Context-Dependent “Upper Anchors” for Learning Progressions

Abstract

In the spirit of model revision, researchers continue to refine the notion of a learning progression. Despite many advances in learning progressions research, one key design element has eluded scholarly critique, the upper anchor. Drawing on science education research and studies of science, this essay argues for a shift from the predominant model of the upper anchor as the fixed, “most sophisticated” way of thinking toward a more expansive “upper reach” that acknowledges plurality and context-dependence in ways of knowing. Three possible models for context-dependent upper reaches are offered.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    The quote “descriptions of the successively more sophisticated ways of thinking about a topic” generated 157 results in GoogleScholar on July 15, 2019.

  2. 2.

    TSTS uses the terms “big idea” and “core idea.” To avoid potential confusion with the more specific use of “core idea” in the Next Generation Science Standards, the term “big idea” is used in Table 1.

  3. 3.

    To clarify, the emphasis here is not on the number of progress variables, but on the kinds of progress variables.

References

  1. Alonzo, A. C. (2018). Exploring the learning progression–formative assessment hypothesis. Applied Measurement in Education, 31(2), 101–103.

    Article  Google Scholar 

  2. Alonzo, A. C., & Elby, A. (2019). Beyond empirical adequacy: learning progressions as models and their value for teachers. Cognition and Instruction, 37(1), 1–37.

    Article  Google Scholar 

  3. Alonzo, A. C., & Steedle, J. T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93(3), 389–421.

    Article  Google Scholar 

  4. American Association for the Advancement of Science (AAAS). (1967). Science – a process approach. Washington, DC: AAAS.

    Google Scholar 

  5. American Association for the Advancement of Science (AAAS). (2001). Atlas of science literacy. Washington, DC: AAAS.

    Google Scholar 

  6. Ball, D. L. (1993). With an eye on the mathematical horizon: dilemmas of teaching elementary school mathematics. The Elementary School Journal, 93(4), 373–397.

    Article  Google Scholar 

  7. Bang, M., & Medin, D. (2010). Cultural processes in science education: supporting the navigation of multiple epistemologies. Science Education, 94(6), 1008–1026.

    Article  Google Scholar 

  8. Baroody, A. J. (2003). The development of adaptive expertise and flexibility: the integration of conceptual and procedural knowledge. In A. J. Baroody & A. Dowker (Eds.), The development of arithmetic concepts and skills: Constructing adaptive expertise (pp. 1–33). Mahwah: Lawrence Erlbaum.

    Google Scholar 

  9. Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: understanding student work and designing supportive instructional contexts. Science Education, 94(5), 765–793.

    Article  Google Scholar 

  10. Bernholt, S., & Sevian, H. (2018). Learning progressions and teaching sequences–old wine in new skins? Chemistry Education Research and Practice. https://doi.org/10.1039/C8RP90009D.

    Article  Google Scholar 

  11. Breslyn, W., McGinnis, J. R., McDonald, R. C., & Hestness, E. (2016). Developing a learning progression for sea level rise, a major impact of climate change. Journal of Research in Science Teaching, 53(10), 1471–1499.

    Article  Google Scholar 

  12. Brigandt, I. (2012). The dynamics of scientific concepts: The relevance of epistemic aims and values. In U. Feeset & F. Steinle (Eds.), Scientific concepts and investigative practice. Berlin studies in knowledge research Vol. 3 (pp. 75–104). Berlin/Boston: De Gruyter.

    Google Scholar 

  13. Castro-Faix, M., Todd, A., Romine, W., & Duncan, R. G. (2018). Do alternative instructional approaches result in different learning progressions?. In Kay, J. and Luckin, R. (Eds.) Rethinking learning in the digital age: making the learning sciences count, 13th International conference of the learning sciences (ICLS) 2018, volume 2. London, UK: International Society of the Learning Sciences.

  14. Catley, K., Lehrer, R., & Reiser, B. (2005). Tracing a prospective learning progression for developing understanding of evolution. Paper commissioned by the National Academies Committee on test design for K-12 science achievement, Washington, DC. National Academies.

  15. Chandler, M. J., & Boutilier, R. G. (1992). The development of dynamic system reasoning. Human Development, 35(3), 121–137.

    Article  Google Scholar 

  16. Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: a theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1–49.

    Article  Google Scholar 

  17. Coman, A., & Ronen, B. (2010). Icarus’ predicament: managing the pathologies of overspecification and overdesign. International Journal of Project Management, 28(3), 237–244.

    Article  Google Scholar 

  18. Confrey, J., Maloney, A., & Gianopulos, G. (2017). Untangling the “messy middle” in learning trajectories. Measurement: Interdisciplinary Research and Perspectives, 15(3–4), 168–171.

    Google Scholar 

  19. Corcoran, T. B., Mosher, F. A., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform. CPRE research reports. Retrieved from http://repository.upenn.edu/cpre_researchreports/53.

  20. Córdova, R. A., & Balcerzak, P. (2016). Co-constructing cultural landscapes for disciplinary learning in and out of school: the next generation science standards and learning progressions in action. Cultural Studies of Science Education, 11(4), 1223–1242.

    Article  Google Scholar 

  21. Dreyfus, B. W., Gupta, A., & Redish, E. F. (2015). Applying conceptual blending to model coordinated use of multiple ontological metaphors. International Journal of Science Education, 37(5–6), 812–838.

    Article  Google Scholar 

  22. Dunbar, K. (1995). How scientists really reason: Scientific reasoning in real-world laboratories. In J. E. Davidson & R. J. Sternberg (Eds.), The nature of insight (pp. 365–395). Cambridge: MIT Press.

    Google Scholar 

  23. Duncan, R. G., & Hmelo-Silver, C. E. (2009). Learning progressions: aligning curriculum, instruction, and assessment. Journal of Research in Science Teaching, 46(6), 606–609.

    Article  Google Scholar 

  24. Duncan, R. G., & Rivet, A. E. (2013). Science learning progressions. Science, 339(6118), 396–397.

    Article  Google Scholar 

  25. Duncan, R. G., Rogat, A. D., & Yarden, A. (2009). A learning progression for deepening students’ understandings of modern genetics across the 5th–10th grades. Journal of Research in Science Teaching, 46(6), 655–674.

    Article  Google Scholar 

  26. Duncan, R. G., Castro-Faix, M., & Choi, J. (2016). Informing a learning progression in genetics: which should be taught first, Mendelian inheritance or the central dogma of molecular biology? International Journal of Science and Mathematics Education, 14(3), 445–472.

    Article  Google Scholar 

  27. Duschl, R. (2008). Science education in three-part harmony: balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32(1), 268–291.

    Article  Google Scholar 

  28. Duschl, R., Maeng, S., & Sezen, A. (2011). Learning progressions and teaching sequences: A review and analysis. Studies in Science Education, 47(2), 123–182.

    Article  Google Scholar 

  29. Elmesky, R. (2013). Building capacity in understanding foundational biology concepts: a K-12 learning progression in genetics informed by research on children’s thinking and learning. Research in Science Education, 43(3), 1155–1175.

    Article  Google Scholar 

  30. Emden, M., Weber, K., & Sumfleth, E. (2018). Evaluating a learning progression on ‘transformation of matter’ on the lower secondary level. Chemistry Education Research and Practice, 19(4), 1096–1116.

    Article  Google Scholar 

  31. Forbes, C. T., Zangori, L., & Schwarz, C. V. (2015). Empirical validation of integrated learning performances for hydrologic phenomena: 3rd grade students’ model-driven explanation construction. Journal of Research in Science Teaching, 52(7), 895–921.

    Article  Google Scholar 

  32. Fortus, D., Shwartz, Y., & Rosenfeld, S. (2016). High school students’ meta-modeling knowledge. Research in Science Education, 46(6), 787–810.

    Article  Google Scholar 

  33. Furtak, E. M. (2009). Toward learning progressions as teacher development tools. In A. Alonzo and A. Gotwals (Eds.), Proceedings of the Learning Progressions in Science (LeaPS) Conference, Iowa City, IA.

  34. Furtak, E. M. (2012). Linking a learning progression for natural selection to teachers’ enactment of formative assessment. Journal of Research in Science Teaching, 49(9), 1181–1210.

    Article  Google Scholar 

  35. Furtak, E. M., & Heredia, S. C. (2014). Exploring the influence of learning progressions in two teacher communities. Journal of Research in Science Teaching, 51(8), 982–1020.

    Article  Google Scholar 

  36. Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. C. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: a meta-analysis. Review of Educational Research, 82(3), 300–329.

    Article  Google Scholar 

  37. Furtak, E. M., Morrison, D., & Kroog, H. (2014). Investigating the link between learning progressions and classroom assessment. Science Education, 98(4), 640–673.

    Article  Google Scholar 

  38. Giere, R. N. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752.

    Article  Google Scholar 

  39. Gooding, D. (1985). ‘In Nature’s school’: Faraday as an experimentalist. In Faraday rediscovered (pp. 105–136). London: Palgrave.

    Google Scholar 

  40. Gotwals, A. W., & Songer, N. B. (2013). Validity evidence for learning progression-based assessment items that fuse core disciplinary ideas and science practices. Journal of Research in Science Teaching, 50(5), 597–626.

    Article  Google Scholar 

  41. Gouveau, J., & Passmore, C. (2017). Models of’ versus ‘models for’: toward an agent-based conception of modeling in the science classroom. Science and Education, 26(1–2), 49–63.

    Article  Google Scholar 

  42. Grove, N. P., & Bretz, S. L. (2010). Perry’s scheme of intellectual and epistemological development as a framework for describing student difficulties in learning organic chemistry. Chemistry Education Research and Practice, 11(3), 207–211.

    Article  Google Scholar 

  43. Gunckel, K. L., Covitt, B. A., Salinas, I., & Anderson, C. W. (2012). A learning progression for water in socio-ecological systems. Journal of Research in Science Teaching, 49(7), 843–868.

    Article  Google Scholar 

  44. Gutiérrez, K. D., & Rogoff, B. (2003). Cultural ways of learning: individual traits or repertoires of practice. Educational Researcher, 32(5), 19–25.

    Article  Google Scholar 

  45. Hadenfeldt, J. C., Neumann, K., Bernholt, S., Liu, X., & Parchmann, I. (2016). Students’ progression in understanding the matter concept. Journal of Research in Science Teaching, 53(5), 683–708.

    Article  Google Scholar 

  46. Hammer, D., & Sikorski, T. R. (2015). Implications of complexity for research on learning progressions. Science Education, 99(3), 424–431.

    Article  Google Scholar 

  47. Hammer, D., Goldberg, F., & Fargason, S. (2012). Responsive teaching and the beginnings of energy in a third grade classroom. Review of Science, Mathematics and ICT Education, 6(1), 51–72.

    Google Scholar 

  48. Herrmann-Abell, C. F., & DeBoer, G. E. (2018). Investigating a learning progression for energy ideas from upper elementary through high school. Journal of Research in Science Teaching, 55(1), 68–93.

    Article  Google Scholar 

  49. Hokayem, H., & Gotwals, A. W. (2016). Early elementary students’ understanding of complex ecosystems: a learning progression approach. Journal of Research in Science Teaching, 53(10), 1524–1545.

    Article  Google Scholar 

  50. Jin, H., Zhan, L., & Anderson, C. W. (2013). Developing a fine-grained learning progression framework for carbon-transforming processes. International Journal of Science Education, 35(10), 1663–1697.

    Article  Google Scholar 

  51. Jin, H., van Rijn, P., Moore, J. C., Bauer, M. I., Pressler, Y., & Yestness, N. (2019). A validation framework for science learning progression research. International Journal of Science Education, 1-23.

  52. Kelly, G. J., & Licona, P. (2018). Epistemic practices and science education. In M. Matthews (Ed.), History, philosophy and science teaching: new research perspectives (pp. 139–165). Springer: Dordrecht.

    Google Scholar 

  53. Knorr-Cetina, K. D. (1981). The manufacture of knowledge: an essay on the constructivist and contextual nature of science. New York: Pergamon.

    Google Scholar 

  54. Koeppen, K., Hartig, J., Klieme, E., & Leutner, D. (2008). Current issues in competence modeling and assessment. Journal of Psychology, 216(2), 61–73.

    Google Scholar 

  55. Krajcik, J., Drago, K., Sutherland, L. A., & Merritt, J. (2012). The promise and value of learning progression research. In S. Bernholt, P. Nentwig, & N. Neumann (Eds.), Making it tangible—learning outcomes in science education (pp. 261–284). Munster: Waxmann.

    Google Scholar 

  56. Lancor, R. (2014). Using metaphor theory to examine conceptions of energy in biology, chemistry, and physics. Science & Education, 23(6), 1245–1267.

    Article  Google Scholar 

  57. Lehrer, R., & Schauble, L. (2012). Seeding evolutionary thinking by engaging children in modeling its foundations. Science Education, 96(4), 701–724.

    Article  Google Scholar 

  58. Lehrer, R., Jaslow, L., & Curtis, C. (2003). Developing understanding of measurement in elementary grades. In D. Clements & G. Bright (Eds.), National Council of teachers of mathematics yearbook on learning and measurement (pp. 100–121). Reston: NCTM.

    Google Scholar 

  59. Liu, X., & Lesniak, K. (2006). Progression in children’s understanding of the matter concept from elementary to high school. Journal of Research in Science Teaching, 43(3), 320–347.

    Article  Google Scholar 

  60. Lombard, F., Merminod, M., Widmer, V., & Schneider, D. K. (2018). A method to reveal fine-grained and diverse conceptual progressions during learning. Journal of Biological Education, 52(1), 101–112.

    Article  Google Scholar 

  61. Merritt, J., & Krajcik, J. (2013). Learning progression developed to support students in building a particle model of matter. In G. Tsaparlis & H. Sevian (Eds.), Concepts of matter in science education. Innovations in science education and technology, vol 19 (pp. 11–45). Dordrecht: Springer.

    Google Scholar 

  62. Mitchell, S. D. (2009). Unsimple truths: science, complexity, and policy. Chicago: University of Chicago Press.

    Google Scholar 

  63. Mohan, L., & Plummer, J. (2012). Exploring challenges to defining learning progressions. In A. C. Alonzo & A. W. Gotwals (Eds.), Learning progressions in science (pp. 139–147). Sense Publishers: Rotterdam.

    Google Scholar 

  64. Mohan, L., Chen, J., & Anderson, C. W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46(6), 675–698.

    Article  Google Scholar 

  65. Mosher, F. (2011). The role of learning progressions in standards-based education reform. CPRE policy briefs. Retrieved from https://repository.upenn.edu/cpre_policybriefs/40.

  66. National National Research Council (NRC). (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: The National Academies Press.

    Google Scholar 

  67. National Research Council (NRC). (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: The National Academies Press.

  68. Nersessian, N. J. (1984). Faraday to Einstein: constructing meaning in scientific theories (Vol. 1). Springer Science & Business Media.

  69. NGSS Lead States. (2013). Next generation science standards: for states, by states. Washington, DC: The National Academies Press.

    Google Scholar 

  70. OECD. (2012). OECD Science, Technology and Industry Outlook 2012. Paris: Organisation for Economic Co-operationand Development.

  71. Osborne, J. F., Collins, S., Ratcliffe, M., Millar, R., & Duschl, R. (2003). What “ideas-about-science” should be taught in school science? A Delphi study of the expert community. Journal of Research in Science Teaching, 40(7), 692–720.

    Article  Google Scholar 

  72. Osborne, J. F., Henderson, J. B., MacPherson, A., Szu, E., Wild, A., & Yao, S. Y. (2016). The development and validation of a learning progression for argumentation in science. Journal of Research in Science Teaching, 53(6), 821–846.

    Article  Google Scholar 

  73. Passmore, C., Gouvea, J. S., & Giere, R. (2014). Models in science and in learning science: Focusing scientific practice on sense-making. In International handbook of research in history, philosophy and science teaching (pp. 1171–1202). Springer: Dordrecht.

    Google Scholar 

  74. Pierson, A. E., Clark, D. B., & Sherard, M. K. (2017). Learning progressions in context: tensions and insights from a semester-long middle school modeling curriculum. Science Education, 101(6), 1061–1088.

    Article  Google Scholar 

  75. Plummer, J. D. (2014). Spatial thinking as the dimension of progress in an astronomy learning progression. Studies in Science Education, 50(1), 1–45.

    Article  Google Scholar 

  76. Plummer, J. D., & Maynard, L. (2014). Building a learning progression for celestial motion: an exploration of students’ reasoning about the seasons. Journal of Research in Science Teaching, 51(7), 902–929.

    Article  Google Scholar 

  77. Plummer, J. D., & Slagle, C. (2009). A learning progression approach to teacher progressional development in astronomy. In A. Alonzo and A. Gotwals (Eds.), Proceedings of the Learning Progressions in Science (LeaPS) Conference, Iowa City, IA.

  78. Plummer, J. D., Palma, C., Flarend, A., Rubin, K., Ong, Y. S., Botzer, B., et al. (2015). Development of a learning progression for the formation of the solar system. International Journal of Science Education, 37(9), 1381–1401.

    Article  Google Scholar 

  79. Radoff, J., Jaber, L. Z., & Hammer, D. (2019). “It’s scary but it’s also exciting”: evidence of meta-affective learning in science. Cognition and Instruction, 37(1), 73–92.

    Article  Google Scholar 

  80. Ribaut, M., Brown, A. G., Boveri, & Baden, C. (1985). A solution to boundary value problems with over-specified boundary conditions. Zeitschrift für angewandte Mathematik und Physik ZAMP, 36(4), 629–634.

    Article  Google Scholar 

  81. Roseman J. E., Caldwell, A., Gogos, A., & Kuth, L. (2006). Mapping a coherent learning progression for the molecular basis of heredity. Paper presented at the International Meeting of the National Association for research in science teaching, San Francisco, CA. Retrieved from http://www.project2061.org/publications/articles/papers/narst2006.pdf.

  82. Ryu, M. (2019). Mixing languages for science learning and participation: an examination of Korean-English bilingual learners in an after-school science-learning programme. International Journal of Science Education, 1-21.

  83. Ryu, M., & Sikorski, T. R. (2019). Tracking a learner’s verbal participation in science over time: analysis of talk features within a social context. Science Education, 103(3), 561–589.

    Article  Google Scholar 

  84. Ryu, M., Tuvilla, M. R. S., & Wright, C. E. (2019). Resettled Burmese refugee youths’ identity work in an afterschool STEM learning setting. Journal of Research in Childhood Education, 33(1), 84–97.

    Article  Google Scholar 

  85. Salinas, I. (2009). Learning progressions in science education: two approaches for development. In A. Alonzo and A. Gotwals (Eds.), Proceedings of the Learning Progressions in Science (LeaPS) Conference, Iowa City, IA.

  86. Sandoval, W. A. (2005). Understanding students’ practical epistemologies and their influence on learning through inquiry. Science Education, 89, 634–656.

    Article  Google Scholar 

  87. Schwab, J. J. (1960). What do scientists do? Behavioral Science, 5(1), 1–27.

    Article  Google Scholar 

  88. Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654.

    Article  Google Scholar 

  89. Schwarz, C., Reiser, B. J., Acher, A., Kenyon, L., & Fortus, D. (2012). MoDeLS: challenges in defining a learning progression for scientific modeling. In A. Alonzo & A. W. Gotwals (Eds.), Learning progressions in science: current challenges and future directions (pp. 101–137). The Netherlands: SensePublishers, Rotterdam.

    Google Scholar 

  90. Sevian, H., & Talanquer, V. (2014). Rethinking chemistry: a learning progression on chemical thinking. Chemistry Education Research and Practice, 15(1), 10–23.

    Article  Google Scholar 

  91. Shavelson, R. J., & Kurpius, A. (2012). Reflections on learning progressions. In A. Alonzo & A. Gotwals (Eds.), Learning progressions in science (pp. 13–26). Brill Sense.

  92. Shepard, L., Daro, P., & Stancavage, F. B. (2013a). The relevance of learning progressions for NAEP. Paper commissioned by the NAEP validity studies (NVS) panel. American Institutes for Research.

  93. Shepard, L., Daro, P., & Stancavage, F. B. (2013b). The relevance of learning progressions for NAEP. American Institutes for Research. Retrieved from https://eric.ed.gov/?id=ED545240.

  94. Shepard, L. A., Daro, P., Stancavage, F. B. (2013c). The relevance of learning progressions for NAEP. Washington, DC: American Institutes for Research. Retrieved from http://www.air.org/files/NVS_combined_study_3_Relevance_of_Learning_Progressions_for_NAEP.pdf.

  95. Shin, N., Stevens, S. Y., Short, H., & Krajcik, J. (2009, June). Learning progressions to support coherence curricula in instructional material, instruction, and assessment design. In A. Alonzo & A. Gotwals (Eds.)., Proceedings of the Learning Progressions in Science (LeaPS) Conference, Iowa City, IA.

  96. Sikorski, T. R., & Hammer, D. (2010). A critique of how learning progressions research conceptualizes sophistication and progress. In Proceedings of the 9th International Conference of the Learning Sciences-Volume 1 (pp. 1032-1039). International Society of the Learning Sciences.

  97. Smith, C., Wiser, M., Anderson, C. W., Krajcik, J., & Coppola, B. (2004). Implications of research on children’s learning for assessment: Matter and atomic molecular theory. Paper commissioned by the Committee on Test Design for K-12 Science Achievement. Center for Education, National Research Council.

  98. Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. (2006). FOCUS ARTICLE: implications of research on children’s learning for standards and assessment: a proposed learning progression for matter and the atomic-molecular theory. Measurement: Interdisciplinary Research & Perspective, 4(1–2), 1–98.

    Google Scholar 

  99. Snively, G., & Corsiglia, J. (2001). Discovering indigenous science: implications for science education. Science Education, 85(1), 6–34.

    Article  Google Scholar 

  100. Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46(6), 610–631.

    Article  Google Scholar 

  101. Steedle, J. T., & Shavelson, R. J. (2009). Supporting valid interpretations of learning progression level diagnoses. Journal of Research in Science Teaching, 46(6), 699–715.

    Article  Google Scholar 

  102. Stevens, S. Y., Delgado, C., & Krajcik, J. S. (2010). Developing a hypothetical multi-dimensional learning progression for the nature of matter. Journal of Research in Science Teaching, 47(6), 687–715.

    Article  Google Scholar 

  103. Stevens, S. Y., Shin, N., & Peek-Brown, D. (2013). Learning progressions as a guide for developing meaningful science learning: a new framework for old ideas. Educación Química, 24(4), 381–390.

    Article  Google Scholar 

  104. Svoboda, J., & Passmore, C. (2013). The strategies of modeling in biology education. Science & Education, 22(1), 119–142.

    Article  Google Scholar 

  105. Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences, 12(3), 435–467.

    Article  Google Scholar 

  106. Todd, A., & Kenyon, L. (2016). Empirical refinements of a molecular genetics learning progression: the molecular constructs. Journal of Research in Science Teaching, 53(9), 1385–1418.

    Article  Google Scholar 

  107. Wang, C. C., Ho, H. C., & Cheng, Y. Y. (2015). Building a learning progression for scientific imagination: a measurement approach. Thinking Skills and Creativity, 17, 1–14.

    Article  Google Scholar 

  108. Wang, C. C., Niemi, H., Cheng, C. L., & Cheng, Y. Y. (2017). Validation of learning progression in scientific imagination using data from Taiwanese and Finnish elementary school students. Thinking Skills and Creativity, 24, 73–85.

    Article  Google Scholar 

  109. Watkins, J., Hammer, D., Radoff, J., Jaber, L. Z., & Phillips, A. M. (2018). Positioning as not-understanding: the value of showing uncertainty for engaging in science. Journal of Research in Science Teaching, 55(4), 573–599.

    Article  Google Scholar 

  110. Wilson, M. (2005). Constructing measures: an item response modeling approach. Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  111. Wilson, M. (2009). Measuring progressions: assessment structures underlying a learning progression. Journal of Research in Science Teaching, 46(6), 716–730.

    Article  Google Scholar 

  112. Wiser, M., Frazier, K. E., & Fox, V. (2013). At the beginning was amount of material: a learning progression for matter for early elementary grades. In G. Tsaparlis & H. Sevian (Eds.), Concepts of matter in science education. Innovations in Science Education and Technology, vol 19. Dordrecht: Springer.

    Google Scholar 

  113. Wyner, Y., & Doherty, J. H. (2017). Developing a learning progression for three-dimensional learning of the patterns of evolution. Science Education, 101(5), 787–817.

    Article  Google Scholar 

  114. Yao, J. X., & Guo, Y. Y. (2018). Validity evidence for a learning progression of scientific explanation. Journal of Research in Science Teaching, 55(2), 299–317.

    Article  Google Scholar 

  115. Yao, J. X., Guo, Y. Y., & Neumann, K. (2017). Refining a learning progression of energy. International Journal of Science Education, 39(17), 2361–2381.

    Article  Google Scholar 

  116. Zabel, J., & Gropengiesser, H. (2011). Learning progress in evolution theory: climbing a ladder or roaming a landscape? Journal of Biological Education, 45(3), 143–149.

    Article  Google Scholar 

Download references

Acknowledgments

I thank three anonymous reviewers, the journal editors, David Hammer, Victoria Winters, and Binyu Yang for very helpful feedback on the manuscript. An early version of this essay was presented at the March 2018 Annual Meeting of the National Association for Research in Science Teaching in Atlanta, GA.

Funding

This material is based upon work supported by the National Science Foundation under Grant No. (1439819).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Tiffany-Rose Sikorski.

Ethics declarations

Conflict of Interest

The author declares no conflict of interest.

Disclaimer

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sikorski, TR. Context-Dependent “Upper Anchors” for Learning Progressions. Sci & Educ 28, 957–981 (2019). https://doi.org/10.1007/s11191-019-00074-w

Download citation