Understanding Teacher Users of a Digital Library Service: A Clustering Approach

Beijie Xu, Mimi Recker


This article describes the Knowledge Discovery and Data Mining (KDD) process and its application in the field of educational data mining (EDM) in the context of a digital library service called the Instructional Architect (IA.usu.edu). In particular, the study reported in this article investigated a certain type of data mining problem, clustering, and used a statistical model, latent class analysis, to group the IA teacher users according to their diverse online behaviors. The use of LCA successfully helped us identify different types of users, ranging from window shoppers, lukewarm users to the most dedicated users, and distinguish the isolated users from the key brokers of this online community. The article concludes with a discussion of the implications of the discovered usage patterns on system design and on EDM in general.

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