Computer Science Curricula 2023 (CS2023) was released in early 2024 by the Joint Task Force of the ACM, IEEE-Computer Society, and AAAI. Viewed from an educational perspective, CS2023 reflects the decennial changes in computer science (CS) since the release of the previous curricular guidelines, Computer Science Curricula 2013. CS2023 comprises curricular content and curricular practices: the former includes updates to the computer science knowledge areas and a competency model framework while curricular practices cover other aspects such as program design and delivery issues.

Studies show that both test anxiety (TA) and self-efficacy (SE) have an impact on academic performance and that different students experience TA at different levels. For example, TA has consistently been shown to be higher and SE to be lower for women than men. In our study, we explore how TA and SE are experienced by CS1 students in a computer-based testing environment and how this changes by demographic group. We build on our prior work, focusing on groups of students based on their prior programming experience (PE) and whether or not they are first in their family (FIF) to attend a post-secondary institution. We measure self-reported TA and SE at five points and relate these to grades. We find that while novices report higher TA, lower SE, and lower grades than their peers, TA is only correlated with grade for experienced students.

Users of parallel patterns need to carefully consider many subtle aspects of software design. In particular, implicit relationships with hardware realities coupled with aggressive strategies for optimization are daunting in this domain. This paper proposes a new way to leverage visual cues in HiLPR, a proposed uniform representation for parallel patterns. We show the application of this approach to three design patterns: Sparse Linear Algebra, Pipeline, and Shared Queue. An evaluation of the combination of a pattern's Forces with its Solution within this representation indicates that this approach holds promise in terms of assisting developers in making better-informed decisions about pattern implementation.

Multi-core programming presents developers with a dramatic paradigm shift. Whereas sequential programming largely allowed the decoupling of source from underlying architecture, it is now impossible to develop new patterns and abstractions in isolation from issues of modern hardware utilization. Synchronization and coordination are now manifested at all levels of the software stack, and developers currently lack the essential tools to even partially automate reasoning techniques and system configuration management. As a first stage to addressing this problem, this paper proposes a framework for a tool suite designed to partially automate the acquisition and management of static system visualization in a feedback loop with dynamic execution properties. This model enables developers to find a best fit system configuration, potentially reconciling resource contention and utilization tensions that are critical to multi-core platforms. The application of a prototype of this suite, Deja View, demonstrates how tool support can aid reasoning about causally related sets of changes across system artifacts.