SEMANTIC MAPPING OF LEARNING ASSETS
SEMANTIC MAPPING OF LEARNING ASSETS

Summary/Abstract: Ensuring that courses touch upon desired goals and objectives is a problem that has long plagued instructional design. While keyword and metadata strategies have provided some benefit these methods suffer from a lack of robustness. As such, they provide only incremental improvements to what remains a highly manual process. A research team at American Public University System addressed these interrelated problems by employing an open source repository and semantic engine for analysis and alignment of content, materials, and learning activities across all courses within the School of Business. The result was a highly detailed, accurate mapping of the programs' knowledge base to established goals and objectives. Content and activities from 29 courses in the APUS Business Program were federated in Common Library. Disaggregation of content yielded 5227 granular level assets and ontological ordering, using Dirichlet analysis, was conducted and categorical structuring was implemented using an iterative, multi-pass approach. A total of 538 goals and objectives, from both the program and course level, were input into the system. Using a natural language approach, these goals and objectives were specified as being representative of over-arching ontological structures.

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