Construction of Collaborative Learning Environment Supported by Cloud-Computing

A Design Framework for Personal Learning Environments

With the popularity of e-learning, to construct a perfect e-learning environment has become a popular research topic in the field of modern distance education. Cloud computing is a new computing model which is based on the grid computing, it is the core technology of the next generation of network computing platform, especially in the field of education, cloud computing is the basic environment and platform of the future e-learning. It provides secure data storage, convenient internet services and strong computing power. This essay mainly focuses on the research of the application of cloud computing in E-learning environment. Taking the “Google collaborative platform” as a case study, one factor of E-learning environment construction-“the collaborative learning platform” has been built, and also the “collaborative learning” has been given detailed illustration.

Objective: This study explores students' perceptions of using Personal Learning Environments (PLEs) in the era of generative artificial intelligence as a learning resource in higher education. The specific objectives are: 1) to determine the current tools that students incorporate into their PLEs and 2) to identify students' perceptions of the use of PLEs for lifelong learning. Theoretical Framework: UNESCO and other institutions recognize lifelong learning as an essential competency for personal and professional development. Personal Learning Environments (PLEs) are considered effective tools for promoting self-directed learning, allowing students to manage their learning processes, especially with the advent of generative artificial intelligence technologies. Method: A qualitative study was conducted with 207 first-semester engineering students from a public university in Peru. Participants were introduced to a training program to develop their PLEs using various digital tools, including generative AI tools such as ChatGPT DALL-E. The instrument used to evaluate students' perceptions was the MUSIC model (Jones, 2009), which focuses on empowerment and usefulness. Results: The results indicate that more than 80% of the students positively perceive PLEs, considering them valuable and empowering tools for achieving their learning objectives. Integrating generative AI tools into PLEs is notable, as it provides a dynamic and personalized learning experience. Most students found PLEs helpful for their future and relevant for promoting lifelong learning skills. Research Implications: The research suggests that PLEs, especially when integrated with generative AI tools, positively impact self-directed learning and promote collaborative and social learning. These findings can inform the development of educational programs and policies that encourage using PLEs as a student-centered learning strategy. Originality/Value: This study contributes to the understanding of the role of PLEs in the context of higher education and generative artificial intelligence. It is one of the first studies to examine students' perceptions of using PLEs with generative AI tools, highlighting their potential to enhance learning autonomy and personalization.

It is commonly argued that learner participation and interaction can be enhanced by the use of Personal Learning Environments (PLEs). However, this idea has yet to be reflected in practice. In this sense, researchers are encouraged to do more empirical research in order to know whether PLEs could allow the transference of the participative and networked nature of Web 2.0 to the institutional sphere. In this sense, this paper presents a differential study in which the authors analyzed student participation and interaction when working with a Virtual Learning Environment (VLE) and a PLE in two online, higher education courses. With that aim in mind, participation measures were calculated and social network analysis was performed on the digital data collected from the learning environments. The findings reflect the effectiveness of a PLE over a VLE for facilitating student participation and for assisting students in the creation of more cohesive learning networks.

Containerization: Practical infrastructure and accessibility efficiency for the Virtual Learning Environment 
 Context and motivation:
 Containers are transforming modern application infrastructure, providing advantages for accessibility and allow many instances of existing legacy applications to be run at the same time on the latest operating systems. This can implement an ideal virtual learning environment which offers a dynamic learning space where instructors upload activities and resources to enhance learning. The goal is to provide a structured learning environment suited to both the student and the instructor, where materials are easily accessed, and that many files can be transferred at one time. The system should cope with the demands of many students occupying the same virtual learning space, providing each student with individual learning experiences. There are challenges when instructors attempt to adapt the learning environment to meet the learning objectives. Technology barriers to the education provider, primarily in the form of high cost of technology infrastructure requirements need to be overcome before widespread adoption of a virtual learning environment is seen. The potential to overcome these barriers through the application of containerisation provides the motivation behind this presentation (Katz & Council of Independent, 2016; Puvaneswary & Siew Hwa, 2019).
 
 What will be demonstrated in this presentation?
 The use of containers provides the ability to supply many instances of the same application running on a single machine. Each instance is isolated, along with any associated dependencies, allowing efficient utilisation of system resources, such as processing and memory. The presentation will demonstrate how many instances of the same application can be activated and the resource advantages gained. The presentation will show a system that has been used to provide a complex structured virtual learning environment to level 7 students and discuss how the system was utilised to provide a positive, individualised learning experience to the students. Security and confidentiality is maintained within each containerized instance. The presentation will show how the system can be configured to be self-healing, respond to scheduling, and to automatically restart single instances as required.
 
 The implications for future practice
 The presentation will show how the advantages of containerization can be integrated at many levels of the virtual learning environment, providing many separate and individual instances of the same application. The number of instances can be easily adjusted as required, and resources are automatically allocated, reducing support overhead and cost of infrastructure. The implications are that each instance can provide a unique experience to each student, whilst reducing the workload of the instructor, and minimizing the cost to the education provider.
 
 
 References
 Katz, P. M., & Council of Independent, C. (2016). High-Tech or High-Touch? Online Learning and Independent Higher Education. Innovations in Teaching and Learning. Research Brief 5: Council of Independent Colleges. Retrieved from http://ezproxy.aut.ac.nz/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=ED569215&site=eds-live
 Puvaneswary, M., & Siew Hwa, Y. (2019). Navigating the Shortcomings of Virtual Learning Environments Via Social Media. International Journal of Virtual and Personal Learning Environments (IJVPLE), 9(2), 1-14. https://doi.org/10.4018/IJVPLE.2019070101
 

Professor Ricardo Torres Kompen is a leading proponent for, and researcher in, personal learning environments (PLEs). During his interview, Torres Kompen clarified his research on PLEs, particularly the digital toolbox within PLEs. He elaborated on experiences with implementing PLE initiatives, personal insights on using social media and Web 2.0 technologies, and the role of PLEs in designing learning for an increasingly complex, networked community in the digital age. Located in Barcelona, Spain, Torres Kompen has traveled extensively and worked in diverse settings. His work has a broad impact, including educators and students, formal and informal learning environments, from primary school students to lifelong learners. The innovative leadership traits referred to as the Innovator's DNA (Dyer, Gregersen, & Christensen, 2011) was used to contextualize major developments in Torres Kompen's contributions to research and practice in PLEs.

A Personal Learning Environment or PLE is described as a process that helps students organize the influx of information and resources that they are faced with on a daily basis into a personalized digital learning space or experience. A PLE is primarily facilitated by cloud-based Web 2.0 technologies and services designed to help students create, organize, and share content, participate in collective knowledge generation, and manage their own meaning making. Hence, a PLE is student-designed around each student’s goals or learning approach. In a PLE, the student develops an individualized digital identity through the perceptual cues and cognitive affordances that the personalized learning environment provides such as what information to share and when, who to share it with, and how to effectively merge formal and informal learning experiences. To gather data on PLEs, Mason students were asked what do they like to learn about; what hardware (devices) and software do they use to learn; what digital tools they wish they had access to for learning; and what their ideal PLE might look like. In this presentation, we will share student responses to these questions and provide recommendations for supporting the development PLEs in teaching and learning contexts.

In the era of digital world that we live in, a new vision for learning is required. Learning is essentially personal, sociocultural, distributed, ubiquitous, flexible, dynamic, and complex in nature. There are multiple challenges, opportunities, and movements in learning that must be considered in the development and implementation of online learning environments. From the emerging computational capacity as a virtualized resource pool available over the network, several benefits can be obtained with regard to the management of computing infrastructures, such as environmental sustainability and improved Personal/Cloud Learning Environment use. In fact, Personal learning environments, Cloud computing, Semantic Web 3.0 and Ontologies are relatively new terms that hold considerable promise for future development and research in higher education contexts. Motivated by the aforementioned perspectives, the purpose of this chapter is to explore and discuss how these terms can be understood towards a more personalized, sociocultural, open, dynamic and encouraging model to support/facilitate teaching and learning processes, fulfilling the integrated view of the educational context presented in Part I of this book.

In this paper we present the results of a naturalistic study carried out with higher education students which describes their learning environments when working on academic tasks in groups and individually. In this study we want to analyse how they organize their learning activities and knowledge processes (reading, reflecting and sharing) and how these learning processes are integrated into their Personal Learning Environments (PLE). In order to achieve that, students had to reflect about the basic ‘components’ that make up their learning process: reading (using not only by text but also multimedia), doing/reflecting (creating cognitive artefacts) and sharing (discussing, showing, and offering and receiving feedback from and to a community of reference). Students formed relationships between those components and any technological tools they used and created mind maps to represent their PLEs. The main objective is to try to understand how PLEs are organized and how they are perceived by learners, not from a technological perspective but from a learning perspective. The main results confirm the strong nature of PLEs as a pedagogical approach with a strong technological base. In addition, those results suggest a close relationship between students’ beliefs and expectations regarding academic tasks as well as the importance they give to each component in their learning process.

This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked learning ideas. The experience opens new perspectives for the design of technology training workshops and for the development of lifelong learning experiences.

Collaborative learning in virtual classrooms has tremendous potential in institutions, as it encourages cooperative knowledge production and skill development. This study investigates how a pedagogical model for virtual learning can incorporate this methodology, emphasizing the significance of meticulous preparation, an appropriate dynamic for establishing groups, the relevance of student practices to everyday technology utilization, an evolution to educator responsibilities and learning autonomy. The research underlines the value of technology in education. This article discusses Virtual Collaborative Learning (VCL) as an effective approach to quality assurance in the age of digitization. It presents design characteristics from an academic standpoint and optimizes these approaches through a qualitative examination of written opinions by VCL participants. The research identifies and prioritizes critical criteria for collaborative learning effectiveness from the students' perspective, generating further multi-perspective design recommendations. Adaptive collaborative virtual learning is a technology-enabled technique that employs algorithms to evaluate student data and adjust to a student's learning style, pace, and accomplishments. It capitalizes on gamification to make learning more engaging and interactive, while artificial intelligence (AI), machine learning, virtual reality (VR), and augmented reality (AR) have transformed conventional education. This article explores at how cutting-edge technologies are being created and implemented into the educational system and classroom for enhancing student education and learning environments. This chapter examines the adoption of AI and machine learning in intelligent learning, emphasizing their potential for enhancing learning experiences, personalize education, and improve outcomes. It also encompasses ethical and security issues, highlighting the importance of stringent laws to protect students’ rights. By implementing these guidelines, educators and policymakers may build a more intelligent and successful classroom setting. This study visualizes AI-enabled learning systems, identifies types of interventions, and discusses popular analytical methodologies. It serves as a reference for future studies regarding the development of AI-enabled educational platforms that address specific learning challenges while enhancing user experiences, ultimately directing future research in this area of study.

With the rapid development of education informatization, the integration and application of technologies such as cloud computing, big data and artificial intelligence are reshaping the shape of online education. This paper constructs and verifies an efficient online collaborative learning mode based on cloud computing platform. Through theoretical analysis and practical validation, the technical architecture of cloud computing platform in online collaborative learning, effectiveness analysis and comparison with traditional learning modes are explored, and optimization algorithms are proposed. The study shows that the collaborative learning mode supported by cloud computing significantly improves the learning efficiency, interaction frequency and learning effect, and provides new theoretical and technical support for education informatization.

Online distance learning, on the one side, makes the learning process more efficient and flexible. However, on the other hand, it produces learning loss within a particular educational period and makes students less enthusiastic about building a collaborative attitude. In general, online learning platform providers have provided support for collaboration activity features, but in reality, they have not been able to support effective interaction. Experts believe that using the Personal Learning Environment (PLE) as an IT tool in the learning process can help create an effective online collaboration environment. This study found that PLE was proven to increase collaborative activities in online learning significantly. The reflection feature is the type of PLE that has the most significant influence on creating a good collaboration environment. The research also found that suitable collaborative activities can increase students' understanding of TVET pedagogy.

This study designed and pilot-tested a learning path in Personal Learning Environments (PLEs) to facilitate learners’ Self-regulated Learning. 122 postgraduates who joined an International English Language Testing System (IELTS) course at a comprehensive university in China were evenly divided into two groups. The experimental group used PLEs with a learning path design, while the control group used PLEs without a learning path design. The pre-and-post-tests of IELTS were conducted to assess participants’ cognitive achievements. The results indicated that both groups improved cognitively, with the experimental group indicating significantly better improvements than the control group. Further, a questionnaire on participants’ satisfaction with the PLEs platform was administered. The results showed that the experimental group was highly satisfied with the path-controlled PLEs, while the controlled group expressed their need for scaffoldings in PLEs. The study could provide learning platform designers with useful guidance to improve learners’ learning performance and satisfaction.

Early CAI efforts focused on the transmission or delivery of knowledge to the student. Subsequent work in intelligent tutoring systems took a more cognitive approach and attempted to emulate the behavior of skilled human tutors via software. More recent work has focused on collaborative learning processes, and the idea of a collaboratory as a virtual space for work. This paper describes a design process to support the development and use of collaborative learning technologies. This process, the Learning Collaboratory Design Framework (LuCiDiFy), integrates methods and concepts from cognitive systems engineering, theories of learning and instruction, distributed computing and computer-supported collaborative learning (CSCL). LuCiDiFy is instantiated in an actual software testbed, the Collaborative Learning Environment for Operational Systems (CLEOS). CLEOS is a collaboratory for teachers, students and practitioners in the physical sciences, and in particular for NMR spectroscopy and X-ray diffraction experiments. CLEOS includes two virtual instrument tutorials, CVS (Collaborative Virtual Spectrometer) and VXRD (Virtual X-Ray Diffractometer); an asynchronous messaging system, the Question Board (QB); a project-based design environment, TOSP (Tool for Organizing and Supervising Projects); and an architecture for developing collaborative apprenticeship learning systems, CALOT (Collaborative Apprenticeship Learning Object Toolkit); an authoring environment, BAUEN (Basic AUthoring ENvironment); and a collaborative multi-user domain (MUD) infrastructure, MudSpot.