Syllabusly is a free, online tool designed to help teachers of introductory computer science refine their syllabus language for improved student self-efficacy and motivation. Instead of altering the underlying class policies, Syllabusly suggests changes to the presentation and framing of those policies. The tool targets instances of language associated with loss-based framing of grades, fixed mindsets, anti-help-seeking, and biased or unwelcoming language. Within its interface, an instructor can accept or reject suggestions made by the algorithm and learn more about why those revisions were suggested. This tool enables teachers to provide better support to their students without adding a significant time commitment to their teaching load. This poster outlines the rationale and goals of this tool, as well as the user-centered design process employed to develop the interface and algorithm. During this poster session, an interactive demo will be presented.

First-year undergraduate computer science students have less investment in the field, making switching to another field of study an easier decision. This study extends understanding of undergraduate women students' experiences in computer science by spotlighting the development or lack of development of peer relationships in the first year of study and how these relate to in-class experiences. Interviews with 36 demographically diverse women from 22 U.S. campuses revealed that classroom teaching practices encourage or discourage development of community and belonging, that peer interactions shape positive and negative perceptions of self-efficacy, and that in the absence of formal curricular interaction, some students sought belonging in extracurricular activities unrelated to their computing major. A fifth of the students described exclusionary behaviors that also influenced sense of belonging, while many more experienced inclusionary behaviors. The poster describes the study, its findings, and relationship to existing research, and concludes with a discussion of implications for CS first-year student experiences.

Self-efficacy has long been linked to student motivation and retention in computing and other fields. While alternatives to traditional points-based grading systems (e.g., specifications grading) are gaining interest in the computing education research community, little research has investigated its impact on students' self-perceptions. Even variations in points-based grading, such as policies regarding attendance and late work, have not been sufficiently analyzed to understand their relationship to student self-efficacy. With this project, my aim is to identify and encourage best-practice grading policies for positive student self-efficacy in introductory computing classes.

Computer science faculty are concerned with retaining students who are admitted into their postsecondary programs of study. Some students leave because their interests change, but others leave for structural reasons that could be addressed by faculty. For example, students may leave because of unbalanced workloads, poor social climate among students, assignments that seem to have no personal relevance, or other issues that can be remedied. An exit survey can give departments actionable evidence about what makes their students leave, including whether some groups of students are more likely to leave than others. Not only can a department benefit by routine formative evaluation, it can benefit by allowing students' voices to be heard, implicitly communicating care for students' experiences. In this paper, we introduce an evidence-driven exit survey that undergraduate computing departments can adapt to encourage feedback from CS leavers. We present our development methodology alongside proof-of-concept data to demonstrate the nature of results and the implications for a department that adopts this tool.

Despite rising enrollment, CS Education struggles with training adequate educators, leading to increased teaching loads. Open-source teaching materials alleviate this by streamlining course preparation. Yet, there is a scarcity of free, open curricula that offer a contemporary coding experience while covering CS fundamentals. To address this gap, we introduce the CS1 Python Bakery curriculum with a "Batteries Included" approach, aiming to furnish instructors with comprehensive teaching resources. This curriculum refines an earlier open-source CS1 with detailed lesson plans, slides, rubrics, reference answers, student answers, and more. We present the learning content in a cross-platform, autograded textbook format and embrace modern Python features such as Dataclasses and static types. We deployed the curriculum in multiple university CS1 courses and collected data on the tradeoffs of our approach. This paper offers a thorough self-assessment based on student learning outcomes, code snapshot analyses, and reflection via the TEC Rubric for curriculum evaluation. Although we improved teacher accessibility, the change in student learning outcomes was unexpectedly minimal. Recognizing room for advancement, we conclude with recommendations for our next iteration to emphasize Equity, Community, and Identity.

As undergraduate computer science enrollments continue to grow, individualized instructor attention becomes increasingly scarce. The impact of social distance between students and their teachers is particularly apparent in large introductory classes, exacerbated by students' lack of common prior experience in computer science. Some institutions remedy class size and experience gaps by hiring advanced undergraduate students as teaching assistants for their introductory courses. However, without the resources to carefully hire, train, and manage undergraduate teaching assistants (uTAs) during the semester, their potential as trustworthy peer mentors and helpful tutors often goes unrealized. This poster presents details of the uTA hiring process, training course, and management strategies used during the 2022-2023 academic year for the introductory computer science courses at the University of Delaware (UD), a large, public, research-focused institution. This system was designed for introductory CS classes, with the goals of low instructor overhead, long-term scalability, and the development of empathetic teaching assistants who could motivate students toward successful learning. Highlights of the system include a hiring process that considers candidates' personality and enthusiasm alongside their technical skills, asynchronous training provided through the university's learning management system, and the use of a head uTA to manage other staff members. Following the implementation of these policies, instructors and uTAs alike reported positive experiences compared to previous semesters without significant changes to students' learning outcomes.

At the University of Delaware, undergraduate teaching assistants have been used for several years to support students in many of the core computer science courses. Meanwhile, like countless other institutions, computer science instructors continue to be stretched to fit the greatest number of students possible. As universities continue to search for additional faculty members, the untrained undergraduate course staff is still a source of untapped educational potential. Unlike some institutions' developed and robust hiring and/or training processes, computer science TAs at UD typically are hired without an interview or written application and are not given training materials by the department at large. With a newly developed hiring process and training course, undergraduate teaching assistants for UD's two CS1 courses, CISC108 (Introduction to Computer Science I) and CISC106 (General Computer Science for Engineers) were carefully selected and prepared to serve our department's newest students during this fall semester, with the goal of ensuring each student's feelings of support throughout the semester.