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.
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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The present study aims to contribute to the body of knowledge on HRD in small businesses by providing a detailed investigation of the role that owner-managers play in enabling social learning and performance in small firms. The investigation focusses particularly on the specific relationships of the social-interdependence orientation and social competence of owner-managers with their social learning behaviour, as well as with the performance of their smallbusinesses within the pig-production sector in the Republic of Korea. A survey was conducted amongst nearly 200 Korean ownermanagers of pig farms. The results indicate that social interdependence orientations and social competencies have a significant relationship with social learning behaviour. Self-promotion and a cooperative orientation are especially important, with selfpromotion taking precedence for social learning behaviour of a more ‘internal’ nature, and a cooperative attitude being more important social learning behaviour of a more ‘external’ nature. Social competence and social interdependence did not have a significant relationship with performance, but social learning behaviour did. The results further highlight the importance of individual social characteristics to social learning behaviour occurring outside highly structured educational settings, in addition to demonstrating that the competence and attitudes required are determined by the type of interaction partner.
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The IMPULS-2020 project DIGIREAL (BUas, 2021) aims to significantly strengthen BUAS’ Research and Development (R&D) on Digital Realities for the benefit of innovation in our sectoral industries. The project will furthermore help BUas to position itself in the emerging innovation ecosystems on Human Interaction, AI and Interactive Technologies. The pandemic has had a tremendous negative impact on BUas industrial sectors of research: Tourism, Leisure and Events, Hospitality and Facility, Built Environment and Logistics. Our partner industries are in great need of innovative responses to the crises. Data, AI combined with Interactive and Immersive Technologies (Games, VR/AR) can provide a partial solution, in line with the key-enabling technologies of the Smart Industry agenda. DIGIREAL builds upon our well-established expertise and capacity in entertainment and serious games and digital media (VR/AR). It furthermore strengthens our initial plans to venture into Data and Applied AI. Digital Realities offer great opportunities for sectoral industry research and innovation, such as experience measurement in Leisure and Hospitality, data-driven decision-making for (sustainable) tourism, geo-data simulations for Logistics and Digital Twins for Spatial Planning. Although BUas already has successful R&D projects in these areas, the synergy can and should significantly be improved. We propose a coherent one-year Impuls funded package to develop (in 2021): 1. A multi-year R&D program on Digital Realities, that leads to, 2. Strategic R&D proposals, in particular a SPRONG/sleuteltechnologie proposal; 3. Partnerships in the regional and national innovation ecosystem, in particular Mind Labs and Data Development Lab (DDL); 4. A shared Digital Realities Lab infrastructure, in particular hardware/software/peopleware for Augmented and Mixed Reality; 5. Leadership, support and operational capacity to achieve and support the above. The proposal presents a work program and management structure, with external partners in an advisory role.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
