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The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a consideration of surrounding stressors such as financial pressures. In a mixed method approach, we compared 28 undergraduate students with disabilities (including mental health concerns) to their peers during 2020, to assess differences and similarities in their educational concerns, stress levels, and COVID-19-related adversities. We found that students with disabilities entered the Spring quarter of 2020 with significantly higher concerns about classes going online, and reported more recent negative life events than other students. These differences between the two groups diminished 3 months later with the exception of recent negative life events. For a fuller understanding of students’ experiences, we conducted qualitative analysis of open-ended interviews. We examined both positive and negative experiences with online learning among students with disabilities and mental health concerns. We describe how online learning enabled greater access—e.g., reducing the need for travel to campus—alongside ways in which online learning impeded academic engagement—e.g., reducing interpersonal interaction. We highlight a need for learning systems to meet the diverse and dynamic needs of students with disabilities.

Although some research highlights the benefits of behavioral routines for individual functioning, other research indicates that routines can reflect an individual's inflexibility and lower well-being. Given conflicting accounts on the benefits of routine, research is needed to examine how routineness versus flexibility in health-related behaviors correspond to personality traits, health, and occupational outcomes. We adopt a nonlinear dynamical systems approach to understanding routine using automatically sensed health-related behaviors collected from 483 information workers over a roughly two-month period. We utilized multidimensional recurrence quantification analysis to derive a measure of health regularity (routineness) from measures of daily step count, sleep duration, and heart rate variability (which relates to stress). Participants also completed measures of personality, health, and job performance at the start of the study and for two months via Ecological Momentary Assessments. Greater regularity was associated with higher neuroticism, lower agreeableness, and greater interpersonal and organizational deviance. Importantly, these results were independent of overall levels of each health indicator in addition to demographics. It is often believed that routine is desirable, but the results suggest that associations with routineness are more nuanced, and wearable sensors can provide insights into beneficial health behaviors.

This paper presents a computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We evaluate the framework with two case studies using datasets of smartphone, Fitbit, and OURA smart ring to evaluate the framework’s ability to (1) detect cyclic biobehavior, (2) model commonality and differences in rhythms of human participants in the sample datasets, and (3) predict their health and readiness status using models of biobehavioral rhythms. Our evaluation demonstrates the framework’s ability to generate new knowledge and findings through rigorous micro- and macro-level modeling of human rhythms from mobile and wearable data streams collected in the wild and using them to assess and predict different life and health outcomes.

Feeling a sense of belonging is a central human motivation that has consequences for mental health and well-being, yet surprisingly little research has examined how belonging shapes mental health among young adults. In three data sets from two universities (exploratory study: N = 157; Confirmatory Study 1: N = 121; Confirmatory Study 2: n = 188 in winter term, n = 172 in spring term), we found that lower levels of daily-assessed feelings of belonging early and across the academic term predicted higher depressive symptoms at the end of the term. Furthermore, these relationships held when models controlled for baseline depressive symptoms, sense of social fit, and other social factors (loneliness and frequency of social interactions). These results highlight the relationship between feelings of belonging and depressive symptoms over and above other social factors. This work underscores the importance of daily-assessed feelings of belonging in predicting subsequent depressive symptoms and has implications for early detection and mental health interventions among young adults.

Aim: Acute cannabis intoxication can impair motor skills and cognitive functions. Given possible impairment related to acute cannabis intoxication, we explored whether mobile phone-based sensors (e.g., GPS, text/phone logs) can detect episodes of acute cannabis intoxication (subjective “high” state) as self-reported in natural environments by young adults. Methods: Young adults (ages 18-25), who reported cannabis use at least twice per week, were recruited by research registry and Craigslist to participate in a mobile phone data collection study (up to 30 days) in Pittsburgh, PA (2017-2019). Participants responded to fixed time phone surveys (3 times/day) and self-initiated reports of cannabis use (start/stop time, rating of subjective high: 0-10, 10=very high). Our mobile AWARE app continuously collected phone sensor data, which was segmented into 5-minute windows for analysis. We built and tested multiple machine learning classifiers (e.g., Support Vector Machine, Light Gradient Boosting Machine (LGBM)) on training (60%), validation (20%), and test (20%) datasets to determine which classifier performed best in distinguishing subjective cannabis “high” (rating=1-10) vs “not high” (rating=0). To minimize the influence of imbalanced data on model performance in the training dataset, we used both over-sampling with Synthetic Minority Over-sampling Technique (SMOTE) and random under-sampling of the majority class, so that both classes (“high”, “not-high”) had the same number of training samples. We also tested the importance of time features (i.e., day of week, time of day: morning, afternoon, evening) relative to smartphone sensor data only on model performance, since time features alone might predict “routines” in cannabis use. Results: Young adults (N=57; 58% female; mean age=19.82 [SD=1.76]; 71.92% White, 15.78% Black, 12.28% Asian and other ethnicity) reported 451 episodes of cannabis use, mean subjective high rating=3.77 (SD=2.64). The sensor dataset included 1,648 datapoints representing reports of subjective ""high"" and 60,580 data points representing ""not high"" reports. For the two time-based features only model, the LGBM classifier had 91% accuracy in detecting subjective cannabis intoxication (vs “not-high”) in the test dataset (Area Under the Curve [AUC]=0.75). Combining smartphone sensor data with the two time-based features (day of week, time of day) improved model performance, with 95% accuracy (AUC=0.93), indicating that smartphone features contribute unique information, and that time features further improve model performance in detecting rating of subjective cannabis ""high"". Among the 102 phone sensor features entered into the analyses (smartphone sensors + time model), some of the most important features (the top 2 were the time features) included travel (GPS: smaller travel radius within a day when feeling ""high”), movement (e.g., smaller number of activity changes when feeling ""high”), and communication/sociability (e.g., increased phone usage interactions, greater voice and noise level around individuals). Conclusion: Results from this proof-of-concept study indicate the feasibility of using phone sensors to detect effects of cannabis intoxication in the natural environment in a population-based model among young adults. Mobile phone sensors show promise for automated and continuous detection of cannabis use in daily life in a sample of young adults, with potential implications for triggering the delivery of just-in-time interventions to minimize marijuana-related harm.

When mobile robots perform tasks in environments with humans, it seems appropriate for the robots to rely on such humans for help instead of dedicated human oracles or supervisors. However, these humans are not always available nor always accurate. In this work, we consider human help to a robot as concretely providing observations about the robot's state to reduce state uncertainty as it executes its policy autonomously. We model the probability of receiving an observation from a human in terms of their availability and accuracy by introducing Human Observation Providers POMDPs (HOP-POMDPs). We contribute an algorithm to learn human availability and accuracy online while the robot is executing its current task policy. We demonstrate that our algorithmis effective in approximating the true availability and accuracy of humans without depending on oracles to learn, thus increasing the tractability of deploying a robot that can occasionally ask for help.

One of the first, most common, and most useful applications that today's computer users access is the World Wide Web (web). One population of users for whom the web is especially important is those with motor disabilities, because it may enable them to do things that they might not otherwise be able to do: shopping; getting an education; running a business. This is particularly important for low bandwidth users: users with such limited motor and speech that they can only produce one or two signals when communicating with a computer. We present requirements for low bandwidth web accessibility, and two tools that address these requirements. The first is a modifified web browser, the second a proxy that modifies HTML. Both work without requiring web page authors to modify their pages.

International audience; Context is of crucial importance for research and applications in many disciplines, as evidenced by many workshops, symposia, seminars, and conferences on specific aspects of context. The International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT), the oldest conference series focusing on context, provides a unique interdisciplinary emphasis, bringing together participants from a wide range of disciplines, including artificial intelligence, cognitive science, computer science, linguistics, organizational science, philosophy, psychology, ubiquitous computing, and application areas such as medicine and law, to discuss and report on context-related research and projects.This proceedings contains the accepted Brevia and Demonstration submissions.Special thanks are due to Isabel Urdapilleta for her preparation of these proceedings and to Jean-Marc Meunier for his assistance in making sure that the technical needs of the demonstrations were met. The contributions reported here will illustrate the rich range of current contributions to context, and will provide a foundation for drawing on the best work from many areas in the next generation of research on context.; Le contexte représente une importance crucial dans beaucoup de disciplines, comme le nombre de manifestations scientifiques le montrent. La Conférence Internationale et Interdisciplinaire sur la Modélisation et l'Utilisation du Contexte (CONTEXT) est la plus ancienne manifestation scientifique qui permet à des chercheurs d'horizons différents de discuter et présenter leurs résultats de leurs recherches et projets en lien avec le contexte.Les actes présents contiennent les Brevia et Démonstrations qui ont été retenus après examen par le Comité Scientifique.Nous souhaitons remercier tout particulièrement Isabel Urdapilleta pour sa préparation soigneuse des Actes et Jean-Marc Meunier pour la prise en charge des aspects techniques de la session Démonstrations. Ces actes montre le large spectre de contributions possible à l'étude du contexte, et fournissent des bases indispensables pour une amélioration notable des recherches dans beaucoup de domaines pour les prochaines générations de recherches sur le contexte.

International audience; Context is of crucial importance for research and applications in many disciplines, as evidenced by many workshops, symposia, seminars, and conferences on specific aspects of context. The International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT), the oldest conference series focusing on context, provides a unique interdisciplinary emphasis, bringing together participants from a wide range of disciplines, including artificial intelligence, cognitive science, computer science, linguistics, organizational science, philosophy, psychology, ubiquitous computing, and application areas such as medicine and law, to discuss and report on context-related research and projects.This proceedings contains the accepted Brevia and Demonstration submissions.Special thanks are due to Isabel Urdapilleta for her preparation of these proceedings and to Jean-Marc Meunier for his assistance in making sure that the technical needs of the demonstrations were met. The contributions reported here will illustrate the rich range of current contributions to context, and will provide a foundation for drawing on the best work from many areas in the next generation of research on context.; Le contexte représente une importance crucial dans beaucoup de disciplines, comme le nombre de manifestations scientifiques le montrent. La Conférence Internationale et Interdisciplinaire sur la Modélisation et l'Utilisation du Contexte (CONTEXT) est la plus ancienne manifestation scientifique qui permet à des chercheurs d'horizons différents de discuter et présenter leurs résultats de leurs recherches et projets en lien avec le contexte.Les actes présents contiennent les Brevia et Démonstrations qui ont été retenus après examen par le Comité Scientifique.Nous souhaitons remercier tout particulièrement Isabel Urdapilleta pour sa préparation soigneuse des Actes et Jean-Marc Meunier pour la prise en charge des aspects techniques de la session Démonstrations. Ces actes montre le large spectre de contributions possible à l'étude du contexte, et fournissent des bases indispensables pour une amélioration notable des recherches dans beaucoup de domaines pour les prochaines générations de recherches sur le contexte.