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With the CS for All movement increasingly gaining traction nationally, students entering colleges and universities are arriving with deeper and broader CS experiences. This in turn can change students' higher education starting point. This panel of CS faculty with expertise in this area will present perspectives and models to describe how higher education choices for placement, credit, and curriculum design affect the efforts to broaden participation in student pathways into computing and related studies.

Over the past decade, politicians, leaders, and pundits have called for computing and computer science education opportunities to be made available earlier and earlier. Such calls have led to the creation of a wide variety of offerings for students at middle-school and even elementary levels, including summer "code camps" targeted at middle-school students. Such camps often emphasize fun aspects of computing, such as games and robots. In contrast, research at the collegiate level suggests that meaningful applications of computing, such as computing for social good, are more successful at building and sustaining interest, particularly among students from groups traditionally underrepresented in computing. In this project, we developed and offered a summer camp that draws upon ideas and approaches from the digital humanities (DH), which explore the use of algorithms and computation in support of broader humanistic inquiry. Because DH reveals different ways to apply algorithmic and computational thinking, DH has the potential to attract students who might not otherwise consider computing. In this paper, we introduce central issues in the digital humanities, explain the rationale for the camp design, describe the camp curriculum, and reflect on successful and less successful aspects of the camp. Among other things, we consider how to introduce digital humanities topics to students who have not yet heard the term "humanities" and explore the utility of such topics for this age group. We also present preliminary data on the short-term effects of the camp on students' self-efficacy and interest in computing.

As outlined in the ACM Computer Science Curricula 2013 Guidelines section on Social Issues and Professional Practice, "Students must also be exposed to the larger societal context of computing to develop an understanding of the relevant social [and] ethical ... issues." In this panel, we demonstrate diverse approaches used to achieve this goal with respect to civic engagement. Drawing from experiences with non-major, introductory computing, mobile applications, software engineering, and interdisciplinary courses, we discuss how to move beyond surface-level discussions of ethical case studies toward an integration of civic engagement activities and personal reflection into standard computing curriculum.

The underrepresentation within computer science of women, domestic students of color, and students with lower socioeconomic-status remains a national issue. Recent studies demonstrate two critical factors: Persistent stereotypes about "who does computer science" can preclude interest in the field for members of these groups; many also perceive computing as "irrelevant" and "asocial". While these issues must be addressed at multiple ages and levels, many suggest that we should start early, before students have developed stereotypes. As a step in combating (mis-)perceptions of ability and relevance, we developed and conducted a spectrum of week-long summer "code camps" for regional middle-school students. These camps emphasize meaningful uses of computing, on building self-efficacy, and on broadening understanding of who does and can do computer science. In this paper, we focus on our "data science for social good" (ds4sg) camp, in which students explored computational approaches to data science through a lens of computing for social good, discovering how computing helps them not only better understand societal issues but also convince others to address problems. We discuss the rationale for the curriculum and its content, including our uses of pair programming, personal projects, and a growth model that brings students from block-based programming to professional Jupyter data notebooks. We consider the short-term effects the camps have on students' self-efficacy and perceptions of computer science. We conclude with recommendations and guidelines for those intending to offer similar camps.

The underrepresentation of women, students of color, and people from lower-SES (socio-economic-status) backgrounds within computer science remains a national issue. Recent studies demonstrate one reason: persistent stereotypes about "who does computer science" can cause minority groups to preclude interest in the field. Unfortunately, these stereotypes are not the only threat to diversifying the discipline. Engaging a diverse group of individuals in computer science is unlikely so long as computing curriculum is perceived as "irrelevant" and "asocial/". Confronting the stigma of triviality applied to computer science, much like approaching student perceptions about who can do computer science, is a necessary step for increasing the perceived applicability of the discipline. As a step in combating the perception that programming curriculum is irrelevant, we developed and ran a week-long summer coding camp for local middle school students that camp applied principles of computing for social good with data science approaches to encourage campers to consider how computing could help them explore societal issues. Throughout the week, campers learned how to extract meaning from their work and gained experience collaborating in pairs to solve problems. In this poster, we discuss the camp curriculum and its emphasis on the applications of computing. We consider the effects of the camp on the self-efficacy of campers and their perceptions of computer science. We then present some recommendations for those intending to conduct similar camps.

As part of the development of a new interdisciplinary initiative in data science that draws from statistics, mathematics, computer science, and the social sciences, we have developed a new introductory CS course that emphasizes data science and that we refer to as DataCSCi. Unlike other introductory data science courses, such as Berkeley's Data 8, our course retains the broad array of concepts necessary not only to introduce programming principles related to data science, but also to prepare students for the second course in our standard introductory computer science sequence. In particular, the course includes coverage of recursion (numeric and structural), unit testing, linked data structures, and other concepts we rely upon in subsequent courses in computer science. At the same time, we introduce students to a wide variety of techniques and approaches that support them in their subsequent work in data science, including techniques for wrangling, cleaning, and visualizing data. We achieve this combination of breadth and depth through two core approaches: We focus on a spiral "use then implement" approach and we focus on a functional model of programming using Scheme/Racket. While Python and R are the most commonly used languages for data science, we find that Scheme works particularly well to introduce students to concepts both complex, like map-reduce, and simple, like list filtering. In this paper, we report on the design of the curriculum, particularly the capstone project and the ways in which we incorporate the burgeoning subfield of data science for social good.

Quick. Think of an outreach activity in computer science and a target audience. If you're like most people, you think of the target audience as middle-school students or high-school students and you think of activities like robotics, app development, or games. As a computer science educator, you might also note that these outreach activities should not just be designed to bring more people to CS, they should be designed to bring more people underrepresented in computing to CS-female students, domestic students of color, and lower-socio-economic-status students. But at the College level, what topics have shown the most efficacy in diversifying the discipline? It's not necessarily robotics, app development, or games. For example, the Media Computing project, led by Mark Guzdial, has shown the power of computing for the arts as a motivating factor for college-age female students. Can such an approach work for younger students? In this project, we developed and presented a week-long "craft of code" camp for elementary school students in which they used block-based languages to explore three kinds of creative computing, two of which had tangible output: programmable embroidery using TurtleStitch, programmable 3D models using BeetleBlocks, and programmable storytelling using Scratch. In this poster, we describe the curriculum for the camp, explore design issues, present results, and suggest approaches for others interested in developing similar camps. Our curriculum and materials are available at https://codecamp.sites.grinnell.edu/craftofcode/.

Student projects play a central role in the CS curriculum and are can be used everywhere, from low-level courses to upper-level capstones. Projects help students tie together ideas from their courses and put them in a "tangible" form for which they can feel a sense of pride. Projects also provide a clear way for students to show off their work to others. When we talk about student projects, we often focus on the functionality of the project: "Build something that does this."The modern Web suggests that software (or at least Web-based software) is often defined by more than a core computational task; in addition to the core functionality, modern Web software situates that core functionality in a broader "ecosystem" that often includes (a) a community of users that share ideas and comment on each others' work; (b) ways to link the software to and from social media sites, such as Facebook and Twitter; and (c) analytic tools to understand how people use the software; and more.I report on an experiment in which I asked students to expand their Web software project (an online drawing application) into a broader ecosystem. The extension from core functionality to broader ecosystem helped students think about different issues in computing, encouraged students to find aspects that most interested them (e.g., some liked analytics, some liked building the functionality, some liked thinking about how to create communities, and some liked designing and testing the interface), and made students think more carefully about their responsibilities as software builders.