In this paper, we introduce the concept of exploring the feature space to aid learning in the context of design space exploration. The feature space is defined as a possible set of features mapped in a 2D plane with each axis representing different interestingness measures, such as precision or recall. Similar to how a designer explores the design space, one can explore the feature space by observing how different features vary in their ability to explain a set of design solutions. We hypothesize that such process helps designers gain a better understanding of the design space. To test this hypothesis, we conduct a controlled experiment with human subjects. The result suggests that exploring the feature space has the potential to enhance the user’s ability to identify important features and predict the performance of a design. However, such observation is limited only to the participants with some previous experience with design space exploration.