The integration of mobile technology in education raises concerns about teachers’ work-life boundaries. Most studies examine boundary issues from a Western, individualistic perspective, prompting the question: How might work-life balance be understood within the context of a collectivist culture? This study examines how Chinese teachers manage boundaries on the all-in-one app WeChat. A survey of 108 teachers shows most view WeChat positively for work, while interviews with 18 teachers reveal that while teachers experience fatigue from blurred boundaries and constant availability, they also view WeChat as indispensable for managing fragmented responsibilities, sustaining relationships, and coordinating collective tasks. Teachers employ workarounds to negotiate expectations of accessibility. These practices highlight what we describe as expected permeability, a relationally constructed rhythm of accessibility shaped by obligations and tie-specific norms, while foregrounding relational agency as a stronger lens for rethinking both platform design and work-life balance theory beyond individualistic framings.

Online communities are increasingly important in forming and maintaining relationships but have also faced criticism for toxic or depression-inducing content. Motivated by research showing that grateful reflection can lead to enhanced well-being, this paper explores how we might design online communities centered on gratitude through a qualitative study with 15 participants. To elicit insights on how people view expressing gratitude in online communities, we built a simple gratitude-centered online community, Gratitude, designed to be similar in nature to many online communities, where users create short posts (notes of gratitude in response to prompts) that other members can browse, react to, and comment on. Participants were first interviewed and then used the Gratitude platform for three weeks, during which they filled out surveys after each prompt. We found that while participants had concerns or questions about the value of expressing gratitude publicly and the risks associated with doing so, they also described experiencing many benefits, such as providing one with a platform for sharing; inspiring reflection and positivity; fostering connection and empathy; and contributing to a cycle of gratitude and vulnerability. Participants raised several areas for design related to better support for interaction and connection, privacy and authenticity, and motivation and engagement. We conclude by discussing implications for future research on the design of prosocial online communities centering cycles of positivity and authentic individual reflection in communal interactions.

Surveys are a powerful tool for collecting data and eliciting insights on social phenomena, and are critical in product design, marketing, scientific research, and other domains. However, traditional open-ended and closed-ended question formats limit researchers' ability to capture data that combines both the richness of qualitative insights and the analytical rigor of quantitative data. Closed-ended questions facilitate structured data collection that is amenable to statistical analysis but limit respondents' answers. In contrast, open-ended questions allow for nuanced responses incorporating new perspectives but require significant effort to interpret due to their unstructured nature. Moreover, traditional survey tools lack mechanisms to prompt respondents for deeper reflections or to facilitate engagement with others' perspectives, limiting the potential for richer insights. To address these problems, we propose Dynamic Surveys, a survey platform that uses Large Language Models (LLMs) to dynamically cluster qualitative responses in real time and to elicit quantitative ratings and rankings on those clusters and qualitative reflections on how their views compare to broader respondent trends, especially helpful in early-stage or exploratory research settings. This process generates a report showing survey creators and respondents the clustered responses as well as each cluster's rank, rating distribution, and follow-up reflections. To evaluate Dynamic Surveys, we conducted two field studies with 93 participants over a 2-month period. In the first study, 52 students provided input for a career workshop, while in the second, 41 students gave feedback on gaps in their academic curriculum. Of these, 44 respondents filled out a survey on their experience using Dynamic Surveys. We also shared the generated report with 4 individuals who were interested in the insights for their work, and interviewed them to understand their perspectives on the results and any contextual risks they saw in the platform design. Our findings suggest that Dynamic Surveys not only provide richer and deeper insights into responses compared with traditional survey tools, but also increase engagement and foster a sense of community. We discuss broader implications for the design of survey platforms that blend qualitative depth with quantitative structure, facilitating richer insights and offering more collaborative interactions.

Though social media platforms contain rich information and insights on professional life, encounters with this content are often fleeting and disconnected, raising questions about the extent social media content is valuable for career identity formation. This paper reports on a research through design study that explores the potential of social media for supporting integrated learning experiences, through investigating and prototyping experiences around the use of TikTok #DayInTheLife videos for career exploration. We conducted semi-structured interviews of 10 college students to understand the value of social media content for career exploration and the feasibility of integrating such content towards reflective learning experiences. A qualitative analysis revealed that #DayInTheLife videos offer firsthand insights into professions that facilitates aspects of career identity formation, and have the potential to prompt and motivate further exploration. However, they are also limited due their short-form, disconnected, entertainment-oriented nature, the distracting context in which they exist, and the potential lack of representation in recommended content. We also had the students participate in an experience prototype in which we used native social media interactions such as comments, mentions, and direct messages to integrate encounters of disparate posts towards holistic and reflective learning experiences. We found that integrating encounters can facilitate more intentional reflection, add interactivity, and provide a sense of agency. We also surfaced contextual risk factors and design factors for designing integrated learning experiences on social media. We build on our findings to introduce and discuss a concept we call SIMPLE apps (Social media Interactions Merged for Purposeful Learning Experiences) and to discuss broader design implications for better harnessing social media content towards purposeful integrated learning.

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.

While mentorship offers many benefits, student access to mentorship is often limited. In this paper, we introduce Compass, a chat platform where industry professionals mentor large cohorts of 30+ students with the support of novel features that enable full engagement without the typical chaos of group chat. Specifically, we conceptualize conversations as composed of not only individual messages, but also multi-person conversational units that collapse large numbers of small but related conversational exchanges into single conceptual units in the main dialogue. Doing so makes it possible to preserve a coherent linear flow of conversation while also supporting non-linear conversational exchanges that can be concisely summarized computationally and built on in the main conversation. We report on design lessons learned over a year of small real-world studies culminating in a final deployment in which 2 industry professionals successfully mentored 30+ students over a 10-week period. We find that both mentors and mentees find the chat UI effective and sometimes preferable, and discuss broader implications for the design of chat UI for large group conversations.

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.

Career development is vital for ensuring a happy and productive workforce, and for maintaining relevance in a rapidly changing economy shaped by technological progress. Yet career development is largely ignored in crowdwork. Crowdwork platforms like Amazon Mechanical Turk (AMT) do not support crowdworkers in reskilling and changing careers. In this paper, we study the career goals of AMT workers and the challenges they face in trying to transition out of crowdwork and into high-skill jobs offline or into specialized freelance work. We performed a qualitative study in which we surveyed 20 AMT workers and interviewed 6 of them about their career goals, how they are currently pursuing them, and the challenges they have faced. We found that crowdworkers aspire to transition out of AMT but face challenges due to lack of career guidance, and limited time and financial resources. Drawing on literature in career studies and organization science, we discuss how crowdworkers' challenges are further aggravated by the enviornment on AMT, and provide implications for future research and design that may better support crowdworkers in making a career change.

While educational technologies such as MOOCs have helped scale content-based learning, scaling situated learning is still challenging. The time it takes to define a real-world project and to mentor learners is often prohibitive, especially given the limited contributions that novices are able to make. This paper introduces micro-role hierarchies, a form of coordination that integrates workflows and hierarchies to help short-term novices predictably contribute to complex projects. Individuals contribute through micro-roles, small experiential assignments taking roughly 2 hours. These micro-roles support execution of the desired work process, but also sequence into learning pathways, resulting in a learning dynamic similar to moving up an organizational hierarchy. We demonstrate micro-role hierarchies through Causeway, a platform for learning web development while building websites for nonprofits. We carry out a proof-of-concept study in which learners built static websites for refugee resettlement agencies in 2 hour long roles.

Though deliberation is a critical component of democratic decision-making, existing deliberative processes do not scale to large groups of people. Motivated by this, we propose a model in which large-scale decision-making takes place through a sequence of small group interactions. Our model considers a group of participants, each having an opinion which together form a graph. We show that for median graphs, a class of graphs including grids and trees, it is possible to use a small number of three-person interactions to tightly approximate the wisdom of the crowd, defined here to be the generalized median of participant opinions, even when agents are strategic. Interestingly, we also show that this sharply contrasts with small groups of size two, for which we prove an impossibility result. Specifically, we show that it is impossible to use sequences of two-person interactions satisfying natural axioms to find a tight approximation of the generalized median, even when agents are non-strategic. Our results demonstrate the potential of small group interactions for reaching global decision-making properties.

The problem of random number generation dates back to Von Neumann's work in 1951. Since then, many algorithms have been developed for generating unbiased bits from complex correlated sources as well as for generating arbitrary distributions from unbiased bits. An equally interesting, but less studied aspect is the structural component of random number generation. That is, given a set of primitive sources of randomness, and given composition rules induced by a device or nature, how can we build networks that generate arbitrary probability distributions? In this paper, we study the generation of arbitrary probability distributions in multivalued relay circuits, a generalization in which relays can take on any of N states and the logical `and' and `or' are replaced with `min' and `max' respectively. These circuits can be thought of as modeling the timing of events which depend on other event occurrences. We describe a duality property and give algorithms that synthesize arbitrary rational probability distributions. We prove that these networks are robust to errors and design a universal probability generator which takes input bits and outputs any desired binary probability distribution.

We present theoretical and empirical results demonstrating the usefulness of social choice functions in crowdsourcing for participatory democracies. First, we demonstrate the scalability of social choice functions by defining a natural notion of epsilon-approximation, and giving algorithms which efficiently elicit such approximations for two prominent social choice functions: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. Second, we apply these ideas to Finland's recent off-road traffic law reform, an experiment on participatory democracy in real life. This allows us to verify the scaling predicted in our theory and show that the constant involved is also not large. In addition, by collecting data on the time that users take to complete rankings of varying sizes, we observe that eliciting partial rankings can further decrease elicitation time as compared to the common method of eliciting pairwise comparisons.

Motivated to understand the role of randomness in biological computation, we study a class of urn models that are characterized by urn functions. At each step, one ball is randomly sampled according to an urn function and replaced with a different colored ball. This process is repeated until the urn contains a single color, at which point the process halts. Such an urn can be thought of as a random switch; depending on the initial ball colors and the urn function, the urn population has some probability of converging to any of the initial ball colors. We find that these probabilities have surprisingly simple closed-form solutions and also derive expressions for the switching time. We demonstrate the application of such urn models to biological systems by deriving the urn function for the genetic network controlling the lysis-lysogeny decision in the Lambda phage virus. By applying our results to this system, we then derive an intriguing hypothesis on the role of dimers in genetic switches. Many open questions exist on further generalizations of such urn models and their applications to the understanding of randomness and biological computation.

The problem of random number generation dates back to von Neumann's work in 1951. Since then, many algorithms have been developed for generating unbiased bits from complex correlated sources as well as for generating arbitrary distributions from unbiased bits. An equally interesting, but less studied aspect is the structural component of random number generation as opposed to the algorithmic aspect. That is, given a network structure imposed by nature or physical devices, how can we build networks that generate arbitrary probability distributions in an optimal way? In this paper, we study the generation of arbitrary probability distributions in multivalued relay circuits, a generalization in which relays can take on any of N states and the logical `and' and `or' are replaced with `min' and `max' respectively. Previous work was done on two-state relays. We generalize these results, describing a duality property and networks that generate arbitrary rational probability distributions. We prove that these networks are robust to errors and design a universal probability generator which takes input bits and outputs arbitrary binary probability distributions.