The structure of a computer science (CS) degree program can create barriers that prevent students from discovering or completing the major and limit broadening participation in the field. This study examines whether ABET accreditation requirements influence the degree structure in undergraduate CS programs by analyzing 34 ABET-accredited programs and 39 non-accredited programs. Using the lens of curricular complexity, we found that ABET-accredited programs have higher curricular complexity than non-accredited programs (mean = 189.6 versus. 129.0 respectively, Cohen's d = 1.6). This result is consistent across three home college categories (Computing, Arts & Sciences, and Engineering), indicating that the increased complexity from ABET is independent of home college. A deeper analysis of degree structure reveals that the increased complexity can be attributed to the number and placement of math and CS courses and the path lengths of the prerequisite chains. Understanding these structural influences can help institutions design CS programs that maintain ABET accreditation while reducing barriers to student success. All students should be able to succeed in CS regardless of the ABET accreditation status of the program.

In 2021-2022, the Computer Science and Math departments at Colorado State University replaced the traditional two-course Calculus sequence (Calculus I and II) with a single-semester course tailored to computer science needs. Unintentionally, this change also led the department to accept classic Calculus I taken by students before they transferred into Computer Science as meeting the major's Calculus requirement. We now have four years of data regarding the success of Computer Science students who only took (some version) of Calculus I, vs. those who took both Calculus I and II.

As computing skills become increasingly vital across disciplines, programs designed as ''bridges'' between prior degrees and a master's in computing have emerged as important pathways for individuals with non-computing backgrounds to prepare for graduate study in computing-related fields. The bridge typically consists of a set of courses providing a foundational understanding of topics often covered in an undergraduate computing program.

CS 2: Data Structures is arguably one of the most central courses in a Computer Science degree, but how it is taught and the exact topics are often an area of debate among educators.

As computer science education continues to evolve, minors in Computer Science represent an important but understudied pathway for students to develop computational thinking skills and interdisciplinary expertise. This study examines 104 Computer Science minor programs from the top 120 universities by graduation rates to understand their current structure, requirements, and accessibility.

The growing demand for computing professionals calls for innovative pathways that welcome individuals from non-computing backgrounds. Post-baccalaureate bridge programs help broaden access by supporting learners from diverse academic disciplines, career trajectories, and socioeconomic backgrounds. The MS Pathways to Computing (MSPC) Consortium, a network of 36 U.S. institutions, advances these efforts to create equitable pathways to master's degrees in computer science.

Curricular analytics can help researchers in computer science (CS) education understand the structural barriers preventing students from discovering and completing the major, especially those underrepresented in tech. Prior research has found an inverse relationship between curricular complexity and percent of CS graduates who identify as women. This study expands upon previous research by focusing specifically on ABET accredited curriculum with respect to complexity and the representation of women. We created curricular maps for 40 ABET and 40 non-ABET programs. Initial findings show that non-ABET programs, on average, have a higher degree of representation of women (33% compared to 21%). Further, the average complexity score for ABET accredited programs is 219, where as non-ABET programs is 133. This poster discusses the use of curricular analytics, our preliminary findings, and further questions to be addressed as part of this work.

Computer science degree requirements often have a rigid pre- and corequisite structure, which can impede a student's progression through a degree and in particular can add one or more semesters to time to completion, particularly for those students who need to retake a course that serves as a prerequisite to other courses and for students who are not calculus-ready when they enter university. In this paper, we present the results of a comparative analysis of curricula before and after a major structural revision. The first curriculum adheres to the conventional, rigid prerequisite structure, while the second emphasizes student choice and multiple pathways through the degree. No changes were made to course content/outcomes between the two versions. Employing curricular metrics such as complexity and centrality, we examine the degree progress of 3010 students over a six-year period. Specifically, our investigation looks at the impact of reducing curricular complexity on student attrition from and attraction to the CS major. The new curriculum, with a 60% reduction in curricular structural complexity, showed both increased retention of students over the old curriculum (67% to 98%) and an increase in the number of students converting from undeclared to computer science (44% to 69%). Our findings demonstrate that reducing curricular complexity need not compromise program rigor and can benefit students by providing greater flexibility and ensuring earlier exposure to (and therefore retention in) CS.

Not all degree programs are created equal. Indeed, the structure, prerequisites and overall complexity of some programs create barriers that impede student success. Inspired by the methodology of previous papers investigating the inverse relationship between curricular complexity and program quality, in this paper we investigate the relationship between curricular complexity and the representation of women earning CS degrees. We created curricular maps of 60 computer science degrees and calculated measures such as program complexity, course blocking, delay factor, and total math/CS credits to understand complexity's correlation with the representation of women CS majors. Our results show that degree complexity, blocking factor, and delay factor are all inversely related to the representation of women. In addition, we present the courses that most commonly impede student progress and provide suggestions to enhance degree programs based on the insights gained.

Students in entry level CS courses come from diverse backgrounds and are learning study and time management skills. Our belief for their success is that they must master a growth mindset and that the final grade should represent their final mastery of topics in the course. Traditional grading systems tend to be too restrictive and hinder a growth mindset. They require strict deadlines that fail to easily account for student accommodations and learning differences. Furthermore, they run into averaging and scaling issues with 59% of a score counting as failing, making it difficult for students to redeem grades even if they later demonstrate mastery of topics.

Intervention in the form of changing one's teaching style is beneficial for boosting student grades and retention. However, in spite of the availability of multiple intervention approaches, a key hindrance is reliance on the belief that students know how to study.

Computer Science (CS 1) offerings in most universities tend to be notoriously difficult. Over the past 60 years about a third of the students either fail or drop out of the course. Past research has focused on improving teaching methods through small changes without changing the overall course structure.

In 2018, Colorado State University redesigned their CS-0 course to become a general education requirement for the university within Arts and Humanities, and a guaranteed transfer course across the state for a similar category in other universities. The first CS course in the State to be accepted as a GT-Pathway course. This redesign had to be carefully done due to a need to introduce liberal arts style topics such as CS History, Philosophy and Ethics and Inclusive Design issues, while maintaining current coding and student success standards that were already expected for the CS-0 at the university. We termed this combination as Culture and Coding. In order to add more without reducing retention, the course was redesigned around the Psychology of Learning and spacing of topics in a "Spiral" Manner. Each topic was briefly introduced, and throughout the semester, students would dive deeper into the topics. This allowed for a 50% reduction of time focused on teaching coding topics, with students performing equivalent on exams compared to previous models of the course that focused 100% of the time on coding topics. Furthermore, students taught by the spiral teaching method outperformed students taught using traditional methods in the follow-on course. Our evaluation suggests that the spiral model of teaching computer science may allow for greater retention of topics, allowing classes to either cover additional concepts or go more in depth on current topics.

The superior performance of students with prior exposure to programming has long been evident to faculty that teach introductory courses. In this paper we replicate previous studies that quantify the difference between students with and without previous programming experience, and we provide further focus on gender differences. Our research is based on an initial CS1 course that we divided into a section with students having previous programming experience (P) and two sections for students without (N). Both sections of CS1 were taught with the same curriculum and assessments. We find that the advantages of prior experience are substantial, with P students outscoring N students by more than 6% on exams and 10% on programming quizzes. However, the performance gap between P and N students narrows considerably by the end of the following CS2 course, with no significant difference in overall scores. Analyzing results by gender, our data shows that 22% of N students in CS1 are female versus only 12% of P students. However, the female students with prior exposure outperform their male peers in all areas. Another finding of our research is the confirmation of a significant difference in confidence between female and male students.