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Web-based learning community allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal/predictive studies). Since Web-based learning community are capable of collecting vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of students, assessments, and the solution strategies adopted by students. In this paper, we propose a new coevolutionary algorithm for the discovery of interesting association rules within a Web-based learning community. Three coevolutionary operators are designed and the mining algorithm is realized in this paper. According to experimentation, the algorithm has been found suitable for association rule mining of the Web-based learning community.