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

When situativity meets objectivity in peer-production of knowledge:the case of the WikiRate platform

PurposeThe purpose of this paper is to further the debate on Knowledge Artefacts (KAs), by presenting the design of WikiRate, a Collective Awareness platform whose goal is to support a wider public contributing to the generation of knowledge on environmental, social and governance (ESG) performance of companies.Design/methodology/approachThe material presented in the paper comes from the first-hand experience of the authors as part of the WikiRate design team. This material is reflexively discussed using concepts from the field of science and technology studies.FindingsUsing the concept of the “funnel of interest”, the authors discuss how the design of a KA like WikiRate relies on the designers’ capacity to translate general statements into particular design solutions. The authors also show how this funnelling helps understanding the interplay between situativity and objectivity in a KA. The authors show how WikiRate is a peer-production platform based on situativity, which requires a robust level of objectivity for producing reliable knowledge about the ESG performance of companies.Originality/valueThis paper furthers the debate on KAs. It presents a relevant design example and offers in the discussion a set of design and community building recommendations to practitioners

A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

Guidelines Towards Better Participation of Older Adults in Software Development Processes using a new SPIRAL Method and Participatory Approach

This paper presents a new method of engaging older participants in the process of application and IT solutions development for older adults for emerging IT and tech startups. A new method called SPIRAL (Support for Participant Involvement in Rapid and Agile software development Labs) is proposed which adds both sustainability and flexibility to the development process with older adults. This method is based on the participatory approach and user empowerment of older adults with the aid of a bootstrapped Living Lab concept and it goes beyond well established user-centered and empathic design. SPIRAL provides strategies for direct involvement of older participants in the software development processes from the very early stage to support the agile approach with rapid prototyping, in particular in new and emerging startup environments with limited capabilities, including time, team and resources

CEPS Task Force on Artificial Intelligence and Cybersecurity Technology, Governance and Policy Challenges Task Force Evaluation of the HLEG Trustworthy AI Assessment List (Pilot Version). CEPS Task Force Report 22 January 2020

The Centre for European Policy Studies launched a Task Force on Artificial Intelligence (AI) and Cybersecurity in September 2019. The goal of this Task Force is to bring attention to the market, technical, ethical and governance challenges posed by the intersection of AI and cybersecurity, focusing both on AI for cybersecurity but also cybersecurity for AI. The Task Force is multi-stakeholder by design and composed of academics, industry players from various sectors, policymakers and civil society. The Task Force is currently discussing issues such as the state and evolution of the application of AI in cybersecurity and cybersecurity for AI; the debate on the role that AI could play in the dynamics between cyber attackers and defenders; the increasing need for sharing information on threats and how to deal with the vulnerabilities of AI-enabled systems; options for policy experimentation; and possible EU policy measures to ease the adoption of AI in cybersecurity in Europe. As part of such activities, this report aims at assessing the High-Level Expert Group (HLEG) on AI Ethics Guidelines for Trustworthy AI, presented on April 8, 2019. In particular, this report analyses and makes suggestions on the Trustworthy AI Assessment List (Pilot version), a non-exhaustive list aimed at helping the public and the private sector in operationalising Trustworthy AI. The list is composed of 131 items that are supposed to guide AI designers and developers throughout the process of design, development, and deployment of AI, although not intended as guidance to ensure compliance with the applicable laws. The list is in its piloting phase and is currently undergoing a revision that will be finalised in early 2020. This report would like to contribute to this revision by addressing in particular the interplay between AI and cybersecurity. This evaluation has been made according to specific criteria: whether and how the items of the Assessment List refer to existing legislation (e.g. GDPR, EU Charter of Fundamental Rights); whether they refer to moral principles (but not laws); whether they consider that AI attacks are fundamentally different from traditional cyberattacks; whether they are compatible with different risk levels; whether they are flexible enough in terms of clear/easy measurement, implementation by AI developers and SMEs; and overall, whether they are likely to create obstacles for the industry. The HLEG is a diverse group, with more than 50 members representing different stakeholders, such as think tanks, academia, EU Agencies, civil society, and industry, who were given the difficult task of producing a simple checklist for a complex issue. The public engagement exercise looks successful overall in that more than 450 stakeholders have signed in and are contributing to the process. The next sections of this report present the items listed by the HLEG followed by the analysis and suggestions raised by the Task Force (see list of the members of the Task Force in Annex 1)

Creative enterprise in west Yorkshire Arts organisations

This report describes and theorises the findings of a workshop discussion, commissioned by WYLLN, into the views of arts organizations on the challenges they face in becoming more enterprising and less grant dependent

A review of GIS-based information sharing systems

GIS-based information sharing systems have been implemented in many of England and Wales' Crime and Disorder Reduction Partnerships (CDRPs). The information sharing role of these systems is seen as being vital to help in the review of crime, disorder and misuse of drugs; to sustain strategic objectives, to monitor interventions and initiatives; and support action plans for service delivery. This evaluation into these systems aimed to identify the lessons learned from existing systems, identify how these systems can be best used to support the business functions of CDRPs, identify common weaknesses across the systems, and produce guidelines on how these systems should be further developed. At present there are in excess of 20 major systems distributed across England and Wales. This evaluation considered a representative sample of ten systems. To date, little documented evidence has been collected by the systems that demonstrate the direct impact they are having in reducing crime and disorder, and the misuse of drugs. All point to how they are contributing to more effective partnership working, but all systems must be encouraged to record how they are contributing to improving community safety. Demonstrating this impact will help them to assure their future role in their CDRPs. By reviewing the systems wholly, several key ingredients were identified that were evident in contributing to the effectiveness of these systems. These included the need for an effective partnership business model within which the system operates, and the generation of good quality multi-agency intelligence products from the system. In helping to determine the future development of GIS-based information sharing systems, four key community safety partnership business service functions have been identified that these systems can most effectively support. These functions support the performance review requirements of CDRPs, operate a problem solving scanning and analysis role, and offer an interface with the public. By following these business service functions as a template will provide for a more effective application of these systems nationally