Collaborative Research: Cyberinfrastructure for Robust Learning of Interconnected Knowledge

提高学生在学科领域的成功是非常有意义的，这些领域是STEM职业生涯的门户课程，如化学和数学。这些科目很难教和学，部分原因是它们涉及一个相互关联的复杂知识网络。目前的在线教学材料一般遵循线性序列，通过知识网络追踪单一的预定路径。相反，在这个项目中开发的CyberBook技术引导学生沿着选择的路径通过知识网络，以优化他们对单个概念和这些概念之间的关系的学习。这些路径适用于个别学生，通过学生与在线材料的互动收集信息来选择最适合该学生的路径。虽然主要的重点是化学和数学，但这种方法也可以应用到其他STEM领域。CyberBook将传统在线课程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，以研究学生是如何学习的。除了学习科学，拟议的研究还有可能推动关于教育(特定领域和一般领域)、教育数据挖掘和教育中的人工智能的文献。该项目将开发几种先进的学习工程方法，以促进创建高质量和有效的在线课件：(1)强化学习的应用程序，以计算主题和脚手架的最佳排序；(2)文本挖掘方法，自动从书面指导和评估的文本中发现要学习的技能；(3)基于网络浏览器的方法，快速创建智能教学系统并将其无缝集成到在线课件中；以及(4)强化学习的应用程序，以对在线课件内容进行基于证据的质量控制。作为概念的证明，将创建大学水平的化学(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，拟议的学习工程方法将应用于现有的两个在线课程平台--Open Learning Initiative和Open edX。拟议干预措施的有效性将通过合作机构的试点评估研究来衡量。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.