Collaborative Research: CyberTraining: CIU: Toward Distributed and Scalable Personalized Cyber-Training

该项目旨在解决为网络基础设施（为存储、共享和处理科学数据提供最先进的云服务的系统）提供分布式、可扩展和个性化培训的挑战。今天，这些快速发展的个性化培训，因此相对没有记录，系统需要培训师监督，动手使用这些系统。这些培训课程要求受训者和训练员同处一处，并向相对较少的受训者提供个性化培训。该项目正在开发新的(a)分布式协作和机器学习中的领域独立技术，以协调一致的方式减少这三个问题，以及(b)针对统计学、物理科学、计算机科学、人文科学和医学领域的受训人员的领域相关培训材料。因此，它符合国家利益，正如NSF的使命所述：促进科学进步；促进国家健康、繁荣和福利。这项工作的一个关键技术见解是，网络基础设施不仅应该支持数据科学，还应该利用数据科学。基于这一见解，该项目正在探索两个相关的创新：(1)协作技术，记录、可视化和共享远程和本地受训者的工作，使培训师能够确定远程或面对面援助的需求。(2)机器学习技术，挖掘受训者和培训师之间的互动，使受训者可以根据培训师和其他受训者之前解决的类似问题，自动指导受训者如何解决他们的问题。该项目利用现有技术和培训技术，为nsf支持的广泛使用的网络基础设施开发，称为CyVerse。该系统是独立于领域的，但到目前为止，其培训材料主要针对植物科学研究。该项目扩展了CyVerse提供的命令解释器和gui。扩展的用户界面允许(a)受训者宣布困难并请求建议，以及(b)培训人员了解远程和本地受训者的进度，并在必要时远程干预。用户界面背后的功能是由基于网络基础设施通用模型的独立于cyverse的服务器实现的，该模型包括受保护文件的共享和可视化、工作流中组成的参数化命令的创建和执行以及可共享的、持久的工作空间等概念。该项目正在调整CyVerse的培训材料，以涵盖新的研究领域，包括地球科学、政治学和生物医学工程。这种扩展的培训材料被用来通过以下培训课程评估拟议的培训技术：(a)统计学、计算机科学、政治学和跨学科课程的学生，(b)分别针对地球科学家、妇女、西班牙裔和美洲原住民的三个会议的与会者，(c)受控实验室研究的受试者，以及(d)多个研究所的研究小组成员。从这些会议中收集的拟议的定性和定量评估数据不仅用于评估拟议的技术和培训材料，还用于评估CyVerse和一般的网络基础设施。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。

This project is addressing the challenge of providing distributed, scalable, and personalized training of cyberinfrastructures - systems that offer state-of-the-art cloud services for storing, sharing, and processing scientific data. Today, personalized training of these rapidly evolving, and hence relatively undocumented, systems requires trainer-supervised, hands-on use of these systems. These training sessions require trainees and trainers to be co-located and provide personalized training to a relatively small number of trainees. The project is developing new (a) domain-independent technologies in distributed collaboration and machine learning to reduce all three problems in a concerted manner, and (b) domain-dependent training material targeted at trainees in statistics, physical sciences, computer science, humanities, and medicine. It, thus, serves the national interest, as stated by NSF's mission: to promote the progress of science; to advance the national health, prosperity and welfare.A key technical insight in this work is that a cyberinfrastructure should not only support data science, but also make use of data science. The project is exploring two related innovations based on this insight: (1) Collaboration technologies that log, visualize and share the work of remote and local trainees to allow trainers to determine the need for remote or face-to-face assistance. (2) Machine-learning technologies that mine trainee and trainer interactions so that trainees can be automatically instructed on how to solve their problems based on similar problems that have been previously solved by trainers and other trainees. The project is leveraging existing technologies and training techniques developed for a widely used NSF-supported cyberinfrastructure, called CyVerse. This system is domain-independent, but so far, its training material has been targeted mainly at plant-science research. The project is extending the command interpreters and GUIs provided by CyVerse. The extended user-interfaces allow (a) trainees to announce difficulties and request recommendations, and (b) trainers to be aware of the progress of remote and local trainees, and remotely intervene when necessary. The functionality behind the user-interfaces is implemented by CyVerse-independent servers based on a general model of cyberinfrastructures, which includes the concepts of sharing and visualization of protected files, creation and execution of parameterized commands composed in workflows, and shareable, persistent work spaces. The project is adapting the CyVerse training material to cover new research domains including Geoscience, Political Science, and Biomedical Engineering. This expanded training material is being used to evaluate the proposed training technologies through training sessions for (a) students in a Statistics, Computer Science, Political Science, and interdisciplinary course, (b) attendees at three conferences targeted at Geoscientists, women, and Hispanics and Native Americans, respectively, (c) subjects in controlled lab studies, and (d) members of research groups at multiple institutes. The proposed qualitative and quantitative evaluation data gathered from these sessions are being used to assess not only the proposed technologies and training material, but also CyVerse and cyberinfrastructures in general.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.