1 On behalf of NASA’s Mars Exploration Program, this lesson was prepared by Arizona State University’s Mars Education Program, under contract to NASA’s Jet Propulsion Laboratory, a division of the California Institute of Technology. These materials may be distributed freely for non-commercial purposes. Copyright 2014; 2012; 2010; 2000. Last edited: April 24, 2014 Marsbound! Mission to the Red Planet

A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas

Engineering education research (EER) has been on the fast track since 2004 with an exponential rise in the number of Ph.D.s awarded and the establishment of new programs, even entire EER departments. The National Research Council’s Discipline-Based Education Research (DBER) report (National Research Council, 2012) captures the state-of-the-art advances in our understanding of engineering and science student learning and highlights commonalities with other science-based education research programs. The DBER report is the consensus analysis of experts in undergraduate education research in physics, chemistry, biology, geosciences, astronomy, and engineering. The study committee, chaired by Susan Singer, also included higher education researchers, learning scientists, and cognitive psychologists. A central aspect of the DBER report is the focus on and application of research in the education, learning, and social-behavioral sciences to science and engineering curricula design and teaching methods. Froyd, Wankat, and Smith (2012) identified five major shifts in engineering education in the past 100 years: 1. A shift from hands-on and practical emphasis to engineering science and analytical emphasis 2. A shift to outcomes-based education and accreditation 3. A shift to emphasizing engineering design 4. A shift to applying education, learning, and social-behavioral sciences research 5. A shift to integrating information, computational, and communications technology in education

Discipline-Based Education Research: Understanding and Improving Learning in

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Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering.

Argumentation is a central goal of science education because it engages students in a complex scientific practice in which they construct and justify knowledge claims. Although there is a growing body of research around argumentation, there has been little focus on developing a learning progression for this practice. We describe a learning progression to understand both students' work in scientific argumentation and the ways in which the instructional environment can support students in that practice. This learning progression describes three dimensions: (1) instructional context, (2) argumentative product, and (3) argumentative process. In this paper, we compare four examples from elementary, middle, and high school science classrooms to explore the ways in which students' arguments vary in complexity across grade level and instructional contexts. Our comparisons suggest that simplifying the instructional context may facilitate students in engaging in other aspects of argumentation in more complex ways. The instructional context may also be used as a tool to support students in argumentation in new content areas and to increase the complexity of their written arguments, which may be weaker than their oral arguments. Furthermore, classroom norms play an important role in supporting students of all ages, including elementary students, in argumentation. © 2010 Wiley Periodicals, Inc. Sci Ed94:765–793, 2010

A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts

∗The book reviewed is Benchmarks for Scientific Literacy, American Association for the Advancement of Science, Project 2061, Oxford University Press, New York and Oxford (1993), pp. 448, $21‐95 (pbk), ISBN 0‐19‐508986‐3.

Benchmarks for Science Literacy: a review symposium∗

Modeling is a core practice in science and a central part of scientific literacy. We present theoretical and empirical motivation for a learning progression for scientific modeling that aims to make the practice accessible and meaningful for learners. We define scientific modeling as including the elements of the practice (constructing, using, evaluating, and revising scientific models) and the metaknowledge that guides and motivates the practice (e.g., understanding the nature and purpose of models). Our learning progression for scientific modeling includes two dimensions that combine metaknowledge and elements of practice—scientific models as tools for predicting and explaining, and models change as understanding improves. We describe levels of progress along these two dimensions of our progression and illustrate them with classroom examples from 5th and 6th graders engaged in modeling. Our illustrations indicate that both groups of learners productively engaged in constructing and revising increasingly accurate models that included powerful explanatory mechanisms, and applied these models to make predictions for closely related phenomena. Furthermore, we show how students engaged in modeling practices move along levels of this progression. In particular, students moved from illustrative to explanatory models, and developed increasingly sophisticated views of the explanatory nature of models, shifting from models as correct or incorrect to models as encompassing explanations for multiple aspects of a target phenomenon. They also developed more nuanced reasons to revise models. Finally, we present challenges for learners in modeling practices—such as understanding how constructing a model can aid their own sensemaking, and seeing model building as a way to generate new knowledge rather than represent what they have already learned. 2009 Wiley Periodicals, Inc. J Res Sci Teach 46: 632-654, 2009

https://people.cs.vt.edu/~kafura/CS6604/Papers/Learning-Progression-Scientific-Modeling.pdf

Andres Achér

Papers

Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners

Models: Challenges in Defining a Learning Progression for Scientific Modeling