Collaboration is a crucial part of computing education. The increase in AI capabilities over the last couple of years is bound to profoundly affect all aspects of systems and software engineering, including collaboration. In this position paper, we consider a scenario where AI agents would be able to take on any role in collaborative processes in computing education. We outline these roles, the activities and group dynamics that software development currently include, and discuss if and in what way AI could facilitate these roles and activities. The goal of our work is to envision and critically examine potential futures. We present scenarios suggesting how AI can be integrated into existing collaborations. These are contrasted by design fictions that help demonstrate the new possibilities and challenges for computing education in the AI era.

Sense of belonging has been shown to be important for student persistence and success in computer science. A student's sense of belonging is related to a variety of factors, including gender, prior programming experience, first generation status, and elements of the learning environment. Prior work has also shown that competitive admission processes have a negative impact on student sense of belonging. This paper explores sense of belonging of students who enter a computer science major through either a direct or competitive admission pathway. We look at how admission pathway, prior programming experience, and self-identification as a minority in computer science affect sense of belonging for these students. We find that sense of belonging does not differ in a significant way between admission pathways, however prior experience and self-identification as a minority do affect sense of belonging.

As teaching-focused positions proliferate and university teaching careers become more professionalized, there is growing attention being paid to how teaching faculty are created. However, how teaching faculty are hired also deserves scrutiny.

MOTIVATION: Iso-Seq RNA long-read sequencing enables the identification of full-length transcripts and isoforms, removing the need for complex analysis such as transcriptome assembly. However, the raw sequencing data need to be processed in a series of steps before annotation is complete. Here, we present nf-core/isoseq, a pipeline for automatic read processing and genome annotation. Following nf-core guidelines, the pipeline has few dependencies and can be run on any of platforms. AVAILABILITY AND IMPLEMENTATION: The pipeline is freely available online on the nf-core website (https://nf-co.re/isoseq) and on GitHub (https://github.com/nf-core/isoseq) under MIT License (DOI: 10.5281/zenodo.7116979).

Undergraduate and graduate teaching assistants (TAs) play a pivotal role in supporting student learning. TAs perform various duties, including grading, facilitating labs and tutorials, supporting collaboration projects, and, in some cases, designing course materials. TAs often receive training to enhance their technical skills but, more recently, some TA training programs have begun to recognize and emphasize the need for skills that help TAs foster inclusive learning spaces. Because TAs often have more opportunities to interact directly with students and hold dual identities as students and teachers in the same community, how they address and mitigate non-inclusive behavior may be different than how faculty would. While some TAs are beginning to receive training on concepts like self-efficacy, growth mindset, equity, inclusion, and bias, our community needs a deeper understanding of what skills TAs need to cultivate inclusive spaces and how these skills can be developed and evaluated within the constraints of the teaching term. In this BOF, we will discuss the skills and competencies that faculty and TAs believe are essential for helping TAs foster inclusive learning spaces, how to teach those skills and competencies in TA training programs, and how to evaluate the effectiveness of those programs.

As with many other institutions, there is currently far more student interest in our undergraduate Computer Science major than we can accommodate. Our institution limits the capacity of the Computer Science major and uses a competitive, grades-based admission process for students to gain admission at the end of their first year of study. This competitive admission process has had a notable negative impact on the student experience in first-year, as students are especially driven to earn top grades. The competition also encourages students to prioritize academics over everything else.

Following publication of the original article [1], the authors identifed an error in the author name of Lél Eöry. Te incorrect author name is: Lél Eöery. Te correct author name is: Lél Eöry. Te author group has been updated above and the original article [1] has been corrected.

BACKGROUND: Genomic and genetic studies often require a target list of genes before conducting any hypothesis testing or experimental verification. With the ever-growing number of sequenced genomes and a variety of different annotation strategies, comes the potential for ambiguous gene symbols, making it cumbersome to capture the "correct" set of genes. In this article, we present and describe the Avian Immunome DB (AVIMM) for easy gene property extraction as exemplified by avian immune genes. The avian immune system is characterised by a cascade of complex biological processes underlaid by more than 1000 different genes. It is a vital trait to study particularly in birds considering that they are a significant driver in spreading zoonotic diseases. With the completion of phase II of the B10K ("Bird 10,000 Genomes") consortium's whole-genome sequencing effort, we have included 363 annotated bird genomes in addition to other publicly available bird genome data which serve as a valuable foundation for AVIMM. CONSTRUCTION AND CONTENT: A relational database with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has been designed and set up. The foundation stone or the "seed" for the initial set of avian immune genes is based on the well-studied model organism chicken (Gallus gallus). Gene annotations, different transcript isoforms, nucleotide sequences and protein information, including amino acid sequences, are included. Ambiguous gene names (symbols) are resolved within the database and linked to their canonical gene symbol. AVIMM is supplemented by a command-line interface and a web front-end to query the database. UTILITY AND DISCUSSION: The internal mapping of unique gene symbol identifiers to canonical gene symbols allows for an ambiguous gene property search. The database is organised within core and feature tables, which makes it straightforward to extend for future purposes. The database design is ready to be applied to other taxa or biological processes. Currently, the database contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species. While the command-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end with download functionality offers sophisticated search functionalities and tracks the origin for each record. AVIMM is publicly accessible at https://avimm.ab.mpg.de .

This report documents the implementation of a self-paced, mastery learning inspired CS1 course. The course was designed to increase the completion rates observed in flipped and online CS1 formats already offered at our institution. We explore the experience of students in the course and evaluate performance outcomes using grade data from all three CS1 formats, student survey responses, and exit interviews. Our evaluation identifies three main challenges in our implementation. First, the course requires significant resources and administering it is significantly more time consuming for instructors than a regular course. Second, students hesitated to treat mastery quizzes as formative. Finally, the flexibility that the course provided, with little structure and few incentives to help students stay on track, led to considerable procrastination. These factors combined to lead students to delay coursework until the end of the semester -- and beyond. As a result, while our data shows an increase in completion relative to the online format, we saw no change in completion relative to the flipped CS1 offering and saw no change in student performance as evaluated by a final exam. However, students reported more deep engagement with and understanding of the material, which encourages us to further develop the course.

Booming interest in computer science and innovations in the way we teach it have allowed us to teach a larger, more demographically and geographically diverse group of students. One of the new challenges facing us now is that it can be harder for instructors to get to know many of their students, and for students to connect with their peers. Building community in our physical and virtual classrooms is difficult, but important for the success and learning experience of our students. Community is especially important for "non-traditional" CS students, who are more likely to struggle with imposter syndrome, or to feel like they don't belong in a CS classroom. Building Community in Large or Virtual Classrooms In this BoF session, we'll discuss the strategies, activities, and interventions we've tried and found successful or unsuccessful in building course community, and helping students believe they belong in CS.