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

该项目正在应对为网络基础设施提供分布式、可扩展和个性化培训的挑战，网络基础设施是为存储、共享和处理科学数据提供最先进的云服务的系统。今天，对这些快速发展的系统进行个性化培训，因此相对没有文档记录，需要在培训员的监督下实际使用这些系统。这些培训课程要求学员和培训员同处一处，并为数量相对较少的学员提供个性化培训。该项目正在开发新的(A)分布式协作和机器学习中独立于领域的技术，以协调一致地减少所有这三个问题，以及(B)针对统计、物理科学、计算机科学、人文和医学的受训者的依赖领域的培训材料。因此，正如NSF的使命所述，它符合国家利益：促进科学进步；促进国家健康、繁荣和福祉。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，而且应该利用数据科学。该项目正在探索基于这一见解的两项相关创新：(1)协作技术，记录、可视化和共享远程和本地受训人员的工作，使培训人员能够确定是否需要远程或面对面的协助。(2)挖掘受训人员和受训人员互动的机器学习技术，以便能够根据受训人员和其他受训人员以前解决的类似问题自动指导受训人员如何解决他们的问题。该项目正在利用为广泛使用的NSF支持的名为CyVerse的网络基础设施开发的现有技术和培训技术。这个系统是独立于领域的，但到目前为止，它的培训材料主要针对植物科学研究。该项目正在扩展CyVerse提供的命令解释器和图形用户界面。扩展的用户界面允许(A)受训人员宣布困难并征求建议，(B)培训人员了解远程和本地受训人员的进展情况，并在必要时进行远程干预。用户界面背后的功能是由独立于CyVerse的服务器基于网络基础设施的一般模型实现的，该模型包括受保护文件的共享和可视化概念、在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工作空间。该项目正在修改CyVerse培训材料，以涵盖地球科学、政治学和生物医学工程等新的研究领域。这份扩展的培训材料正被用于评估拟议的培训技术，培训对象为：(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、拉美裔人和美洲原住民的三个会议的与会者，(C)受控实验室研究的对象，以及(D)多个研究所的研究小组成员。从这些会议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，而且还用于评估CyVerse和网络基础设施的总体情况。该奖项反映了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。

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.