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  1. Gresalfi, M. ; Horn, I. S. (Ed.)
  2. Gresalfi, M. ; Horn, I. S. (Ed.)
    Sociologists and historians of science have documented the salience of meritocracy and technocracy in engineering (Cech, 2014; Slaton, 2015; Riley, 2008). Meritocracy is often paired with a technocratic ideology, which distinguishes technical and “soft” skills and assigns more worth to the technical. Scholars have shown how technocracy and meritocracy contribute to marginalization within engineering education (Slaton, 2015; Foor et al., 2007; Secules et al., 2018). Our team has been iteratively redesigning a pedagogy seminar for engineering peer educators to disrupt such forces of marginalization. We study peer educators because they can do harm if these ideologies aren't challenged, and theymore »have the potential to disrupt these ideologies. Using tools from discourse analysis and the ideology-in-pieces framework (Philip, 2011), we analyze how technocratic stances are reproduced or challenged in engineering peer educators’ talk. Such analyses can help others to recognize technocratic reasoning and see some of its negative consequences.« less
  3. Gresalfi, M. ; Horn, I. S. (Ed.)
    There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the fieldmore »of STEM and CT integration.« less
  4. Gresalfi, M. ; Horn, I. S. (Ed.)
    As human society advances, new scientific challenges are constantly emerging. The use of systems thinking (ST) and computational thinking (CT) can help elucidate these problems and bring us closer to a possible solution. The construction and use of models is one of the most widely used tools when trying to understand systems. In this paper, we examine four case studies of student pairs who were engaged in building and using system models in an NGSS-aligned project-based learning unit on chemical kinetics. Using a theoretical framework that describes how CT and ST practices are manifested in the modeling process we examinemore »the progression of students’ models during their model revisions and explore strategies they employ to overcome modeling challenges they face. We discuss some suggestions to scaffold students’ progression in constructing computational system models and prepare teachers to support their students in engaging in CT and ST practices.« less
  5. Gresalfi, M. ; Horn, I. S. (Ed.)
    As human society advances, new scientific challenges are constantly emerging. The use of systems thinking (ST) and computational thinking (CT) can help elucidate these problems and bring us closer to a possible solution. The construction and use of models is one of the most widely used tools when trying to understand systems. In this paper, we examine four case studies of student pairs who were engaged in building and using system models in an NGSS-aligned project-based learning unit on chemical kinetics. Using a theoretical framework that describes how CT and ST practices are manifested in the modeling process we examinemore »the progression of students’ models during their model revisions and explore strategies they employ to overcome modeling challenges they face. We discuss some suggestions to scaffold students’ progression in constructing computational system models and prepare teachers to support their students in engaging in CT and ST practices.« less
  6. Gresalfi, M. ; Horn, I. S. (Ed.)
    Computational Thinking (CT) is increasingly being targeted as a pedagogical goal for science education. As such, researchers and teachers should collaborate to scaffold student engagement with CT alongside new technology and curricula. We interviewed two high school teachers who implemented a unit using dynamic modeling software to examine how they supported student engagement with CT through modeling practices. Based on their interviews, they believed that they supported student engagement in CT and modeling through preliminary activities, conducting classroom demonstrations of the phenomenon, and engaging students in model revisions through dialogue.
  7. Gresalfi, M. ; Horn, I. S. (Ed.)
    There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the fieldmore »of STEM and CT integration.« less
  8. Gresalfi, M. ; Horn, I. S. (Ed.)
    Computational Thinking (CT) is increasingly being targeted as a pedagogical goal for science education. As such, researchers and teachers should collaborate to scaffold student engagement with CT alongside new technology and curricula. We interviewed two high school teachers who implemented a unit using dynamic modeling software to examine how they supported student engagement with CT through modeling practices. Based on their interviews, they believed that they supported student engagement in CT and modeling through preliminary activities, conducting classroom demonstrations of the phenomenon, and engaging students in model revisions through dialogue.
  9. Gresalfi, M. ; Horn, I. S. (Ed.)
    This study investigated the impact of instructional prompts on how learners interacted with two forms of 3D models during a science education task: tangible (3D prints) or digital (desktop-based) 3D models of fossils from a natural history museum collection. Learners observed and reasoned with 3D models to answer a scientific question. Two forms of instructional prompts were compared: functional prompts to manipulate the models in ways that explored how the fossil may have functioned, and general prompts to manipulate the models in ways that help answer the scientific question. Results suggest that functional prompts encouraged different participant interactions with tangiblemore »models, but not with digital models.« less
  10. Gresalfi, Melissa ; Horn, I. S. (Ed.)
    This paper shares the design and process of development for a data visualization project that centers computing squarely in social studies classroom instruction for social justice. Circuit Playground Expresses are programmed to engage students in engaging with and creating visualizations of the Great Migration of Black folx from the American South during the Jim Crow era.