Learning Progressions in Science Education


Learning progressions embody a developmental approach to learning by describing hypothetical paths that students might take as they develop progressively more sophisticated ways of reasoning about scientific concepts and practices in a domain over extended periods of time (Smith, Wiser, Anderson & Krajcik, 2006). There are four key features that characterize LPs in science (Corcoran et al., 2009). First, LPs are centered on a few foundational and generative disciplinary ideas and inquiry practices. Second, LPs are bounded by a lower anchor that describes assumptions about the prior knowledge and skills of learners as they enter the progression, and an upper anchor that describes the expected outcomes by the end of the progression. The upper anchor is predominantly determined by societal expectations and analyses of the domain. Third, LPs describe the development of students’ understandings as intermediate steps or levels between the two anchors. These levels are derived from analyses of research on student learning in the domain. LPs also include descriptions of expected learning performances at each level that can be used to track student progress. Fourth, LPs are mediated by targeted instruction and curriculum. That is, they describe learning as facilitated by carefully designed learning environments. It is important to note that LPs by their very nature are hypothetical; they are conjectural models of learning over time that need to be empirically validated. In validating an LP one is trying to determine if the nature and order of the intermediate steps fit empirical data on student thinking from cross-sectional studies (e.g. Mohan, Chen & Anderson, 2009;), and teaching experiments (e.g. Lehrer & Schauble, 2012).

Syllabi and Slides

Learning Progressions slides by Ravit Duncan

Video Resources

Listen to the Learning Progressions in Science Education webinar


Basic Reading
  • Corcoran, T., Mosher, F. A., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform. NY: Center on Continuous Instructional Improvement, Teachers College — Columbia University. [Access Online]
Additional Reading
  • Rivet, A. & Kastens, K. (2012). Developing a construct-based assessment to examine students’ analogical reasoning around physical models in earth science. Journal of Research in Science Teaching, 49(6), 713-743.
  • Sikorski, T., & Hammer, D. (2010). A critique of how three learning progressions conceptualize sophistication and progress. In K. Gomez, L. Lyons & J. Radinsky (Eds.), Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010) – Volume 1, Full Papers. Chicago IL: International Society of the Learning Sciences.
  • Shea, N., & Duncan, R. G. (2012). From theory to data: Refining a learning progression. Journal of the Learning Sciences, 22(1), 7-32.
  • Songer, N. B., Kelcey, B., and Gotwals, A. (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), 610-633.
  • 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.
  • Wiser, M., Smith, C., & Doubler, S. (2012). Learning Progressions as tool for curriculum development: Lessons from the Inquiry Project. In A. Alonzo & A. Gotwals (Eds.), Learning Progressions in Sciences. Sense Publishing.

Learning Scientists Who Have Researched This Topic

  • Alicia Alonzo
  • Charles (Andy) Anderson
  • Derek Briggs
  • Ravit Duncan
  • Erin Furtak
  • Amelia Gotwals
  • Joseph Krajcik
  • Richard Lehrer
  • Kathleen Metz
  • Knut Neumann
  • Leona Schuable
  • Carol Smith
  • Marianne Weiser
  • Mark Wilson