In this session, members of the ACM Data Science (DS) Task Force will present the final draft of Computing Competencies for Undergraduate Data Science Programs. Drafting this document has been a three-year process, in which the task force has released preliminary drafts, sought input from the community, and responded to the community's helpful feedback. Our intent is that the session be an exchange that will clarify the contents of the report and provide participants with ways to put the report into practice at their own institutions. This session should be of interest to all SIGCSE attendees, but especially to faculty developing college-level curricula in Data Science.

We have created, piloted and are growing the Align program, a Master of Science in Computer Science (MS in CS) for post-secondary graduates who did not major in CS. Our goal is to create a pathway to CS for all students, with particular attention to women and underrepresented minorities. Indeed, women represent 57% and underrepresented minorities represent 25% of all bachelor's recipients in the U.S., but only 19.5% and 12.6% of CS graduates, respectively. If we can fill this opportunity gap, we will satisfy a major economic need and address an issue of social equity and inclusion. In this paper, we present our "Bridge'' curriculum, which is a two-semester preparation for students to then join the traditional MS in CS students in master's-level classes. We describe co-curricular activities designed to help students succeed in the program. We present our empirical findings around enrollment, demographics, retention and job outcomes. Among our findings is that Align students outperform our traditional MS in CS students in grade point average. To date we have graduated 137 students and 827 are enrolled.

The ACM Data Science Taskforce was established by the ACM Education Council and tasked with articulating the role of computing discipline-specific contributions to this emerging field. This special session seeks to provide an update of the work of the ACM Data Science Taskforce as well as to engage the SIGCSE community in this effort. Members of the taskforce will report on version 2 of a draft report released Fall 2019, and the activities to-date, including a summary of data science curricular efforts to date, as well as the current articulation of computing competencies. This session should be of interest to all SIGCSE attendees, but especially faculty developing college-level curricula in Data Science.

In August 2017, the ACM Education Council initiated a task force to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force is seeking to define what the computing contributions are to this new field, in order to provide guidance for computer science or similar departments offering data science programs of study at the undergraduate level. The ACM Data Science Task Force has completed the initial draft of a curricular report. The computing-knowledge areas identified in the report are drawn from across computing disciplines and include several sub-areas of AI. This short paper describes the overall project, highlights AI-relevant areas, and seeks to open a dialog about the AI competencies that are to be considered central to a data science undergraduate curriculum.

Data science encompasses elements of statistics, computer science, and mathematics as well as domain-specific knowledge, suggesting that with interdisciplinary conversations and collaborations we can make data science curricula more comprehensive and successful. Development of such programs provide an exciting collaboration opportunity for faculty in statistical and computer and mathematical sciences as well as a wide array of other disciplines. This BOF provides an opportunity for faculty involved/interested in the development of undergraduate data science curricula to come together and discuss forming collaborations across disciplines and what the disciplines can learn from each other. The discussion will be led by faculty from computer science and statistics departments who have been involved with both disciplinary and interdisciplinary data science education initiatives. One of the goals of this BOF is to jumpstart conversations across disciplines, which we hope the audience will continue at their home and/or nearby institutions.

The ACM Data Science Task Force was established by the ACM Education Council and tasked with articulating the role of computing discipline-specific contributions to this emerging field. This special session seeks to introduce the work of the ACM Data Science Task Force as well as to engage the SIGCSE community in this effort. Members of the task force will introduce key components of a draft report, including a summary of data science curricular efforts to date, results of ACM academic and industry surveys on data science, as well as the initial articulation of computing competencies for undergraduate programs in data science. This session should be of interest to all SIGCSE attendees, but especially faculty developing college-level curricula in Data Science.

Undergraduate research is a critical component of high-quality education in any discipline, including Computer Science (CS). Over the past few years, there has been a dramatic increase in CS undergraduate research activities at colleges and universities, and predominantly undergraduate institutions (PUIs) have an important role to play. Not every university has abundant resources to devote to research, and teaching-focused institutions may face the greatest challenges in this respect. Faculty at PUIs, for example, may face funding and infrastructure challenges and may find themselves stretched thin due to especially high teaching and service expectations. A frequently asked question by new faculty at these institutions is: Is it really possible to conduct meaningful research in such a fast-paced discipline as CS, while juggling a very high teaching and service load? Not only is the answer to this question "Yes!" but there are advantages to conducting research at a non-research institution. Faculty here has access to some of the brightest young minds who will potentially be future graduate students in research-intensive universities. They may have the freedom to do research that is too risky for graduate students. They can work on projects they are interested in, rather than those they know must work. With good time management techniques and careful selection of collaborators and student researchers, faculty here really can conduct important CS research. Thus, the focus of this BOF is to share methods that are helpful in conducting significant and meaningful CS research in a primarily undergraduate or teaching institution.

Roughly once per decade, the ACM and IEEE-Computer Society form a joint task force to produce curricular guidelines for undergraduate computer science programs. The latest guidelines document, Computer Science Curricula 2013 (CS2013), was released in December 2013. CS faculty at many institutions are interested in understanding CS2013, evaluating their curricula against it, and adopting some or all of the recommendations. This task is non-trivial at any institution, but it can be particularly challenging at small or liberal arts institutions. For instance, small schools with few faculty are limited in the number of courses they can offer, and many liberal arts colleges place limits on the number of courses that can be required for a major. How might a department with limited resources trade off covering the core while still providing electives to students? A meta-issue involves mapping CS2013 at all: Given the demands on a small department, are there ways to improve and update a curriculum without having to go through a complete CS2013 mapping? In this workshop we will: (1) give a brief overview of CS2013, (2) describe experiences mapping individual courses or an entire curriculum to CS2013, (4) split participants into working groups for course or curricular mapping based on starting points and goals, (4) re-group to share lessons learned from the mapping experience. Laptop Strongly Recommended.

No abstract available.

Involving undergraduates in Computer Science research has many benefits. It's an exciting way for students to gain independent problem solving skills. It exposes them to interesting projects and the research process, thereby keeping them in computer science, even encouraging them to go to graduate school. And especially in primarily teaching institutions, it's a rewarding way for faculty to remain engaged in their own research. In this workshop we will (1) present best practices for mentoring undergraduate research, (2) equip participants with resources for mentoring their own students, and (3) further develop (1) and (2) through breakout sessions on concerns of interest to attendees. For more, please see www.cs.williams.edu/~andrea/SIGCSE2013. This workshop is intended for all college level computer science educators. Laptop Optional.

Beginning with the publication of Curriculum 68, ACM and IEEE-Computer Society have sponsored various efforts to establish international curricular guidelines for undergraduate programs in computing. Work on the next volume, Computer Science 2013 is well underway, with the Ironman draft out shortly before SIGCSE 2013. The Ironman draft includes course and curricular exemplars, which should serve as a rich resource for those trying to meet the curriculum standards. This special session highlights the exemplar section of the Ironman report through a description of its purpose, presentation of several exemplars, and an invitation to the SIGCSE community to participate by submitting exemplars and providing feedback on what they would find useful in this section of the CS 2013 final report.

Because of the growing importance of concurrent programming, many people are trying to figure out where in the curriculum to introduce students to concurrency. In this paper we discuss the use of concurrency in an introductory computer science course. This course, which has been taught for ten years, introduces concurrency in the context of event-driven programming. It also makes use of graphics and animations with the support of a library that reduces the syntactic overhead of using these constructs. Students learn to use separate threads in a way that enables them to write programs that match their intuitions of the world. While the separate threads do interact, programs are selected so that race conditions are generally not an issue.

Many undergraduate liberal arts institutions offer computer science majors. This article illustrates how quality computer science programs can be realized in a wide variety of liberal arts settings by describing and contrasting the actual programs at five liberal arts colleges: Williams College, Kalamazoo College, the State University of New York at Geneseo, Spelman College, and Calvin College. While the example programs differ in size, mission, and the nature of their home institutions, all take advantage of their liberal arts setting to offer rich computer science educations. Comparing these programs to each other and to the latest ACM/IEEE Computer Society computer science curriculum shows that the liberal arts programs are distinguishable from the ACM/Computer Society recommendations, but at the same time are strong undergraduate majors.

The approach to teaching recursion in introductory programming courses has changed little during the transition from procedural to object-oriented languages. It is still common to present recursion late in the course and to focus on traditional, procedural examples such as calculating factorials or solving the Towers of Hanoi puzzle. In this paper, we propose that the shift to object-oriented programming techniques calls for a significant shift in our approach to teaching recursion. First, we argue that in the context of object-oriented programming students should be introduced to examples of simple recursive structures such as linked lists and methods that process them, before being introduced to traditional procedural examples. Second, we believe that this material should be presented before students are introduced to structures such as arrays. In our experience, the early presentation of recursive structures provides the opportunity to reinforce the fundamentals of defining and using classes and better prepares students to appreciate the reasons to use classes to encapsulate access to other data structures when they are presented.

We have recently designed a CS 1 course that integrates event-driven programming from the very start. Our experience teaching this course runs counter to the prevailing sense that these techniques would add complexity to the content of CS 1. Instead, we found that they were simple to present and that they also simplified the presentation of other material in the course. In this paper, we explain the approach we used to introduce event-driven methods and discuss the factors underlying our success.

In this paper we describe a library we have developed that supports an "OO-from-the-beginning" approach to CS 1. The use of real graphics "objects" and event-driven programming are important components of our approach. The design of interactive graphical programs helps students to both use objects and write methods early while designing and implementing interesting programs.