Reflections on skill development theory and simulation game-play practice, using MS Flight Simulator and the MSP Challenge Simulation Platform as cases.
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This paper describes experiments with a game device that was used for early detection of delays in motor skill development in primary school children. Children play a game by bi-manual manipulation of the device which continuously collects ac- celerometer data and game state data. Features of the data are used to discriminate between normal children and children with delays. This study focused on the feature selection. Three features were compared: mean squared jerk (time domain); power spectral entropy (fourier domain) and cosine similarity measure (quality of game play). The discriminatory power of the features was tested in an experiment where 28 children played games of different levels of difficulty. The results show that jerk and cosine similarity have reasonable discriminatory power to detect fine-grained motor skill development delays especially when taking the game level into account. Duration of a game level needs to be at least 30 seconds in order to achieve good classification results.
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The present study investigated whether text structure inference skill (i.e., the ability to infer overall text structure) has unique predictive value for expository text comprehension on top of the variance accounted for by sentence reading fluency, linguistic knowledge and metacognitive knowledge. Furthermore, it was examined whether the unique predictive value of text structure inference skill differs between monolingual and bilingual Dutch students or students who vary in reading proficiency, reading fluency or linguistic knowledge levels. One hundred fifty-one eighth graders took tests that tapped into their expository text comprehension, sentence reading fluency, linguistic knowledge, metacognitive knowledge, and text structure inference skill. Multilevel regression analyses revealed that text structure inference skill has no unique predictive value for eighth graders’ expository text comprehension controlling for reading fluency, linguistic knowledge and metacognitive knowledge. However, text structure inference skill has unique predictive value for expository text comprehension in models that do not include both knowledge of connectives and metacognitive knowledge as control variables, stressing the importance of these two cognitions for text structure inference skill. Moreover, the predictive value of text structure inference skill does not depend on readers’ language backgrounds or on their reading proficiency, reading fluency or vocabulary knowledge levels. We conclude our paper with the limitations of our study as well as the research and practical implications.
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As the Dutch population is aging, the field of music-in-healthcare keeps expanding. Healthcare, institutionally and at home, is multiprofessional and demands interprofessional collaboration. Musicians are sought-after collaborators in social and healthcare fields, yet lesser-known agents of this multiprofessional group. Although live music supports social-emotional wellbeing and vitality, and nurtures compassionate care delivery, interprofessional collaboration between musicians, social work, and healthcare professionals remains marginal. This limits optimising and integrating music-making in the care. A significant part of this problem is a lack of collaborative transdisciplinary education for music, social, and healthcare students that deep-dives into the development of interprofessional skills. To meet the growing demand for musical collaborations by particularly elderly care organisations, and to innovate musical contributions to the quality of social and healthcare in Northern Netherlands, a transdisciplinary education for music, physiotherapy, and social work studies is needed. This project aims to equip multiprofessional student groups of Hanze with interprofessional skills through co-creative transdisciplinary learning aimed at innovating and improving musical collaborative approaches for working with vulnerable, often older people. The education builds upon experiential learning in Learning LABs, and collaborative project work in real-life care settings, supported by transdisciplinary community forming.The expected outcomes include a new concept of a transdisciplinary education for HBO-curricula, concrete building blocks for a transdisciplinary arts-in-health minor study, innovative student-led approaches for supporting the care and wellbeing of (older) vulnerable people, enhanced integration of musicians in interprofessional care teams, and new interprofessional structures for educational collaboration between music, social work and healthcare faculties.
Teachers have a crucial role in bringing about the extensive social changes that are needed in the building of a sustainable future. In the EduSTA project, we focus on sustainability competences of teachers. We strengthen the European dimension of teacher education via Digital Open Badges as means of performing, acknowledging, documenting, and transferring the competencies as micro-credentials. EduSTA starts by mapping the contextual possibilities and restrictions for transformative learning on sustainability and by operationalising skills. The development of competence-based learning modules and open digital badge-driven pathways will proceed hand in hand and will be realised as learning modules in the partnering Higher Education Institutes and badge applications open for all teachers in Europe.Societal Issue: Teachers’ capabilities to act as active facilitators of change in the ecological transition and to educate citizens and workforce to meet the future challenges is key to a profound transformation in the green transition.Teachers’ sustainability competences have been researched widely, but a gap remains between research and the teachers’ practise. There is a need to operationalise sustainability competences: to describe direct links with everyday tasks, such as curriculum development, pedagogical design, and assessment. This need calls for an urgent operationalisation of educators’ sustainability competences – to support the goals with sustainability actions and to transfer this understanding to their students.Benefit to society: EduSTA builds a community, “Academy of Educators for Sustainable Future”, and creates open digital badge-driven learning pathways for teachers’ sustainability competences supported by multimodal learning modules. The aim is to achieve close cooperation with training schools to actively engage in-service teachers.Our consortium is a catalyst for leading and empowering profound change in the present and for the future to educate teachers ready to meet the challenges and act as active change agents for sustainable future. Emphasizing teachers’ essential role as a part of the green transition also adds to the attractiveness of teachers’ work.
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.
