Browsing by Author "Kinshuk, Taiyu Lin"

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    Open Access
    Application of Learning Styles Adaptivity in Mobile Learning Environments
    (2004-07) Kinshuk, Taiyu Lin
    PCF3 // Availability of advanced mobile technologies, such as high bandwidth infrastructure, wireless technologies, and handheld devices, have started to extend e-learning towards mobile learning (m-learning) (Sharples, 2000). This phenomenon fits well with the new paradigm “anytime, anywhere computing” (Lehner and Nösekabel, 2002). However, the development of m-learning is still at rather early stage and many issues have yet to be resolved. One of these issues is the potential of individualization of learning process for the learners. // This paper explores how to improve learning process by adapting course content presentation to student learning styles in multi-platform environments such as PC and PDA. A framework has been developed to comprehensively model student’s learning styles and present the appropriate subject matter, including the content, format, media type, and so on, to suit individual student. The work is based on the Felder-Silverman Learning Style Theory. The framework uses traditional web-based intelligent tutorial architecture, with two additional components: ‘learning style analysis module’ and ‘access device analysis module’. The learning style analysis module takes care of modeling student learning style and communicates with student model, whereas the access device analysis module identifies the access device profile and provides the information to tutorial module. The tutorial module creates the suitable content, based on the student model (including individual learning styles) and access device profile, and presents to the student. // Based on the framework, a prototype for the domain of PHP programming course has been developed. With this system, students are able to learning PHP programming with course content that matches their own learning style and the device used to access the content. A formative evaluation is planned to assess the student satisfaction, learning efficiency, and effectiveness of the system while providing various presentations of the same content to different users on different devices.
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    Improving Mobile Learning Environments by Applying Mobile Agents Technology
    (2004-07) Kinshuk, Taiyu Lin
    PCF3 // Availability of high bandwidth infrastructure such as GPRS, 3G and UMTS networks, advances in wireless technologies (Chen and Nahrstedt, 2000; Chiang, et al., 1998; Johnson and Maltz, 1996; Chen and Lai, 2000; Lin and Liu, 1999) and popularity of handheld devices (Microsoft, 2001) provide a new way for education by extending the learning time and space. One important emerging application of handheld devices is mobile learning (Sharples, 2000). With the new paradigm “anytime, anywhere computing”, e-learning has been extending to mobile learning (m-learning) (Lehner and Nösekabel, 2002). // In recent years, numerous efforts have been making in the direction of using handheld devices for educational purposes (m-learning project, 2003; MOBIlearn project, 2003; Chen, et al., 2002; Chang and Sheu, 2002; Becta Report, 2003; Megan Fox, 2003). However, while mobile devices are approaching ubiquity today, the mlearning industry is still in its infancy. The current research base is insufficient to wholly explore the value of wireless internet learning devices (Roschelle, et al., 2002). The m-learning has a number of common deficiencies, such as access to course materials is slow; courseware does not adapt to individual students; the real time interaction between student and system is hard to achieve because of the connection unreliability and bandwidth limitations. // Intelligent Agents are one of the ʺhotʺ topics in Information Systems Research and Development at the moment. The last ten years have seen a marked interest in agentoriented technology and a distinct trend has evolved to the research and development work on intelligent agents. This trend relates to the diversification in the types of agents being investigated and most popular types include user interface agents, information agents, multi agent system, and mobile agent and so on. Intelligent agents, and in particular, mobile agents (Cardelli, 1995; White, 1996; Gray, 1997; Aglet system, 2003; Bee-gent and Plangent, 2003; Minar, 2000), have huge potential to address those deficiencies. In this paper, we shall demonstrate how mobile agents can address the problems that limit the potential mobile learning environment development.