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Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify differentially frequent patterns between groups of students (e.g., experimental versus control conditions or high versus low performers). We extend this techniquedoi:10.5281/zenodo.3554617 fatcat:s22hvepeqfcyrik6wd2mcfze2i