This paper reports on a summative analysis of Exploratory Reading Groups (ERGs), a low time-commitment, relational, student-led reading group program designed to provide students from any background and year with a broad exploration of computing research. Since prior work, the program was institutionalized as a 1-credit course with a greater emphasis on strengthening pipelines into research labs. In analyzing 3 quarters of data from 136 participants, we found diverse indicators of impact. Surprisingly, despite the lightweight nature of the program (∼ 2 hours/week), we observed a statistically significant increase in satisfaction with their intellectual development at the university; confidence in reading, presenting, and communicating about their field; sense of belonging for women and minoritized ethnic groups; alignment with faculty goals in joining research labs (greater desire to make a research contribution and publish, decreased desire to join for the purpose of exploration); and engagement in the ‘reconsideration’ dimension of career identity formation. Over 70% of the participants continued on into group research projects for undergraduate students. The effectiveness of this scalable, lightweight initiative shows the promise of ERGs as a tool to support students in computing when connected to group research projects and points to future research directions on designing other lightweight, relational, scalable learning experiences.

Learning qualitative analysis requires personalized feedback and in-depth discussion not possible for educators to provide in a large course, resulting in many students obtaining only a shallow exposure to qualitative user research and interpretative skills. To overcome this challenge, we introduce a learnersourcing method that builds on the Dawid-Skene expectation maximization (EM) algorithm to generate peer-based AI hints that support students in one aspect of qualitative analysis: determining what sentences are relevant to the research question. After one annotation round, class-wide annotations are used to predict relevant sentences and to generate hints prompting students to revisit missed or incorrectly annotated sentences. An in-the-wild deployment within a large course (N=122) showed that our algorithm converged to comparatively high accuracy despite noisy student labels, and after only ∼ 20 students. An analysis of student interviews found that peer-based AI hints helped improve understanding of research questions, led to more careful examination of transcript annotations, and improved understanding of when they were over-annotating or under-annotating.

We explore the challenges individuals face when seeking like-minded partners to pursue shared interests in small groups, specifically outside the workplace, classroom, or other externally organized contexts. Conducting nine 90-minute semi-structured interviews involving a hypothetical small group formation process, we found four compounding challenges that make it difficult to form and maintain stable groups. In contrast to other kinds of small groups discussed in the literature, the unique "combination" of these challenges bring into focus a class of interest-based small groups we term " shared interest pods ": self-assembled small groups united by shared interests, whose members derive benefits from sustained, synchronous interactions that hinge on the level of fit among them. The need to discover and maintain collective fit, the considerable commitment required for regular interactions, their small size, and the lack of external enforcement, makes shared interest pods fragile and unstable, and an information-rich site for studying dynamics of self-organized consensus-seeking in underexplored areas of Lee and Paine's Model of Coordinated Action, and for considering the tension between supporting personal agency through organic group formation and optimizing stability through algorithmic assignments. A central finding is how dynamics of maintaining and giving up control co-exist in shared interest pods in their search for collective fit. People seek to maintain control over certain aspects of finding fit (e.g. in determining group membership), but are simultaneously willing to give up control to organizers or algorithms under certain conditions (e.g. when balancing desired quality with social and ethical considerations). We discuss design implications for supporting the formation of shared interest pods, and more broadly, for exploring self-organized consensus-seeking in other societal collective decision-making contexts.

In nationwide efforts to promote computing education, undergraduate research experiences (UREs) have been identified as an effective way to not only motivate research and graduate studies, but to also increase participation and retention, promote disciplinary knowledge and diversity in the field, develop strong soft and technical skills, and integrate students in the community. Yet, despite these many benefits, opportunities for UREs are still limited. And even though the challenge of finding faculty mentors is one of the major barriers, there is still limited research exploring the faculty perspective in greater depth. In this paper, we leveraged an onboarding process for a new program to interview twelve engineering faculty doing computing-related research in a large public research institution about their experiences and perspectives towards UREs. These faculty members ranged in experience from working regularly with many undergraduates to having limited or no prior experience. Our analysis uncovered four main themes on barriers to UREs around the central concept of misalignment: misalignments between the educational system and research, misalignments between faculty and student goals, misalignments between expectations of research and reality, and the challenge of getting to and maintaining steady state when trying to create alignment. We conclude by discussing design implications for designing programs and computational ecosystems that scale access to UREs by helping to overcome misalignments.

Most CS education initiatives focus primarily on teaching raw computational skills and domain knowledge. While this is certainly important, it ignores the creative and real-world aspects that are needed for applying skills to societal problems, and for attracting more diverse populations of students. One way to impart this type of learning is through undergraduate research. Unfortunately, these opportunities are limited and hard to scale due to their need for mentorship. In this paper, we introduce exploratory reading groups as a way to support creativity and intrinsic motivation in a scalable manner. In contrast to graduate journal clubs, exploratory reading groups are designed for broad exploration of ideas, and their lightweight, student-driven nature makes them easily scalable. We present design patterns for structuring groups learned over a two-year time period of running and iterating on exploratory reading groups through a user-centered process. This time period saw the program grow from a group of 6 students to having served over 260 students, all participating completely voluntarily. In surveys and interviews of participants, we found that students valued the experience tremendously, but that successful exploration took on diverse meanings. We found that surprisingly strong relational ties developed from a simple intervention, and describe three clusters of participant motivations and experiences that emerged.