Collaborative learning using service-oriented architecture: A framework design

doi:10.1016/j.knosys.2009.01.003

Knowledge-Based Systems

Volume 22, Issue 4, May 2009, Pages 271-274

https://doi.org/10.1016/j.knosys.2009.01.003 Get rights and content

Abstract

Collaborative learning serves as an important part of e-learning, increasing interactivity and accessibility to various learning resources either synchronously or asynchronously among users. Distributed interactivity through Web services thus forms the focus of this paper. The paper reviews related work on service-oriented architecture (SOA), distributed infrastructure, business process management (BPM) and highlights the need to integrate SOA technologies for meaningful and interactive collaborative learning processes. The significance of the study is an SOA approach to enhance the interoperability, flexibility and reusability of e-learning content in a collaborative environment.

Introduction

Service-Oriented Computing (SOC) is widely accepted as a new paradigm for delivering useful functionality in a cost effective way. It forms a new trend to be adopted by organizations in mitigating legacy system problems as in to maximize interoperability, reusability, flexibility and cost efficiency. Service-oriented architecture (SOA) is suitable for modularization, and the resulting SOA-framework can be used as a basis for implementing specialized e-learning services, specified by future standard frameworks and reference models. Grewal et al. [1] presents a model of Web Services-based e-learning lifecycle. The lifecycle defines all the functionality needed for the interactions between the service provider and the service requester. Many research have identified and created common services, which are essential for the creation and authoring stages of a typical e-learning system.

SOA research has mainly dealt with code reusability in LMS and less on its use for collaborative learning. There is a need to realize the SOA design in the e-learning domain as there is not much implementation done based on the designed models or architectures. For this paper, our scope focuses on designing the scope of services and business processes, which are to be published in the service registry. The business processes are directly dealing with collaborative activities from the consumer (learners’) perspective.

The research questions for this paper are:

(1)
How can SOA (conceptually and technically) be designed to allow flexible interaction between different components in LMSs in enhancing collaborative learning environments?
(2)
How can we utilize business process management (BPM) to manage learning processes?

The outline of the paper is as follows: Section 2 presents related works on service-oriented architecture and its benefits to e-learning. Section 3 focuses on related works concerning business process management. Section 4 presents our proposed collaborative learning framework design using a Web Service approach. The paper is concluded in Section 5.

Section snippets

Why SOA?

The typical learning management system is built based on a component-based architecture. However, Web services provide a better alternative as services are loosely coupled and can be subscribed anytime any place. Furthermore, leveraging web standards in an e-learning environment will allow the dynamic integration of applications distributed over the web and encourage reuse of learning objects. An example of the significance of Web services is in the context of collaborative learning. According

BPM and e-learning

Helic [5] discusses the possibilities of using BPM technology for the management of collaborative learning processes. The generic learning processes framework is shown in Fig. 1.

Business process is a set of linked activities that create value by transforming an input into a more valuable output. Typically, e-learning adopts content-oriented, tool-oriented or the task-oriented approach [5]. However, none of these approaches deals with the learning process itself, but addresses only certain parts

Collaborative learning framework design using SOA

The uses of learning objects, which can benefit from SOA, are such as Reusability, Interoperability, Accessibility and Modularization. Thus, different components/learning objects can be implemented as Web services so that the system components and content can be distributed all over the Web, and by different service providers. Section 4.1 explains how the SOA methodology can be used in designing the collaborative learning framework. Further details are provided in Section 4.2.

Conclusion

Many of the collaborative learning systems are mainly based on client-server architectures, which give rise to the problems of poor flexibility, scalability and interoperability. Collaborative learning using service-oriented architecture, which serves as a novel approach in the e-learning domain, helps in distributing the learning content more efficiently and promotes reusability. Most research has not addressed the implementation aspects. This paper has addressed the implementation details for

References (8)

A. Grewal, S. Rai, R. Phillips, C.C. Fung, The e-learning lifecycle and its services: the web services approach, in:...
W.J. Chang, H.H. Chen, Y.S. Chu, K.J. Chen, M.J. Chen, Application of knowledge management learning system to adult...
S. Wilson, K. Blinco, D. Rehak, Service-oriented frameworks: modelling the infrastructure for the next generation of...
C. Allison, M. Bateman, R. Nicoll, A. Ruddle, Adaptive QoS for collaborative service-oriented learning environments,...

There are more references available in the full text version of this article.

Cited by (45)

Modularisation and containerisation of the Digital Process Twin
2023, Procedia CIRP
The concept of Digital Process Twins is designed to map, monitor and orchestrate entire processes. The current monolithic architecture of the Digital Process Twin does not yet meet the requirements of the industry including variable computing power, reliability and fail safety. This work addresses this research gap. It presents an architecture in which the components of the digital process twins are modularised and containerised. Operation on single host as well as distributed systems is addressed and realised in a prototypical implementation. Moreover, the overall concept is demonstrated in a case study of a cube-satellite assembly process.
Distributed agent-based deep reinforcement learning for large scale traffic signal control
2022, Knowledge-Based Systems
Citation Excerpt :
In this section, we propose Nash Equilibrium based actor–critic algorithms, named Nash Advantage Actor–Critic (Nash-A2C) and Nash Asynchronous Advantage Actor–Critic (Nash-A3C) inspired by the DRL model of Actor–Critic [33] algorithms and the equilibrium optimizer [34]. Moreover, we develop a distributed computing IoT architecture and optimize the traffic simulation environment in this section inspired by the service-oriented architecture [35] and RL framework [36]. Algorithms of DRL are divided into two types: value-based and policy-based.
Traffic signal control (TSC) is an established yet challenging engineering solution that alleviates traffic congestion by coordinating vehicles’ movements at road intersections. Theoretically, reinforcement learning (RL) is a promising method for adaptive TSC in complex urban traffic networks. However, current TSC systems still rely heavily on simplified rule-based methods in practice. In this paper, we propose: (1) two game theory-aided RL algorithms leveraging Nash Equilibrium and RL, namely Nash Advantage Actor–Critic (Nash-A2C) and Nash Asynchronous Advantage Actor–Critic (Nash-A3C); (2) a distributed computing Internet of Things (IoT) architecture for traffic simulation, which is more suitable for distributed TSC methods like the Nash-A3C deployment in its fog layer. We apply both methods in our computing architecture and obtain better performance than benchmark TSC methods by 22.1% and 9.7% reduction of congestion time and network delay, respectively.
Collaborative approaches in smart learning environment: A case study
2020, Procedia Computer Science
A smart city can be defined as a community in which citizens, businesses and institutions work in a collaborative environment. At a time when the Internet is the most used medium in the world, collaboration in a smart university (SU) is based on collaborative learning (CL) as one of the best practices used to create a practical learning environment for acquire and share knowledge; it is a relevant concept that adopts smart interactions to promote modern methods of collaboration between teams of learners. More specifically, smart interactions may be recommended to refine and ensure learner participation and communication in a learning environment. In this article, we cite key criteria that assess intelligent collaboration and that serve to improve collaboration performance and provide an effective way to create a collaborative learning environment, which mainly based on recommendation, and refers to learner profile and evaluations.
A computational simulation tool for training autistic reasoning about mental attitudes
2013, Knowledge-Based Systems
Citation Excerpt :
It increases interactivity and accessibility to various learning resources either synchronously or asynchronously among users. Distributed interactivity through Web services thus forms the focus of [59] who review service-oriented architecture, distributed infrastructure and highlight the need to integrate service-oriented technologies for meaningful and interactive collaborative learning processes. The need for providing learners with web-based learning content that match their accessibility needs and preferences, as well as providing ways to match learning content to user’s devices has been identified as an important issue in accessible educational environment [45].
It has been discovered more than a decade ago that autistic people cannot properly understand and reproduce mental states and emotions. We hypothesize that people with autism suffer from difficulties in learning social rules from examples. Many remediation strategies have not taken this into account. Therefore an appropriate remediation strategy is to teach not simply via examples but to teach the rule along with it. In this study we suggest a reasoning rehabilitation strategy, based on playing with a computer based mental simulator that is capable of modeling mental and emotional states of the real world. A model of the mental world is presented in 12 steps. We describe our implementation of a natural language multiagent system that simulates this model. In addition we describe the system’s user interface for autistic rehabilitation. This system is subject to short-term and long-term evaluation of rehabilitation of autistic reasoning. Case studies with children who used it extensively are presented. Implications specifically in terms of autistic rehabilitation as well as generally in terms of reasoning about mental states are discussed.
AI Enabled SaaS Framework for Fashion Designing
2023, EPiC Series in Computing
Service design approaches and applications in higher education: A thematic literature review
2020, Art, Design and Communication in Higher Education

View all citing articles on Scopus

View full text