Panning for gold: designing pedagogically-inspired learning nuggets

Tools to support teachers and learning technologists in the creation of effective learning designs are currently in their infancy. This paper describes a metadata model, devised to assist in the conception and design of new learning activities, that has been developed, used and evaluated over a period of three years. The online tool that embodies this model was not originally intended to produce runtime executable code such as IMS-LD, but rather focussed on assisting teachers in the thought processes involved in selecting appropriate methods, tools, student activities and assessments to suit the required learning objectives. Subsequently, we have modified the RELOAD editor such that the output from our tool can be translated into IMS-LD. The contribution of this paper is the comparison of our data model with that of IMS-LD, and the analysis of how each can inform the other

Personality representation: predicting behaviour for personalised learning support

The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles.This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods.This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support?Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services.The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach.The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support.The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming

A service-orientated architecture for adaptive and collaborative e-learning systems

This research proposes a new architecture for Adaptive Educational Hypermedia Systems (AEHS). Architectures in the context of this thesis refer to the components of the system and their communications and interactions. The architecture addresses the limitations of AEHS regarding interoperability, reusability, openness, flexibility, and limited tools for collaborative and social learning. It presents an integrated adaptive and collaborative Web-based learning environment. The new e-learning environment is implemented as a set of independent Web services within a service-oriented architecture (SOA). Moreover, it uses a modern Learning Management System (LMS) as the delivery service and the user interface for this environment. This is a two-way solution, whereby adaptive learning is introduced via a widely adopted LMS, and the LMS itself is enriched with an external - yet integrated - adaptation layer. To test the relevance of the new architecture, practical experiments were undertaken. The interoperability, reusability and openness test revealed that the user could easily switch between various LMS to access the personalised lessons. In addition, the system was tested by students at the University of Nottingham as a revision guide to a Software Engineering module. This test showed that the system was robust; it automatically handled a large number of students and produced the desired adaptive content. However, regarding the use of the collaborative learning tools, the test showed low levels of such usage

A service-orientated architecture for adaptive and collaborative e-learning systems

This research proposes a new architecture for Adaptive Educational Hypermedia Systems (AEHS). Architectures in the context of this thesis refer to the components of the system and their communications and interactions. The architecture addresses the limitations of AEHS regarding interoperability, reusability, openness, flexibility, and limited tools for collaborative and social learning. It presents an integrated adaptive and collaborative Web-based learning environment. The new e-learning environment is implemented as a set of independent Web services within a service-oriented architecture (SOA). Moreover, it uses a modern Learning Management System (LMS) as the delivery service and the user interface for this environment. This is a two-way solution, whereby adaptive learning is introduced via a widely adopted LMS, and the LMS itself is enriched with an external - yet integrated - adaptation layer. To test the relevance of the new architecture, practical experiments were undertaken. The interoperability, reusability and openness test revealed that the user could easily switch between various LMS to access the personalised lessons. In addition, the system was tested by students at the University of Nottingham as a revision guide to a Software Engineering module. This test showed that the system was robust; it automatically handled a large number of students and produced the desired adaptive content. However, regarding the use of the collaborative learning tools, the test showed low levels of such usage

A Real-time Personalization Service for SCORM

Despite SCORM’s (Shareable Content Object Reference Model’s) advantages, many scholars argue that SCORM confines learners to a static pool of learning resources organized in a predefined narrative structure. Hence users are unable to explore around the current topic, for example, in order to access materials that respond to their preferred learning styles. To address these limitations, we propose an architecture to provide SCORM with an independent ‘service’ that supplies the learner with dynamic personalized links to alternative resources. We demonstrate the feasibility of our architecture by implementing a service supporting users’ Learning Styles