A Context-Aware Self-Adaptive Fractal Based Generalized Pedagogical Agent Framework for Mobile Learning

A Context-Aware Self-Adaptive Fractal Based Generalized Pedagogical Agent Framework for Mobile Learning

Soufiane Boulehouache (Department of Computer Science, Université 20 Août 1955-Skikda, Skikda, Algeria), Ramdane Maamri (Faculty of New Information and Communication Technologies, Université Constantine 2-Abdelhamid Mehri, Constantine, Algeria) and Zaidi Sahnoun (Faculty of New Information and Communication Technologies, Université Constantine 2-Abdelhamid Mehri, Constantine, Algeria)
Copyright: © 2015 |Pages: 28
DOI: 10.4018/IJDET.2015100101
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Abstract

The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component based Generalized Pedagogical Agent (CASA FBGPA) framework for Mobile Learning. The proposed framework allows for the construction of PA that self-reconfigures its structure, the functional part, to conform to the unpredictable changing runtime context. To carry out the context-awareness, the PA embeds a distinct Search based Adapting Engine that dynamically monitors and assembles the appropriate linear combination of Fractal components. In addition, to avoid the rules associated conceptual holes, to deal with the conflicting objectives and to reduce the substantial overhead, the components selection is formulated as a multiobjective problem and it is tackled using a metaheuristic search method. Furthermore, to evaluate the design and the feasibility of the proposed framework, a use case and a discussion are provided.
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1. Introduction

The Pedagogical Agents (PAs) for Mobile Learning (m-learning) constitute a leading learning model for the next decade. M-learning allows learners to obtain learning materials anywhere and anytime using mobile technologies and the Internet (Ozdamli & Cavus, 2011). With the emergence of m-learning, the mobile learner will carry multiple heterogeneous wearable and handheld devices and he/she is able to continually learn wherever he/she is moving without any mobility, time and other restrictions (Economides, 2008). Consequently, the learner carries these portable devices to learn as well as to store the student model and the studied material (Lee et al., 2009). When coming with the mobile learning, the wide variety of technical characteristic and standards of devices (notebook computers, cellular phones, Personal Communication System (PCS), Personal Digital Assistants (PDAs)…) faces us to take into account new features in the adaption process: the device “preferences” (Chorfi, Sevkli, & Bousbahi, 2012). However, the unpredictable changing of the learner device at runtime influences the pedagogical efficiency of the Pedagogical Agents (PAs). It is the case when users move and the computing and the communication environment vary dynamically (Hallsteinsen Geihs, Paspallis, Eliassen, Horn, Lorenzo, & Papadopoulos, 2012). Accordingly, where execution conditions often change, adaptation at run-time is required in context-aware applications (Grondin, Bouraqadi, & Vercouter, 2006). This forces the systems to self-adapt and improve their own behavior while trying to cope with such changes (Andrew, Viviane, & Carlos, 2009).

On the other side, the Self-adaptation seems a promising technique to implement self-adaptive pedagogical agents. The Self-adaptive software is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. Such a system is required to monitor itself and its runtime context, detect significant changes, decide how to react, and act to execute such decisions (Salehie, & Tahvildari, 2009). In this perspective, context-aware systems, as a type of self-adaptive system, are able to adapt their operations to the current context without explicit user intervention and thus aim at increasing usability and effectiveness by taking environmental context into account (Baldauf, Dustdar, & Rosenberg, 2012). Context-aware adapting is an important requirement to keep the quality of the provided services at a high level. Since, it is imperative to expect that the software works as intended and that the software provides us with the largest possible utility always and everywhere. Thus, in the case of PA, they must be able not only to adapt the teaching to the student cognitive level and profile but also to ensure the pedagogical efficiency within unpredictable contexts. But, deciding what reactions a system has to a certain context is one of the hardest points in context-aware applications (Petrelli, Not, Zancanaro & Strapparava, 2001).

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