I-Corps: Fragmented Learning App based on Knowledge Graphs

I-Corps项目更广泛的影响/商业潜力是基于知识图谱的碎片化学习应用程序的开发，以提高碎片化学习的有效性和效率，作为传统课堂集体学习的补充。这个目标是检验碎片化学习方式与（主要使用的）集体学习方式对不同类型领域知识的影响。提出的技术将探索知识图如何表示和组织不同领域的知识（例如，概念、定理、原理、规则），并使知识推理和推导成为可能。收集各类用户的数据，根据最新的学习需求，丰富和发展知识图谱。这个I-Corps项目是基于支持碎片化学习的技术发展，使用户可以随时随地、在各种平台上学习，提高学习效果和效率。特别地，该技术将知识图与机器/深度学习和自然语言处理模型相结合，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。该应用程序既为一个知识领域构建了完整的知识图谱，也为个人用户构建了个性化的知识图谱，从而生成适合个人学习需求的个性化学习计划。碎片化学习App利用知识图来表示和组织来自大数据源（文本和视频教程）的知识，捕捉知识的显性和隐性关系，并派生新的知识和关系，以防止领域知识的碎片化。此外，该技术还结合了社交平台和观察者角色，用于用户交互和知识交换。该奖项反映了美国国家科学基金会的法定使命，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.