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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. Exploring Computer Science Identity Development Among Undergraduate Computer Science Majors

   https://doi.org/10.22318/icls2023.957078  

   Apedoe, Xornam; Li, Wen; Rollins, Sami; Engle, Sophie; Joshi, Alark; Malensek, Matthew; Brooks, Chris (October 2023, Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023)

   Blikstein, P.; Van Aalst, J.; Kizito, R.; Brennan, K. (Ed.)
3. Understanding Professional Identity of Computer Science Teachers: Design of the Computer Science Teacher Identity Survey

   https://doi.org/10.1145/3446871.3469766  

   Ni, Lijun; McKlin, Tom; Hao, Han; Baskin, Jake; Bohrer, Jason; Tian, Yan (August 2021, ICER 2021: Proceedings of the 17th ACM Conference on International Computing Education Research)

   null (Ed.)

   Motivation: Recent efforts to expand K-12 computer science education highlight the great need for well-prepared computer science (CS) teachers. Teacher identity theory offers a particular conceptual lens for us to understand computer science teacher preparation and professional development. The emerging literature suggests that teacher identity is central to sustaining motivation, efficacy, job satisfaction, and commitment, and these attributes are crucial in determining teacher retention. While the benefits associated with a strong sense of teacher identity are great, teachers face unique challenges and tensions in developing their professional identity for teaching computer science. Objectives: This exploratory study attempts to operationalize computer science teacher identity through discussing the potential domains, proposing and testing a quantitative instrument for assessing computer science teachers’ professional identity. Method: We first discussed the potential domains of computer science teacher identity based on recent teacher identity literature and considerations on some unique challenges for computer science teachers. Then we proposed the computer science teacher identity scale, which was piloted through a national K-12 computer science teacher survey with 3,540 completed responses. The survey results were analyzed with a series of factor analyses to test the internal structure of the computer science teacher identity scale. Results: Our analyses reveal a four-factor solution for the computer science teacher identity scale, which is composed of CS teaching commitment, CS pedagogical confidence, confidence to engage students, and sense of community/belonging. There were significant differences among the teachers with different computer science teaching experiences. In general, teachers with more computer science teaching experience had higher computer science teacher identity scores on all four factors. Discussion: The four-factor model along with a large national dataset invites a deeper analysis of the data and can provide important benchmarks. Such an instrument can be used to explore developmental patterns in computer science teacher identity, and function as a pedagogical tool to provoke discussion and reflection among teachers about their professional development. This study may also contribute to understanding computer science teachers’ professional development needs and inform efforts to prepare, develop, and retain computer science teachers. 

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4. 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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5. 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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