Supporting student success requires collaboration among multiple stakeholders. Researchers have explored machine learning models for academic performance prediction; yet key challenges remain in ensuring these models are interpretable, equitable, and actionable within real-world educational support systems. First, many models prioritize predictive accuracy but overlook human-centered principles, limiting trust among students and reducing their usefulness for educators and institutional decision-makers. Second, most models require at least a month of data before making reliable predictions, delaying opportunities for early intervention. Third, current models primarily rely on sporadically collected, classroom-derived data, missing broader behavioral patterns that could provide more continuous and actionable insights. To address these gaps, we present three modeling approaches-LR, 1D-CNN, and MTL-1D-CNN-to classify students as low or high academic performers. We evaluate them based on explainability , fairness , and generalizability to assess their alignment with key social values. Using behavioral and self-reported data collected within the first week of two Spring terms, we demonstrate that these models can identify at-risk students as early as week one. However, trade-offs across human-centered principles highlight the complexity of designing predictive models that effectively support multi-stakeholder decision-making and intervention strategies. We discuss these trade-offs and their implications for different stakeholders, outlining how predictive models can be integrated into student support systems. Finally, we examine broader socio-technical challenges in deploying these models and propose future directions for advancing human-centered, collaborative academic prediction systems.

Social virtual reality (VR) avatars hold promise for allowing people to represent themselves as they want to be seen. But most social VR environments constrain avatar options in ways that limit the accurate presentation of age and promote the assumption of youth. Through individual interviews with ten women in midlife, we explore experiences representing age and other aspects of identity in social VR. Participants expressed a desire to show age and gender with increased nuance: they sought more gradations of color, texture, and body types, and disliked the hypersexualization that resulted from integrated clothing and body parts. As they customized their avatars, participants struggled to depict both physical and personal characteristics, taking into account how others might evaluate their self-perception. These findings highlight opportunities for nuanced representations of physical attributes as well as options for representing the self at other levels: psychological, social, and aesthetic.

During the COVID-19 pandemic, risk negotiation became an important precursor to in-person contact. For young adults, social planning generally occurs through computer-mediated communication. Given the importance of social connectedness for mental health and academic engagement, we sought to understand how young adults plan in-person meetups over computer-mediated communication in the context of the pandemic. We present a qualitative study that explores young adults’ risk negotiation during the COVID-19 pandemic, a period of conflicting public health guidance. Inspired by cultural probe studies, we invited participants to express their preferred precautions for one week as they planned in-person meetups. We interviewed and surveyed participants about their experiences. Through qualitative analysis, we identify strategies for risk negotiation, social complexities that impede risk negotiation, and emotional consequences of risk negotiation. Our findings have implications for AI-mediated support for risk negotiation and assertive communication more generally. We explore tensions between risks and potential benefits of such systems.

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users’ data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms’ generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

A deep understanding of how discrimination impacts psychological health and well-being of students could allow us to better protect individuals at risk and support those who encounter discrimination. While the link between discrimination and diminished psychological and physical well-being is well established, existing research largely focuses on chronic discrimination and long-term outcomes. A better understanding of the short-term behavioral correlates of discrimination events could help us to concretely quantify such experiences, which in turn could support policy and intervention design. In this paper we specifically examine, for the first time, what behaviors change and in what ways in relation to discrimination. We use actively-reported and passively-measured markers of health and well-being in a sample of 209 first-year college students over the course of two academic quarters. We examine changes in indicators of psychological state in relation to reports of unfair treatment in terms of five categories of behaviors: physical activity, phone usage, social interaction, mobility, and sleep. We find that students who encounter unfair treatment become more physically active, interact more with their phone in the morning, make more calls in the evening, and spend more time in bed on the day of the event. Some of these patterns continue the next day. Our results further our understanding of the impact of discrimination and can inform intervention work.