This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features individually for each pattern. Consequently, MFH-SPAM operates on a larger space of patterns in the activity sequences. In this paper, we employ a differential version of MFH-SPAM to extract a small set of patterns that best differentiate students with different learning behavior profiles in the Betty's Brain system. Our results illustrate that: (1) MFH-SPAM identifies important patterns missed by traditional sequence mining approaches; and (2) the differential patterns provide additional information for characterizing learning behaviors. This has implications for developing targeted and adaptive scaffolding in open-ended learning environments. [For complete proceedings, see ED560503.]