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Undergraduate Computer Science (CS) curricular guidelines have been published regularly since 1968, and the latest released in 2013. From early 2021, a task force of the ACM, IEEE-Computer Society, and the Association for the Advancement of Artificial Intelligence (AAAI) has worked on a decennial revision titled the ACM/IEEE-CS/AAAI Computer Science 2023 Curricula (CS2023). The CS2023 task force includes a 17-member steering committee, 17 knowledge area subcommittees, and an international group of disciplinary experts. CS2023 provides curricular content – a knowledge model largely backward compatible with CS2013, supplemented by a competency model – and curricular practices, comprising articles by independent experts on program design and delivery that complement curricular content guidelines. CS2023 will inform educators and administrators on the what, why, and how to cover undergraduate CS over the next decade. Ongoing work on CS2023 has been disseminated widely over the past two years: via the task force website; presentations at computing education conferences, e.g., SIGCSE Technical Symposium 2023; articles, e.g., ACM Inroads; emails to various computing education mailing lists; gathering community feedback via surveys and special sessions; and soliciting and receiving expert blind peer reviews. Building on earlier drafts, a gamma draft was released in September 2023, with the final version due by the end of 2023. This panel examines CS2023 from different perspectives. All panelists serve on the CS2023 steering committee and have an intimate understanding of CS2023. The moderator will lay out its overall vision and structure while panelists will emphasize three major perspectives of CS education: software development fundamentals; systems development; and the increased role of societal, ethical, and professional aspects crucial to a modern CS graduate. Strong interdependencies exist between these perspectives, along with tensions arising from how much can be squeezed into a tight undergraduate CS curriculum. Attendees will take home an understanding of the approach taken by the CS2023 task force, the constraints on curriculum design, and how best to use the CS2023 guidelines to educate the next generation of CS graduates. 

Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of computing education. This paper presents a thorough systematic review of metacognition and self-regulation work in the context of computer programming and an in-depth discussion of the theories that have been leveraged in some way. We also discuss several prominent metacognitive and self-regulation theories from the literature outside of computing education – for example, from psychology and education – that have yet to be applied in the context of programming education. In our investigation, we built a comprehensive corpus of papers on metacognition and self-regulation in programming education, and then employed backward snowballing to provide a deeper examination of foundational theories from outside computing education, some of which have been explored in programming education, and others that have yet to be but hold much promise. In addition, we make new observations about the way these theories are used by the computing education community, and present recommendations on how metacognition and self-regulation can help inform programming education in the future. In particular, we discuss exemplars of studies that have used existing theories to support their design and discussion of results as well as studies that have proposed their own metacognitive theories in the context of programming education. Readers will also find the article a useful resource for helping students in programming courses develop effective strategies for metacognition and self-regulation. 

For decades computing educators have been trying to identify and predict at-risk students, particularly early in the first programming course. These efforts range from the analyzing demographic data that pre-exists undergraduate entrance to using instruments such as concept inventories, to the analysis of data arising during education. Such efforts have had varying degrees of success, have not seen widespread adoption, and have left room for improvement. We analyse results from a two-year study with several hundred students in the first year of programming, comprising majors and non-majors. We find evidence supporting a hypothesis that engagement with extra credit assessment provides an effective method of differentiating students who are not at risk from those who may be. Further, this method can be used to predict risk early in the semester, as any engagement – not necessarily completion – is enough to make this differentiation. Additionally, we show that this approach is not dependent on any one programming language. In fact, the extra credit opportunities need not even involve programming. Our results may be of interest to educators, as well as researchers who may want to replicate these results in other settings.