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1. WIP: An Ecosystems Metaphor for Propagation

   Nolen, S. B.; Koretsky, M. D. (June 2020, ASEE annual conference proceedings)

   In this work-in-progress paper, we apply the ecosystems metaphor to develop a model to address the ways a technology-based tool, the Concept Warehouse (Koretsky et al., 2014), propagates in diverse settings and to how students use the tool in their learning. The ecosystem model goes beyond previous research using the Diffusion of Innovations framework (Rogers, 2005). While Diffusion of Innovations has been applied to educational innovations in engineering education (Borrego et al., 2010), physics education (Henderson and Dancy, 2008), and medical education (Rogers, 2002), it does not adequately account for the ways in which instructional and learning practices are socially situated within specific educational ecosystems, nor how those systems influence the ways in which practices are taken up by individuals and groups. 

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2. Integrating Computer Science in Science: Considerations for Scale

   Krakowski, A; Greenwald, E; Duke, J; Comstock, M; Roman, N (June 2020, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF THE LEARNING SCIENCES 2020)

   Facility with foundational practices in computer science (CS) is increasingly recognized as critical for the 21st century workforce. Developing this capacity and broadening participation in CS disciplines will require learning experiences that can engage a larger and more diverse student population (Margolis et al., 2008). One promising approach involves including CS concepts and practices in required subjects like science. Yet, research on the scalability of educational innovations consistently demonstrates that their successful uptake in formal classrooms depends on teachers’ perceived alignment of the innovations with their goals and expectations for student learning, as well as with the specific needs of their school context and culture (Blumenfeld et al., 2000; Penuel et al., 2007; Bernstein et al., 2016). Research is nascent, however, about how exactly to achieve this alignment and thereby position integrated instructional models for uptake at scale. To contribute to this understanding, we are developing and studying two units for core middle school science classrooms, known as Coding Science Internships. The units are designed to support broader participation in CS, with a particular emphasis on females, by expanding students’ perception of the nature and value of coding. CS and science learning are integrated through a simulated internship model, in which students, as interns, apply science knowledge and use computer programming as a tool to address real-world problems. In one unit, students gain first-hand experience with sequences, loops, and conditionals as they program and debug an interactive scientific model of a coral reef ecosystem under threat. The second unit engages students in learning concepts related to data analysis and visualization, abstraction, and modularity as they code data visualizations using real EPA air quality data. A core goal for both units is to provide students experience with some of the increasingly prevalent ways that computer science is integrated into the work of scientists. 

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3. Using an Analytic Model to Gauge the Potential of Innovative Pedagogies of Approximation in Mathematics Teacher Education.

   Brown, Amanda; Herbst, Patricio; Hanby, Kristi (December 2022, Mathematics teacher education and development)

   To leverage pedagogies of approximation to improve teacher education, educational researchers must first tackle the methodological question, “How can we determine the potential of pedagogies of approximation for supporting the growth of prospective teachers’ knowledge and practices for teaching?” In this paper, we offer a model called the Dual Action Cycles of Approximations of Practice for identifying the pedagogical practices made available to pre-service teachers within various approximations of practice and describe its use to empirically investigate the potential of StoryCircles—a facilitated process of collaboratively representing a lesson using a multimedia storyboarding tool. We illustrate ways the model was used to make observations of pre-service teachers during StoryCircles. A key feature of the model is that it provides a bi-focal perspective on both the preactive and interactive phases of teaching, which help bring into focus the interdependent nature of these two phases. We close by suggesting that the development of new forms of approximation needs to be accompanied by research frameworks capable of investigating the potential of these various innovations. 

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4. Cultivating a culture of scholarly teaching and learning: An ecological approach to institutional change in engineering education

   Sochacka, N. W.; Walther, J.; Morelock, J. R.; Hunsu, N. J.; Carnell, P. H. (December 2019, Australian Association for Environmental Education)

   Over the past two decades, there has been a significant increase in the production of engineering education research. Worldwide, this increase is reflected in the growing number of papers that are submitted to engineering education-focused conferences; engineering education-focused journal outlets; and the increasing number of new schools and departments of engineering education, and tenure-track faculty positions opening up in the United States. In spite of these developments, it is often argued that there remains a gap between engineering education research and educational practice. Some studies attribute this gap to a focus on the dissemination of evidence-based practices, as opposed to working with instructors to adapt evidence-based practices to “fit” into new contexts (Froyd et al., 2017). Other research points to the need for broader cultural change, for example at the level of the school or department, in order to create the conditions that enable and encourage instructors to sustainably engage with scholarly teaching and learning practices (Henderson, Beach, & Finkelstein, 2011). In this paper, we describe a novel institutional model, currently embodied in the Engineering Education Transformations Institute (EETI) at the University of Georgia (UGA), which is designed to create such conditions (Morelock, Walther, & Sochacka, 2019). Philosophically, our model is based on a propagation (versus a dissemination) paradigm (Froyd et al., 2017), grounded in a strengths (Saleebey, 2012) (versus a deficit) approach to existing instructional capacity, and broadly informed by complex systems theory (Laszlo, 1996; Meadows & Wright, 2008). Practically, the model leverages ecological design principles (Hemenway, 2009) to inform the day-to-day operations of the effort. This paper describes these philosophical and practical underpinnings and investigates the following research question: How can ecological design principles be operationalized to cultivate a culture of innovative and scholarly teaching and learning in a college of engineering? 

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5. AI and formative assessment: The train has left the station

   https://doi.org/10.1002/tea.21885  

   Zhai, Xiaoming; Nehm, Ross H. (June 2023, Journal of Research in Science Teaching)

   Abstract In response to Li, Reigh, He, and Miller's commentary,Can we and should we use artificial intelligence for formative assessment in science, we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has outpaced the research community. We challenge the somewhat restrictive view of formative assessment presented by Li et al., highlighting the significant contributions of AI in providing formative feedback to students, assisting teachers in assessment practices, and aiding in instructional decisions. We contend that AI‐generated scores should not be equated with the entirety of formative assessment practice; no single assessment tool can capture all aspects of student thinking and backgrounds. We address concerns raised by Li et al. regarding AI bias and emphasize the importance of empirical testing and evidence‐based arguments in referring to bias. We assert that AI‐based formative assessment does not necessarily lead to inequity and can, in fact, contribute to more equitable educational experiences. Furthermore, we discuss how AI can facilitate the diversification of representational modalities in assessment practices and highlight the potential benefits of AI in saving teachers’ time and providing them with valuable assessment information. We call for a shift in perspective, from viewing AI as a problem to be solved to recognizing its potential as a collaborative tool in education. We emphasize the need for future research to focus on the effective integration of AI in classrooms, teacher education, and the development of AI systems that can adapt to diverse teaching and learning contexts. We conclude by underlining the importance of addressing AI bias, understanding its implications, and developing guidelines for best practices in AI‐based formative assessment. 

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