Mining and Identifying Relationships Among Sequential Patterns in Multi-Feature, Hierarchical Learning Activity Data

Computer-based learning environments can produce a wealth of information on each student action, which can often be represented at multiple levels of abstraction and with a variety of features. This paper extends an exploratory sequence mining methodology for assessing and comparing students’ learning behaviors by autonomously identifying abstraction levels in a hierarchical taxonomy of actions and their potential features. We apply this methodology to action data gathered from the Betty’s Brain learning environment. The results illustrate the potential of this methodology in identifying and comparing learning behavior patterns across groups of students with complex, hierarchical action and action feature definitions.