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We present a new algorithm for active learning embedded within an interactive calendar management system that learns its users' scheduling preferences. When the system receives a meeting request, the active learner selects a set of alternative solutions to present to the user; learning is then achieved by noting the user's preferences for the selected schedule over the others presented. To achieve the goals of presenting solutions that meet the user's needs while enhancing the preference-learning process, we introduce a new approach to active learning that makes online decisions about the technique to use in selecting the schedules to present in response to each meeting request. The decision is based on the entropy of the available options: a highly diverse set of possible solutions calls for a selection technique that chooses instances that are different from one another, maximizing coarse-grained learning, whereas a set of possible solutions containing little diversity is met with a selection strategy that promotes fine-grained learning. We present experimental results that indicate that our entropy-driven approach provides a better balance between learning efficiency and user satisfaction than static selection techniques.