The design and use of online materials for blended learning have been in the spotlight of educational development over the last decade. With respect to didactical courses, however, the potential of online and blended learning seems to be underexplored; little is known about its affordances for teacher education, and for domain specific didactical courses in particular. To investigate this potential, as well as the ways to organize the co-design of such learning units, we carried out a small and short-term research project in which teacher educators in the Netherlands engaged in a co-design process of developing and field-testing open online learning units for mathematics and science didactics. We focused on the features of the designed online learning units, on the organization of the co-design process, and on the experiences with the learning units in teacher education practice. A first conclusion was that it was most fruitful to design building blocks rather than ready-to-use courses, and that students should have play a role in the materials. With respect to the co-design process, intensive meetings of small design teams seemed an efficient approach. The experiences in the field tests revealed that the learning units were inspiring, but needed finalization, and educators needed time to prepare the incorporation in their existing educational practices. In the future, the resulting learning units will be maintained and extended, and are expected to contribute to a community of practice of mathematics and science educators.
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From the article: "The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in."
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Learning objects are bits of learning content. They may be reused 'as is' (simple reuse) or first be adapted to a learner's particular needs (flexible reuse). Reuse matters because it lowers the development costs of learning objects, flexible reuse matters because it allows one to address learners' needs in an affordable way. Flexible reuse is particularly important in the knowledge economy, where learners not only have very spefic demands but often also need to pay for their own further education. The technical problems to simple and flexible are rapidly being resolved in various learning technology standardisation bodies. This may suggest that a learning object economy, in which learning objects are freely exchanged, updated and adapted, is about to emerge. Such a belief, however, ignores the significant psychological, social and organizational barriers to reuse that still abound. An inventory of these problems is made and possible ways to overcome them are discussed.
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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.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
The pressure on the European health care system is increasing considerably: more elderly people and patients with chronic diseases in need of (rehabilitation) care, a diminishing work force and health care costs continuing to rise. Several measures to counteract this are proposed, such as reduction of the length of stay in hospitals or rehabilitation centres by improving interprofessional and person-centred collaboration between health and social care professionals. Although there is a lot of attention for interprofessional education and collaborative practice (IPECP), the consortium senses a gap between competence levels of future professionals and the levels needed in rehabilitation practice. Therefore, the transfer from tertiary education to practice concerning IPECP in rehabilitation is the central theme of the project. Regional bonds between higher education institutions and rehabilitation centres will be strengthened in order to align IPECP. On the one hand we deliver a set of basic and advanced modules on functioning according to the WHO’s International Classification of Functioning, Disability and Health and a set of (assessment) tools on interprofessional skills training. Also, applications of this theory in promising approaches, both in education and in rehabilitation practice, are regionally being piloted and adapted for use in other regions. Field visits by professionals from practice to exchange experiences is included in this work package. We aim to deliver a range of learning materials, from modules on theory to guidelines on how to set up and run a student-run interprofessional learning ward in a rehabilitation centre. All tested outputs will be published on the INPRO-website and made available to be implemented in the core curricula in tertiary education and for lifelong learning in health care practice. This will ultimately contribute to improve functioning and health outcomes and quality of life of patients in rehabilitation centres and beyond.
