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Background and Context: Though computer science education is marked by a pronounced racial participation gap, there is little research about effective ways that teachers can be supported in creating racially-just and equitable computer science learning opportunities for students. Objectives: This paper examines how teachers engage in learning about race and equity in computer science education during professional development. Method: Drawing from data collected from 94 participants attending a summer week-long Exploring Computer Science workshop, this mixed-methods study revealed how key curricular and instructional features of professional development foster race-conscious discussions and shifts in beliefs and agency around broadening participation in computing. Findings: Given the importance of developing teacher capacity to increase opportunities for students of color in computing classrooms, the findings of this study highlight how intentionally integrating race education in the center of professional development for teachers can support teachers' equity-based beliefs and practices. Implications: Professional development for teachers that seeks to broaden participation in computing is supported by long-term professional learning that explicitly center issues of race and equity in the curriculum. [ABSTRACT FROM AUTHOR]

To support teachers to implement Computer Science curricula into classrooms from the very first year of school, teachers, schools and organisations seek quality curriculum resources to support implementation and teacher professional development. Until now, many Computer Science resources and outreach initiatives have targeted K-12 school-age children, with the intention to engage children and increase interest, rather than to formally teach concepts and skills. What is the educational quality of existing Computer Science resources and to what extent are they suitable for classroom learning and teaching? In this paper, an assessment framework is presented to evaluate the quality of online Computer Science resources. Further, a semi-systematic review of available online Computer Science resources was conducted to evaluate resources available for classroom learning and teaching and to identify gaps in resource availability, using the Australian curriculum as a case study analysis. The findings reveal a predominance of quality resources, however, a number of critical gaps were identified. This paper provides recommendations and guidance for the development of new and supplementary resources and future research. [ABSTRACT FROM PUBLISHER]

Reduction is one of the key techniques used for problem-solving in computer science. In particular, in the theory of computation and complexity (TCC), mapping and polynomial reductions are used for analysis of decidability and computational complexity of problems, including the core concept of NP-completeness. Reduction is a highly abstract technique that involves revealing close non-trivial connections between problems that often seem to have nothing in common. As a result, proper understanding and application of reduction is a serious challenge for students and a source of numerous misconceptions. The main contribution of this paper is detection of such misconceptions, analysis of their roots, and proposing a way to address them in an undergraduate TCC course. Our observations suggest that the main source of the misconceptions is the false intuitive rule “the bigger is a set/problem, the harder it is to solve”. Accordingly, we developed a series of exercises for proactive prevention of these misconceptions. [ABSTRACT FROM PUBLISHER]

Computer science education should not be based on short-term developments but on content that is observable in multiple domains of computer science, may be taught at every intellectual level, will be relevant in the longer term, and is related to everyday language and/or thinking. Recently, a catalogue of central concepts for computer science education such as algorithm, computer, and data has been empirically determined. This paper now concentrates on central processes relevant for computer science education. Experts of computer science rated 44 general-education processes regarding four criteria. By using a cluster analysis approach the following central processes for computer science education have been identified: problem solving and problem posing, analyzing, generalizing, finding relationships, classifying, and investigating. [ABSTRACT FROM AUTHOR]