Team roles offer an interpretable lens on collaboration, yet computational studies of roles often rely on domain-specific personas or datadriven clustering rather than theory-grounded taxonomies.We operationalize a taxonomy of eight communication roles grounded in education literature and annotate a corpus of 6,307 Slack messages from 55 students across 18 teams in a semester-long computer science course project.We evaluate whether LLMs can approximate expert labels, enabling scalable, taxonomy-driven role annotation.Using these role labels, we characterize role dynamics over teams' lifecycles, finding that different roles peak at different moments and that students enact a more diverse set of roles as projects progress.To evaluate the utility of our role constructs, we use them to predict peer recognition, outperforming lexical, conversational, and LLM-prompting baselines.To assess generalizability beyond the educational context, we apply the same role constructs to a public dataset (DeliData) to predict team performance improvement after deliberation, again exceeding prior performance.

Teamwork assessment methods, such as peer evaluations, often fail to accurately capture the collaboration processes in team projects. While log data from digital collaboration tools provide objective evidence of contributions, effectively leveraging these data remains challenging. We interviewed 10 instructors and 16 students, then surveyed additional 51 students to identify their valued teamwork behaviors to guide log data usage, and investigate their perceived benefits and concerns. Students prioritized work-related behaviors such as equitable contribution and timeliness, whereas instructors additionally valued interaction behaviors like mutual support. Both groups highlighted significant limitations in raw log data, including omission of offline contributions, insufficient representation of work quality, and misattributing collaborative work to only the person who interacts with the tool. Many students reviewed logs on their own to monitor project progress and workload equity, but lacked structured guidance to meaningfully interpret the insights. Our findings underscore the need for student-centered teamwork assessment approaches that enable students not only to annotate and contextualize their own data, but also to improve their teamwork by reflecting on a rubric of valued teamwork behaviors.

Peer evaluations are a well-established tool for evaluating individual and team performance in collaborative contexts, but are susceptible to social and cognitive biases. Current peer evaluation tools have also yet to address the unique opportunities that online collaborative technologies provide for addressing these biases. In this work, we explore the potential of one such opportunity for peer evaluations: data traces automatically generated by collaborative tools, which we refer to as "activity traces". We conduct a between-subjects experiment with 101 students and MTurk workers, investigating the effects of reviewing activity traces on peer evaluations of team members in an online collaborative task. Our findings show that the usage of activity traces led participants to make more and greater revisions to their evaluations compared to a control condition. These revisions also increased the consistency and participants' perceived accuracy of the evaluations that they received. Our findings demonstrate the value of activity traces as an approach for performing more reliable and objective peer evaluations of teamwork. Based on our findings as well as qualitative analysis of free-form responses in our study, we also identify and discuss key considerations and design recommendations for incorporating activity traces into real-world peer evaluation systems.

Receiving feedback on preliminary work allows content creators to gain insight and improve outcomes. However, many creators only share in-progress work at late stages of the creative process and lose opportunities to address conceptual issues in the work. To contribute to the base of knowledge of factors that shape one’s decision to seek feedback on their work, we conducted 24 semi-structured interviews with creators in product, interaction and graphic design domains. The results yielded a taxonomy of process-related, social, and cognitive factors that affected a creator’s choice to seek feedback. Next, we administered a survey to quantify the prevalence of these factors at different design stages and for different levels of expertise. Our results show feedback strategy varies by expertise—experts are more likely to create personal deadlines to seek feedback than novices—and by stage. At the early stage, creators sought feedback when testing multiple alternatives. At the late stage, creators are most likely to consider revision granularity prior to seeking feedback. We demonstrate future possibilities for new features in creativity tools through speculative design sketches motivated by our findings.

Peer evaluations are critical for assessing teams, but are susceptible to bias and other factors that undermine their reliability. At the same time, collaborative tools that teams commonly use to perform their work are increasingly capable of logging activity that can signal useful information about individual contributions and teamwork. To investigate current and potential uses for activity traces in peer evaluation tools, we interviewed (N=11) and surveyed (N=242) students and interviewed (N=10) instructors at a single university. We found that nearly all of the students surveyed considered specific contributions to the team outcomes when evaluating their teammates, but also reported relying on memory and subjective experiences to make the assessment. Instructors desired objective sources of data to address challenges with administering and interpreting peer evaluations, and have already begun incorporating activity traces from collaborative tools into their evaluations of teams. However, both students and instructors expressed concern about using activity traces due to the diverse ecosystem of tools and platforms used by teams and the limited view into the context of the contributions. Based on our findings, we contribute recommendations and a speculative design for a data-centric peer evaluation tool.