Research in Science Education

, Volume 48, Issue 3, pp 549–573 | Cite as

Development and Validation of Measures of Secondary Science Teachers’ PCK for Teaching Photosynthesis

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Abstract

This paper describes procedures by which two types of measures of Pedagogical Content Knowledge (PCK) were developed and validated: (a) PCK Survey and (b) PCK Rubric. Given the topic-specificity of PCK, the measures centered on photosynthesis as taught in high school classrooms. The measures were conceptually grounded in the pentagon model of PCK and designed to measure indispensable PCK that can be applied to any teacher, in any teaching context, for the given topic. Because of the exploratory nature of the study, the measures focus on two key components of PCK: (a) knowledge of students’ understanding in science and (b) knowledge of instructional strategies and representations. Both measures have established acceptable levels of reliability as determined by internal consistency and inter-rater agreement. Evidence related to content validity was gathered through expert consultations, while evidence related to construct validity was collected through analysis of think-aloud interviews and factor analyses. Issues and challenges emerging from the course of the measure development, administration, and validation are discussed with strategies for confronting them. Directions for future research are proposed in three areas: (a) relationships between PCK and teaching experiences, (b) differences in PCK between science teachers and scientists, and (c) relationships between PCK and student learning.

Keywords

Pedagogical Content Knowledge (PCK) Knowledge for teaching Photosynthesis PCK survey PCK rubric Think-aloud interview PCK measures 

References

  1. Abell, S. K. (2007). Research on science teacher knowledge. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 1105–1149). Mahwah: Lawrence Earlbaum Associates.Google Scholar
  2. Abell, S. K. (2008). Twenty years later: does pedagogical content knowledge remain a useful idea? International Journal of Science Education, 30(10), 1405–1416. doi: 10.1080/09500690802187041.CrossRefGoogle Scholar
  3. Academies, U. S. (2006). Rising above the gathering storm: energizing and employing America for a brighter economic future.Google Scholar
  4. American Association for the Advancement of Science (AAAS). (1993). Benchmarks for science literacy. New York: Longman.Google Scholar
  5. Ball, D. L., & Hill, H. C. (2009). Measuring teacher quality in practice. In D. H. Gitomer (Ed.), Measurement issues and assessment for teaching quality (pp. 80–98). Los Angeles: Sage.CrossRefGoogle Scholar
  6. Barnett, J., & Hodson, D. (2000). Pedagogical context knowledge: toward a fuller understanding of what good science teachers know. Science Education, 85, 426–453. doi: 10.1002/sce.1017.CrossRefGoogle Scholar
  7. Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., & Tsai, Y. M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180. doi: 10.3102/0002831209345157.CrossRefGoogle Scholar
  8. Baxter, J. A., & Lederman, N. G. (1999). Assessment and measurement of pedagogical content knowledge. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge (pp. 147–161). Dordrecht: Kluwer Academic Publishers.Google Scholar
  9. Blumer, H. (1969). The methodological position of symbolic interactionism. Symbolic interactionism: perspective and method, 1–60.Google Scholar
  10. Borko, H., & Putnam, R. T. (1996). Learning to teach. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 673–708). New York: Macmillan.Google Scholar
  11. Brennan, R. L. (1992). Generalizability theory. Educational Measurement: Issues and Practice, 11(4), 27–34.CrossRefGoogle Scholar
  12. Bullough, R. V., Jr. (2001). Pedagogical content knowledge circa 1907 and 1987: a study in the history of an idea. Teaching and Teacher Education, 17(6), 655–666. doi: 10.1016/S0742-051X(01)00022-1.CrossRefGoogle Scholar
  13. Calderhead, J. (1996). Teachers: beliefs and knowledge. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 709–725). New York: Macmillan.Google Scholar
  14. Carifio, J., & Perla, R. J. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences, 3(3), 106–116.CrossRefGoogle Scholar
  15. Carter, K. (1990). Teachers’ knowledge and learning to teach. In W. R. Houston (Ed.), Handbook of research on teacher education (pp. 291–310). New York: Macmillan.Google Scholar
  16. Cattell, R. B. (1956). Validation and intensification of the sixteen personality factor questionnaire. Journal of Clinical Psychology, 12(3), 205–214.Google Scholar
  17. Church, A. H. (1993). Estimating the effect of incentives on mail survey response rates: a meta-analysis. Public Opinion Quarterly, 57(1), 62–79.CrossRefGoogle Scholar
  18. Clermont, C. P., Borko, H., & Krajcik, J. S. (1994). Comparative study of the pedagogical content knowledge of experienced and novice chemical demonstrators. Journal of Research in Science Teaching, 31, 419–441. doi: 10.1002/tea.3660310409.CrossRefGoogle Scholar
  19. Cochran, K. F. (1992). Pedagogical content knowledge: teachers’ transformations of subject matter. Research matters… to the science teacher. National Association for Research in Science Education (NARST), Monograph, (5).Google Scholar
  20. Cochran, K. F., Deruiter, J. A., & King, R. A. (1993). Pedagogical content knowing: an integrative model for teacher preparation. Journal of Teacher Education, 44(1), 263–272.CrossRefGoogle Scholar
  21. Davis, E. A. (2004). Knowledge integration in science teaching: analysing teachers’ knowledge development. Research in Science Education, 34(1), 21–53. doi: 10.1023/B:RISE.0000021034.01508.b8.CrossRefGoogle Scholar
  22. De Jong, O., Van Driel, J. H., & Verloop, N. (2005). Preservice teachers’ pedagogical content knowledge of using particle models in teaching chemistry. Journal of Research in Science Teaching, 42, 947–964.CrossRefGoogle Scholar
  23. Dykema, J., Stevenson, J., Klein, L., Kim, Y., & Day, B. (2013). Effects of e-mailed versus mailed invitations and incentives on response rates, data quality, and costs in a web survey of university faculty. Social Science Computer Review, 31(3), 359–370.CrossRefGoogle Scholar
  24. Edwards, P., Roberts, I., Clarke, M., DiGuiseppi, C., Pratap, S., Wentz, R., & Kwan, I. (2002). Increasing response rates to postal questionnaires: systematic review. British Medical Journal, 324, 1183.CrossRefGoogle Scholar
  25. Ekici, F., Ekici, E., & Aydin, F. (2007). Utility of concept cartoons in diagnosing and overcoming misconceptions related to photosynthesis. International Journal of Environmental and Science Education, 2(4), 111–124.Google Scholar
  26. Falk, A. (2012). Teachers learning from professional development in elementary science: reciprocal relations between formative assessment and pedagogical content knowledge. Science Education, 96(2), 265–290. doi: 10.1002/sce.20473.CrossRefGoogle Scholar
  27. Friedrichsen, P., Van Driel, J. H., & Abell, S. K. (2011). Taking a closer look at science teaching orientations. Science Education, 95, 358–376. doi: 10.1002/sce.20428.CrossRefGoogle Scholar
  28. Gess-Newsome, J. (1999). Pedagogical content knowledge: an introduction and orientation. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge (pp. 3–20). Dordrecht: Kluwer Academic Publishers.Google Scholar
  29. Gess-Newsome, J. (2015). A model of teacher professional knowledge and skill including PCK: results of the thinking from the PCK Summit. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 28–42). London: Routledge Press.Google Scholar
  30. Gess-Newsome, J., & Carlson, J. (2013). An international perspective on pedagogical content knowledge. Paper presented at the annual conference of the Association for Science Teacher Education, Charleston, SC.Google Scholar
  31. Gitomer, D. H. (Ed.). (2009). Measurement issues and assessment for teaching quality. SAGE.Google Scholar
  32. Goldberg, A., Russell, M., & Cook, A. (2003). The effect of computers on student writing: a meta-analysis of studies from 1992 to 2002. The Journal of Technology, Learning and Assessment, 2(1).Google Scholar
  33. Green, P. E. (1978). Analyzing multivariate data (p. 38). Hinsdale: Dryden Press.Google Scholar
  34. Grossman, P. L. (1990). The making of a teacher: teacher knowledge and teacher education. New York: Teachers College Press.Google Scholar
  35. Hand, B. (Ed.). (2008). Science inquiry, argument and language. Rotterdam: Sense Publishers.Google Scholar
  36. Hashweh, M. Z. (2005). Teacher pedagogical constructions: a reconfiguration of pedagogical content knowledge. Teachers and Teaching, 11(3), 273–292. doi: 10.1080/13450600500105502.CrossRefGoogle Scholar
  37. Haslam, F., & Treagust, D. F. (1987). Diagnosing secondary students’ misconceptions of photosynthesis and respiration in plants using a two-tier multiple choice instrument. Journal of Biological Education, 21(3), 203–211.CrossRefGoogle Scholar
  38. Hill, H. C., Rowan, B., & Ball, D. L. (2005). Effects of teachers’ mathematical knowledge for teaching on student achievement. American Educational Research Journal, 42(2), 371–406. doi: 10.3102/00028312042002371.CrossRefGoogle Scholar
  39. Howitt, C. (2007). Pre-service elementary teachers’ perceptions of factors in a holistic methods course influencing their confidence in teaching science. Research in Science Education, 37(1), 41–58. doi: 10.1007/s11165-006-9015-8.CrossRefGoogle Scholar
  40. Jüttner, M., & Neuhaus, B. J. (2012). Development of items for a pedagogical content knowledge test based on empirical analysis of pupils’ errors. International Journal of Science Education, 34(7), 1125–1143.CrossRefGoogle Scholar
  41. Kaya, O. N. (2009). The nature of relationships among the components of pedagogical content knowledge of preservice science teachers: ‘ozone layer depletion’ as an example. International Journal of Science Education, 31(7), 961–988. doi: 10.1080/09500690801911326.CrossRefGoogle Scholar
  42. Knapp, T. R. (1990). Treating ordinal scales as interval scales: an attempt to resolve the controversy. Nursing Research, 39, 121–123.CrossRefGoogle Scholar
  43. Kongsved, S. M., Basnov, M., Holm-Christensen, K., & Hjollund, N. H. (2007). Response rate and completeness of questionnaires: a randomized study of Internet versus paper-and-pencil versions. Journal of medical Internet research, 9(3). doi:  10.2196/jmir.9.3.e25.
  44. Krauss, S., Brunner, M., Kunter, M., Baumert, J., Blum, W., Neubrand, M., & Jordan, A. (2008). Pedagogical content knowledge and content knowledge of secondary mathematics teachers. Journal of Educational Psychology, 100(3), 716. doi: 10.1037/0022-0663.100.3.716.CrossRefGoogle Scholar
  45. Lawrence, I. M., & Dorans, N. J. (1987). An assessment of the dimensionality of SAT Mathematical. Paper presented at the annual meeting of the National Council on Measurement in Education, Washington, DC.Google Scholar
  46. Lederman, N. G., & Gess-Newsome, J. (1992). Do subject matter knowledge, pedagogical knowledge, and pedagogical content knowledge constitute the ideal gas law of science teaching? Journal of Science Teacher Education, 3(1), 16–20.CrossRefGoogle Scholar
  47. Lee, H. S., Liu, O. L., & Linn, M. C. (2011). Validating measurement of knowledge integration in science using multiple-choice and explanation items. Applied Measurement in Education, 24(2), 115–136. doi: 10.1080/08957347.2011.554604.CrossRefGoogle Scholar
  48. Loughran, J., Mulhall, P., & Berry, A. (2004). In search of pedagogical content knowledge in science: developing ways of articulating and documenting professional practice. Journal of Research in Science Teaching, 41(4), 370–391. doi: 10.1002/tea.20007.CrossRefGoogle Scholar
  49. Loughran, J. J., Berry, A., & Mulhall, P. (2006). Understanding and developing science teachers’ pedagogical content knowledge. Rotterdam: Sense Publishers.Google Scholar
  50. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84.CrossRefGoogle Scholar
  51. Magnusson, S., Krajcik, J., & Borko, H. (1999). Nature, sources, and development of pedagogical content knowledge for science teaching. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge (pp. 95–132). Netherlands: Springer.Google Scholar
  52. Marsh, H. W. (1994). Confirmatory factor analysis models of factorial invariance: a multifaceted approach. Structural Equation Modeling, 1, 5–34.CrossRefGoogle Scholar
  53. Métioui, A., Matoussi, F., & Trudel, L. (2015). The teaching of photosynthesis in secondary school: a history of the science approach. Journal of Biological Education, 1–15.Google Scholar
  54. Mintzes, J. J., & Wandersee, J. H. (2005). Research in science teaching and learning: a human constructivist view. In J. J. Mintzes, J. H. Wandersee, & J. D. Novak (Eds.), Teaching science for understanding: a human constructivist view (pp. 59–92). San Diego: Academic.CrossRefGoogle Scholar
  55. National Academy of Sciences (2014). STEM integration in K-12 education: status, prospects, and an agenda for research. National Academies Press.Google Scholar
  56. National Research Council. (1996). National science education standards. Washington, DC: National Academy Press.Google Scholar
  57. National Research Council. (2012). A framework for K–12 science education: practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.Google Scholar
  58. National Science Board (2010). Science and engineering indicators 2010, appendix tables. Arlington, VA, USA: National Science Foundation (National Science Board 10-01).Google Scholar
  59. National Science Board (NSB). (2007). A National action plan for addressing the critical needs of the U.S. science, technology, engineering, and mathematics education system. Arlington: National Science Foundation.Google Scholar
  60. Nelson, M. M., & Davis, E. A. (2012). Preservice elementary teachers’ evaluations of elementary students’ scientific models: an aspect of pedagogical content knowledge for scientific modeling. International Journal of Science Education, 34(12), 1931–1959. doi: 10.1080/09500693.2011.594103.CrossRefGoogle Scholar
  61. Nilsson, P. (2008). Teaching for understanding: the complex nature of pedagogical content knowledge in pre‐service education. International Journal of Science Education, 30(10), 1281–1299. doi: 10.1080/09500690802186993.CrossRefGoogle Scholar
  62. Park, S., & Oliver, J. S. (2008). Revisiting the conceptualisation of pedagogical content knowledge (PCK): PCK as a conceptual tool to understand teachers as professionals. Research in Science Education, 38(3), 261–284. Google Scholar
  63. Park, S., & Chen, Y-C. (2012). Mapping out the integration of the components of pedagogical content knowledge (PCK) for teaching photosynthesis and heredity. Journal of Research in Science Teaching, 49(7), 922–941.Google Scholar
  64. Park, S., & Suh, J. (2015). Trajectory from portraying toward assessing PCK: Drives, dilemmas, and directions for future research. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 104–119). London: Routledge Press.Google Scholar
  65. Park, S., Jang, J., Chen, Y-C., & Jung, J. (2011). Is pedagogical content knowledge (PCK) necessary for reformed science teaching?: Evidence from an empirical study. Research in Science Education, 41, 245–260.Google Scholar
  66. Paulhus, D. L., & Vazire, S. (2007). The self-report method. Handbook of research methods in personality psychology, 224–239.Google Scholar
  67. Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: standardized observation can leverage capacity. Educational Researcher, 38(2), 109–119. doi: 10.3102/0013189X09332374.CrossRefGoogle Scholar
  68. Rodriguez, M. C. (2005). Three options are optimal for multiple‐choice items: a meta‐analysis of 80 years of research. Educational Measurement: Issues and Practice, 24(2), 3–13. doi: 10.1111/j.1745-3992.2005.00006.x.CrossRefGoogle Scholar
  69. Ross, P. A., Tronson, D., & Ritchie, R. A. J. (2005). Modelling photosynthesis to increase conceptual understanding. Journal of Biological Education, 40(2), 84–88.CrossRefGoogle Scholar
  70. Roth, K. J. (1990). Developing meaningful conceptual understanding in science. In B. Jones & L. Idol (Eds.), Dimensions of thinking and cognitive instruction (pp. 139–175). Hillsdale: Erlbaum.Google Scholar
  71. Roth, K. J., Garnier, H. E., Chen, C., Lemmens, M., Schwille, K., & Wickler, N. Z. (2011). Videobased lesson analysis: effective science PD for teacher and student learning. Journal of Research in Science Teaching, 48(2), 117–148. doi: 10.1002/tea.20408.CrossRefGoogle Scholar
  72. Rowan, B., Schilling, S. G., Ball, D. L., Miller, R., Atkins-Burnett, S., & Camburn, E. (2001). Measuring teachers’ pedagogical content knowledge in surveys: an exploratory study. Ann Arbor: Consortium for Policy Research in Education, University of Pennsylvania.Google Scholar
  73. Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., & Miller, J. L. (2013). The influence of teachers’ knowledge on student learning in middle school physical science classrooms. American Educational Research Journal, 50(5), 1020–1049.CrossRefGoogle Scholar
  74. Sanders, W. L. (2000). Value-added assessment from student achievement data: opportunities and hurdles CREATE NATIONAL EVALUATION INSTITUTE July 21, 2000. Journal of Personnel Evaluation in Education, 14(4), 329–339. doi: 10.1023/A:1013008006096.CrossRefGoogle Scholar
  75. Schon, D. A. (1983). The reflective practitioner. New York: Basic Books.Google Scholar
  76. Schon, D. A. (1987). Educating the reflective practitioner: toward a new design for teaching and learning in the professions. San Francisco: Jossey-Bass.Google Scholar
  77. Sellmann, D., Liefländer, A. K., & Bogner, F. X. (2015). Concept maps in the classroom: a new approach to reveal students’ conceptual change. The Journal of Educational Research, 108(3), 250–257.CrossRefGoogle Scholar
  78. Shulman, L. (1986). Those who understand: knowledge growth in teaching. Educational Researcher, 15(1), 4–14.CrossRefGoogle Scholar
  79. Shulman, L. (1987). Knowledge and teaching: foundations of the new reform. Harvard Educational Review, 57(1), 1–22.CrossRefGoogle Scholar
  80. Shulman, L. (2015). PCK: Its genesis and exodus. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 3–13). London: Routledge Press.Google Scholar
  81. Singer, E., & Ye, C. (2013). The use and effects of incentives in surveys. The Annals of the American Academy of Political and Social Science, 645(1), 112–141.CrossRefGoogle Scholar
  82. Smith, P. S., & Banilower, E. R. (2015). Assessing PCK: a new application of the uncertainty principle. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 88–103). London: Routledge Press.Google Scholar
  83. Stavy, R., Eisen, Y., & Yaakobi, D. (1987). How students aged 13‐15 understand photosynthesis. International Journal of Science Education, 9(1), 105–115.CrossRefGoogle Scholar
  84. Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(1), 63.Google Scholar
  85. Tamir, P. (1988). Subject matter and related pedagogical knowledge in teacher education. Teaching and Teacher Education, 4(2), 99–110. doi: 10.1016/0742-051X(88)90011-X.CrossRefGoogle Scholar
  86. Thompson, B., & Melancon, J. G. (1996). Using item ‘testlets’/‘parcels’ in confirmatory factor analysis: an example using the PPSDQ-78. Paper presented at the annual meeting of the Mid-South Educational Research Association, Tuscaloosa, AL.Google Scholar
  87. Thorn, C. J., Bissinger, K., Thorn, S., & Bogner, F. X. (2016). “Trees live on soil and sunshine!”-coexistence of scientific and alternative conception of tree assimilation. PloS One, 11(1), e0147802.CrossRefGoogle Scholar
  88. Van Driel, J. H., Verloop, N., & de Vos, W. (1998). Developing science teachers’ pedagogical content knowledge. Journal of Research in Science Teaching, 35(6), 673–695.CrossRefGoogle Scholar
  89. Van Driel, J. H., Beijaard, D., & Verloop, N. (2001). Professional development and reform in science education: the role of teachers’ practical knowledge. Journal of Research in Science Teaching, 38(2), 137–158.CrossRefGoogle Scholar
  90. Varma, S. (2006). Preliminary item statistics using point-biserial correlation and p-values. Morgan Hill: Educational Data Systems Inc.Google Scholar
  91. Weigle, S. C. (2002). Assessing writing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of STEM EducationNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Teaching and LearningUniversity of IowaIowa CityUSA
  3. 3.Department of Biological SciencesMinnesota State University, MankatoMankatoUSA

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