Success in introductory computing (CS1) courses requires more than just learning the material; students must also be able to identify their own knowledge gaps and study effectively. Failure to accurately self-assess may lead to inefficient study behaviors and poor learning outcomes. While theories of self-regulated learning (SRL) emphasize planning, performance, and self-reflection as essential phases of effective study, there is limited evidence on how to help learners put these phases into practice. In this context, Mastery-Based Tests (MBT)–which allows students to retake tests after receiving feedback–has shown promise for improving learning outcomes. However, prior work in computer science is largely observational and does not directly test whether MBT affects SRL behaviors. This paper presents a controlled experiment examining how MBT influences SRL behaviors in CS1 courses. We implemented an introductory Python course in OLI Torus and conducted a between-subjects pilot study (N = 6) in which the control group studied freely, while the experimental group completed an MBT prior to studying. Learners who first completed an MBT demonstrated higher metacognitive accuracy, reported using MBT feedback to guide their practice, and achieved higher post-test scores. These findings provide initial causal evidence that MBTs can scaffold effective SRL behaviors in CS1.