Design of a scrutable learning system

Personal Learning Environments (PLEs) refer to systems that allow individual learners to manage and control their own learning in their own space and at their own pace. In this work we explore the different ways in which a learning experience can be informal, and propose a 4D model of informal learning to characterise the informal aspects of a learning experience.The model includes dimensions for learning objectives, the learning environment, learning activities and learning tools, and reveals how much of the experience is really under the control of the learner. In an analysis of mobile tools presented in the mLearn 2008 conference we show that many emerging m-learning systems focused on informality in the environment dimension but not in the others.To solve this problem this report proposes a scrutable learning model approach that allows personal learners to take control of their learning objectives while still allowing the system to intelligently support them with appropriate learning activities and resources. In addition an experimental design is described based around a prototype of a scrutable learning system for mobile devices

A scrutable adaptive hypertext

Fuelled by the popularity and uptake of the World Wide Web since the 1990s, many researchers and commercial vendors have focussed on Adaptive Hypermedia Systems as an effective mechanism for disseminating personalised information and services. Such systems store information about the user, such as their goals, interests and background, and use this to provide a personalised response to the user. This technology has been applied to a number of contexts such as education systems, e-commerce applications, information search and retrieval systems. As an increasing number of systems collect and store personal information about their users to provide a personalised service, legislation around the world increasingly requires that users have access to view and modify their personal data. The spirit of such legislation is that the user should be able to understand how personal information about them is used. There literature has reported benefits of allowing users to access and understand data collected about them, particularly in the context of supporting learning through reflection. Although researchers have experimented with open user models, typically the personalisation is inscrutable: the user has little or no visibility in to the adaptation process. When the adaptation produces unexpected results, the user may be left confused with no mechanism for understanding why the system did what it did or how to correct it. This thesis is the next step, giving users the ability to see what has been personalised and why. In the context of personalised hypermedia, this thesis describes the first research to go beyond open, or even scrutable user models; it makes the adaptivity and associated processes open to the user and controllable. The novelty of this work is that a user of an adaptive hypertext system might ask How was this page personalised to me? and is able to see just how their user model affected what they saw in the hypertext document. With an understanding of the personalisation process and the ability to control it, the user is able to steer the personalisation to suit their changing needs, and help improve the accuracy of the user model. Developing an interface to support the scrutinisation of an adaptive hypertext is difficult. Users may not scrutinise often as it is a distraction from their main task. But when users need to scrutinise, perhaps to correct a system misconception, they need to easily find and access the scrutinisation tools. Ideally, the tools should not require any training and users should be able to use them effectively without prior experience or if have not used them for a long time, since this is how users are likely to scrutinise in practice. The contributions of thesis are: (1) SASY/ATML, a domain independent, reusable framework for creation and delivery of scrutable adaptive hypertext; (2)a toolkit of graphical tools that allow the user to scrutinise, or inspect and understand what personalisation occurred and control it; (3) evaluation of the scrutinisation tools and (4) a set of guidelines for providing support for the scrutinisation of an adaptive hypertext through the exploration of several forms of scrutinisation tools

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be ``holistic'', i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: rst, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best t the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of ``holistic'' RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

Modelling Physical Activity in Virtual Reality Games

This thesis was inspired by the possibility that virtual reality (VR) games, which are designed primarily to be fun, could also provide exercise. It aimed to gain insights about this by exploring whether people can gain beneficial levels of exercise while playing VR games and how they might use VR games for exercise over several weeks. Furthermore, this work also focuses on how the level of physical activity that can be captured during gameplay and how a long-term user model can be created for individual players, as a foundation for supporting the user in gaining personal informatics insights about their exertion as well as being used for personalisation and external recommendation for VR games. The key contributions of this research are: • The first study of a diverse set of commercial VR games to gain insights about the level of actual and perceived exertion players have. • The first long-term study of VR games in a sedentary workplace to gain insights about the ways people utilise it and the levels of exertion they gain. • Based on reflections on the above studies, this thesis presents a framework and guidelines for designing physical activity VR games. • The systematic creation of a user model for representing a person’s long-term fitness and their VR gameplay, exertion and preferences. • A study of the ways that people can scrutinise their long-term personal informatics user model of exertion from VR game play and incidental walking. These contributions provide a foundation for future researchers and industry practitioners to design VR games that provide beneficial levels of exertion and allow people to gain insights into the relative contribution of the exercise from gameplay

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

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 framework for adaptive personalised e-advertisements

The art of personalised e-advertising relies on attracting the user‟s attention to the recommended product, as it relates to their taste, interest and data. Whilst in practice, companies attempt various forms of personalisation; research of personalised e-advertising is rare, and seldom routed on solid theory. Adaptive hypermedia (AH) techniques have contributed to the development of personalised tools for adaptive content delivery, mostly in the educational domain. This study explores the use of these theories and techniques in a specific field – adaptive e-advertisements. This is accomplished firstly by structuring a theoretical framework that roots adaptive hypermedia into the domain of e-advertising and then uses this theoretical framework as the base for implementing and evaluating an adaptive e-advertisement system called “MyAds”. The novelty of this approach relies on a systematic design and evaluation based on adaptive hypermedia taxonomy. In particular, this thesis uses a user centric methodology to design and evaluate the proposed approach. It also reports on evaluations that investigated users‟ opinions on the appropriate design of MyAds. Another set of evaluations reported on users‟ perceptions of the implemented system, allowing for a reflection on the users‟ acceptance level of e-advertising. The results from both implicit and explicit feedback indicated that users found the MyAds system acceptable and agreed that the implemented user modelling and AH features within the system contributed to achieving acceptance, within their e-advertisement experience due to the different personalisation methods