Convergence Accelerator Phase I (RAISE): Scalable Knowledge Network to Enable Intelligent Textbooks

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。正确教育未来的STEM领导者需要超越通过纸质教科书和讲座从教师向学习者传递知识。这个融合加速器第一阶段项目的更广泛的影响和潜在的社会效益是启动教科书开放知识网络（TOKN）。TOKN项目将由认知科学家、机器学习和人工智能工程师以及莱斯大学OpenStax和斯坦福大学的教育工作者组成的多学科团队执行，他们致力于创建智能教科书库，并随着项目的进展吸引更多合作伙伴。开始，这个项目将建立知识图谱，捕捉教育概念之间的复杂关系。知识图使人工智能算法能够为智能教科书提供增强和个性化的功能。因此，智能教科书可以提供更强大的课件功能（文本、视频、模拟等），学习分析和个性化辅导，例如自动生成教科书内容的摘要，为学生生成有用的练习，提供与学生的互动对话，以帮助他们更好地理解和掌握底层源材料，等等。 将这项智能技术集成到完整的OpenStax免费开放图书馆中，有可能影响数百万中等和高等教育学生的学术成果，同时显著提高全球教育水平。智能教科书为学生提供了更好的学习机会。然而，它们需要大量的时间、金钱和专业知识的投资。适当的知识图是智能教科书的核心，通常是智能教科书创建的最大挑战，因为需要人类学科专家来开发术语和思想的语义连接。TOKN旨在开发新的、可扩展的流程和支持技术，为智能教科书生成高质量和可扩展的知识图。这项研究旨在降低生成高质量知识图所需的成本和时间。与使用主题专家相比，该项目建议使用机器学习算法和学生知识众包的组合。众包不仅将为知识图谱提供数据，而且还将提供一个机会来评估概念图对学生学习的教学效果。该项目的第一阶段将包括一个概念证明，为OpenStax生物学的一章构建和验证知识图，OpenStax生物学是一个免费的开源文本，超过30%的学生在大学生物学课程中使用。总体目标是最终在第二阶段大规模应用这种方法，为整个OpenStax图书馆的38本普通教育教科书生成知识图谱，将其转化为面向社会的智能开放教科书。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 NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. Properly educating the STEM leaders of tomorrow requires moving beyond knowledge being transmitted from teacher to learner via paper textbooks and lectures. The broader impact and potential social benefit of this Convergence Accelerator Phase 1 project is to launch the Textbook Open Knowledge Network (TOKN). The TOKN project will be executed by a multidisciplinary team of cognitive scientists, machine learning and artificial intelligence engineers, and educators from Rice University's OpenStax and Stanford University who are committed to creating a library of intelligent textbooks, engaging more partners as the project progresses. To begin, this project will build knowledge graphs that capture the complex relationships between educational concepts. A knowledge graph enables artificial intelligence algorithms to provide enhanced and personalized functionality to intelligent textbooks. Consequently, the intelligent textbook can provide more robust courseware functionality (text, videos, simulations, etc.), learning analytics, and personalized tutoring, such as automatically generating summaries of textbook content, generating useful practice exercises for students, providing interactive dialogues with students to help them better understand and master the underlying source material, and more. Integrating this intelligent technology into the full OpenStax free and open library has the potential to impact academic outcomes for millions of students in both secondary and higher education, while significantly advancing the state of education worldwide. Intelligent textbooks provide an opportunity to facilitate better learning for students. However, they require major investments of time, money, and expertise. An appropriate knowledge graph is at the heart of an intelligent textbook and is often the biggest challenge to intelligent textbook creation due to the need for human subject experts to develop the semantic connectivity of terms and ideas. TOKN aims to develop new, scalable processes and supporting technologies for generating high-quality and extensible knowledge graphs for intelligent textbooks. The proposed research aims to lower both the cost and time required to produce high-quality knowledge graphs. In contrast to using subject matter experts, this project proposes to use a combination of machine learning algorithms and crowdsourcing of knowledge from students. Crowdsourcing will not only provide data for knowledge graphs, but it will also provide an opportunity to evaluate the pedagogical effectiveness of concept mapping on student learning. Phase 1 of this project will include a proof of concept to construct and validate a knowledge graph for one chapter of OpenStax Biology, a free and open-source text used by more than 30% of students in college biology programs. The overarching goal is to eventually apply this approach at scale during Phase 2 to generate knowledge graphs for the entire OpenStax library of 38 general educational textbooks, transforming them into intelligent open textbooks for society.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.