108 research outputs found

    A meta level to LAG for adaptation language re-use

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    Recently, a growing body of research targets authoring of content and adaptation strategies for adaptive systems. The driving force behind it is semantics-based reuse: the same adaptation strategy can be used for various domains, and vice versa. E.g., a Java course can be taught via a strategy differentiating between beginner and advanced users, or between visual versus verbal users. Whilst using an Adaptation Language (LAG) to express reusable adaptation strategies, we noticed, however, that: a) the created strategies have common patterns that, themselves, could be reused; b) templates based on these patterns could reduce the designers' work; c) there is a strong preference towards XML-based processing and interfacing. This has lead us to define a new meta-language for the LAG Adaptation Language, facilitating the extraction of common design patterns. This paper provides more insight into the LAG language, as well as describes this meta-language, and shows how introducing it can overcome some redundancy issues

    A Plugable Web Based Intelligent Tutoring System

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    With the development of WWW technology, web-based ITSs are becoming mainstream area of research and development. The major benefit of web-based ITS is that, the ITS installed and supported in one place can be used by thousands of learners all over the world. Although many web-based educational systems appeared recently, most of them emerged from their predecessor legacy standalone systems. Therefore, they not only restrict themselves in functionality, but also fail to take advantage of distributed nature of Internet. This paper describes an open architecture based adaptable web-based intelligent tutoring system with pluggable domain modules. The system is based client/server architecture and has distinct and separable domain modules and a generic module. Such architecture not only provides salability in the Internet environment but also allows the same architecture to be used for multiple subject domains

    Domain Modeling for Personalized Guidance

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    This chapter attempts to untangle the relationships between personalized guidance and domain modeling, as well as to explain how domain modeling could be used to provide personalized guidance. The problem of personalized guidance has a long history in the area of adaptive educational systems (AES). In fact, the very first recognized AES SCHOLAR (Carbonell, 1970) focused on guiding students to the most relevant facts and questions about the geography of South America. The SCHOLAR functionality was based on a domain model in the form of a semantic network and an overlay student model. Since that time, a considerable share of research in the field of AES has focused on different kinds of personalized guidance, and the majority of this work relied heavily on domain modeling—which makes these two research directions heavily interconnected

    Adaptive intelligent tutoring for teaching modern standard Arabic

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    A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyThe aim of this PhD thesis is to develop a framework for adaptive intelligent tutoring systems (ITS) in the domain of Modern Standard Arabic language. This framework will comprise of a new approach to using a fuzzy inference mechanism and generic rules in guiding the learning process. In addition, the framework will demonstrate another contribution in which the system can be adapted to be used in the teaching of different languages. A prototype system will be developed to demonstrate these features. This system is targeted at adult English-speaking casual learners with no pre-knowledge of the Arabic language. It will consist of two parts: an ITS for learners to use and a teachers‘ tool for configuring and customising the teaching rules and artificial intelligence components among other configuration operations. The system also provides a diverse teaching-strategies‘ environment based on multiple instructional strategies. This approach is based on general rules that provide means to a reconfigurable prediction. The ITS determines the learner‘s learning characteristics using multiple fuzzy inferences. It has a reconfigurable design that can be altered by the teacher at runtime via a teacher-interface. A framework for an independent domain (i.e. pluggable-domain) for foreign language tutoring systems is introduced in this research. This approach allows the system to adapt to the teaching of a different language with little changes required. Such a feature has the advantages of reducing the time and cost required for building intelligent language tutoring systems. To evaluate the proposed system, two experiments are conducted with two versions of the software: the ITS and a cut down version with no artificial intelligence components. The learners used the ITS had shown an increase in scores between the post-test and the pre-test with learning gain of 35% compared to 25% of the learners from the cut down version

    E-Tester: A computer-based tool for auto-generated question and answer assessment

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    Adaptive E-Learning systems are needed to efficiently support lifelong learning activities. To check goal attainment, learners need to take tests or assessment activities. Large efficiency gains may be made if the assessment and associated grading or evaluation process could be supported with adaptive systems sensitive to user parameters. Hand made tests are a time consuming and tiresome task. If the tests are hand scored there is the inevitability of either interrupting the learning whilst the assessment outcome is pending, or the danger of proceeding with the attempt at learning possibly unsuitable content. As a contribution to solving the problem stated so far we have designed the E-TESTER. In this paper we describe the implementation solution and its integration into the AdeLE system, point out the potential of the E-TESTER by an application example, and discuss the experiences of the prototype implementation

    The hybrid model, and adaptive educational hypermedia frameworks

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    The amount of information on the web is characterised by being enormous, as is the number of users with different goals and interests. User models have been utilized by adaptive hypermedia systems generally and adaptive educational hypermedia systems (AEHS) particularly to personalize the amount of information they have with respect to each individual's knowledge, background and goals. As a result of the research described herein, a user model called the Hybrid Model has been developed. This model is both generic and abstract, and it extends other models used by AEHS by measuring users' knowledge levels with respect to different knowledge domains simultaneously by utilising well known techniques in the world of user modelling, specifically the Overlay model (which has been modified) and the Stereotype model. Therefore, using the Hybrid Model, AEHS will not be restricted to a single knowledge domain at anyone time. Thus, by implementing the Hybrid model, those systems can manage users' knowledge globally with respect to the deployed knowledge domains. The model has been implemented experimentally in an educational hypermedia system called WHURLE (Web-based Hierarchal Universal Reactive Learning Environment) to verify its aim - managing users' knowledge globally. Moreover, this implementation has been tested successfully through a user trial as an adaptive revision guide for a Biological Anthropology Course. Furthermore, the infrastructure of the WHURLE system has been modified to embrace the objective of the Hybrid Model. This has led to a novel design that provides the system with the capability of utilising different user models easily without affecting any of its component modules

    Personalized Students' Profile Based on Ontology and Rule-based Reasoning

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    Nowadays, most of the existing e-learning architecture provides the same content to all learners due to "one size fits for all" concept. E-learning refers to the utilization of electronic innovations to convey and encourage training anytime and anywhere. There is a need to create a personalized environment that involves collecting a range of information about each learner. Questionnaires are one way of gathering information on learning style, but there are some problems with their usage, such as reluctance to answer questions as well as guesses the answer being time consuming. Ontology-based semantic retrieval is a hotspot of current research, because ontologies play a paramount part in the development of knowledge. In this paper, a novel way to build an adaptive student pro le by analysis of learning patterns through a learning management system, according to the Felder-Silverman learning style model (FSLSM) and Myers-Briggs Type Indicator (MBTI) theory is proposed

    CAIN-21: Automatic adaptation decisions and extensibility in an MPEG-21 adaptation engine

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    This paper presents the progress and final state of CAIN-21, an extensible and metadata driven multimedia adaptation in the MPEG-21 framework. CAIN-21 facilitates the integration of pluggable multimedia adaptation tools, automatically chooses the chain of adaptations to perform and manages its execution. To drive the adaptation, it uses the description tools and implied ontology established by MPEG-21. The paper not only describes the evolution and latest version of CAIN-21, but also identifies limitations and ambiguities in the description capabilities of MPEG-21. Therefore, it proposes some extensions to the MPEG-21 description schema for removing these problems. Finally, the pros and cons of CAIN-21 with respect to other multimedia adaptation engines are discussed
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