Many cognitive, behavioral, and environmental factors im- pact 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) behav- ior of a group of undergraduates. We show that there are a number of important behavioral factors automatically in- ferred from smartphones that significantly correlate with term and cumulative GPA, including time series analysis of ac- tivity, conversational interaction, mobility, class attendance, studying, and partying. We propose a simple model based on linear regression with lasso regularization that can accu- rately 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.