Injecting Pedagogical Constraints into Sequential Learning Pattern Mining

Data mining techniques have been applied to educational research in various ways. Given a large sample of learning logs, it is common for sequential mining to return a large number of patterns, only a portion of which are educationally meaningful. In this paper, we proposed a constraint-based pattern filtering method to help researchers discover meaningful, interpretable, and relevant patterns by injecting research contexts and domain knowledge into the pattern filtering process. We discussed six different types of constraints researchers can use to further extract meaningful or relevant patterns for pedagogical decision-making and illustrated the viability and usefulness of such constraint-based pattern filtering mechanisms with nStudy logs.