This article reflects on the previous articles in this special issue by discussing some common themes and raising some proposals for future research on the topic of workplace learning and its boundaries. The article subsequently discusses objects and results of workplace learning, the issue of learning through acquisition and/or participation, the interplay between organisational and individual factors, the issue of access to learning opportunities, the role of workplace learning in education and HRD, respectively.
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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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Abstract Many innovations in education are not completed, even if they are well thought out in advance. One of the main causes is the organization's lack of learning ability, combined with a shortage of teachers' and students' ownership with respect to the renewal of ideas and design. In communities of learners, teachers and students collaborate and learn together in order to shape innovations in their daily practice. Their ability to learn collectively is a key factor in developing a learning organization. So far, insights into how processes of collective learning can be designed effectively, and which critical factors play a role, have been based on limited empirical research. This article's goal is to contribute to the development of these insights, using the results of a study based on 48 cases of collective learning in communities of learners in primary schools and teacher education institutes. The results suggest that although collective learning rarely takes place in most cases many outcomes are created that affect all community members. This leads to the conclusion that some participants create outcomes, not only on behalf of themselves, but also on behalf of others.
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Conference Paper From the article: Abstract Learning analytics is the analysis and visualization of student data with the purpose of improving education. Literature reporting on measures of the effects of data-driven pedagogical interventions on learning and the environment in which this takes place, allows us to assess in what way learning analytics actually improves learning. We conducted a systematic literature review aimed at identifying such measures of data-driven improvement. A review of 1034 papers yielded 38 key studies, which were thoroughly analyzed on aspects like objective, affected learning and their operationalization (measures). Based on prevalent learning theories, we synthesized a classification scheme comprised of four categories: learning process, student performance, learning environment, and departmental performance. Most of the analyzed studies relate to either student performance or learning process. Based on the results, we recommend to make deliberate decisions on the (multiple) aspects of learning one tries to improve by the application of learning analytics. Our classification scheme with examples of measures may help both academics and practitioners doing so, as it allows for structured positioning of learning analytics benefits.
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From the article: "The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in."
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poster voor de EuSoMII Annual Meeting in Pisa, Italië in oktober 2023. PURPOSE & LEARNING OBJECTIVE Artificial Intelligence (AI) technologies are gaining popularity for their ability to autonomously perform tasks and mimic human reasoning [1, 2]. Especially within the medical industry, the implementation of AI solutions has seen an increasing pace [3]. However, the field of radiology is not yet transformed with the promised value of AI, as knowledge on the effective use and implementation of AI is falling behind due to a number of causes: 1) Reactive/passive modes of learning are dominant 2) Existing developments are fragmented 3) Lack of expertise and differing perspectives 4) Lack of effective learning space Learning communities can help overcome these problems and address the complexities that come with human-technology configurations [4]. As the impact of a technology is dependent on its social management and implementation processes [5], our research question then becomes: How do we design, configure, and manage a Learning Community to maximize the impact of AI solutions in medicine?
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The workforce in the EU is ageing, and this requires investment in older workers so that the organisations in which they work remain competitive and viable. One such investment takes the form of organising and facilitating intergenerational learning: learning between and among generations that can lead to lifelong learning, innovation and organisational development. However, successfully implementing intergenerational learning is complex and depends on various factors at different levels within the organisation. This multidisciplinary literature review encompasses work from the fields of cognitive psychology, occupational health, educational science, human resource development and organisational science and results in a framework that organisations can use to understand how they can create the conditions needed to ensure that the potential of their ageing workforce is tapped effectively and efficiently. Although not a comprehensive review, this chapter serves as a basis for further empirical research and gives practitioners an insight into solving a growing problem.
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Evaluation of the effect of Problem Based Learning course
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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.
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The present study aimed to develop a football-specific self-report instrument measuring self-regulated learning in the context of daily practice, which can be used to monitor the extent to which players take responsibility for their own learning. Development of the instrument involved six steps: 1. Literature review based on Zimmerman's (2006) theory of self-regulated learning, 2. Item generation, 3. Item validation, 4. Pilot studies, 5. Exploratory factor analysis (EFA), and 6. Confirmatory factor analysis (CFA). The instrument was tested for reliability and validity among 204 elite youth football players aged 13-16 years (Mage = 14.6; s = 0.60; 123 boys, 81 girls). The EFA indicated that a five-factor model fitted the observed data best (reflection, evaluation, planning, speaking up, and coaching). However, the CFA showed that a three-factor structure including 22 items produced a satisfactory model fit (reflection, evaluation, and planning; non-normed fit index [NNFI] = 0.96, comparative fit index [CFI] = 0.95, root mean square error of approximation [RMSEA] = 0.067). While the self-regulation processes of reflection, evaluation, and planning are strongly related and fit well into one model, other self-regulated learning processes seem to be more individually determined. In conclusion, the questionnaire developed in this study is considered a reliable and valid instrument to measure self-regulated learning among elite football players.
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