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1. Integrating Computer Science in Science Classrooms: Learning Computational Thinking and Expanding Perceptions of Computer Science

   Greenwald, Eric; Krakowski, Ari (April 2021, NARST 2021 ANNUAL INTERNATIONAL CONFERENCE)

   null (Ed.)

   We will present emerging findings from an ongoing study of instruction at the intersection of science and computer science for middle school science classrooms. This paper focuses on student knowledge and dispositional outcomes in relation to a 2 week/10-lesson learning sequence. Instruction aims to broaden participation in STEM pathways through a virtual simulated internship in which students inhabit the role of interns working to develop a restoration plan to improve the health of coral reef populations. Through this collaborative work, students construct understanding of biotic and abiotic interactions within the reef and develop a computational model of the ecosystem. Analysis of pre/post surveys for n=381 students revealed that students who participated in the 2 week/10 lesson integrated computational thinking in science learning sequence demonstrated significant learning gains on an external measure of CT (0.522***; effect size=0.32). Drawing on scales from the Activation Lab suite of measures, pre/post surveys revealed increased competency beliefs about computer programming (mean difference =1.13***; effect size=1.01), and increased value assigned to STEM (0.78***; effect size=0.945). We also discuss the design of the instructional sequence and the theoretical framework for its development. 

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2. Computer Science Curriculum Trends

   https://doi.org/10.1145/3626253.3635501  

   Mackay, Sean; Decker, Adrienne (March 2024, SIGCSE)

   Since the 1960s, the ACM has provided routinely updated guidelines for what concepts constitute a computer science curriculum, with the latest version currently in development in 2023. These guidelines have traditionally provided a model curriculum from which universities can choose to adopt or modify for their own purposes. What is unclear, however, is to what degree schools follow the curriculum recommendations that the ACM provides. While most faculty and students likely have knowledge of their own institution's curriculum, as well as what courses are offered at a small selection of other schools, the goal of the work presented in this poster is to distill a cohesive view of what computer science curriculums in their second and third years look like across a broad range of universities across a range of institutions. Our goal with this work was to answer the following question: What do computer science course requirements look like at a wide range of different institutions? We believe the work will help those who are trying to develop curriculum changes within their own institutions and aims to provide a more cohesive view of what trends and patterns exist in course offerings and degree requirements for computer science in the second and third years across a wide range of universities. 

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3. Undergraduate Computer Science Curricula

   https://doi.org/10.1145/3624729  

   Simha, Rahul; Kumar, Amruth N.; Raj, Rajendra K. (February 2024, Communications of the ACM)

   There can be many conflicting goals for the design of a computer science curriculum including: immediate employability in industry, preparation for long-term success in an ever-changing discipline and preparation for graduate (that is, post-graduate) study. Emphasis on immediate employability may lead to prioritizing current tools and techniques at the expense of foundational and theoretical skills as well as broader liberal-arts education that are crucial to long-term career success and for graduate study. The implications of these conflicting goals include allocation of finite resources (time, courses in the curriculum), unwillingness of students to invest in the mathematics that they see as irrelevant to their immediate career goals, and reluctance of faculty to have their courses driven by a continually evolving marketplace of tools and APIs. A balanced curriculum benefits all stakeholders: students, employers, and faculty. Would a data-driven approach help faculty design curricula that effectively balance these multiple goals? For example, if we ask graduates of computer science programs to reflect on the impact of their undergraduate education, explicitly focusing on short and long-term impact, will there be enough meaningful data to significantly inform curricular design? A recent survey of industry professionals undertaken by the ACM/IEEE-CS/AAAI 2023 Computer Science Curricular Task Force (CS2023) points the way. This column presents one aspect of that survey—a focus on comparing short-term versus long-term views—and calls for similar surveys of industry professionals to be conducted on an ongoing basis to refine our understanding of the role played by various elements of undergraduate computer science curricula in the success of graduates. 

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4. Reimagining Computer Science Education

   Twarek, Bryan (July 2024, Computer Science Teachers Association)

   Presentation of the community-wide redefinition of foundational high school computer science content to the NSF CISE Principal Investigator community 

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5. 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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