Operationalizing Optimization in a Middle School Virtual Engineering Internship
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Operationalizing Optimization in a Middle School Virtual Engineering Internship Ryan Montgomery 1
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Eric Greenwald 1
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Samuel Crane 2
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Ari Krakowski 1
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Jacqueline Barber 1
# This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020
Abstract New national science standards have elevated attention to student performance with a core set of science and engineering practices, yet guidance about how to assess these practices is only just emerging in the literature. This is particularly true for the set of engineering design–focused concepts and practices articulated in the Next Generation Science Standards’ (NGSS) Engineering, Technology, and Application of Science (ETS) standards. In this work, we present a model of student cognition for assessing student facility with the engineering design practice of optimization. We operationalize this model of cognition within a set of engineering-focused units for middle school, framed as Virtual Engineering Internships (VEIs). To operationalize the engineering design practice of optimization within our VEIs, we first broke optimization down into two more specific subbehaviors: exploration and systematicity. We then designed metrics that provide evidence of those behaviors and would be observable given student clickstream data from a digital design tool. We normalized these metrics based on the obtained distributions from a research trial. We discuss the existing correlations between these behaviors and metrics. Keywords Learning analytics . Engineering design . NGSS . Optimization . Middle school . Virtual internships
Motivation Traditional approaches to automatically scorable assessments are inadequate to meet the demands of recent education reforms, which call for performance-based demonstrations of understanding (National Research Council [NRC] 2012; NGSS Lead States 2013). The need for more robust assessments is especially salient when attempting to assess science and engineering practices, because these are patterns of behavior that (1) take time to perform and (2) do not lend themselves to discrete correct or incorrect answers but rather a continuum of abilities (Pellegrino et al. 2014). These two aspects of engineering design practices make traditional assessments (e.g., multiple-choice items, which are of short duration and are scored dichotomously) an awkward fit for diagnosing student facility with engineering design practices. Teachers need new approaches to assessment that will enable them to
* Ryan Montgomery [email protected] 1
Lawrence Hall of Science, University of California, Berkeley, CA, USA
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Amplify Education Inc., Brooklyn, NY, USA
monitor and support student progress in these complex science and engineering practices (Pellegrino et al. 2014). Extended performance assessments show promise for assessing complex science and engineering practices (Wertheim et al. 2016). Previous work has developed models of student cognition for problem solving within a science context, for ex
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