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The increasing use of AI systems opens up opportunities to use learner and teacher-related data for reflection, supporting instruction and professional growth. However, teacher-related data is rarely collected in technology-enhanced learning environments. Understanding teacher preferences can inform the design of AI reflection systems that align with their priorities. The current survey study explores how K-12 teachers' reflection practices and preferences for analytic data types (e.g., learning-related vs. engagement-related) vary by experience, an aspect often overlooked in prior works. Findings from our survey study with N=100 teachers in the U.S. revealed significant differences. Less experienced teachers focus on classroom behavior and prefer analytics on classroom issues, while experienced teachers prefer broader strategies to improve student learning and favor sustained data analysis. Our results reflect the need to consider teachers’ experience level by tuning the complexity and concreteness of analytic recommendations to support ongoing professionalization.