To adequately deal with the challenges faced within residential care for older people, such as the increasing complexity of care and a call for more person-centred practices, it is important that health care providers learn from their work. This study investigates both the nature of learning, among staff and students working within care for older people, and how workplace learning can be promoted and researched. During a longitudinal study within a nursing home, participatory and democratic research methods were used to collaborate with stakeholders to improve the quality of care and to promote learning in the workplace. The rich descriptions of these processes show that workplace learning is a complex phenomenon. It arises continuously in reciprocal relationship with all those present through which both individuals and environment change and co-evolve enabling enlargement of the space for possible action. This complexity perspective on learning refines and expands conventional beliefs about workplace learning and has implications for advancing and researching learning. It explains that research on workplace learning is itself a form of learning that is aimed at promoting and accelerating learning. Such research requires dialogic and creative methods. This study illustrates that workplace learning has the potential to develop new shared values and ways of working, but that such processes and outcomes are difficult to control. It offers inspiration for educators, supervisors, managers and researchers as to promoting conditions that embrace complexity and provides insight into the role and position of self in such processes.
Deze uitgave is een eerste verkenning van de mogelijkheden om learning analytics in te zetten bij open en online onderwijs en de Grand Challenges die daarbij spelen. Vijf experts uit de special interest groups Open Education en Learning Analytics identificeerden daartoe de uitdagingen in één van beide gebieden. Per uitdaging is een literatuurstudie uitgevoerd en is onderzocht welke concrete vragen er bestaan, welke nationale en internationale voorbeelden er zijn en welke punten nader onderzoek verdienen.
Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
MULTIFILE
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
The Academy for Leisure & Events has always been one of the frontrunners when it comes to the development, design and implementation of cultural tourism and creative industry business models as well as lifelong learning programmes.These programmes are attended by a variety of leisure and tourism professionals, including public authorities in leisure, culture and nature fields.The CULTURWB project addresses the need for strengthening the development of the cultural tourism industry.The experts from BUas together with the other project partners have utilised diverse research methodologies (marketing and branding, strategy business planning, digital tourism, sustainable development, strategy and action plan implementation, etc.) to develop and pilot a toolkit for Lifelong Learning courses in the field of cultural tourism and heritage. They have also designed and implemented a master’s programme in the WB countries and created an online platform for communication between stakeholders, industry leaders, managers, workforce, and academia.PartnersHochschule Heibronn, FH Joanneum Gesellschaft, World University Service - Österreichisches Komitee (WUS Austria), Dzemal Bijedic University of Mostar (UNMO), University of East Sarajevo (UES), The University of Banja Luka (UBL), University of NIS (UNI), University of Montenegro (UoM), Sarajevo Meeting of Cultures (SMOC), rovincial Institute for the Protection of Cultural Monuments (PZZZSK), Tourism Organisation of Kotor Municipality (TO Kotor)
Zijn data-analyse en bio-informatica de sleutel naar voorspellingen over de invloed van giftige stoffen op de gezondheid van mensen? Het project DART Pathfinder is een vervolgonderzoek naar een dierproefvrije testmethode. Met moderne ICT-technieken proberen we die voorspellingen te doen.Doel Het doel van dit project is om gegevens over giftige stoffen uit verschillende data bronnen samen te brengen. In het onderzoek gebruiken we technieken uit de bio-informatica. Zo willen we de eigenschappen van giftige stoffen beter in kaart brengen en (nadelige) effecten van soortgelijke stoffen kunnen voorspellen. Veel bedrijven maken producten of stoffen, die getest moeten worden of ze veilig zijn. Met dit project helpen we bedrijven om o.b.v. bestaande gegevens een betere keuze te maken welke testen ze hiervoor het beste kunnen gebruiken. Resultaten Kennis over computer modellen die voorspellingen doen, zoals machine learning, regression tree-based models; Nieuwe algoritmen (instructies om berekeningen uit te voeren) Inzicht in nieuwe biologische mechanismen obv data science Nieuwe statische methoden om data te analysen en voorspellingen te doen. Looptijd 01 februari 2018 - 01 februari 2022 Aanpak Met de gegevens uit het onderzoek maken we een computermodel dat voorspelt of giftige stoffen invloed hebben op de voortplanting en ontwikkeling van mensen. Die voorspelling gebeurt via machine learning, algoritmen en statistische methoden. Voor dit model wordt informatie uit publieke databases over fysische en chemische eigenschappen van mogelijk gevaarlijke stoffen samengevoegd met de gegevens over de invloed van deze stoffen op levende organismen. Net als in het eerste onderzoek (PreDART) werken we met rondwormen (C.elegans) en embryo's van zebravissen, met als doel geen proeven meer met ratten en konijnen te hoeven doen.
