Reusing adaptation strategies in adaptive educational hypermedia systems

Due to the difficulty and thus effort and expenses involved in creating them, personalization strategies in learning environments have to demonstrate a higher return-on-investment (ROI), if they are to be a viable component of the learning setting of the future. One feature that can increase this ROI is the reusability of adaptation strategies in Adaptive Educational Hypermedia Systems. This research looks into various ways of enhancing this reusability. Using multiple modular adaptation strategies (MAS) with a controlling meta-strategy is proposed as a more efficient way of authoring adaptation strategies. This renders the reuse of adaptation strategies faster and easier for course authors. A method for semi-automatically breaking down complex adaptation strategies into smaller modular adaptation strategies is described. Potential problems with using multiple strategies are described and ways to solve them are discussed. Finally, some evaluation points are illustrated, conclusions are drawn and further research areas are identified

Towards an aspect weaving BPEL engine

This position paper proposes the use of dynamic aspects and the visitor design pattern to obtain a highly configurable and extensible BPEL engine. Using these two techniques, the core of this infrastructural software can be customised to meet new requirements and add features such as debugging, execution monitoring, or changing to another Web Service selection policy. Additionally, it can easily be extended to cope with customer-specific BPEL extensions. We propose the use of dynamic aspects not only on the engine itself but also on the workflow in order to tackle the problems of Web Service hot deployment and hot fixes to long running processes. In this way, composing aWeb Service "on-the-fly" means weaving its choreography interface into the workflow

Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

Generic adaptation framework for unifying adaptive web-based systems

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the most general architectures of the present and future, including conventional AHS, and different types of personalization-enabling systems and applications such as recommender systems (RS) personalized web search, semantic web enabled applications used in personalized information delivery, adaptive e-Learning applications and many more. At the same time GAF is trying to bring together two (seemingly not intersecting) views on the adaptation: a classical pre-authored type, with conventional domain and overlay user models and data-driven adaptation which includes a set of data mining, machine learning and information retrieval tools. To bring these research fields together we conducted a number GAF compliance studies including RS, AHS, and other applications combining adaptation, recommendation and search. We also performed a number of real systems’ case-studies to prove the point and perform a detailed analysis and evaluation of the framework. Secondly it introduces a number of new ideas in the field of AH, such as the Generic Adaptation Process (GAP) which aligns with a layered (data-oriented) architecture and serves as a reference adaptation process. This also helps to understand the compliance features mentioned earlier. Besides that GAF deals with important and novel aspects of adaptation enabling and leveraging technologies such as provenance and versioning. The existence of such a reference basis should stimulate AHS research and enable researchers to demonstrate ideas for new adaptation methods much more quickly than if they had to start from scratch. GAF will thus help bootstrap any adaptive web-based system research, design, analysis and evaluation

Interoperability and FAIRness through a novel combination of Web technologies

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

The development of intelligent hypermedia courseware, for design and technology in the English National Curriculum at Key Stage 3, by the sequential combination of cognition clusters, supported by system intelligence, derived from a dynamic user model

The purpose of this research was to develop an alternative to traditional textbooks for the teaching of electronics, within Design and Technology at Key Stage 3, in the English National Curriculum. The proposed alternative of intelligent hypermedia courseware was investigated in terms of its potential to support pupil procedural autonomy in task directed, goal oriented, design projects. Three principal design criteria were applied to the development of this courseware: the situation in which it is to be used; the task that it is to support; and the pedagogy that it will reflect and support. The discussion and satisfaction of these design criteria led towards a new paradigm for the development of intelligent hypermedia courseware, i.e. the sequential combination of cognition clusters, supported by system intelligence, derived from a dynamic user model. A courseware prototype was instantiated using this development paradigm and subsequently evaluated in three schools. An illuminative evaluation method was developed to investigate the consequences of using this courseware prototype. This evaluation method was based on longitudinal case studies where cycles of observation, further inquiry and explanation are undertaken. As a consequence of following this longitudinal method, where participants chose to adopt the courseware after the first trial, the relatability of outcomes increased as subsequent cycles were completed. Qualitative data was obtained from semi-structured interviews with participating teachers. This data was triangulated against quantitative data obtained from the completed dynamic user models generated by pupils using the courseware prototype. These data were used to generate hypotheses, in the form of critical processes, by the identification of significant features, concomitant features and recurring concomitants from the courseware trials. Four relatable critical processes are described that operate when this courseware prototype is used. These critical processes relate to: the number of computers available; the physical environment where the work takes place; the pedagogical features of a task type match, a design brief frame match and a preferred teaching approach match; and the levels of heuristic interaction with the courseware prototype