Ontologies for Personalised Adaptive Learning

In recent years there has been an increasing interest in individual education. Consequently, one of the hot research topics is to adapt learning content to learner’s learning needs. Furthermore, recent developments in the field of semantic web have led to a renewed attention with focus in ontology-based e-learning system. This paper proposes an innovative ontological approach to design a personalised e-learning system which creates tailored contents for individual learners. The learning content associated with sequencing logic provides a clear separation between the domain and content models to increase the reusability and flexibility of the system. Additionally, in the proposed approach learner’s profiles are modelled to describe learner’s characteristics

A personalized adaptive e-learning approach based on semantic web technology

Recent developments in semantic web technologies heightened the need for online adaptive learning environment. Adaptive learning is an important research topic in the field of web-based systems as there are no fixed learning paths which are appropriate for all learners. However, most studies in this field have only focused on learning styles and habits of learners. Far too little attention has been paid on understanding the ability of learners. Therefore, it is becoming increasingly difficult to ignore adaptation in the field of e-learning systems. Many researchers are adopting semantic web technologies to find new ways for designing adaptive learning systems based on describing knowledge using ontological models. Ontologies have the potential to design content and learner models required to create adaptive e-learning systems based on various characteristics of learners. The aim of this paper is to present an ontology-based approach to develop adaptive e-learning system based on the design of semantic content, learner and domain models to tailor the teaching process for individual learner’s needs. The proposed new adaptive e-learning has the ability to support personalization based on learner’s ability, learning style, preferences and levels of knowledge. In our approach the ontological user profile is updated based on achieved learner’s abilities

Next Generation Context Aware Adaptive Services

Situational information can enrich the interactions between a user and the services they wish to utilize. Such information encompasses details about the user, the physical environment and the computing resources. There are at least three key aspects in addressing this issue. Firstly, it is important to accurately capture or infer the requirements of the users in a timely fashion. Without precise information on what the users are hoping to achieve it is difficult to identify suitable services or sub-services that may fulfill (in part or fully) their information needs. Secondly, the nature of the available services determines the modes in which they may be adapted to the users’ needs. Rigid, inflexible services may be difficult to tune to the information requirements of the users. Adaptive services, on the other hand, are well suited to dynamically modifying their behavior, within defined constraints. The third issue to be addressed is the on-the-fly combination of services to meet the users’ requirements. This paper argues that current modeling (both of users and services) techniques, adaptive axes and personalization techniques used in current personalized information services, such as Adaptive Hypermedia Systems, may supply the basis for next generation adaptive collaborative services

Multi-model, metadata driven approach to adaptive hypermedia services for personalized eLearning

peer-reviewedOne of the major obstacles in developing quality eLearning content is the substantial development costs involved and development time required [12]. Educational providers, such as those in the university sector and corporate learning, are under increasing pressure to enhance the pedagogical quality and technical richness of their course offerings while at the same time achieving improved return on investment. One means of enhancing the educational impact of eLearning courses, while still optimizing the return on investment, is to facilitate the personalization and repurposing of learning objects across multiple related courses. However, eLearning courses typically differ strongly in ethos, learning goals and pedagogical approach whilst learners, even within the same course, may have different personal learning goals, motivations, prior knowledge and learning style preferences. This paper proposes an innovative multi-model approach to the dynamic composition and delivery of personalized learning utilizing reusable learning objects. The paper describes an adaptive metadata driven engine that composes, at runtime, tailored educational experiences across a single content base. This paper presents the theoretical models, design and implementation of the adaptive hypermedia educational service. This service is currently being successfully used for the delivery of undergraduate degree courses in Trinity College, Dublin as well as being used as part of a major EU research trial

M.: Multi-Model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning

Abstract. One of the major obstacles in developing quality eLearning content is the substantial development costs involved and development time required [12]. Educational providers, such as those in the university sector and corporate learning, are under increasing pressure to enhance the pedagogical quality and technical richness of their course offerings while at the same time achieving improved return on investment. One means of enhancing the educational impact of eLearning courses, while still optimizing the return on investment, is to facilitate the personalization and repurposing of learning objects across multiple related courses. However, eLearning courses typically differ strongly in ethos, learning goals and pedagogical approach whilst learners, even within the same course, may have different personal learning goals, motivations, prior knowledge and learning style preferences. This paper proposes an innovative multi-model approach to the dynamic composition and delivery of personalized learning utilizing reusable learning objects. The paper describes an adaptive metadata driven engine that composes, at runtime, tailored educational experiences across a single content base. This paper presents the theoretical models, design and implementation of the adaptive hypermedia educational service. This service is currently being successfully used for the delivery of undergraduate degree courses in Trinity College, Dublin as well as being used as part of a major EU research trial.

Third international workshop on Authoring of adaptive and adaptable educational hypermedia (A3EH), Amsterdam, 18-22 July, 2005

The A3EH follows a successful series of workshops on Adaptive and Adaptable Educational Hypermedia. This workshop focuses on models, design and authoring of AEH, on assessment of AEH, conversion between AEH and evaluation of AEH. The workshop has paper presentations, poster session and panel discussions