graduate School of Education, Nazarbayev University, Kabanbay Batyr 53, Astana, 01000, Kazakhstan

    Kathy Lea Malone

  2. Department of Biology Teaching and Learning, University of Minnesota, Minneapolis, MN, 53455, USA

    Anita Schuchardt

Authors
  1. Kathy Lea Malone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anita Schuchardt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kathy Lea Malone .

Editor information

Editors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    H. Chad Lane

  2. Anderson Academic Commons, University of Denver, Denver, CO, USA

    Susan Zvacek

  3. School of Engineering, University of Ulster, Newtownabbey, UK

    James Uhomoibhi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Malone, K.L., Schuchardt, A. (2020). Population Growth Modelling Simulations: Do They Affect the Scientific Reasoning Abilities of Students?. In: Lane, H.C., Zvacek, S., Uhomoibhi, J. (eds) Computer Supported Education. CSEDU 2019. Communications in Computer and Information Science, vol 1220. Springer, Cham. https://doi.org/10.1007/978-3-030-58459-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58459-7_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58458-0

  • Online ISBN: 978-3-030-58459-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation