A smartphone app that can predict your grades

The first-of-its-kind smartphone app uses cellphone data on studying and leisure habits to predict grade points. Read on for details..

Scientists have developed the first smartphone app that automatically predicts college students' grade point average based on their cellphone data that tracks their study, party and other habits.

The findings offer new ways to improve students' performance, providing real-time feedback on their studying, partying, sleeping, exercising and other conscious and unconscious behaviours to help them stay on track academically, researchers said.

The SmartGPA app builds on the Dartmouth College researchers' earlier StudentLife study, which created the first smartphone app that automatically reveals college students' mental health, academic performance and behavioural trends.

"Many cognitive, behavioural and environmental factors impact student learning during college," said Dartmouth computer science Professor Andrew Campbell, senior author of the SmartGPA study.

"Our SmartGPA results show there are a number of important study and social behaviours automatically inferred from smartphone sensing data that significantly correlate with term and cumulative GPA," said Campbell.

The researchers installed the SmartGPA app on the smartphones of 30 Dartmouth undergraduate students and monitored them across a 10-week academic term.

The app uses automatic sensing data on the phone and in the cloud and machine learning algorithms around the clock to infer higher level behaviours, including partying (frequency and duration) and studying (duration and focus).

It also tracks behavioural changes for the students, such as class attendance, sleep, physical activity and sociability (face to face conversation and indoor and outdoor mobility).

The app works behind the scenes with no user input.

"The results show the app, along with periodic self-reports from students, can predict their GPA within 17 hundredths of a point against their cumulative GPA from their transcripts," researchers said.

The results are statistically significant even for a small cohort.

The computational model uses no prior knowledge of students' academic performance, such as SAT scores and IQ. 

Lead image used for representational purposes only