It is well established that repetition, especially when spaced over multiple sleep cycles, significantly enhances learning and long-term retention. However, introductory computer science (CS) courses present a unique pedagogical challenge: students enter with vastly different levels of prior experience. For some, the material is entirely new, offering no opportunity for repetition; for others, it is largely review. Traditional instructional models tend to privilege those who grasp content on the first pass, while offering little structural support for students who need more time. Meanwhile, more equitable strategies like mastery-based grading are difficult to scale in large-enrollment settings due to resource constraints. In response to these challenges, we designed a lesson structure that builds repetition into the course flow without penalizing students for slower initial understanding and without increasing workload for educators. Our solution is a weekly workbook composed of four components: a conceptual overview, followed by three linked assignments. These assignments progress in emphasis from assignment completion (to build confidence, familiarity, and provide early feedback) to correctness (to support mastery). Early results suggest improved retention and higher performance among our students. This model encourages re-engagement with material across multiple contexts and time points, giving all students – regardless of background – space to improve through repetition while still balancing the resource demand of a large foundational class.