Introduction of Science Learning Progression

• In Event: Modern Measurement for Science Learning Progressions (Book by Routledge)

Abstract

This presentation introduces the background, theories, methodologies, applications, and important implications. Learning progressions (LPs) are “descriptions of successively more sophisticated ways of thinking about how learners develop key disciplinary concepts and practices within a grade level and across multiple grades” (Fortus & Krajcik, 2012, p. 784). LP research provides a promising venue to support various learners to develop and monitor usable knowledge. Learning environments should facilitate students’ learning of big ideas in and across disciplines and developing capabilities to apply those ideas in real situations. These big ideas are essential because they can explain a range of phenomena in our world. The science education community in particular has adopted learning progressions to organize and align content, assessment, instruction, and learning experiences to provide learners the opportunities to develop deep knowledge of big scientific ideas and practices (National Research Council, 2007; Smith et al., 2006). To serve as valuable guides for such learning, LPs should pro¬vide a progression of integrated sets of ideas and capabilities across disciplines, from early elementary grades through secondary education and into college. In addition, LPs focus on how learners need to connect and relate relevant ideas and experiences to use them appropriately, as opposed to knowing individual, discreet facts. A vital aspect of a LP is showing how to support learners in developing knowledge of big ideas and practices over time. Big ideas systematically develop across grade levels in contexts that require learners to apply the ideas more deeply and in more sophisticated situations. While there is no single trajectory or pathway that all learners will follow, learners need to develop certain scientific ideas before they can learn other, more sophisticated ideas, as some ideas are dependent on others.
In this symposium, we present four papers related to modern measurement for science LP, including a discussion of major critiques targeting the validity of LPs, validation of an LP for science-in-use, and Bayesian networks used to monitor students’ progression along the LP, and AI used to develop an LP for three-dimensional science learning.

Author

• Joseph S. Krajcik, Michigan State University