May I Suggest? Comparing Three PLE Recommender Strategies

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general

May I Suggest? Comparing Three PLE Recommender Strategies

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general

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

Supporting self-regulated learning

Self-regulated learning (SRL) competences are crucial for lifelong learning. Their cultivation requires the right balance between freedom and guidance during the learning processes. Current learning systems and approaches, such as personal learning environments, give overwhelming freedom, but also let weak learners alone. Other systems, such as learning management systems or adaptive systems, tend to institutionalise learners too much, which does not support the development of SRL competences. This chapter presents possibilities and approaches to support SRL by the use of technology. After discussing the theoretical background of SRL and related technologies, a formal framework is presented that describes the SRL process, related competences, and guidelines. Furthermore, a variety of methods is presented, how learners can be supported to learn in a self-regulated way

Building a flexible CBT model based on structured data for the COPE app

Master's Thesis in InformaticsINF399MAMN-PROGMAMN-IN

Responsive Open Learning Environments at the Open University

Personal Learning Environments (PLEs) offer new opportunities for supporting personalized and self regulated learning both in formal and in informal education. The Open University in the UK is an early adopter of PLEs through a number of different initiatives, one of which is the European project ROLE (Responsive Open Learning Environments). This paper presents some of the lessons learned and best practices from the introduction of ROLE technologies within an informal learning test-bed at the Open University

XEL Group Learning – A Socio-technical Framework for Self-regulated Learning

We describe XEL-Group Learning, a socio-technical framework for socially oriented e-learning. The aim of the presented framework is to address the lack of holistic pedagogical solutions that take into account motivational theories, socio–technical factors, and cultural elements in social learning networks. The presented framework provides initiatives for collaboration by providing a dynamic psycho-pedagogical recommendation mechanism with validation properties. In this paper, we begin by highlighting the socio-technical concept associated with socially-oriented e-learning. Next, we describe XEL-GL’s main mechanisms such as group formation and the semantic matching framework. Moreover, through semantic similarity measurements, we show how cultural elements, such as the learning subject, can enhance the quality of recommendations by allowing for more accurate predictions of friends networks

The problem of behaviour and preference manipulation in AI systems

Statistical AI or Machine learning can be applied to user data in order to understand user preferences in an effort to improve various services. This involves making assumptions about either stated or revealed preferences. Human preferences are susceptible to manipulation and change over time. When iterative AI/ML is applied, it becomes difficult to ascertain whether the system has learned something about its users, whether its users have changed/learned something or whether it has taught its users to behave in a certain way in order to maximise its objective function. This article discusses the relationship between behaviour and preferences in AI/ML, existing mechanisms that manipulate human preferences and behaviour and relates them to the topic of value alignment