Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

Case-based analysis in user requirements modelling for knowledge construction

Context: Learning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations. Objectives: The current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose. Method: An experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement. Result: The research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications. Conclusion: The current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements

An Integrated Approach for Automatic\ud Aggregation of Learning Knowledge Objects

This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain\ud knowledge acquisition from textual documents for knowledge-based systems. First, the\ud Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’\ud content through natural language processing and machine learning technologies. Second,\ud it employs a new content model, the Knowledge Puzzle Content Model, which aims to model\ud learning material from annotated content. Annotations are performed semi-automatically based\ud on IBM’s Unstructured Information Management Architecture and are stored in an Organizational\ud memory (OM) as knowledge fragments. The organizational memory is used as a knowledge\ud base for a training environment (an Intelligent Tutoring System or an e-Learning environment).\ud The main objective of these annotations is to enable the automatic aggregation of Learning\ud Knowledge Objects (LKOs) guided by instructional strategies, which are provided through\ud SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects\ud from these LKOs

A Model-Driven Architecture based Evolution Method and Its Application in An Electronic Learning System

Software products have been racing against aging problem for most of their lifecycles, and evolution is the most effective and efficient solution to this problem. Model-Driven Architecture (MDA) is a new technique for software product for evolving development and reengineering methods. The main steps for MDA are to establish models in different levels and phases, therefore to solve the challenges of requirement and technology change. However, there is only a standard established by Object Management Group (OMG) but without a formal method and approach. Presently, MDA is widely researched in both industrial and research areas, however, there is still without a smooth approach to realise it especially in electronic learning (e-learning) system due to the following reasons: (1) models’ transformations are hard to realise because of lack of tools, (2) most of existing mature research results are working for business and government services but not education area, and (3) most of existing model-driven researches are based on Model-Driven Development (MDD) but not MDA because of OMG standard’s preciseness. Hence, it is worth to investigate an MDA-based method and approach to improve the existing software development approach for e-learning system. Due to the features of MDA actuality, a MDA-based evolution method and approach is proposed in this thesis. The fundamental theories of this research are OMG’s MDA standard and education pedagogical knowledge. Unified Modelling Language (UML) and Unified Modelling Language Profile are hired to represent the information of software system from different aspects. This study can be divided into three main parts: MDA-based evolution method and approach research, Platform-Independent Model (PIM) to Platform-Specific Model (PSM) transformation development, and MDA-based electronic learning system evolution. Top-down approach is explored to develop models for e-learning system. A transformation approach is developed to generate Computation Independent Model (CIM), Platform-Independent Model (PIM), and Platform-Specific Model (PSM); while a set of transformation rules are defined following MDA standard to support PSM’ s generation. In addition, proposed method is applied in an e-learning system as a case study with the prototype rules support. In the end, conclusions are drawn based on analysis and further research directions are discussed as well. The kernel contributions are the proposed transformation rules and its application in electronic learning system