Collaborative Research: Cyberinfrastructure for Robust Learning of Interconnected Knowledge

协作研究：用于互联知识稳健学习的网络基础设施

• 批准号: 2016929
• 负责人: Norman Bier
• 金额: $ 36.3万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016929&HistoricalAwards=false
• 关键词: Collaborative; Research; Cyberinfrastructure; Robust; Learning

Improving student success in subject domains that serve as gateway courses to STEM careers, such as chemistry and math, is of great interest. These subjects are difficult to teach and learn, partly because they involve a complex network of interconnected knowledge. Current online instructional materials generally follow a linear sequence, tracing a single predetermined pathway through the knowledge network. The CyberBook technology developed in this project instead guides students through the knowledge network along pathways chosen to optimize their learning of both the individual concepts and the relations between these concepts. The pathways adapt to individual students, with information gathered from a student’s interaction with the online materials to select a pathway that is optimal for that individual student. Although the primary focus is on chemistry and mathematics, the approach can be applied to other STEM domains. CyberBook combines traditional online courseware with intelligent tutoring systems. It also provides a research platform for learning scientists to conduct studies on how students learn. Beyond learning science, the proposed research has the potential to advance the literature on education (both domain-specific and domain-general), educational data mining, and artificial intelligence in education. This project will develop several advanced learning-engineering methods to facilitate the creation of high-quality and effective online courseware: (1) an application of reinforcement learning to compute optimal sequencing of topics and scaffolding, (2) a text-mining method to automatically discover skills to be learned from text in written instructions and assessments, (3) a web browser-based method to rapidly create intelligent tutoring systems and seamlessly integrate them into online courseware, and (4) an application of reinforcement learning for an evidence-based quality control for online courseware content. As a proof of concept, instances of CyberBook for a college level chemistry (stoichiometry) and high school math (coordinate geometry) will be created. To validate the feasibility of implementation, the proposed learning engineering methods will be applied to two existing online course platforms—Open Learning Initiative and Open edX. The effectiveness of the proposed intervention will be measured through a pilot evaluation study at the partner institutions.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.

提高学生在作为STEM职业生涯门户课程的学科领域的成功，如化学和数学，是非常有趣的。这些学科很难教和学，部分原因是它们涉及到一个复杂的相互关联的知识网络。目前的在线教学材料通常遵循线性序列，通过知识网络追踪单一的预定路径。在这个项目中开发的CyberBook技术，而是引导学生通过知识网络沿着路径选择，以优化他们的学习的个人概念和这些概念之间的关系。这些途径适合个别学生，从学生与在线材料的互动中收集信息，以选择最适合该学生的途径。虽然主要重点是化学和数学，但该方法可以应用于其他STEM领域。CyberBook将传统的在线课件与智能辅导系统相结合。它还为学习科学家提供了一个研究平台，以研究学生如何学习。除了学习科学，拟议的研究有可能推进教育文献（特定领域和一般领域），教育数据挖掘和教育中的人工智能。该项目将开发几种先进的学习工程方法，以促进高质量和有效的在线课件的创建：（1）应用强化学习来计算主题和支架的最佳排序，（2）文本挖掘方法来自动发现要从书面说明和评估中的文本中学习的技能，（3）一种基于网络浏览器的快速创建智能辅导系统并将其无缝集成到在线课件中的方法，以及（4）强化学习在在线课件内容的循证质量控制中的应用。作为概念证明，将创建大学水平化学（化学计量学）和高中数学（坐标几何）的CyberBook实例。为了验证实施的可行性，提出的学习工程方法将被应用到两个现有的在线课程平台-开放学习倡议和开放edX。建议的干预措施的有效性将通过在伙伴机构进行的试点评估研究来衡量。该奖项反映了NSF的法定使命，并被认为值得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来支持。