Doing assignments is a very important part of learning. Students' assignment submission time provides valuable information on study attitudes and habits which strongly correlate with academic performance. However, the number of assignments and their submission deadlines vary among university courses, making it hard to use assignment submission time as a feature to predict academic performance. In this paper, we propose a new method called Relative Assignment Submission Time (RAST) which uses the assignment submission information of peer students to improve the correlation with course grades. Experiments on real-life data of 20 courses show that RAST has a high correlation with students' academic performance. We also build a machine learning model using RAST as a feature to detect students who would suffer from poor grades. Our method outperforms the traditional method by up to 61% on f1-score. We believe that our proposed method can help other studies on assignment submission time to improve the prediction accuracy on academic performance and detecting at-risk students. [For the full proceedings, see ED623995.]