College has a lot of unwritten rules, and some of our students will matriculate knowing those rules while others don't. Research has shown that revising homework assignments to make these unwritten rules explicit can help students' academic confidence and sense of belonging. This tutorial is intended for faculty who may be interested in revising their assignments to make those unwritten rules clear to students. We will focus on Transparent Assignment Design, a framework used to make explicit the purpose, task, and criteria of assignments. Come to this tutorial for a crash course on making your assignments transparent followed by hands-on help! Please bring an assignment you are interested in revising (or we will provide samples) and your choice of editing tool (e.g., laptop, pen/paper).

In this paper, we describe a CS+Social Science Python project that can be integrated directly into high school classrooms, enabling students to explore social science questions using computer science. The project uses the pandas library and Google Colab to give students an authentic experience with data science tools. We present teachers' experiences and students feedback from implementing the project in three high school classes, one non-STEM class and two AP CS classes. The project is designed to be simple enough for students with no CS background to succeed, but creative and open-ended enough to allow students with experience to develop their skills further. Students from both courses report the project was interesting and useful. Our work builds upon the body of literature examining ways to include CS in non-STEM high school courses, but also appears to fit well into CS curricula.

Background: Instructional transparency makes a course’s learning goals, evaluation criteria, and path to success clear to students, with the goal of improving equity in higher education. Increased transparency may improve equity by bolstering students’ self-efficacy and sense of belonging in computing, both of which are correlated with persistence in the field. Purpose: We aim to understand whether there are group differences in how students perceive and benefit from instructional transparency. We are additionally interested in understanding whether perceiving instructional transparency is positively correlated with students’ self-efficacy and sense of belonging and, therefore, can contribute to the persistence of students from historically underrepresented groups in computing. Methods: To investigate these relationships, we used linear regressions to analyze survey responses from 11,046 undergraduate students from 203 institutions. Findings: We found that there are group differences in students’ perception of transparency in their CS courses: students who identify as women, first-generation college students, and/or disabled reported perceiving less instructional transparency than their peers. We also found that perceiving more transparency has a positive correlation with students’ self-efficacy and sense of belonging in computing while controlling for important confounding variables, such as prior CS experience. We further demonstrated that this relationship is different for certain groups of students: first, for Black students and first-generation college students, perceiving transparency has a larger positive impact on their self-efficacy, and second, for Hispanic students, perceiving transparency has a smaller positive impact on their sense of belonging. Contributions: Our work constitutes one of the first empirical, multi-institutional investigations of the perceptions and benefits of transparency in CS classrooms that focuses on group differences. Our work also includes a theoretical articulation of the mechanisms through which transparent teaching practices may influence students’ self-efficacy and sense of belonging in computing. Taken together, our empirical findings and theoretical argument provide important evidence for the benefits of instructional transparency in CS courses, particularly as it relates to improving equity in computing.

Broadening participation in computing (BPC) is a focus in industry and academia. Extant research focuses on what broadening participation in computing (BPC) efforts are pursued, while we propose focusing on how change happens. Our qualitative study applied John Kotter's (2012) eight-stage change framework to analyze interviews with faculty and staff engaged in BPC efforts. Illustrative examples from our interviews elucidate each of the eight stages and how they can be applied to pursue organizational change efforts that support BPC.

Racial and gender representation among computer science (CS) students continues to lag behind national demographics in the U.S. One way to improve students' interests in CS is to connect CS to other fields to expand students' perceptions of what constitutes CS. While CS+X programs, which combine CS and another field into a single interdisciplinary degree, are expanding at the undergraduate level, there is room to further expand related opportunities in K-12 spaces to encourage more students to pursue CS. To this end, in this experience report we present a new CS+X topics course for high school students that teaches about the intersections of CS with several non-STEM "+X" fields. The course was designed by a team of educators with experience in K-12 curriculum design and broadening participation programs. We piloted the course at a high school in Spring 2023 with 11 students. We present our course design and breakdown of decisions made during the course design process. Further, we provide results from our evaluation survey, featuring thematic analysis of students' commentary and a breakdown of course topics and components students favored. Our students reported that their interests in computing and understanding of computing's broad impacts on society improved. We provide a reflection on the course's future refinements and our plans for further testing of the course in more high school environments to prepare it for wider community adoption.

No abstract available.

In traditional scientific publishing, studies are designed, conducted and written-up, then submitted to a conference or journal for peer review. This means that any feedback from reviewers on the design of the study is too late to be acted upon. It also means that reviewers cannot detect questionable practices such as bending of the written-up study aims to match the found data, or modification of the analysis to produce a statistically significant result. Registered Reports are a new kind of scientific publication process where the study design is written-up and peer reviewed before the study is conducted. This way, changes to data collection or procedure can still be made in response to reviewer comments, and the analysis has been stated on the record. The accept/reject decision is also made in principle at this stage, which avoids the decision being based on whether results are significant and can increase researcher confidence that the work will be published. At the time of writing, Registered Reports are available at the Computer Science Education journal and are being added at ACM Transactions on Computer Education. If not publishing at these venues, preregistration is a voluntary alternative where the design is published at a trusted third-party site like the Open Science Foundation (with an embargo), but without the peer review and early accept/reject. This birds of a feather session will bring together researchers with an interest in registering their study, reviewing such studies, or who have questions or interest in this new process.

Students' goals may be one of many factors contributing to the underrepresentation of women, people who identify as Black, Hispanic, Latinx/a/o/*, or Native, and first-generation college students in computing. This study examines whether students who desire a career that enables them to pursue communal goals: goals of working with or for the benefit of others (e.g., have a social impact, serve humanity, help others, or give back to their community) may be deterred from computing if they perceive it as incompatible with those goals. Using survey data from over 45,000 undergraduate students, results show that women, compared to men of their same racial/ethnic identity, endorse social impact goals at higher rates, and that the relationship between a student's sense of belonging in computing and their goals is moderated by their perception of the communal goal affordances of computing.

In this workshop, participants will learn to use physical Java memory models to help students develop a deep conceptual understanding of Java variables. These physical models use the metaphor of a "remote control" to introduce references. Using table-top versions of the physical models, participants will discuss and be prepared to use physical objects to help students understand: (1) how primitive variables and references variables behave in similar and different ways, (2) how reference variables that both reference an object or array can both modify its content, (3) how calling a method with an argument creates a local variable within that method, and (4) inside an object method, the variable this references the object we called the method on. Participants will receive a set of these physical models to use in their classroom.