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

该项目旨在解决提供分布式、可扩展和个性化的网络基础设施培训的挑战-这些系统提供最先进的云服务，用于存储、共享和处理科学数据。如今，这些快速发展的系统的个性化培训，因此相对没有记录，需要培训师监督，动手使用这些系统。这些培训班要求学员和培训员在同一地点，并向人数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领域独立的技术，以协调一致的方式减少所有三个问题，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培训材料。因此，它服务于国家利益，正如NSF的使命所述：促进科学的进步;促进国家的健康，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，而且还应该利用数据科学。该项目正在探索基于这一认识的两项相关创新：（1）协作技术，记录、可视化和分享远程和当地受训人员的工作，使培训人员能够确定是否需要远程或面对面的援助。 (2)机器学习技术，挖掘学员和培训师的互动，以便学员可以自动指导如何根据培训师和其他学员以前解决的类似问题解决他们的问题。该项目正在利用为广泛使用的NSF支持的网络基础设施（称为CyVerse）开发的现有技术和培训技术。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。 该项目正在扩展CyVerse提供的命令解释器和GUI。扩大的用户界面使（a）受训人员能够宣布困难并要求提出建议，（B）教员能够了解远程和当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由CyVerse独立服务器基于网络基础设施的一般模型实现，其中包括受保护文件的共享和可视化，工作流程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的概念。该项目正在调整CyVerse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。这一扩充的培训材料正被用于通过培训课程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、西班牙裔和美洲土著人的三次会议的与会者;（c）受控实验室研究的受试者;（d）多个研究所研究小组的成员。从这些会议中收集的拟议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和网络基础设施。该奖项反映了NSF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.