This paper reviews and discusses the neuroscience of a dynamic, contextual and polycultural self. Advances in neuro-science suggests that: (1) the brain can acquire contradictory cultural systems at the same time; (2) all three groups ofbi/multi/ and mono-cultural individuals can activate corresponding cultural patterns of the self, based on the cultural cues given in a specific cultural context; (3) individuals may be born with some genetic predispositions and these interact with the cultural environment, such that the same genetic predisposition may have opposite expressions of the self in different cultural contexts. Based on these insights, future research could invest more in (1) understanding theneuroscience of polycultural and global citizens who may have a universal identity; (2) advancing new identity development models for monocultural individuals who have the potential of a dynamic, contextual and polycultural self,but don’t benefit from living in a diverse cultural environment; and (3) because people can be both products and producers of culture, future research can focus on ‘technologies of the self’, in the sense that individuals, organisationsand governments can promote human agency (i.e. people as producers/authors of culture), proactively raise awareness and support the cultivation of a dynamic, contextual and polycultural self.
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De zogenoemde “21th century skills” worden, aldus het Ministerie van Onderwijs, steeds belangrijker. Het zijn eigenschappen die we terugvinden in de eindtermen van vrijwel alle hbo-opleidingen en die – in de woorden van Donald Schön – de kern zijn van een “reflective practitioner” : een vakvrouw of –man, die zichzelf in complexe situaties kan sturen en daardoor productief blijft. Eerder onderzoek van het lectoraat Pedagogiek van de Beroepsvorming heeft aangetoond dat een leeromgeving gericht op zelfsturing aan drie condities moet voldoen: er moet sprake zijn van praktijkgestuurd onderwijs, studenten moeten de kans krijgen een dialoog aan te gaan over de zin en betekenis van hun ervaringen in het praktijkgestuurde onderwijs en studenten moeten medezeggenschap hebben over hun eigen leerproces. Met name het realiseren van een dialoog blijkt echter heel moeilijk te zijn. Zowel docenten als studenten (en ook de onderwijsmanagers) zijn gewend aan onderwijs waarin zin en betekenis nauwelijks ter discussie staat. Het gevolg is dat ze vooral gericht zijn op reproductief en niet op betekenis-gericht leren. Zelfsturing vereist evenwel deze laatste vorm van leren. Zelfsturing vereist een dialoog over de zin en betekenis van ervaringen die de student “raken”. Dergelijke ervaringen roepen veelal emoties op die in eerste instantie niet begrepen worden. Zin en betekenis zijn “geen dingen in een doosje”; ze worden gaandeweg duidelijk in een gesprek waarin de docent verklaart noch verheldert, maar samen met de student op zoek gaat naar de juiste woorden. Dat zijn woorden waarvan de student voelt dat ze haar in staat stellen iets uit te drukken dat voorheen nog niet onder woorden gebracht kon worden. In dit boek wordt vanuit verschillende perspectieven en op basis van empirisch onderzoek ingegaan op de vraag in hoeverre het hbo er in slaagt een dergelijke dialoog met haar studenten te realiseren. Tevens wordt stilgestaan bij methoden om zo’n dialoog te realiseren.
From September 2024 onwards we will start the development of an educational innovation for Dutch primary schools to design a dynamic school day (a school day in which sedentary learning is regularly interrupted by moments of physical activity) for their local context. A number of Dutch primary schools already successfully implemented a more dynamic school day. In this qualitative study, we set out to assess the facilitators and barriers that several stakeholders faced during the implementation of the dynamic school day. We also set out to assess preferences of pupils with respect to a more dynamic school day. In preparation of the development phase, we will conduct semi-structured interviews with stakeholders of 3 Dutch primary schools (spring 2024). The interview guide will be based on the MRC guideline for conducting process evaluations of complex interventions. For each school, we seek to include: 1) the physical education teacher, 2) a classroom teacher who finds it easy to organize physical activities during the school day, 3) a classroom teacher who finds it difficult to organize physical activities during the school day, 4) a member of the management team. If relevant, we will also interview other stakeholders involved in the implementation of the dynamic school day. We will present the factors that may facilitate or hinder the implementation of a dynamic school day in the Dutch context. We will use these results to develop a set of potential implementation strategies that can serve as a source of inspiration for other Dutch primary schools in their process to develop a dynamic school day for their local context.
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The transition towards an economy of wellbeing is complex, systemic, dynamic and uncertain. Individuals and organizations struggle to connect with and embrace their changing context. They need to create a mindset for the emergence of a culture of economic well-being. This requires a paradigm shift in the way reality is constructed. This emergence begins with the mindset of each individual, starting bottom-up. A mindset of economic well-being is built using agency, freedom, and responsibility to understand personal values, the multi-identity self, the mental models, and the individual context. A culture is created by waving individual mindsets together and allowing shared values, and new stories for their joint context to emerge. It is from this place of connection with the self and the other, that individuals' intrinsic motivation to act is found to engage in the transitions towards an economy of well-being. This project explores this theoretical framework further. Businesses play a key role in the transition toward an economy of well-being; they are instrumental in generating multiple types of value and redefining growth. They are key in the creation of the resilient world needed to respond to the complex and uncertain of our era. Varta-Valorisatielab, De-Kleine-Aarde, and Het Groene Brein are frontrunner organizations that understand their impact and influence. They are making bold strategic choices to lead their organizations towards an economy of well-being. Unfortunately, they often experience resistance from stakeholders. To address this resistance, the consortium in the proposal seeks to answer the research question: How can individuals who connect with their multi-identity-self, (via personal values, mental models, and personal context) develop a mindset of well-being that enables them to better connect with their stakeholders (the other) and together address the transitional needs of their collective context for the emergence of a culture of the economy of wellbeing?
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
