Introductory programming (CS1) remains a major barrier to persistence in computer science, with high DFW (D, F, Withdrawal) rates and wide disparities in prior programming experience. At Tennessee Technological University, historical CS1 DFW rates ranged from 25-31%. Existing solutions reduce inequities but often add credit requirements or create administrative burdens. To address these challenges, we developed a self-selected, experience-based grouping (EBG) model in CS1. After receiving instructor guidance in the first week, students choose between Purple (novice/slower pace) or Gold (experienced/faster pace) groups, with structured opportunities to switch groups during the semester. Unlike track-based models, the EBG approach operates within a single course section, offering flexibility without altering credit structures. Grounded in Self-Determination Theory, this design embeds autonomy, competence, and peer connectedness to promote motivation and belonging. Across three semesters, EBG reduced DFW rates from historical averages of 25–31% to 18–19%. Course GPAs also improved, alongside stronger survey-reported confidence, belonging, and motivation. In Spring 2024, these results led my institution to adopt the EBG intervention across all CS1 sections. My dissertation includes this work as well as investigating the longitudinal impacts of using the EBG model on student preparedness, motivation, confidence, and persistence in CS2/CS3, and persistence in the computer science major.

The rapid evolution of computing demands curricula that reflect modern practices, yet many CS1 and CS2 courses continue to emphasize only sequential programming. This NSF-funded project addresses that gap by designing and disseminating exemplar CS1 and CS2 courses that integrate parallel, distributed, and event-driven computing as core concepts. The materials include unplugged activities and programming labs for both C++ and Java. To ensure broad applicability and adoption, development occurred in collaboration with instructors from six diverse institutions who are now implementing the materials. Evaluation includes surveys, assignment-specific instruments, and cross-team analysis. This poster presents the project’s vision, methods, and resources, highlighting how others can adopt and adapt them to teach modern computing.

Computer Science education, and all education for that matter, is being disrupted by Generative AI. While there have been few truly transformational technologies similar to AI, other incremental but impactful advances have helped shape the computing ecosystem. Other recent examples include the transition to multicore systems (requiring the promotion of parallel computing from an elective topic), the shift to graphical interfaces (raising expectations for assignments and motivating the creation of Media Computation), and the emergence of object-oriented programming. In this Birds of a Feather Session, we ask the question ''How might we redesign our CS1 and CS2 courses to better prepare students for emerging and future computing paradigms while maintaining strong foundations in problem solving, programming, and computational thinking?'' Using collaborative brainstorming techniques, participants will create a list of potential future paradigms (either disruptive or incremental) that are relevant to CS1/CS2, and develop proposed roadmaps that identify how those paradigms can be leveraged as contexts for teaching the existing CS1 and CS2 courses within the CS2023 curriculum.

Parallel and distributed computing (PDC) has become pervasive in all aspects of computing, and thus it is essential that students include parallelism and distribution in the computational thinking that they apply to problem solving, from the very beginning. Computer science education is still teaching a 20th century model of algorithmic problem solving, where sequence, branch, and loop are the only organizing principles needed for algorithms. We invest considerable time in showing how best to sequentially process large volumes of data. All computing devices that students use currently have multiple cores as well as a GPU in many cases. Most of their favorite applications use multiple cores and distributed resources. Often concurrency offers simpler solutions than sequential approaches. In this tutorial we overview key PDC concepts and provide examples of how they may naturally be incorporated in early computing classes. We lead participants through plugged and unplugged curriculum modules that have been successfully integrated and tested in existing computing classes at multiple institutions. We also discuss recent efforts at integrating AI methods, including LLMs, into early classes. In addition, we highlight other CDER activities for integration of PDC and AI into undergraduate computing curricula. Additional Information: No equipment or prior PDC experience is required, although a laptop that can run C++, Java and Python is recommended for following along with some code examples if desired.

Parallel and distributed computing (PDC) has become pervasive in all aspects of computing, so it is essential that students include parallelism and distribution in the computational thinking that they apply to problem solving, from the beginning of their computing education. With all computing devices that students use having multiple cores as well as a GPU in many cases, many students' favorite applications use multiple cores and/or distributed processors. However, we are still teaching them to solve problems using only sequential thinking. Why?

This work in progress innovative practice paper documents ways in which current introductory computing courses are designed for an earlier generation of computers. We describe our plans for updating these courses for modern systems and programming practices and share details of the development of exemplar courses that will be adoptable by diverse institutions and programs teaching introductory programming courses.

This Innovative Practice Full Paper addresses the challenges of distribution of prior experience among students in the first course for computing majors (CS1) by allowing students to self-select an experience-based group for their lecture class. Rising enrollments in computer science have caused many challenges for computer science educators in student learning, engagement, and success. Included in these challenges is the diversity of previous programming experience of new students. To address these challenges, we introduced a student-centered learning approach in a test section of our CS1 course whereby students were afforded the ability to self-select an experience-based group. We also had a control section of our CS1 course that remained in the traditional setting of not having experience-based groups. The results from our collected data suggest that having self-selected experience-based groups improves retention and student success.

Mirroring the trend of the growth of Computer Science (CS) programs nation and worldwide, the CS program in the College of Engineering at Tennessee Technological University has experienced similar growth in the number of students enrolling in its B.S., M.S., and Ph.D. programs. This growth of enrollment in CS has been accompanied by a growth in another student population at the university that is often overlooked: Interdisciplinary Studies - Interest in Computer Science (ICSC) majors. This population represents students who have qualified for admission at Tennessee Tech, but have not qualified for entry into the CS program. Indeed, just as the freshman class of CS has grown 44%, the ICSC program has grown 63%. To address the problem of retention and migration into CS from ICSC, we have developed the pre-CS Redshirt program, which is aimed at providing increased advising, peer mentoring, tutoring, and connections to faculty. Launched in Fall 2020, the challenges facing these students have been compounded by COVID-19. In order to study initial effectiveness, we measured Fall-Spring retention, comparative GPAs for students in the CS and ICSC programs, and conducted a survey of students to measure students' sense of belongingness with the measured population including students of all levels currently enrolled in the CS program as well as the pre-CS Redshirt students.

This Innovative Practice Full Paper addresses the challenges of teaching data structures and algorithms to blind students using tactile media and a user-centered approach. Computer Science educators have have long used diagrams and other visualizations to assist in teaching data structures and algorithms. As enrollments in computer science rise, a more diverse student body has led to widespread access to computer science, including an increase in the number of students with sight impairments that are seeking computer science degrees. In the Department of Computer Science at Tennessee Technological University we have been actively engaging in the development of methodologies and approaches necessary to facilitate creation of tactile documents suitable for helping students with sight impairments to understand and effectively use common data structures. Using tools generally available to practitioners, we applied a user-centered iterative process to develop standards necessary to create visual idioms that capture various data structures and algorithms as diagrams expressed using tactile documents. With feedback from our visually impaired students, we created several diagrams that represent various data structures using common drawing tools such as Microsoft Visio with Braille font. These documents were then printed using swell paper and a tactile printer. Students using these diagrams have reported gaining an increased understanding of concepts that were previously static abstractions only read about in their textbooks. Many lessons have been learned along the way that range from the general (i.e., how to properly space words in order to not affect ambiguity in the layout of diagrams) to the specific (i.e., how to demonstrate movement of data in a data structure using the tactile medium). In this paper, we report on the approaches used to create tactile diagrams representing various data structures, the ways in which we interacted with students in order to gain a better understanding of how to represent visual idioms in tactile form, challenges faced in creating the documents, and lessons learned. In addition, we discuss the work in the context of the state of the art, and suggest future investigations.