As the COVID-19 pandemic spread throughout the United States in Spring 2020, universities around the country conducted the unprecedented feat of moving essentially their entire curricula online. This abrupt mid-semester shift in modality was the first of its kind at scale, but it will not be the last. Now that our experiences during the pandemic have demonstrated that an online transition is possible, we may see similar transitions proposed during smaller scale crises, especially as an alternative to school closures. We present an experience report focusing on computer science instructors at a large Midwestern university during the Spring 2020 online transition: the challenges they faced, as well as the interventions they implemented. As more data and analysis is generated from the first mass, unwilling modality shift, it is nevertheless important to focus on the visceral reactions and lessons learned from teachers and students in the moment as they responded to an unprecedented situation. We therefore also present our analysis of critical challenges and preliminary pedagogical recommendations for consideration during future rapid transition "involuntarily online" semesters.

Pair programming in CS1 courses has been shown to have many benefits (both to students and instructors), and it is widely employed. Other than assignment by instructors, how do students select their partners, and what are their perceptions of working with a partner? In our study, students in a CS1 course for non-majors were required to complete a brief, anonymized survey after each homework assignment; we analyzed their survey responses over the course of the semester and compared the results of students who worked with a partner and students who worked alone. Pairs and individuals judged assignments to be about the same difficulty, but pairs spent less time on assignments. Individuals generally worked alone out of preference, but a minority of them reported interest in working with partners but could not find one on their own. Through our analysis we model relationships between survey constructs, student success, and student satisfaction.

Introduction to programming courses employ pair programming with benefits to both students and instructors. When selecting their own partners, students without extant support or social networks are at a significant disadvantage relative to well-connected students. The present work will use individual and pair programming activities to create a matching program to suggest effective programming partners to students, with the goal of improving the experience of partner work in introductory CS classes. In doing so, we will explore the relevant matching traits in productive pairs and gain a deeper understanding of (un)productive pair partnerships.

MOTIVATION: Alternative splicing and other processes that allow for different transcripts to be derived from the same gene are significant forces in the eukaryotic cell. RNA-Seq is a promising technology for analyzing alternative transcripts, as it does not require prior knowledge of transcript structures or genome sequences. However, analysis of RNA-Seq data in the presence of genes with large numbers of alternative transcripts is currently challenging due to efficiency, identifiability and representation issues. RESULTS: We present RNA-Seq models and associated inference algorithms based on the concept of probabilistic splice graphs, which alleviate these issues. We prove that our models are often identifiable and demonstrate that our inference methods for quantification and differential processing detection are efficient and accurate. AVAILABILITY: Software implementing our methods is available at http://deweylab.biostat.wisc.edu/psginfer. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.