Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined with showing learning analytics on skill mastery and visual "what-if" explanations, can support students in practice contexts requiring high degrees of self-regulation, such as homework. Semi-structured interviews with six middle school students revealed three key insights: (1) participants highly valued learner control for an enhanced learning experience and better self-regulation, especially because most wanted to avoid losses in skill mastery; (2) only seeing their skill mastery estimates often made participants base problem selection on their weaknesses; and (3) "what-if" explanations stimulated participants to focus more on their strengths and improve skills until they were mastered. These findings show how explainable learning analytics could shape students' selection strategies when they have control over what to practice. They suggest promising avenues for helping students learn to regulate their effort, motivation, and goals during practice with tutoring systems. [This paper will be published in: "LAK25: The 15th International Learning Analytics and Knowledge Conference (LAK 2025), March 3-7, 2025, Dublin, Ireland," ACM, 2025. Additional funding provided by Research Foundation Flanders.]