SBIR Phase II: Semantically Intelligent Knowledge Hub for Course Authoring

This Small Business Innovation Research Phase II project aims to develop a system to dramatically simplify how course content is shared, discovered, curated and managed in educational institutions. The vast majority of content in educational institutions is in multiple silo-ed repositories. Hence, compliance tracking, aligning curricula with competencies and course authoring are manual and expensive processes. This project aims to unlock these silos and make it simple to search, tag and curate courseware at topic-level across multiple platforms. The technology also extracts student feedback and assessment information and maps it against granular learning objectives to provide new insights to track and improve learning outcomes. By unlocking courseware stuck in silo-ed college systems, this project facilitates a cross-institutional marketplace that enables any institution to share and/or monetize curated high quality content, and conversely have access to curated course packs that may be taught by adjunct faculty, a $2B market opportunity. Simplifying faculty access to Open Educational Resources and curated learning content from other colleges will help drive down student course-pack costs significantly. The key technical innovation in this project is seamless extraction of learning content from multiple educational repositories coupled with a syllabus-driven semantically intelligent search and recommendation engine woven into the course curation process. Developing an abstracted mechanism to interface with different types of learning management systems and other repositories, and making it portable without requiring a common content format is a significant innovation. Natural Language Processing techniques combined with curated controlled vocabularies and taxonomies are applied to simplify curation of a course, notably automatic topic identification and instructional tagging. The curation data is leveraged in the semantic search engine to improve recommendation of courseware during course authoring. The goal of the adaptive recommendation engine is to improve discovery and usage of both open educational resources and courseware developed in other colleges, without requiring expensive professional services. Another goal of the project is to extract student feedback seamlessly using IMS defined standards regardless of which learning management system is used to deliver the content. The student feedback, assessment data, grades and other information pulled from various college systems are then correlated to provide rich analytics and visibility of student outcomes and alignment of courseware with competencies.