From the organizational perspective, the overwhelming amount of information associated with online learning, that resides in silo structures, presents a challenge in terms of the efficacy of instructional design workflows and codification of tacit knowledge for accreditation by external agencies. In the Learning or Content Management System environments, content management frequently translates into a single-purpose allocation of content resources, with cataloging and meta-tagging being a haphazard affair. This can lead to potential duplication of content and significant time loss associated with asset retrieval for incorporation into new curricula as well as less effective, poorly constructed, and mis-aligned learning objectives. Because content is created with the notion that all contributors have knowledge of the underlying taxonomies or common vernacular that information is based upon, it is difficult for organizations to survey their content universe for existing objects that can be incorporated into emerging workflows.

 By automating the meta-tagging and gap analysis process, semantic analysis allows one to not only smartly survey existing learning objects in a specific curriculum area, but examine more learning objects across unrealized curriculums. The ability to determine content interrelationships through the mapping of assets across the content universe enables one to effectively and efficiently facilitate object reusability towards curricular goal and objective fulfillment. This process allows for the actualizing of opportunities to locate learning objects to fulfill course level objectives for alignment across course level objectives, programmatic outcomes and industry standards. Improvement of instructional outcomes, through the ingestion of work products from implementation of content distillation and semantic analysis, increases return on investment and time on task. The opportunity to provide detailed analysis reporting that demonstrates curricular alignment to accrediting bodies and others is effectuated.

At American Public University System (APUS) the Instructional Design and Development (IDD) Team created an Instructional Design Process Model to design and develop curricula. The APUS ID Process Model (see http://tinyurl.com/2bpbcb4) is a continuous course development process allowing for the collaboration with Subject Matter Experts (SMEs), Instructional Designers (IDs), Graphic and Media specialists, and Content Area experts both internal and external to the institution. Through the APUS Instructional Design Process Model, a derivative of the ADDIE model (Molenda, 2003) was incorporated with the foundational principles of the Community of Inquiry (CoI) (Arbaugh, Cleveland-Innes, Diaz, Garrison, Ice, Richardson & Swan, 2008) and applied to every step of the process; planning, pre-development, development, design, and evaluation, and maintenance.

 The balance six-phase process model is an architecture which enables the team to focus on three important elements of the courseware development. First, the team implements accepted best practices in online Instructional Design (ID) systems pedagogy, andragogy, and heutagogy modeling to structure overall courseware development. Next, the CoI is applied to the online learning framework in the courseware to ensure successful student outcomes. Finally, the team utilizes agile project management principles to allow for collaboration and communication, both internally and with subject matter experts, while still maintaining discipline, quality, and rigor throughout the project’s lifecycle (Staley, Gibosn & Ice, 2010).

Though highly efficient and effective in rapidly creating quality content across a network of over 130 geographically dispersed contributors, the APUS IDD is still confronted with three problems that consistently confound the field. First, content and learning activities created with the premise that SMEs have a mastery of their area and will ensure that goals and objectives are met through tacit embedding of area knowledge within course structures. However, subject matter experts are human and prone to the same tendency to have expert blind spots; a phenomenon long noted in the traditional classroom (McKeachie, 1986). Second, metatagging data is a time intensive process that even when executed with a high degree of accuracy provides little more than key word associations. Finally, because all contributors have knowledge of the underlying taxonomies or common vernacular that information is based upon, it is difficult for organizations to survey their content universe for existing objects that can be incorporated into emerging workflows.

As with all institutions, APUS is also confronted with the issue of providing adequate data for accreditation by external entities. Within the accreditation process is the task of demonstrating that curricula fulfills both course level and program level goals and objectives. While the IDD process, described above, is designed to help expedite goal and objective alignment, the limitations previously described still limit the robustness of execution.

To solve this problem, APUS investigated the feasibility of using semantic analysis to: 1. match program and course level goals and objectives to course content and activities; 2. create a gap analysis to note where additional resources should be applied to meet goals and objectives; and 3. create robust content repositories that have granular associations between course components and over-arching ontologies that can be applied in a cross-curricular fashion.

Similar to contemporary object oriented programming language, semantic analysis is reliant on defining data in terms of classes with attributes and instances. The vision of the semantic aware applications builds upon this concept by refining these ontologies through comparisons of associated metadata. Currently, there are two approaches for developing semantic applications; the bottom-up approach and the top-down approach. The bottom-up approach is problematic in that it assumes metadata will be added to each piece of content to include information about its context; tagging at the concept level, if you will. The top-down approach appears to have a far greater likelihood of success, as it focuses on developing natural language search capability that can make those same kinds of determinations without any special metadata (Johnson, Levine & Smith, 2009).

Interestingly though the NMC / ELI Horizon Report 2009 indicates that semantically aware applications are not likely to become mainstream for four to five years, a few cutting edge prototypes are currently being utilized. Even as these applications are still undergoing refinement the prototypes demonstrate the potential power of semantic applications for both formal and informal learning. The IDD team at APUS vetted several of these cutting edge solutions, both open source and proprietary for the purposes previously described. The Common Library solution, which is open source and available under an Apache 2 license on Source Forge, was ultimately selected.

Developed from the ground-up to address specific needs in education, Common Library (http://commonlibrary.org) is the first standards-based content management system to enable true collaborative potential through the integration of content development and social networking.  The Common Library latent semantic search engine defines a unique and powerful aspect of the application. In the current 2.0 implementation of the system, the metadata and content of each learning object are compared against defined standards systems. This higher-order logic enables Common Library to dynamically suggest interconnections between content items and applicable state standards, providing immediate value for users in the K-12 educational market. This functionality also defines the potential for constructing dynamic relationships between state standard systems that evolve over time. Implementation of search and aggregate technology generates references that feed new granularly addressable connections between content and curriculum structures as more is learned about a specific users requirements. A PowerPoint detailing the Semantic Analysis process can be found at: http://tinyurl.com/2a6bp2k

An instance of this solution was stood up for the APUS IDD team and all course components for the APUS Business Program were federated. After disaggregation of the materials a granular analysis was conducted using Common Library's underlying latent Dirichlet analysis engine. A full mapping of program goals and objectives was created across the content universe. Where incidents of deficiency were noted, remedial action was taken to provide additional resources.