Network resource management in support of QoS in ubiquitous learning

Abstract

Ubiquitous learning involves a large-scale service network organized as a social infrastructure. These environments weave together context service dissemination, learner profiling as well as autonomic control of the induced network traffic. The objective of the research presented in this paper is to derive a Quality of Service aware model of ubiquitous learning services based on typical learning schemes. These pedagogical patterns are designed to match various learning situations in terms of learning context, learner profile and network infrastructure. They particularly represent classes of services in ubiquitous learning environments to prioritize traffic so that less important traffic does not consume network bandwidth and slow down or halt the delivery of more important traffic. We analyze formally and empirically the network traffic requirements of a proposed learning service quality controller to support providers of learning services allocating resources in a pervasive learning environment.

Introduction

Learning technology has lately been driving the move to transform education across an increasing number of institutions from “anywhere, anytime, anyone” form of learning to “the right information at the right time in the right place” (Fischer, 2010). These technology-committed institutions are increasingly populating our educational landscape. This trend is expected to accelerate further the transformation of our universities to house smart campuses. The rapid and wide proliferation of technology into our societies, and the soaring enthusiasm of learners for smart gadgets are increasing and enriching the integration pace of novel educational experiences in these structures. This evolution of education is paving the way to pervasive learning environments where ubiquitous learning or u-learning processes persuade learners to connect with, and learn from their environments (Khan and Zia, 2007). Pervasive learning environments bring context awareness through u-learning platforms (Hwang et al., 2010), to deliver in-situ instruction.

In Ubiquitous Learning, a concept may be reproduced across several learning services following different pedagogical processes (Wessa et al., 2011) and quality schemes to match various contexts and personalized profiles (Huang et al., 2008). Typically, u-learning processes occur in a pervasive learning environment, which integrates three dimensions of instruction to compose ambient learning spaces as shown in Fig. 1. In these spaces, learning adaptation is augmented by the provision of context data (e.g. surrounding physical spaces) and social peers or tutors (Smith, 2009, Jin et al., 2010). Each ambient learning space is controlled by three driving forces as illustrated in Fig. 1, namely learning services, their providers and the quality schemes or patterns along which these services are delivered. A learning scheme dictates the QoS requirements of a class of learning services (Huang et al., 2008). QoS capability is a decisive characteristic to distinguish services with identical functionalities and determine their groupings to prioritize traffic. In this paper, we identify learning schemes and their QoS requirements into which the traffic can be divided to support u-learning processes. We also propose a theoretical formulation and analysis of these schemes to help learning service providers ensuring adequate control of their resources and deliver QoS aware learning services in a smart campus environment.

A campus is a natural candidate for u-learning since it comprises all u-learning dimensions which promote instructional scaffolding processes (Zhao and Okamoto, 2011). A smart campus environment is a digitally augmented physical campus. Pervasively instrumented objects and spaces are made intelligently perceptive and responsive to the state of this campus environment and its inhabitants. In this environment, the use of computing and communication services is not limited to solitary moments at an office desk or a classroom, but extended in multifaceted ways to all aspects of learning situations (Fischer and Konomi, 2005). Traveling across Web pathways, these services provide wider informational accessibility and extended operational control. The widespread proliferation of these services (and their schemes) induces local (i.e. within the ambient space) and global (i.e. within the smart campus) costs. To regulate these costs and manage QoS delivery of learning services locally and globally, we propose CLASS, a Configurable LeArning ServiceS platform, which optimizes u-learning experiences in ambient learning spaces.

The remaining sections of this paper are organized as follows. We first motivate our work and review some related work in Section 2. Then, we introduce our platform for ubiquitous learning in Section 3, where we propose the concept and instances of learning schemes. Then, in Section 4, we reveal and analyze our Quality of Service controller model to regulate the communication traffic induced by the proposed learning schemes. Here, we use our model to experiment and evaluate the effect of learning scheme demands on the overall traffic performance. Finally, Section 5 concludes the paper with a summary of the proposed work.