Date of Award:

5-2012

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Instructional Technology and Learning Sciences

Committee Chair(s)

Mimi Recker

Committee

Mimi Recker

Committee

James Dorward

Committee

Anne R. Diekema

Committee

Jamison Fargo

Committee

Andrew Walker

Abstract

This dissertation examined how users of the Instructional Architect (IA; http://ia.usu.edu) utilized the system in order to find online learning resources, place them in a new online instructional activities, and share and use them with students. The online learning resources can be found in educational digital libraries such as the National Science Digital Library (NSLD; http://nsdl.org) or the wider Web. Usage data from 22 months of IA use were processed to form usage features that were analyzed in order to find clusters of user behavior using latent class analysis (LCA).

The users were segmented into two samples with 5–90 and 10–90 logins each. Four clusters were found in each sample to be nearly identical in meaning and were named according to their usage characteristics: 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 a 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.

Because this work was in the field of educational digital libraries and educational data mining, the process and results are discussed in that context with an eye to assist novice data miners become familiar with the process and caveats. Implications of user behavior clusters for the IA developers and professional development planners are discussed. The importance of using clustering for the users of educational digital library and end-user authoring tools lies in at least two different areas. First, the clusters can be a very useful framework in which to conduct future studies about how users use the system, from which the tool may be redesigned or simply enhanced to better support different kinds of use. Second, the ability to develop different instructional or promotional outreach for each class can help users accomplish their purposes more effectively, share ideas with other users, and help those struggling with the use of technology.

Checksum

0a6b33ba99580b8e2930b0e467eb8832

Comments

This work made publicly available electronically on May 10, 2012.

Download

Share

COinS

Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .