Skip to main content
Log in

A Resource for Eliciting Student Alternative Conceptions: Examining the Adaptability of a Concept Inventory for Natural Selection at the Secondary School Level

  • Published:
Research in Science Education Aims and scope Submit manuscript

Abstract

The Conceptual Inventory of Natural Selection (CINS) is an example of a research-based instrument that assesses conceptual understanding in an area that contains well-documented alternative conceptions. Much of the CINS’s use and original validation has been relegated to undergraduate settings, but the information learned from student responses on the CINS can also potentially be a useful resource for teachers at the secondary level. Because of its structure, the CINS can have a role in eliciting alternative conceptions and induce deeper conceptual understanding by having student ideas leveraged during instruction. In a first step toward this goal, the present study further investigated the CINS’s internal properties by having it administered to a group (n = 339) of students among four different biology teachers at a predominantly Latino, economically disadvantaged high school. In addition, incidences of the concept inventory’s use among the teachers’ practices were collected for support of its adaptability at the secondary level. Despite the teachers’ initial enthusiasm for the CINS’s use as an assessment tool in the present study, results from a principal components analysis demonstrate inconsistencies between the original and present validations. Results also reveal how the teachers think CINS items may be revised for future use among secondary student populations.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Anderson, D. L., Fisher, K. M., & Norman, G. J. (2002). Development and evaluation of the conceptual inventory of natural selection. Journal of Research in Science Teaching, 39, 952–978.

    Article  Google Scholar 

  • Anderson, D. L., Fisher, K. M., & Smith, M. U. (2010). Support for the CINS as a diagnostic conceptual inventory: Response to Nehm and Schonfeld (2008). Journal of Research in Science Teaching, 47, 354–357.

    Google Scholar 

  • Beeth, M. E., & Hewson, P. W. (1999). Learning goals in an exemplary science teachers’ practice: cognitive and social factors in teaching for conceptual change. Science Education, 83, 738–760.

    Article  Google Scholar 

  • Black, P., & Wiliam, D. (1998a). Assessment and classroom learning. Assessment in Education, 5, 7–74.

    Article  Google Scholar 

  • Black, P., & Wiliam, D. (1998b). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80, 139–148.

    Google Scholar 

  • Cohen, D. K., Raudenbush, S. W., & Ball, D. L. (2003). Resources, instruction, and research. Educational Evaluation and Policy Analysis, 25, 119–142.

    Article  Google Scholar 

  • Danish, J. A., Peppier, K., Phelps, D., & Washington, D. (2011). Life in the hive: Supporting inquiry into complexity within the zone of proximal development. Journal of Science Education and Technology, 20(5), 454–467.

    Article  Google Scholar 

  • Dickes, A. C., & Sengupta, P. (2013). Learning natural selection in 4th grade with multi-agent-based computational models. Research in Science Education, 43, 921–953.

    Article  Google Scholar 

  • diSessa, A. A. (1994). Speculations on the foundations of knowledge and intelligence. In D. Tirosh (Ed.), Implicit and explicit knowledge: An educational approach (pp. 1–54). Norwood, NJ: Ablex.

    Google Scholar 

  • Duckworth, E. R. (2006). “The having of wonderful ideas” and other essays on teaching and learning (3rd ed.). New York, NY: Teachers College Press.

    Google Scholar 

  • Duncan, R. G., & Hmelo-Silver, C. E. (2009). Learning progressions: Aligning curriculum, instruction, and assessment. Journal of Research in Science Teaching, 46, 606–609.

    Article  Google Scholar 

  • Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: National Academies Press.

    Google Scholar 

  • Elby, A. (2000). What students’ learning of representations tells us about constructivism. Journal of Mathematical Behavior, 19(4), 481–502.

    Article  Google Scholar 

  • Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1319030111.

    Google Scholar 

  • Garvin-Doxas, K., Klymkowsky, M., & Elrod, S. (2007). Building, using, and maximizing the impact of concept inventories in the biological sciences: Report on a National Science Foundation-sponsored conference on the construction of concept inventories in the biological sciences. CBE—Life Sciences Education, 6, 277–282.

    Article  Google Scholar 

  • Gregory, T. R. (2009). Understanding natural selection: essential concepts and common misconceptions. Evolution: Education and Outreach, 2, 156–175.

    Google Scholar 

  • Hake, R. R. (1998). Interactive-engagement vs. traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66, 64–74.

    Article  Google Scholar 

  • Hatano, G., & Inagaki, K. (1994). Young children’s naïve theory of biology. Cognition, 50, 171–188.

    Article  Google Scholar 

  • Heller, P., & Huffman, D. (1995). Interpreting the force concept inventory: A reply to Hestenes and Halloun. The Physics Teacher, 33, 503–511.

    Article  Google Scholar 

  • Hennessey, M. G. (2003). Metacognitive aspects of students’ reflective discourse: implications for intentional conceptual change teaching and learning. In G. M. Sinatra & P. R. Pintrich (Eds.), Intentional conceptual change (pp. 103–132). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Hestenes, D., & Halloun, I. (1995). Interpreting the force concept inventory: A response to March 1995 critique by Huffman and Heller. The Physics Teacher, 33, 502–506.

    Article  Google Scholar 

  • Hestenes, D., Wells, M., & Swackhammer, G. (1992). Force concept inventory. The Physics Teacher, 30, 141–158.

    Article  Google Scholar 

  • Hovardas, T., & Korfiatis, K. J. (2006). Word associations as a tool for assessing conceptual change in science education. Learning and Instruction, 16, 416–432.

    Article  Google Scholar 

  • Huffman, D., & Heller, P. (1995). What does the force concept inventory actually measure? The Physics Teacher, 33, 138–143.

    Article  Google Scholar 

  • Jackson, J. E. (1991). A user’s guide to principal components. New York, NY: Wiley.

    Book  Google Scholar 

  • Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas (automated readability index, fog count, and Flesch reading ease formula) for navy enlisted personnel (Research Branch Report 8–75). Millington, TN: Naval Technical Training Command.

    Google Scholar 

  • Klymkowsky, M. W., Garvin-Doxas, K., & Zeilik, M. (2003). Bioliteracy and teaching efficacy: What biologists can learn from physicists. Cell Biology Education, 2, 155–161.

    Article  Google Scholar 

  • Larkin, D. (2012). Misconceptions about “misconceptions”: Preservice secondary science teachers’ views on the value and role of student ideas. Science Education, 96, 927–959.

    Article  Google Scholar 

  • 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, NJ: Wiley.

    Google Scholar 

  • Lehrer, R., Schauble, L., Carpenter, S., & Penner, D. (2000). The inter-related development of inscriptions and conceptual understanding. In P. Cobb, E. Yackel, & K. McClain (Eds.), Symbolizing and communicating in mathematics classrooms: perspectives on discourse, tools, and instructional design. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Lucero, M.M., & Petrosino, A.J. (2012). Secondary teachers’ predictions of students’natural selection alternative conceptions. Paper presented at the annualmeeting of the American Educational Research Association, Vancouver, BC, Canada.

  • Marbach-Ad, G., McAdams, K. C., Benson, S., Briken, V., Cathcart, L., Chase, M., & Smith, A. C. (2010). A model for using a concept inventory as a tool for students’ assessment and faculty professional development. CBE—Life Sciences Education, 9, 408–416. doi:10.1187/cbe.10-05-0069.

    Article  Google Scholar 

  • Mazur, E. (1997). Peer instruction: A user’s manual. Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Mintzes, J., Wandersee, J., & Novak, J. (2000). Assessing science understanding: A human constructivist view. San Diego, CA: Academic.

    Google Scholar 

  • Nadelson, L. S., & Southerland, S. A. (2010). Development and preliminary evaluation of the measure of understanding of macroevolution: Introducing the MUM. The Journal of Experimental Education, 78, 151–190.

    Article  Google Scholar 

  • National Research Council. (2001). Knowing what students know: The science and design of educational assessment. In J. W. Pellegrino, N. Chudowsky, & R. Glaser (Eds.), Committee on the Foundations of Assessment, Board on Testing and Assessment, Center for Education. Washington, DC: National Academies Press.

    Google Scholar 

  • National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. In R.A. Duschl, H.A. Schweingruber, & A.W. Shouse (Eds.), Committee on Science Learning Kindergarten Through Eighth Grade. Washington, DC: National Academies Press.

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Committee on a Conceptual Framework for New K-12 Science Education Standards, Board on Science Education, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press.

    Google Scholar 

  • Nehm, R. H., & Schonfeld, I. S. (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, 1131–1160.

    Article  Google Scholar 

  • Nehm, R. H., & Schonfeld, I. S. (2010). The future of natural selection knowledge measurement: A reply to Anderson et al. (2010). Journal of Research in Science Teaching, 47, 358–362.

    Google Scholar 

  • Novick, L. R., & Catley, K. M. (2012). Assessing students’ understanding of macroevolution: Concerns regarding the validity of the MUM. International Journal of Science Education, 34(17), 2679–2703.

    Article  Google Scholar 

  • Piaget, J. (1983). Piaget’s theory. In P. Mussen (Ed.), Handbook of child psychology (4th ed., Vol. 1). New York, NY: Wiley.

    Google Scholar 

  • Richardson, J. T. E. (2005). Instruments for obtaining student feedback: A review of the literature. Assessment & Evaluation in Higher Education, 30, 387–415.

    Article  Google Scholar 

  • Rivet, A. E., & Krajcik, J. S. (2008). Contextualizing instruction: Leveraging students’ prior knowledge and experiences to foster understanding of middle school science. Journal of Research in Science Teaching, 45, 79–100.

    Article  Google Scholar 

  • Scott, P., Asoko, H., & Leach, J. (2007). Student conceptions and conceptual learning in science. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 31–56). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Smith, J.P., diSessa, A.A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. The Journal of the Learning Sciences, 3(2), 115–163.

  • Smith, J. I., & Tanner, K. (2010). The problem of revealing how students think: concept inventories and beyond. CBE Life Sciences Education, 9, 1–5.

    Article  Google Scholar 

  • Smith, M.K., Wood, W.B., & Knight, J.K. (2008). The genetics concept assessment: A new concept inventory for gauging student understanding of genetics. CBE Life Sciences Education, 7, 422–230.

  • State Education Agency (2015). 2014 comprehensive biennial report on (state’s) public schools (Document No. GE15 601 04). (Publication information withheld to protect state’s anonymity).

  • Thornton, R. K., & Sokoloff, D. R. (1998). Assessing student learning of Newton’s laws: The force and motion conceptual evaluation and the evaluation of active learning laboratory and lecture curricula. American Journal of Physics, 66, 338–352.

    Article  Google Scholar 

  • van Zee, E. H., Iwasyk, M., Kurose, A., Simpson, D., & Wild, J. (2001). Student and teacher questioning during conversations about science. Journal of Research in Science Teaching, 38(2), 159–190.

    Article  Google Scholar 

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24(2), 171–209.

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the teachers and students of the study site for allowing access and participating in this study. We also thank the study site’s assigned undergraduate tutors from the local university for their assistance with the on-site organization of data sources. This research was made possible with the major support of James Barufaldi and the Center for STEM Education at the University of Texas at Austin and supplemental support from the National Science Foundation (Grant Nos. DRL-0833726 and MSP-0831811). Our sincere appreciation goes to Angelo Collins and anonymous reviewers for their comments on the drafts of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Margaret M. Lucero.

Electronic Supplementary Material

ESM 1

(PDF 302 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lucero, M.M., Petrosino, A.J. A Resource for Eliciting Student Alternative Conceptions: Examining the Adaptability of a Concept Inventory for Natural Selection at the Secondary School Level. Res Sci Educ 47, 705–730 (2017). https://doi.org/10.1007/s11165-016-9524-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11165-016-9524-z

Keywords

Navigation