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3De ontwikkelingen en veranderingen in de gezondheidszorg maken het noodzakelijk dat verpleegkundigen door middel van bij- en nascholing hun deskundigheid op peil houden. Deskundigheid is de basis waarop herregistratie in het BIG-register zal gaan plaatsvinden. Per 1 januari 2009 moeten zorgverleners na vijf jaar hun deskundigheid aantonen door te voldoen aan de werkervaringseis en, als ze daar niet aan voldoen, de scholingseis1. Deskundigheidsbevordering en Lifelong Learning - levenslang leren - gaan hand in hand. Lifelong Learning is het principe dat mensen gedurende hun hele leven in staat en gemotiveerd zijn om te leren en dat de omgeving daartoe mogelijkheden biedt2, 3. E-learning wordt geassocieerd met leeractiviteiten die plaatsvinden op een zelfgekozen moment waarbij een met een computernetwerk verbonden computer interactief gebruikt wordt. ‘Any place, any time’ is een wezenlijk aspect van e-learning. E-learning is belangrijk voor het levenslang leren van verpleegkundigen.
DOCUMENT
Educational programs teaching entrepreneurial behaviour and knowledge are crucial to a vital and healthy economy. The concept of building a Communities of Practice (CoP) could be very promising. CoP’s are formed by people who engage in a process of collective learning in a shared domain of human endeavour (Wenger, McDermott and Snyder, 2002). They consist of a group of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly. Normally CoP’s are rather homogeneous. Saxion institute Small Business & Retail Management (SB&RM) started a CoP with entrepreneurs September 2007. Typical in the this community, are the differences between the partners. The Community consists of students, entrepreneurs and members of an institution for higher education. They have different characteristics and they don’t share the same knowledge. Thus, building long-lasting relations can be complicated. Solid relations for longer periods are nevertheless inevitable in using CoP as a mean in an educational concept that takes approximately 4 years. After one year an evaluation took place on the main aspects of a lasting partnership. The central problem SB&RM in Deventer faces is to design the CoP in a way possible members will join and stay for a longer period and in a way it ensures entrepreneurial learning. This means important design characteristics have to be identified, and the CoP in Deventer has to be evaluated to assess whether it meets those design characteristics in an effective and efficient way. The main target of the evaluation is to determine which key factors are important to make sure continuity in partnership is assured and entrepreneurial learning is best supported. To solve the problem, an investigation on how a CoP works, what group dynamics take place, and how this can be measured has to be conducted. Furthermoreusing the CoP as a tool for entrepreneurship means key aspects of entrepreneurial learning have to be identified. After that the CoP in Deventer has to be examined on both aspects. According to literature CoP’s define themselves along three dimensions: domain (indicating what is it about), community (defining how it functions), and practice (indicating what capabilities it has produced) (Wenger, 1998). This leads to meaningful, shared and coordinated activities (Akkerman et al, 2007): Key aspects of a successful CoP lie in both hard and soft sides of creating a partnership. It means on one hand a CoP has to deal with defining their own overall vision, formulating long term goals and targets on the short term. They have to formulate how to achieve those targets and create meaningful activities (reification). On the other hand a CoP has to deal with relations, trust, norms and values (participation). Reification and participation as design characteristic can provide indicators on which the CoP in Deventer can be evaluated. A lasting partnership means joining the CoP and staying. Weick provides us with a suitable model that enables us to do research and evaluate whether the CoP in Deventer is successful or not, Weick’s model of means convergence. To effectively ensure entrepreneurial learning the process in the CoP has to provide or enable actionoriented forms through Project-based activity, accompanied by reflection, with high emotional exposure (or cognitive affection) preferably caused by discontinuities to be suitable as a tool in entrepreneurial learning. Furthermore it should be accompanied by the right preconditions to work effectively and efficiently. The evaluation of the present CoP in Deventer is done by interviewing all participants at the end of the first year of the partnership. In a structured interview, based on literature studies, all participants were separately questioned
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Morssink-Santing, V. E., van der Zee, S., Klaver, L. T., de Brouwer, J., andamp; Sins, P. H. (2024). The long-term effect of alternative education on self-regulated learning: A comparison between Montessori, Dalton, and traditional education. Studies in Educational Evaluation, 83, 101380. https://doi.org/10.1016/j.stueduc.2024.101380
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Uit het vooronderzoekvan het project Duurzamelearning communities: Oogstenin de Greenportblijkt dat12 factorenhierbijvan belangrijk zijn. Deze succesfactoren staan centraal in de interactieve tool Seeds of Innovation. Ook komen uit het vooronderzoek, aangevuld met inzichten uit de literatuur en tips om de samenwerking door te ontwikkelen en meer gebruik te maken van de opbrengsten 12 succesfactoren met toelichting, belangrijkste bevindingen en tips voor ‘hoe nu verder’, Poster, Walk through, De app die learning communities helptde samenwerkingnaareenhogerplan te tillenen innovatieveopbrengstenoptimaalte benutten.
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Als relatief nieuw begrip in de context van e-learning krijgt ‘mobile learning’ steeds meer aandacht, wat ten dele kan worden verklaard door de ontwikkeling en verspreiding van mobiele technologie. Als we de pleitbezorgers van ‘mobile learning’ moeten geloven, dan wordt deze vorm van leren belangrijker en is het denkbaar dat sommige leerprocessen in de toekomst volledig op die wijze vormgegeven zullen worden. Probleem is dat een eenduidige definitie van ‘mobile learning’ nog altijd ontbreekt, dat er meningsverschillen zijn over de technologie die tot het domein van ‘mobile learning’ behoort, en dat er betrekkelijk weinig resultaten zijn van succesvolle inzet van mobiele technologie in leerprocessen. Daarbij wordt onder succesvol verstaan dat het heeft bijgedragen aan de effectiviteit van het leren, en daarmee aan een beter leerresultaat en een efficiënter leerproces, waarbij onder het laatste verstaan wordt dat het maximale leereffect wordt bereikt met een beperkte inzet van mensen en middelen. Deze notitie beoogt enige duidelijkheid te scheppen in de definitiekwestie en in de visies op leren die een rol spelen bij ‘mobile learning’. Vanuit dat perspectief wordt vervolgens ingegaan op kenmerken van mobiele technologie en ontwikkelingen die daarin verwacht worden. Aansluitend wordt er dieper ingegaan op leerprocessen en de rol die mobiele technologie daarin zou kunnen vervullen, waarna de notitie wordt afgesloten met een kijkkader om de mogelijke inzet en betekenis van ‘mobile learning’ in onderwijssituaties te kunnen duiden en beoordelen.
DOCUMENT
Background: Continuing professional development (CPD) in nursing is defined as ‘a life-long process of active participation in learning activities that assist in developing and maintaining continuing competences, enhancing professional practice and supporting achievement of career goals’. Research has shown that inability to access resources and activities for CPD influences quality of care and adversely affects nurses’ satisfaction, recruitment and retention. Although more and more research regarding CPD is done, a comprehensive overview about the needs of nurses for successful CPD is missing. Conclusions: All nurses strive for CPD. However, organizations need to recognize nurses' personal goals and unique strategies as this leads to different needs in CPD. In addition, resources must be made available and accessible before CPD can be successfully pursued by all nurses.
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Learning objects are bits of learning content. They may be reused 'as is' (simple reuse) or first be adapted to a learner's particular needs (flexible reuse). Reuse matters because it lowers the development costs of learning objects, flexible reuse matters because it allows one to address learners' needs in an affordable way. Flexible reuse is particularly important in the knowledge economy, where learners not only have very spefic demands but often also need to pay for their own further education. The technical problems to simple and flexible are rapidly being resolved in various learning technology standardisation bodies. This may suggest that a learning object economy, in which learning objects are freely exchanged, updated and adapted, is about to emerge. Such a belief, however, ignores the significant psychological, social and organizational barriers to reuse that still abound. An inventory of these problems is made and possible ways to overcome them are discussed.
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As more and more older adults prefer to stay in their homes as they age, thereandapos;s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in long-term care.
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As more and more older adults prefer to stay in their homes as they age, there’s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in longterm care. Methods: A three round modified Delphi study was conducted, consisting of two online questionnaires and one focus group. A group of 48 experts participated in the study: nurses, innovators, developers, researchers, managers and educators. In the first two rounds experts assessed clarity and relevance on a proposed list of competencies, with the opportunity to provide suggestions for adjustments or inclusion of new competencies. In the third round the items without consensus were bespoken in a focus group. Findings: After the first round consensus was reached on relevance and clarity on n = 46 (72 %) of the competencies, after the second round on n = 54 (83 %) of the competencies. After the third round a final list of 10 competency domains and 61 sub-competencies was finalized. The 10 competency domains are: Fundamentals of AI, Participation in AI design, Patient-centered needs assessment, Personalisation of AI to patients’ situation, Data reporting, Interpretation of AI output, Integration of AI output into clinical practice, Communication about AI use, Implementation of AI and Evaluation of AI use. These competencies span from basic understanding of AIdriven lifestyle monitoring, to being able to integrate it in daily work, being able to evaluate it and communicate its use to other stakeholders, including patients and informal caregivers. Conclusion: Our study introduces a novel framework highlighting the (sub)competencies, required for nurses to work with AI-driven lifestyle monitoring in long-term care. These findings provide a foundation for developing initial educational programs and lifelong learning activities for nurses in this evolving field. Moreover, the importance that experts attach to AI competencies calls for a broader discussion about a potential shift in nursing responsibilities and tasks as healthcare becomes increasingly technologically advanced and data-driven, possibly leading to new roles within nursing.
DOCUMENT
Background: As more and more older adults prefer to stay in their homes as they age, there’s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in longterm care. Methods: A three round modified Delphi study was conducted, consisting of two online questionnaires and one focus group. A group of 48 experts participated in the study: nurses, innovators, developers, researchers, managers and educators. In the first two rounds experts assessed clarity and relevance on a proposed list of competencies, with the opportunity to provide suggestions for adjustments or inclusion of new competencies. In the third round the items without consensus were bespoken in a focus group. Findings: After the first round consensus was reached on relevance and clarity on n = 46 (72 %) of the competencies, after the second round on n = 54 (83 %) of the competencies. After the third round a final list of 10 competency domains and 61 sub-competencies was finalized. The 10 competency domains are: Fundamentals of AI, Participation in AI design, Patient-centered needs assessment, Personalisation of AI to patients’ situation, Data reporting, Interpretation of AI output, Integration of AI output into clinical practice, Communication about AI use, Implementation of AI and Evaluation of AI use. These competencies span from basic understanding of AIdriven lifestyle monitoring, to being able to integrate it in daily work, being able to evaluate it and communicate its use to other stakeholders, including patients and informal caregivers. Conclusion: Our study introduces a novel framework highlighting the (sub)competencies, required for nurses to work with AI-driven lifestyle monitoring in long-term care. These findings provide a foundation for developing initial educational programs and lifelong learning activities for nurses in this evolving field. Moreover, the importance that experts attach to AI competencies calls for a broader discussion about a potential shift in nursing responsibilities and tasks as healthcare becomes increasingly technologically advanced and data-driven, possibly leading to new roles within nursing.
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