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: 2016966
• 负责人: Noboru Matsuda
• 金额: $ 38.69万
• 依托单位: North Carolina State 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=2016966&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职业的门户课程（例如化学和数学）中的门户课程的主题领域的成功。这些主题很难教书和学习，部分原因是它们涉及复杂的相互联系的知识网络。当前的在线教学材料通常遵循线性序列，并通过知识网络追踪单个预定的途径。该项目中开发的网络手册技术相反，沿选择的路径可以指导学生通过知识网络，以优化他们对个人概念的学习以及这些概念之间的关系。该路径适应个别学生，并从学生与在线材料的互动中收集了信息，以选择最适合该个人学生的途径。尽管主要的重点是化学和数学，但该方法可以应用于其他STEM领域。网络手册将传统的在线课程与智能辅导系统结合在一起。它还为学习科学提供了一个研究平台，以对学生的学习方式进行研究。除了学习科学外，拟议的研究还可以推进有关教育文献（领域特异性和领域），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，以促进创建高质量和有效的在线课程：（1）增强学习以计算主题和脚手架的最佳测序的应用，（2）一种文本挖掘方法，一种自动从文本中从文本中学习的技能，将基于网络中的智能及其智能的智能培养在本文本中，（3） （4）在线课程内容的基于证据的质量控制中，增强学习的应用。作为概念的证明，将创建用于大学级化学的网络手册（化学计量学）和高中数学（坐标几何学）。为了验证实施的可行性，提议的学习工程方法将应用于两个现有的在线课程平台 - 开放学习计划和开放性EDX。拟议的干预措施的有效性将通过合作伙伴机构的试点评估研究来衡量。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为通过评估被认为是宝贵的支持。

项目成果

Latent Skill Mining and Labeling from Courseware Content

课件内容中的潜在技能挖掘和标记

• 发表时间: 2022
• 期刊: Journal of educational data mining
• 作者: Matsuda, N.;Wood, J.;Shrivastava, R.;Shimmei, M.;Bier, N.
• 通讯作者: Bier, N.

Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions

• DOI: 10.1007/978-3-030-78270-2_57
• 发表时间: 2021
• 作者: Machi Shimmei;Noboru Matsuda