Adaptive governance describes the purposeful collective actions to resist, adapt, or transform when faced with shocks. As governments are reluctant to intervene in informal settlements, community based organisations (CBOs) self-organize and take he lead. This study explores under what conditions CBOs in Mathare informal settlement, Nairobi initiate and sustain resilience activities during Covid-19. Study findings show that CBOs engage in multiple resilience activities, varying from maladaptive and unsustainable to adaptive, and transformative. Two conditions enable CBOs to initiate resilience activities: bonding within the community and coordination with other actors. To sustain these activities over 2.5 years of Covid-19, CBOs also require leadership, resources, organisational capacity, and network capacity. The same conditions appear to enable CBOs to engage in transformative activities. How-ever, CBOs cannot transform urban systems on their own. An additional condition, not met in Mathare, is that governments, NGOs, and donor agencies facilitate, support, and build community capacities. This is the peer reviewed version of the following article: Adaptive governance by community-based organisations: Community resilience initiatives during Covid‐19 in Mathare, Nairobi. which has been published in final form at doi/10.1002/sd.2682. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
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In higher education, students often misunderstand teachers’ written feedback. This is worrisome, since written feedback is the main form of feedback in higher education. Organising feedback conversations, in which feedback request forms and verbal feedback are used, is a promising intervention to prevent misunderstanding of written feedback. In this study a 2 × 2 factorial experiment (N = 128) was conducted to examine the effects of a feedback request form (with vs. without) and feedback mode (written vs. verbal feedback). Results showed that verbal feedback had a significantly higher impact on students’ feedback perception than written feedback; it did not improve students’ self-efficacy, or motivation. Feedback request forms did not improve students’ perceptions, self-efficacy, or motivation. Based on these results, we can conclude that students have positive feedback perceptions when teachers communicate their feedback verbally and more research is needed to investigate the use of feedback request forms.
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Many institutes for initial teacher education struggle to organise effective performance feedback within the context of student teaching practicum. As the cooperating teachers who provide this feedback bring their individualised ontologies, feedback practices have been characterised by inconsistencies in the amount and quality of performance feedback. In this small-scale study carried out in the Netherlands, we explored affordances of eCoaching using a standardised feedback taxonomy. With the help of Bluetooth technology and the Synchronous Online Feedback Taxonomy, four teacher educators provided eCoaching to eight preservice teachers over the course of three lessons. We interviewed teacher educators and preservice teachers about their experiences with eCoaching using the feedback taxonomy during secondary school practicum. Overall, both groups of participants were positive about eCoaching using the taxonomy. Teacher educators observed preservice teachers self-regulating when implementing prior feedback in their lessons. Preservice teachers indicated increased confidence following the lessons with eCoaching.
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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.
Het probleem dat deze projectaanvraag adresseert is de hoge werkdruk van zorgprofessionals in de dementiezorg. Door een stijging in het aantal ouderen met dementie, stijgt de zorgvraag, terwijl het tekort aan zorgprofessionals groeit. Door de inzet van slimme technologische innovaties zoals een Intelligente Zorgomgeving kan deze werkdruk sterk verminderd worden. Een Intelligente Zorgomgeving maakt gebruik van sensortechnieken en gebruikt Artificiële Intelligentie (AI) om gepersonaliseerde zorg te leveren door de zorgbehoefte in kaart te brengen en daarop te reageren. De Intelligente Zorgomgeving werkt daarbij samen met de zorgprofessional. Deze oplossingsrichting wordt in dit project verder uitgewerkt samen met vier zorgpartijen en drie innovatieve MKB. Aan de hand van de casus “Ondersteuning bij eten en drinken” worden Just-in-time adaptive interventions (JITAI) ontwikkeld zodat de zorgprofessional de zorgprofessional ondersteund wordt in het uitvoeren van bepaalde zorgtaken. Een voorbeeld van een interventie is het op het juiste moment geven van op de persoon aangepaste zintuigelijke prikkels (geluiden, lichten en projecties) die senioren stimuleren om te eten. Door dergelijke interventies wordt de druk op de zorgprofessional verminderd en neemt de kwaliteit van de zorg toe. Niet alleen de integratie van de AI-modules is van belang maar ook hoe de AI ‘getoond’ wordt aan de zorgprofessional. Daarom wordt er in dit project ook extra aandacht besteed aan de interactie tussen zorgprofessional en de Intelligente Zorgomgeving waardoor het gebruiksgemak wordt verhoogd en zowel cliënt als zorgprofessional een hogere mate van autonomie kunnen ervaren. Door het prototype van de Intelligente Zorgomgeving verder te ontwikkelen in zorginstellingen in samenwerking met verschillende zorgprofessionals en aandacht te besteden aan het ontwikkelen van AI en Interactie met het systeem kunnen de wensen en behoeften van de zorgprofessionals worden geïntegreerd in de Intelligente Zorgomgeving. Dit gebeurt in drie iteraties waarbij de drie opeenvolgende beschikbare living labs in toenemende mate complex en realistisch zijn.
