SBIR Phase II: Semantically Intelligent Knowledge Hub for Course Authoring

这个小企业创新研究第二阶段项目旨在开发一个系统，以极大地简化教育机构中课程内容的共享、发现、策划和管理。教育机构中的绝大多数内容都在多个竖井存储库中。因此，遵从性跟踪、将课程与能力相结合以及课程编写都是手动且昂贵的过程。这个项目旨在解开这些孤岛，使搜索、标记和策划跨多个平台的主题级课件变得简单。该技术还可以提取学生反馈和评估信息，并将其映射到精细的学习目标中，从而为跟踪和改善学习成果提供新的见解。通过解锁受困于高校系统的课件，该项目促进了一个跨机构的市场，使任何机构都能分享和/或将精心策划的高质量内容货币化，反过来，也可以访问由兼职教师教授的精心策划的课程包，这是一个价值20亿美元的市场机会。简化教师访问开放教育资源和从其他学院策划的学习内容将有助于显著降低学生的课程包成本。该项目的关键技术创新是从多个教育知识库中无缝提取学习内容，并将教学大纲驱动的语义智能搜索和推荐引擎编织到课程管理过程中。开发一种抽象机制来与不同类型的学习管理系统和其他存储库进行接口，并使其在不需要通用内容格式的情况下具有可移植性，这是一项重要的创新。将自然语言处理技术与精心策划的受控词汇和分类法相结合，应用于简化课程策划，特别是自动主题识别和教学标记。在课程创作过程中，在语义搜索引擎中利用策划数据来改进课件的推荐。自适应推荐引擎的目标是提高开放教育资源和其他学院开发的课件的发现和使用，而不需要昂贵的专业服务。该项目的另一个目标是使用IMS定义的标准无缝地提取学生反馈，而不管使用哪个学习管理系统来交付内容。然后将学生反馈、评估数据、成绩和从各个大学系统中提取的其他信息关联起来，提供丰富的分析和学生成果的可见性，并将课件与能力相结合。

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.