Online Self-Service Learning Platform for Application-Inspired Cloud Development and Operations (DevOps) Curriculum

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2024-07-24 DOI:10.1109/TLT.2024.3428842

Roshan Lal Neupane;Prasad Calyam;Songjie Wang;Kiran Neupane;Ashish Pandey;Xiyao Cheng;Durbek Gafurov;Hemanth Sai Yeddulapalli;Noah Glaser;Kanu Priya Singh;Yuanyuan Gu;Shangman Li;Sharan Srinivas

{"title":"Online Self-Service Learning Platform for Application-Inspired Cloud Development and Operations (DevOps) Curriculum","authors":"Roshan Lal Neupane;Prasad Calyam;Songjie Wang;Kiran Neupane;Ashish Pandey;Xiyao Cheng;Durbek Gafurov;Hemanth Sai Yeddulapalli;Noah Glaser;Kanu Priya Singh;Yuanyuan Gu;Shangman Li;Sharan Srinivas","doi":"10.1109/TLT.2024.3428842","DOIUrl":null,"url":null,"abstract":"Cloud-hosted services are being increasingly used in hosting business and scientific applications due to cost-effectiveness, scalability, and ease of deployment. To facilitate rapid development, change and release process of cloud-hosted applications, the area of development and operations (DevOps) is fast evolving. It is necessary to train the future generation of scientific application development professionals such that they are knowledgeable in the DevOps-enabled continuous integration/delivery automation. In this article, we present the design and development of our “Mizzou Cloud DevOps platform,” an online self-service platform to learn cutting-edge Cloud DevOps tools/technologies using open/public cloud infrastructures for wide adoption amongst instructors/students. Our learning platform features scalability, flexibility, and extendability in providing Cloud DevOps concepts knowledge and hands-on skills. We detail our “application-inspired learning” methodology that is based on integration of real-world application use cases in eight learning modules that include laboratory exercises and self-study activities. The learning modules allow students to gain skills in using latest technologies (e.g., containerization, cluster and edge computing, data pipeline automation) to implement relevant security, monitoring, and adaptation mechanisms. The evaluation of our platform features a knowledge growth study to assess student learning, followed by a usability study to assess the online learning platform, as well as the curriculum content as perceived by instructors and students across multiple hands-on workshops.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1946-1960"},"PeriodicalIF":2.9000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/10608442/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

Abstract

Cloud-hosted services are being increasingly used in hosting business and scientific applications due to cost-effectiveness, scalability, and ease of deployment. To facilitate rapid development, change and release process of cloud-hosted applications, the area of development and operations (DevOps) is fast evolving. It is necessary to train the future generation of scientific application development professionals such that they are knowledgeable in the DevOps-enabled continuous integration/delivery automation. In this article, we present the design and development of our “Mizzou Cloud DevOps platform,” an online self-service platform to learn cutting-edge Cloud DevOps tools/technologies using open/public cloud infrastructures for wide adoption amongst instructors/students. Our learning platform features scalability, flexibility, and extendability in providing Cloud DevOps concepts knowledge and hands-on skills. We detail our “application-inspired learning” methodology that is based on integration of real-world application use cases in eight learning modules that include laboratory exercises and self-study activities. The learning modules allow students to gain skills in using latest technologies (e.g., containerization, cluster and edge computing, data pipeline automation) to implement relevant security, monitoring, and adaptation mechanisms. The evaluation of our platform features a knowledge growth study to assess student learning, followed by a usability study to assess the online learning platform, as well as the curriculum content as perceived by instructors and students across multiple hands-on workshops.

查看原文本刊更多论文

应用启发式云开发与运营（DevOps）课程在线自助学习平台

云托管服务具有成本效益高、可扩展性强和易于部署等优点，因此越来越多地用于托管业务和科学应用程序。为了促进云托管应用程序的快速开发、变更和发布过程，开发和运营（DevOps）领域正在快速发展。有必要对新一代科学应用程序开发专业人员进行培训，使他们掌握 DevOps 支持的持续集成/交付自动化知识。在本文中，我们将介绍 "Mizzou 云 DevOps 平台 "的设计和开发，这是一个在线自助服务平台，可利用开放/公共云基础设施学习尖端的云 DevOps 工具/技术，供教师/学生广泛采用。我们的学习平台在提供云 DevOps 概念知识和实践技能方面具有可扩展性、灵活性和可扩展性。我们详细介绍了我们的 "应用启发式学习 "方法，该方法基于在八个学习模块中整合真实世界的应用案例，包括实验室练习和自学活动。通过这些学习模块，学生可以掌握使用最新技术（如容器化、集群和边缘计算、数据管道自动化）实施相关安全、监控和适应机制的技能。对我们平台的评估包括一项知识增长研究，以评估学生的学习情况；随后是一项可用性研究，以评估在线学习平台，以及指导教师和学生在多个实践研讨会中感知到的课程内容。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.