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In this experience report, we describe the Investigating Air Quality curriculum unit that integrates computational data practices with science learning in middle school science classrooms. The unit is part of the Coding Science Internship instructional model, designed to broaden access to computer science (CS) learning through scalable integration in core science courses, and through confronting barriers to equitable participation in STEM. In this report, we describe the core features of the unit and share preliminary findings and insights from student experiences in 13 science classrooms. We discuss affordances and challenges for student learning of computational data practices in formal science classrooms, and conclude with emerging recommendations for instructional designers. 

Abstract Studies of model organisms have provided important insights into how natural genetic differences shape trait variation. These discoveries are driven by the growing availability of genomes and the expansive experimental toolkits afforded to researchers using these species. For example, Caenorhabditis elegans is increasingly being used to identify and measure the effects of natural genetic variants on traits using quantitative genetics. Since 2016, the C. elegans Natural Diversity Resource (CeNDR) has facilitated many of these studies by providing an archive of wild strains, genome-wide sequence and variant data for each strain, and a genome-wide association (GWA) mapping portal for the C. elegans community. Here, we present an updated platform, the Caenorhabditis Natural Diversity Resource (CaeNDR), that enables quantitative genetics and genomics studies across the three Caenorhabditis species: C. elegans, C. briggsae and C. tropicalis. The CaeNDR platform hosts several databases that are continually updated by the addition of new strains, whole-genome sequence data and annotated variants. Additionally, CaeNDR provides new interactive tools to explore natural variation and enable GWA mappings. All CaeNDR data and tools are accessible through a freely available web portal located at caendr.org. 

In order to expand opportunities to learn computer science (CS),there is a growing push for inclusion of CS concepts and practices, such as computational thinking (CT), in required subjects like science. Integrated, transdisciplinary (CS/CT+X) approaches have shown promise for broadening access to CS and CT learning opportunities, addressing potential self-selection bias associated with elective CS coursework and afterschool programs, and promotinga more expansive and authentic contextualization of CS work. Emerging research also points to pedagogical strategies that can transcend simply broadening access, by also working to confront barriers to equitable and inclusive engagement in CS. Yet, approaches to integration vary widely, and there is little consensus on whether and how different models for CS and CT integration contribute to desired outcomes. There has also been little theory development that can ground systematic examination of the affordances and tradeoffs of different models. Toward that end, we propose a typology through which to examine CT integration in science (CT+S). The purpose of delineating a typology of CT+S integration is to encourage instantiation, implementation, and inspection of different models for integration, and to promote shared understanding among learning designers, researchers, and practitioners working at the intersection of CT and science. For each model in the typology, we characterize how CT+S integration is accomplished, the ways in which CT learning supports science learning, and the affordances and tensions for equity and inclusion that may arise upon implementation in science classrooms.