For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task, and to select an appropriate new learning task in response to that self-assessment. This study investigated the use of video-based modeling examples to teach self-assessment and task-selection skills. Students in both the experimental and control condition observed the model performing a problem solving task; students in the experimental condition additionally observed the model engaging in self-assessment and task selection. Results show that students in both conditions acquired problem-solving skills from the examples, as indicated by a substantial pretest to posttest knowledge gain. Moreover, students in the experimental condition also acquired self-assessment and task-selection skills from the examples: they demonstrated higher self-assessment and task-selection accuracy on the posttest than students in the control condition.