Self-regulated learning is among the most important factors affecting learning outcomes in the digital age. Visualization feedback is widely used to support students' self-regulated learning. However, low motivation often leads to insufficient self-regulated learning engagement. Therefore, based on achievement goal theory, this study developed motivation-driven visualization feedback and employed a quasi-experimental design to investigate its effects on self-regulated learning. Sixty-four sophomores from a Chinese university volunteered for this study and were randomly assigned to two groups. We measured students' self-regulated learning abilities using a 24-item five-point Likert scale, collected insights from the experimental group's experiences with the feedback through semi-structured interviews, and analyzed their learning behavior paths through clickstream data. Data analysis procedures included t-test, statistical analysis, and process mining. The results indicated that, on the one hand, motivation-driven visualization feedback significantly improved specific self-regulated learning abilities, including goal setting, task strategy, time management, and help-seeking. Students expressed high satisfaction with the motivation-driven visualization feedback, believing that the feedback improved their learning motivation and made them more focused on applying self-regulated learning strategies. On the other hand, the feedback allowed students to exhibit a more efficient learning path by increasing their attention to the control and reflection phases of self-regulated learning. This study presents design principles for motivation-driven visualization feedback and analyzes the causal relationship between such feedback and self-regulated learning. The findings reveal how enhancing learning motivation can improve self-regulated learning and provide actionable insights for future educational research.