I-Corps: Fragmented Learning App based on Knowledge Graphs

这个I-Corps项目更广泛的影响/商业潜力是开发一个基于知识图谱的碎片化学习应用程序，以提高碎片化学习的有效性和效率，作为课堂环境中传统集体学习的补充。这个目标是检查的影响，分散的学习方式对各种类型的领域知识与（主要使用）集体学习方式。所提出的技术将探索知识图如何表示和组织知识（例如，概念、定理、原理、规则），并且使得能够进行知识推理和推导。 I-Corps是一个基于技术开发的支持碎片化学习的项目，它允许用户随时随地在各种平台上学习，提高他们的学习效果和效率。特别地，所提出的技术是通过将知识图与机器/深度学习和自然语言处理模型相结合来设计的，以基于连贯和不断发展的知识图而不是用户构建的关键字搜索来推荐相关的学习材料。该应用程序为知识领域构建完整的知识图，并为个人用户构建个性化的知识图，以生成适合他/她个人学习需求的个性化学习计划。利用知识图谱来表示和组织来自大数据源（文本和视频教程）的知识，碎片化学习应用程序捕获知识的显式和隐式关系，并导出新的知识和关系，以防止领域知识碎片化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a Fragmented Learning App based on knowledge graphs to improve the effectiveness and efficiency of fragmented learning as a complement to the traditional collective learning in the classroom setting. This goal is to examine the impact of the fragmented learning style on various types of domain knowledge versus the (predominantly used) collective learning style. The proposed technology will explore how knowledge graphs may represent and organize knowledge (e.g., concepts, theorems, principles, rules) in various domains and enable knowledge reasoning and derivation. Data from various types of users will be collected to enrich and evolve the knowledge graphs based on the latest learning needs.This I-Corps project is based on the development of technology to support fragmented learning, which allows users to study anytime and anywhere as well as on various platform, improving their learning effectiveness and efficiency. Specially, the proposed technology is designed by integrating knowledge graphs with machine/deep learning and Natural Language Processing models to recommend relevant learning materials based on coherent and evolving knowledge graphs rather than user-constructed keyword search. The app constructs both a complete knowledge graph for a knowledge domain and a personalized knowledge graph for the individual user to generate a personalized learning plan adapted to his/her individual learning needs. Leveraging knowledge graphs to represent and organize knowledge from big data sources (text and video tutorials), the Fragmented Learning App captures explicit and implicit relations of knowledge and derives new knowledge and relations to prevent fragmenting the domain knowledge. In addition, the technology incorporates the social platform and observer roles for user interaction and knowledge exchange.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.