OCCTIVE is a library of short, animated videos introducing foundational computing concepts. The primary goal is to support faculty outside of Computer Science who are teaching computing in their courses but often lack the class time or background to cover foundational computing concepts that underlie their course materials.

Computing is everywhere, and it's here to stay. Computing is crucial in many disciplines and influences every discipline. It's unlikely we'll willingly return to a society unmediated by computing. How do our institutions proceed?

ACM/IEEE curriculum guidelines for computer science, such as CS2013 or the forthcoming CS2023, provide well-researched and detailed guidance about the content and skills that make up an undergraduate computer science (CS) program. Liberal arts CS programs often struggle to apply these guidelines within their institutional context and goals. Historically, this has been addressed through the development of model CS curricula tailored for the liberal arts context. We take a different position: that no single model curriculum can apply across the wide range of liberal arts institutions. Instead, we argue that liberal arts CS educators need best practices for using guidelines such as CS2023 to inform curriculum design. These practices must acknowledge the opportunities and priorities of a liberal arts philosophy as well as a program's mission and identity. This paper reviews the context and motivation behind computing in the liberal arts. We also review the history of liberal arts CS educators and ACM/IEEE curriculum guidelines. We present data and trends about liberal arts computing programs, discussing how this informs curriculum design. Finally, we propose a process that guides programs to work with curriculum guidelines through the lens of institutional and program missions and identities, goals, and situational factors.

As computing becomes increasingly relevant to all disciplines, interdisciplinary computing degrees become increasingly important. These interdisciplinary majors: 1) address the increasing need for computing knowledge across all disciplines; 2) have the potential to increase a student's employability; 3) give employers the opportunity to hire students who are trained in two fields relevant to the company; 4) by reducing the number of requirements for the computing degree, can alleviate some of the pressure faced by CS departments from booming enrollments ; and 5) broaden participation in computing - in particular, to increase the percentage of women. Despite these opportunities, as of 2022 there are only a few schools that have embraced this mission. There are significant implementation challenges to interdisciplinary majors: 1) university/college budget models often financially discourage interdisciplinary degrees; 2) difficulty determining the "unit" that will administer the combined degree; 3) already large demands on CS faculty time; 4) advisors/faculty must be trained to advise interdisciplinary majors; and 5) both disciplines have to agree to reduce their major course requirements. In this BoF session we will discuss these and other obstacles identified at the range of institutions represented at SIGCSE and how to overcome these implementation challenges.

Typical CS 1 classes are about many things. The problems and examples are drawn from a variety of domains, with a goal of teaching a computational problem-solving approach and specific language constructs. Many CS 1 courses begin with writing programs that perform simple calculations, transition to more substantial simulations or games in the middle, and perhaps one or two SIGCSE Nifty Assignments at the end. In this panel, we explore the 'strongly-themed' approach in which many of the programs and examples used in the course come from a single domain which provides a theme for the course.

Computer science curricula has been well defined for many years through the publication of the Computer Science Curricula reports developed jointly by the two major professional societies, the Association for Computing Machinery (ACM) and the IEEE Computer Society. These documents define computer science curricula by providing knowledge areas and course exemplars. The most recent curriculum report, the Computer Science Curricula 2013 (CSC13 [1]), provides 18 knowledge areas (KAs). Though it stresses that KAs do not necessary represent courses, computer science departments have traditionally created courses around the KAs. Indeed, the course exemplars presented in the CSC13 report, for the most part, center around KAs.

In this special session, we share a variety of ways in which CS 1 courses can broaden their focus beyond programming. These courses strive to effectively introduce the fundamental ideas of computer science, and the importance of computing in broader contexts, without sacrificing programming rigor.

No abstract available.

This workshop focuses on the NSF proposal review process. Via close examination of the review process, participants gain an understanding of how to write good reviews and how to improve their own proposal writing. The workshop covers the following topic areas: the proposal review process from submission of a proposal to award or decline; elements of a good review; NSF merit criteria (intellectual merit and broader impacts); elements of a good proposal; and how to volunteer to review proposals. The workshop uses a structured guided-interactive methodology to lead participants through each topic by introducing related issues, engaging participants in group exercises designed to explore and share their understanding of the issues, and then providing some "expert" opinion on these issues. Good and bad examples and a Top Ten List of Do's and Don'ts will be provided.

This session will provide information about the current status of Federal budget as it impacts NSF and the computing community. Representatives from both Education and Human Resources (EHR) and Computer and Information Science and Engineering (CISE) directorates will provide the most up-to-date information about available funding opportunities.

No abstract available.

In his SIGCSE 2007 keynote, Grady Booch exhorted us to share the "passion, beauty, joy and awe" (PBJA) of computing. This led to sessions that have provided a forum for sharing: What we've done: Highlighting successful PBJA initiatives the presenters have undertaken or seen and wish to trumpet; What we should do (curriculum): Pointing out where our curriculum is lacking in PBJA, and how to fix it; How we should do it (pedagogy): Sharing how a change in attitude/focus/etc. can make strides to improving PBJA.

No abstract available.

The number of undergraduates entering computer science has declined in recent years. This is paralleled by a drop in the number of high school students taking the CS AP exam and the number of high schools offering computer science courses. The declines come at a time when career opportunities in CS continue to grow and computer science graduates are seen as crucial in building a globally competitive workforce for the 21st century. Efforts aimed at reversing the declining interest in computer science include curriculum revisions at the undergraduate level at many institutions, a re-design of computer science AP courses [1], and the inclusion of computational thinking into disciplines outside computer science [3].

With the declining interest among incoming college students and the nationwide drop in computer science (CS) enrollments ([2, 1]), many CS departments would like to broaden participation in computing by reaching out to other departments on their campuses. One tactic is to encourage interdisciplinary work that has computing as a significant component. These efforts may result in more CS majors, increased enrollments in certain CS courses, and a student population that is better equipped to utilize computing within the contexts of their disciplines.

Researchers and practitioners in the field of expert systems all generally agree that to be useful, any fielded intelligent system must be adequately verified and validated. But what does this mean in concrete terms? What exactly is verification? What exactly is validation? How are they different? Many authors have attempted to define these terms and, as a result, several interpretations have surfaced. It is our opinion that there is great confusion as to what these terms mean, how they are different, and how they are implemented. This paper, therefore, has two aims—to clarify the meaning of the terms validation and verification as they apply to intelligent systems, and to describe how several researchers are implementing these. The second part of the paper, therefore, details some techniques that can be used to perform the verification and validation of systems. Also discussed is the role of testing as part of the above-mentioned processes.