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:

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.

Is there a rhythm to the Dartmouth term?

We have a hypothesis that there is a signature to the Dartmouth term. That is, if we gave StudentLife to all students at Dartmouth we'd find a common rhythm to the intense Dartmouth term. You'll see some examples of the "dart-rhythm" below. Take a look at the paper for more detailed results.

The study captured behavioral trends across the Dartmouth term. For example, students returned from spring break feeling good about themselves, relaxed (i.e., low stress levels), sleeping well and going to the gym regularly. That all changed once the Dartmouth term picked up speed toward midterm and finals, as shown in the plot.

The timeline above shows conversation frequency and duration across the term. Students start the term by having long (possibly) social conversations. They'd just returned form spring break after all.

As midterm approaches they start having more frequent conversations but they are shorter in length. During midterm week conservation is much more businesslike in comparison to the start of term; that is, students have fewer, shorter conversations. As the term draws to an end things switch around and people have more frequent, longer conservations. Does that sound like your Dartmouth term?

The term starts with students being very active. Strangely enough they don't get much sleep during the first week of term. Why is that? Possibly partying and socializing at the start of term. Class attendance is excellent. As the term gets underway things change. Activity drops sharply to its lowest level during midterm and stays that way for the rest of the term. Sleep follows a similar pattern -- drops to a low point during midterm and then plummets at the end of term.

Interestingly enough, class attendance drops off steadily across the term to a low point where students are only attending 25% of their classes on average. Our results also indicate that there is no correlation between class attendance and grade.

Predicting GPA from Phones

Many cognitive, behavioral, and environmental factors impact student learning during college. The SmartGPA study uses passive sensing data and self-reports from students’ smartphones to understand individual behavioral differences between high and low performers during a single 10-week term. We propose new methods for better understanding study (e.g., study duration) and social (e.g., partying) behavior of a group of undergraduates. We show that there are a number of important behavioral factors automatically inferred from smartphones that significantly correlate with term and cumulative GPA, including time series analysis of activity, conversational interaction, mobility, class attendance, studying, and partying. We propose a simple model based on linear regression with lasso regularization that can accurately predict cumulative GPA.

The predicted GPA strongly correlates with the ground truth from students’ transcripts (r = 0.81 and p < 0.001) and predicts GPA within ±0.179 of the reported grades. Our results open the way for novel interventions to improve academic performance. [pdf]

The two plots show when students party and study. This data is automatically inferred by the students’ phones and correlates with the party and study scene at Dartmouth.

This plot shows the partying trends across the complete term. Most partying happens at the start of term and then declines until the spring festival week (called Green Key) then there is little in the way of partying as students buckle down to the end of term and finals.

This is an interesting plot. Class attendance drops after the midterm period but there is an opposite increase in studying? Students are missing classes but studying more.

StudentLife Dataset

The StudentLife dataset is a large, longitudinal dataset that is rich in formation and deep. Importantly, the dataset is anonymized protecting the privacy of the participants in the study.

The dataset is from 48 undergrads and grad students at Dartmouth over the 10 week spring term. It includes over 53 GB of continuous data, 32,000 self-reports, and pre-post surveys; specifically it comprises:

Dataset detailed description here

Download the StudentLife dataset here

StudentLife 2.0

What's next for StudentLife? In a word we want to add intervention to the app. In the future, we hope StudentLife will help students boost their academic performance while living a balanced life on campus.

One could imagine that an app like StudentLife could be used for real-time feedback on campus safety, students at risk; or answer questions like "how stressed is campus right now?" or instead of waiting until the end of term to assess class quality such a tool could give immediate reaction about the quality of teaching at any moment in time.

We purposely provided students with no feedback because we didn't want to use StudentLife as a behavioral change tool. We simply wanted to "record" their time on campus. Providing feedback and intervention is the next step. For example, we might inform students of risky behavior; such as, partying too much, poor levels of sleep for peak academic performance, poor eating habits, too socially isolated, not flourishing, struggling, etc.

There are many stakeholders in student life on campus (see image above): students, faculty, student deans, docs, friends and family. All have only partial state information. We imagine StudentLife 2.0 will allow students to "connect" stakeholders by sharing their data. Such a vision represents a massive privacy problem that needs to be solved. However by connecting the stakeholders StudentLife could provide new forms of intervention to promote healthy living and safety on campus as well as help students modulate their behavior (e.g., could be as simple as not pulling all-nights and getting regular sleep) to improve GPA and life on campus.

We feel that StudentLife 1.0 is just the start. Stay tuned.

StudentLife Team

Dror Ben-Zeev, Andrew Campbell, Fanglin Chen, Zhenyu Chen, Tianxing Li, Rui Wang and Xia Zhou (Dartmouth College), Gabriella Harari (University of Texas at Austin), Stefanie Tignor (Northeastern University)

We would like to thank the following people for their input and guidance is getting the study going.

Ethan Berke (DHMC), Tanzeem Choudhury (Cornell), Randy Colvin (Northeasten), Sam Gosling (UT Austin) and Catherine Norris (Swarthmore)


Rui Wang, Min S. H. Aung, Saeed Abdullah, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A. Scherer, Vincent W. S. Tseng, and Dror Ben-Zeev. "CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia." In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 886-897. ACM, 2016. [pdf]

Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T. Campbell. "SmartGPA: How Smartphones Can Assess and Predict Academic Performance of College Students." To be presented at ACM Conference on Ubiquitous Computing (UbiComp 2015), Osaka, Japan from Sep. 7-11, 2015 Honorable Mention Award [pdf]

Rui Wang, Xia Zhou, and Andrew T. Campbell. "Using Opportunistic Face Logging from Smartphone to Infer Mental Health: Challenges and Future Directions." To be presented at 4th ACM Workshop on Mobile Systems for Computational Social Science (MCSS 2015), Osaka, Japan from Sep. 7-11, 2015 [pdf]

Sophia Haim, Rui Wang, Sarah E. Lord, Lorie Loeb, Xia Zhou, and Andrew T. Campbell. "The Mobile Stress Meter: A New Way to Measure Stress Using Images." To be presented at 4th ACM Workshop on Mobile Systems for Computational Social Science (MCSS 2015), Osaka, Japan from Sep. 7-11, 2015 [pdf]

Dror Ben-Zeev, Emily A. Scherer, Rui Wang, Haiyi Xie, and Andrew T. Campbell. "Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health." Psychiatric Rehabilitation Journal, Vol 38(3), Sep 2015 [pdf]

Randy Colvin, Stefanie Tignor, Rui Wang, Andrew T. Campbell, Inching Closer to Objective Personality Assessment: The Promise of Smartphone Data, the Society for Personality and Social Psychology (SPSP), Long Beach, CA February, 2015.

Gabriella M. Harari, Samuel D. Gosling, Rui Wang, Andrew T. Campbell, Capturing Situational Information with Smartphones and Mobile Sensing Methods, European Journal of Personality, 2015.[pdf]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. "StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones." In Proceedings of the ACM Conference on Ubiquitous Computing. 2014. Nominated for the best paper award (top 5% of all papers) [pdf]

Fanglin Chen, Rui Wang, Xia Zhou, and Andrew T. Campbell. "My smartphone knows i am hungry." In Proceedings of the 2014 workshop on physical analytics, pp. 9-14. ACM, 2014. [pdf]


Harari, G. M., Wang, R., Campbell, A. T., & Gosling, S. D. (2016, January). Capturing Sociability Behaviors Using Smartphone Sensing. Presented at the Society for Personality and Social Psychology, San Diego, CA.

Andrew Campbell "StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students", GVU Center Brown Bag Seminar Series: Andrew Campbell, November, 2015

Rui Wang "SmartGPA: How Smartphones Can Assess and Predict Academic Performance of College Students", ACM UbiComp, Sept 11, 2015

Andrew Campbell "StudentLife",Keynote on StudentLife, AAAI ICWSM Conference, Oxford, May 2015

Andrew Campbell "What happens when life throws you a googly?", Wireless Health Conference, 2014

Rui Wang "StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones", ACM UbiComp, Sept 15, 2014

Rui Wang "My smartphone knows i am hungry.", Workshop on Physical Analytics, June 16, 2014

In Press

Students under surveillance, July 2015

Predicting GPA and student success? Dartmouth researchers say there’s an app for that, during NPR's Morning Edition and All Things Considered shows, June 8, 2015

The Quantified Student: An App That Predicts GPA, June 2015

App claims to know your GPA just by looking at your phone, June 2015

The app that can predict your GPA, June 2015

Researchers use an app to predict GPA based on smartphone use, June 2015

Can a New Smartphone App Predict GPA?, June 2015

Dartmouth Predicts GPA Based on Phone Tracking App, June 2015

Really smart phones: Now they can predict your GPA, June 2015

Dartmouth Researchers Create First Smartphone App That Predicts GPA, May 2015

Diagnosing depression with an app, The Independent, November 2014

Smartphone apps could be next tool for mental wellbeing, ABC (listen), Nov 2014

Mental Health App, BBC World News TV Impact, October 2014

Smartphone app knows when students are feeling stressed, BBC CAPITAL, October 2014

New App Measures Students' Moods and Mental Health, Chronicle of Higher Education, October 2014

App Can Tell When Students Are Stressed Out, September 2014

Sensing Depression, Radio interview with Nora Young on CBC/NPR Spark, September 2014

Your App, Yourself, Editorial, September 2014

Failing students saved by stress-detecting app - our work on the StudentLife study at Dartmouth is featured in the New Scientist, September 2014 - the article appeared in the printed version of NS under the headline "Phone in your feelings"

Sensitive Smart Phones Decipher The Habits Of Successful Students, September 2014

This App Passively Tracks Your Mental Health, September 2014

Stressed Out? Your Smartphone Could Know Even Before You Do, September 2014

This Phone App Knows If You're Depressed, MIT Technology Review, September 2014

Your Smartphone Thinks You're Sad, CBS News, September 2014

Smartphone App Knows When You're Feeling Blue, CNET, September 2014

Dartmouth Teacher Makes Health App, Valley News, September 2014

Dartmouth's StudentLife App Can Tell You If Your Mental Health Is Hurting Your Grades, engadget, September 2014


If you have any questions regarding the study or dataset contact