For many students, attending college is a dramatic but necessary change. To gain a better understanding of experiences that are unique to autistic college students, we conducted a mixed-method study with 20 students (10 autistic and 10 neurotypical). We collected physiological, contextual, experience, and environmental data from their natural environment using Fitbit and smartphones. We found that stress patterns, emotional states, and physical states are similar for both groups. Our autistic participants prioritized academic success over everything else, often intentionally confining their movements among academic, resident, and work locations to engage themselves with academic work as much as possible. They had a small number of friends, always preferring quality over quantity and sometimes regarding friends as close as family members. To maintain a better social life, they extensively used social media. They slept more than neurotypical participants per day; however, they experienced lower sleep quality.

Autistic college students face significant challenges in college settings and have a higher dropout rate than neurotypical college students. High physiological distress, depression, and anxiety are identified as critical challenges that contribute to this less than optimal college experience. In this paper, we leverage affordable mobile and wearable devices to collect large amounts of physiological and contextual data (biomarkers) and leverage a data-driven analysis approach for building stress prediction models. Such models can be used to provide real-time intervention for better stress management. We conducted a mixed-method study where we collected physiological and contextual data from 20 college students (10 neurotypical and 10 autistic). Our proposed data-driven analysis pipeline leverages an unsupervised representation learning technique with a semi-supervised label approximation method to predict the onset of stress based on biomarkers for autistic students, neurotypical students, and both populations with accuracies 69%, 72%, and 70%, respectively.

Individuals with Autism Spectrum Disorder (ASD) experience difficulty in social interaction resulting in a lack of close social connections. Online social networking sites show promise in enabling individuals with ASD to make connections as the media of communication is often void of complexities present in real-life situations (e.g., real-time interpretation of complex social and emotional cues). We conducted a systematic content analysis of the Autism and Asperger's subreddits to understand user types, interaction patterns and trends, and user's experience, expectations, and unmet needs pertaining to these online communities. Our analysis revealed several interesting findings including the presence of 15 different topic areas, the tone and preference of the community, the difference in communication style between individuals with ASD and other related members (e.g., parents, therapists). Our analysis signified a need for an accessible and inclusive social networking site design that will cater to the specific needs of this community.