This chapter explores qualitative career assessment as an identity learning process where meaning-oriented learning is essential and distinguished from conditioned or semantic types of learning. In order to construct a career identity in the form of a future-oriented narrative, it is essential that learners are helped through cognitive learning stages with the help of a dialogue about concrete experiences which aims to pay attention to emotions and broadens and deepens what is expressed.
When studying the acquisition of social-communicative competence, it is important to take students' individual learning theories into account. Increased insight into the role ILTs play can be of help in improving social work education.
Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
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Middels een RAAK-impuls aanvraag wordt beoogd de vertraging van het RAAK-mkb project Praktische Predictie t.g.v. corona in te halen. In het project Praktische Predictie wordt een prototype app ontwikkeld waarmee fysiotherapeuten in een vroeg stadium het chronisch worden van lage rugpijn kunnen voorspellen. Om chronische rugpijn te voorkomen is het belangrijk om in een vroeg stadium de kans hierop in te schatten door psychosociale en mogelijk andere risicofactoren op chronische pijnklachten te herkennen en hierop te interveniëren. Fysiotherapeuten zijn met deze vraag naar het lectoraat Werkzame factoren in Fysiotherapie en Paramedisch Handelen van de Hogeschool van Arnhem en Nijmegen gegaan en dit heeft aanleiding gegeven een onderzoek op te zetten waarin een dergelijke methodiek ontwikkeld wordt. De voorgestelde methodiek betreft een Clinical Decision Support Tool waarmee een geïndividualiseerde kans op chronische rugpijn kan worden bepaald gekoppeld aan een behandeladvies conform de lage rugpijn richtlijn. Hiervoor is eerst geïnventariseerd welke methoden fysiotherapeuten reeds gebruiken en welke in de literatuur worden genoemd. Op basis hiervan is een keuze gemaakt ten aanzien van data die digitaal verzameld worden in minimaal 16 fysiotherapiepraktijken waarbij patiënten gedurende 12 weken gevolgd worden. Met de verzamelde data worden met machine learning algoritmes ontwikkeld voor het berekenen van de kans op chroniciteit. De algoritmes worden ingebouwd in de Clinical Decision Support Tool: een gebruiksvriendelijke prototype app. Bij het ontwikkelen van de tool worden eindgebruikers (fysiotherapeuten en patiënten) intensief betrokken. Op deze manier wordt gegarandeerd dat de tool aansluit bij de wensen en behoeften van de doelgroep. De tool berekent de kans op chroniciteit en geeft een behandeladvies. Daarnaast kan de tool gebruikt worden om patiënten te informeren en te betrekken bij de besluitvorming. Vanwege de coronacrisis is er een aanzienlijke vertraging in de patiënten-instroom (doel n= 300) ontstaan die we met ondersteuning van een RAAK-impuls subsidie willen inlopen.
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.
Denim Democracy from the Alliance for Responsible Denim (ARD) is an interactive exhibition that celebrates the journey and learning of ARD members, educates visitors about sustainable denim and highlights how companies collaborate together to achieve results. Through sight, sound and tactile sensations, the visitor experiences and fully engages sustainable denim production. The exhibition launches in October 2018 in Amsterdam and travels to key venues and locations in the Netherlands until April 2019. As consumers, we love denim but the denim industry, like other sub-sectors in the textile, apparel and footwear industries, faces many complex sustainability challenges and has been criticized for its polluting and hazardous production practices. The Alliance for Responsible Denim project brought leading denim brands, suppliers and stakeholders together to collectively address these issues and take initial steps towards improving the ecological sustainability impact of denim production. Sustainability challenges are considered very complex and economically undesirable for individual companies to address alone. In denim, small and medium sized denim firms face specific challenges, such as lower economies of scale and lower buying power to affect change in practices. There is great benefit in combining denim companies' resources and knowledge so that collective experimentation and learning can lift the sustainability standards of the industry and lead to the development of common standards and benchmarks on a scale that matters. If meaningful, transformative industrial change is to be made, then it calls for collaboration between denim industry stakeholders that goes beyond supplier-buyer relations and includes horizontal value chain collaboration of competing large and small denim brands. However collaboration between organizations, and especially between competitors, is highly complex and prone to failure. The research behind the Alliance for Responsible Denim project asked a central research question: how do competitors effectively collaborate together to create common, industry standards on resource use and benchmarks for improved ecological sustainability? To answer this question, we used a mixed-method, action research approach. The Alliance for Responsible Denim project mobilized and facilitated denim brands to collectively identify ways to reduce the use of water and chemicals in denim production and then aided them to implement these practices individually in their respective firms.
