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Towards Requirements for Supporting Course Redesign with Learning Analytics

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Published:02 November 2014Publication History

ABSTRACT

With increased focus on quality, many Colleges and Universities run large enrollment online courses by maintaining course masters or frameworks. A framework enables curriculum managers to instantiate hundreds of course sections by keeping under control the quality of common elements of instruction. Current research in learning analytics tools suggests their applicability for the purposes of course design however, the current set of tools were built to support either course or student interventions. Little work has been done to examine the challenges of developing tools for the purpose of supporting the course redesign process. This research is the first phase of a larger project aimed at developing a "Best Practice Finder" which would aggregate section level data to help curriculum managers improve the design of their frameworks. This phase of the research seeks to investigate key requirements for the support of the use of learning analytic systems for the purpose of course redesign. Responses from 71 program chairs of Ivy Tech Community College of Indiana were collected to gauge the relative importance of curricular, institutional, and statistical knowledge they would need to make sense of learning analytics. Three main approaches to sense making emerged: a course centered, an institution centered, and an information centered approach. This work paves the way for an articulation of a full set of requirements for supporting course design focused learning analytics.

References

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              • Published in

                cover image ACM Conferences
                SIGUCCS '14: Proceedings of the 42nd annual ACM SIGUCCS conference on User services
                November 2014
                122 pages
                ISBN:9781450327800
                DOI:10.1145/2661172

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 2 November 2014

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                SIGUCCS '14 Paper Acceptance Rate24of58submissions,41%Overall Acceptance Rate123of170submissions,72%

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