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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Context When the pandemic hit the world, teachers were forced to change their education from onsite to virtual overnight Understandably, teaching quality decreased in the beginning, as there was little experience in how to adapt the educational design Zuyd University of Applied Sciences ( recognized the problem that teachers were on different didactic and pedagogical levels when it comes to online education Unfortunately, the pandemic made it hard for teachers to connect with each other In the Domain of Health and Welfare, this led to the idea of establishing a professional learning community A professional learning community ( can be seen as an informal group of people who share knowledge and experiences among each other on a common topic they are all highly interested in Zuyd’s vision “passion for development” sets a good basis for the start of such a community. Steps we took In order to find out how a professional learning community can look like in Zuyd, the following steps were taken Firstly, we collected and evaluated literature and best practices around the topic Based on our findings we developed an interview guideline and conducted interviews with eight teachers from the Domain of Health and Welfare Throughout the whole report a SWOT analysis was performed with the literature and best practices filling opportunities and threats and the interviews providing content for strengths and weaknesses Main findings From these sources, we derived enablers for a successful learning community, which led to recommendations for Zuyd on how to strategically position, implement and organize a PLC One of our major recommendations is to make didactic and pedagogical skills an important topic within Zuyd in order to strategically implement the learning community into Zuyd’s strategy Furthermore, we recommend giving the lead in organizing and facilitating the PLC to the blended learning task force To collect a diverse set of interested employees to the core group, the educational managers should personally approach teachers that might be interested The sense of urgency around the topic needs to be addressed regularly through the directors of the Domain, the task force of blended learning, as well as the PLC itself In this way, interest in the topic of didactic and pedagogical skills and blended learning can be enhanced In the report we go into greater detail on how to organize and apply these recommendations. We are convinced that implementing these steps will pay off in the future and will successfully enhance competencies on blended learning and didactic and pedagogical skills through knowledge exchange.
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In this chapter it is argued that self-direction is currently well above the head of the majority of youngsters and even of many adults. Evidence for this conclusion stems from developmental and brain research. However, for various reasons it is important that people develop the competences that are necessary for self-direction. To what degree is it possible to develop these competences? Are they 'learnable'? What can education contribute?
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The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
In recent years, ArtEZ has worked on a broadly supported strategic research agenda on the themes New Ecologies of Matter (ecological challenges), Social Equity (social-societal issues), (Un)Learning Practices (educational innovations) and (Non)CybernEtic Fabric (technological developments). Building on these strategic themes, the ArtEZ Research Collective as developed an international research strategy to become a valuable partner in the relevant Horizon Europe (HEU) areas of Environment, Industry and Social science and humanities. With its specific knowledge position and approach from arts and creativity, ArtEZ is convinced that it can play a distinctive role in European consortia to tackle various challenges in these areas, in particular from the perspective and research topics of the professorships Fashion and Tactical Design. To achieve its ambitions and goals in its targeted research topics, ArtEZ is convinced that a combination of international connections and local applications is key for successful impact. Building upon existing relations and extending the international research position requires extra efforts, e.g., by developing a strong international framework of state-of-the-art research results, impacts and ambitions. Therefore ArtEZ needs to (further) build on both its international network and its supportive infrastructure. With this proposal ArtEZ is presenting its goals and efforts to work on its international recognition as a valuable research partner, and to broaden its international network in cutting-edge research and other stakeholders. With regards to its supporting infrastructure, ArtEZ has the ambition to expand the impact of the Subsidy Desk to become a professional partner to the professorships. This approach requires a further professionalization and extension of both the Subsidy Desk organization and its services, and developing and complementing skills, expertise and competences to comply to the European requirements.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
