How can we better understand the mental steps learners take as they come to understand science topics? Recent insights: Learning Progressions and Trajectories

Learning Progressions Based on the notion that students come to understand big ideas in progressively more sophisticated ways over time [extended periods, e.g. K-12] (Smith, Wiser, Anderson, Krajcik, & Coppola, 2004). “Conjectures about… the levels (or steps) learners go through as they develop expertise in the domain” (Shea & Duncan, 2013). Help guide development of learning environments that align curriculum, instruction, and assessment [often applied in science education] (Duschl et al., 2011).

Learning Trajectories Typical, predictable sequences of thinking that emerge as students develop understanding of an idea (Daro, Mosher, & Corcoran, 2011). Describe the development of student thinking over shorter ranges of specific topics [often applied in math education] (Daro, Mosher, & Corcoran, 2011).

How do we develop a more sophisticated understanding over time? What knowledge is needed to keep progressing? Learning to Ride a Bike Level 1 Level 2 Level 3 Level 4

Example topic: Causes of Earth’s seasons Learners’ understandings of scientific topics

Another example topic Image from NOAA:

Sample learning progression for the water cycle Water is only in isolated, visible locations (puddles, oceans) Water and substances in water move along multiple paths with visible and invisible boundaries; detailed connections between natural and human- engineered systems Water moves through connected environmental systems using multiple paths (e.g. underground aquifers, water vapor, human- engineered systems) Water can move from one visible location to another (river  lake; soil  clouds) Water can move from one visible location to another (river  lake; soil  clouds) Based on Gunckel, Covitt, Salinas, & Anderson (2012)

Teaching and learning progressions for sea level rise NGSS-aligned teaching progression: /SLRTeachingProgression.pdf Draft hypothesized learning progression: /SLR-LP.pdf

This presentation was designed by the MADE CLEAR Learning Sciences Research Team at the University of Maryland, College Park ( This material is based upon work supported by the National Science Foundation under Grant No Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This presentation was designed by the MADE CLEAR Learning Sciences Research Team at the University of Maryland, College Park ( This material is based upon work supported by the National Science Foundation under Grant No Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.