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CS4All initiatives nationwide have been working to increase and diversify student participation in computer science (CS). One intentional effort to broaden participation in CS was the launch of the Advanced Placement (AP) CS Principles (CSP) course, which sought to increase the number of students enrolling in CS overall as well as from groups historically underrepresented in CS. Early AP CSP implementation results are encouraging and have identified the need to better understand essential supports for quality implementation, differential student experiences and outcomes, and students’ motivations for course enrollment. In this paper, we explore the motivations that affect student decisions to take AP CSP using survey data collected during fall 2019 in the New York City public schools, the largest school district in the U.S. This work is part of an ongoing research-practice partnership that provides teacher and school supports for AP CSP implementation and aims to improve outcomes especially for female, Black, and Latinx students in high-need schools. In particular, we examine how students’ reasons and influences for enrolling in AP CSP may differ based on self-identified gender and race/ethnicity. Our findings indicate that while most students shared an interest in learning more about CS, students from communities historically underrepresented in computing are more likely to report being placed in the course and to be influenced by guidance counselors. The implications of these results highlight the importance of understanding why students choose AP CSP in developing recruitment resources, student engagement strategies, and supports for implementation. 

Seymour Papert’s 1972 paper “Teaching Children to be Mathematicians Versus Teaching About Mathematics” started with the summary statement “The important difference between the work of a child in an elementary mathematics class and that of a mathematician is not in the subject matter…but in the fact that the mathematician is creatively engaged….” Along with “creative,” a key term Papert kept using is project rather than the common notion of problem. A project is not simply a very large problem. It centrally includes a focus on sustained and active engagement. The projects in his illustrations were essentially research projects, not just multi-step, fullyprescribed, build-a-thing tasks, no matter how nice the end product might be. A mathematical playground with enough attractive destinations in it draws children naturally to pose their own tasks and projects—as they universally do in their other personal and group playgrounds—and to learn to act and think like mathematicians. They even acquire conventionally taught content through that play. Physical construction was always available, and appealed to such thinkers as Dewey, but for Papert computer programming, newly available to school, suggested a more flexible medium and a model for an ideal playground. A fact about playgrounds is that children choose challenge. In working and playing with children I’ve seen that puzzles tap some of the same personally chosen challenge that a programming centric playground offers. Children are naturally drawn to intellectual challenges of riddles (ones they learn and ones they invent) and puzzles; and adults are so lured by puzzles that even supermarkets sell books of them. So what’s the difference between real puzzles and school problems? What’s useful about creating a puzzle or posing a problem? How might puzzles and problem posing support mathematical learning? And what’s constructionist about this? This plenary will try to respond to these questions, invite some of your own responses, let you solve and create some puzzles, and explore how problem posing in programming and puzzling can support mathematics even in an age of rigid content constraints.