The distributed practice effect suggests that students retain learning content better when they pace their practice over time. The key factors are practice dosage (intensity) and timing (when to practice and how in between). Inspired by the thriving development of image recognition, this study adopts one of the successful techniques, multiresolution analysis (MRA), to model distributed and spaced practice (SP). We consider a sequence of practice sessions as a signal of the student's learning strategy. Then, we apply the stationary wavelet transform (SWT) to extract practice patterns spaced by three periods: small, medium, large. The result reveals a positive correlation between the small-spaced practice and the exam grade. The benchmark against baseline feature models shows that the SP patterns significantly improve the goodness-of-fit and complements the baseline models. This work successfully demonstrates 1) the use of MRA in modeling sequential patterns by event intensity and event timing; 2) the MRA approach can be used as an alternative method to improve existing student models of practice effort. [For the full proceedings, see ED615472.]