Date of Award:
Doctor of Philosophy (PhD)
Instructional Technology and Learning Sciences
This dissertation was situated in the crossroads of educational data mining (EDM), educational digital libraries (such as the National Science Digital Library; http://nsdl.org), and examination of teacher behaviors while creating online learning resources in an end-user authoring system, the Instructional Architect (IA; http://ia.usu.edu). The knowledge from data/database (KDD) framework for preparing data and finding patterns in large amounts of data served as the process framework in which a latent class analysis (LCA) was applied to IA user data. Details of preprocessing challenges for web usage data are included. A meaningful IA activity framework provided four general areas of user behavior features that assisted in the interpretation of the LCA results: registration and usage, resource collection, project authoring, and project usage. Four clusters were produced on two samples (users with 5–90 logins and those with 10–90 logins) from 22 months of data collection. The analyses produced nearly identical models with both samples. The clusters were named according to their usage behaviors: one-hit wonders who came, did, and left and we are left to wonder where they went; focused functionaries who appeared to produce some content, but in only small numbers and they did not share many of their projects; popular producers who produced small but very public projects that received a lot of visitors; and prolific producers who were very verbose, created many projects, and published a lot to their students with many hits, but they did not publish much for the public. Information about EDM within the context of digital libraries is discussed and implications for the IA, its professional development workshop, and the larger context of educational digital libraries are presented.
Palmer, Bart C., "Web Usage Mining: Application To An Online Educational Digital Library Service" (2012). All Graduate Theses and Dissertations. 1215.
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