Study in Student Life

StudentLife is the first study that uses passive and automatic sensing data from the phones of a class of 48 Dartmouth students over a 10 week term to assess their mental health (e.g., depression, loneliness, stress), academic performance (grades across all their classes, term GPA and cumulative GPA) and behavioral trends (e.g., how stress, sleep, visits to the gym, etc. change in response to college workload -- i.e., assignments, midterms, finals -- as the term progresses).
Much of the stress and strain of student life remains hidden. In reality faculty, student deans, clinicians know little about their students outside of the classroom. Students might know about their own circumstances and patterns but know little about classmates. To shine a light on student life we develop the first of a kind StudentLife smartphone app and sensing system to automatically infer human behavior. Why do some students do better than others? Under similar conditions, why do some individuals excel while others fail? Why do students burnout, drop classes, even drop out of college? What is the impact of stress, mood, workload, sociability, sleep and mental health on academic performance (i.e., GPA)? The study used an android app we developed for smartphones carried by 48 students over a 10 week term to find answers to some of these pressing questions.
We use computational methods and machine learning algorithms on the phone to assess sensor data and make higher level inferences (i.e., sleep, sociability, activity, etc.) The StudentLife app that ran on students' phones automatically measured the following human behaviors 24/7 without any user interaction:

• bed time, wake up time and sleep duration
• the number of conversations and duration of each conversation per day
• physical activity (walking, sitting, running, standing)
• where they were located and who long they stayed there (i.e., dorm, class, party, gym)
• the number of people around a student through the day
• outdoor and indoor (in campus buildings) mobility
• stress level through the day, across the week and term
• positive affect (how good they felt about themselves)
• eating habits (where and when they ate)
• app usage
• in-situ comments on campus and national events: dimension protest, cancelled classes; Boston bombing.

We used a number of well-known pre and post mental health surveys and spring and cumulative GPA as ground truth for evaluation of mental-health and academic performance, respectively.
Below you will find papers that report on some of the findings from the StudentLife dataset. Because we are interested in spurring work in mining human behavior we have released an anonymized version of the StudentLife dataset (see below).
Feel free to contact us if you have any questions relating to the project, findings or dataset.