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

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

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

Supporting teachers in collaborative student modeling: a framework and an implementation

Collaborative student modeling in adaptive learning environments allows the learners to inspect and modify their own student models. It is often considered as a collaboration between students and the system to promote learners’ reflection and to collaboratively assess the course. When adaptive learning environments are used in the classroom, teachers act as a guide through the learning process. Thus, they need to monitor students’ interactions in order to understand and evaluate their activities. Although, the knowledge gained through this monitorization can be extremely useful to student modeling, collaboration between teachers and the system to achieve this goal has not been considered in the literature. In this paper we present a framework to support teachers in this task. In order to prove the usefulness of this framework we have implemented and evaluated it in an adaptive web-based educational system called PDinamet.Postprint (author's final draft

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

Light-weight ontologies for scrutable user modelling

This thesis is concerned with the ways light-weight ontologies can support scrutability for large user models and the user modelling process. It explores the role that light-weight ontologies can play, and how they can be exploited, for the purpose of creating and maintaining large, scrutable user models consisting of hundreds of components. We address problems in four key areas: ontology creation, metadata annotation, creation and maintenance of large user models, and user model visualisation, with a goal to provide a simple and adaptable approach that maintains scrutability. Each of these key areas presents a number of challenges that we address. Our solution is the development of a toolkit, LOSUM, which consists of a number of tools to support the user modelling process. It incorporates light-weight ontologies to fulfill a number of roles: aiding in metadata creation, providing structure for large user model visualisation, and as a means to reason across granularities in the user model. In conjunction with this, LOSUM also features a novel visualisation tool, SIV, which performs a dual role of ontology and user model visualisation, supporting the process of ontology creation, metadata annotation, and user model visualisation. We evaluated our approach at each stage with small user studies, and conducted a large scale integrative evaluation of these approaches together in an authentic learning context with 114 students, of whom 77 had exposure to their learner models through SIV. The results showed that students could use the interface and understand the process of user model construction. The flexibility and adaptability of the toolkit has also been demonstrated in its deployment in several other application areas

User model interoperability in education: sharing learner data using the experience API and distributed ledger technology

Learning analytics and data mining require gathering and exchanging learner data for further processing and designing of activities tailored to learner’s characteristics, context, and needs. Currently, systems that store learners’ attributes should, ideally, be operated and controlled by responsible and trustworthy authorities that guarantee the protection and sovereignty of data and use objective criteria to protect and represent all parties’ interests. This chapter introduces a peer-to-peer method for storing and exchanging learner data with minimal trust. The proposed approach, underpinned by the Experience API standard, eliminates the need of a mediator authority by using distributed ledger technology

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Making Legacy LMS adaptable using Policy and Policy templates

Koesling, A., Herder, E., De Coi, J., & Abel, F. (2008). Making Legacy LMS adaptable using Policy and Policy templates. In J. Baumeister & M. Atzmüller, Proceedings of the 16th Workshop on Adaptivity and User Modeling in Interactive System, ABIS 2008 (pp. 35-40). October, 6-8, 2008, Würzburg, Germany: University of Würzburg. Website with link to proceedings: http://lwa08.informatik.uni-wuerzburg.de/Wiki.jsp?page=FGABIS08In this paper, we discuss how users and designers of existing learning management systems (LMSs) can make use of policies to enhance adaptivity and adaptability. Many widespread LMSs currently only use limited and proprietary rule systems defining the system behaviour. Personalization of those systems is done based on those rule systems allowing only for fairly restricted adaptation rules. Policies allow for more sophisticated and flexible adaptation rules, provided by multiple stakeholders and they can be integrated into legacy systems. We present the benefits and feasibility of our ongoing approach of extending an existing LMS with policies. We will use the LMS ILIAS as a hands-on example to allow users to make use of system personalization.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Adaptive applications to assist students with autism in succeeding in higher education

In this demo we discuss a few possible scenarios showing adaptation of presentation and information to assist autistic students in succeeding in higher education. These students not only have specific information need, they are also more concerned about their privacy. We use WiBAF (Within Browser Adaptation Framework) for user modeling and adaptation to give users control over the sharing of their data