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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This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive constructivism focuses on the active role of the learner, and on real-life learning. Social-learning theories, comprising the socio-historical, socio-cultural theories as well as the situated-learning and community-of-practice approaches, emphasise learning as being a process within and a product of the social context. Critical-learning theory stresses that this social context is a man-made construction, which should be approached critically and transformed in order to create a better world. We propose to view these different approaches as contributions to our understanding of the learning-environment relationship, and their educational impact as questions to be addressed to educational contexts.
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 Academy for Leisure & Events has always been one of the frontrunners when it comes to the development, design and implementation of cultural tourism and creative industry business models as well as lifelong learning programmes.These programmes are attended by a variety of leisure and tourism professionals, including public authorities in leisure, culture and nature fields.The CULTURWB project addresses the need for strengthening the development of the cultural tourism industry.The experts from BUas together with the other project partners have utilised diverse research methodologies (marketing and branding, strategy business planning, digital tourism, sustainable development, strategy and action plan implementation, etc.) to develop and pilot a toolkit for Lifelong Learning courses in the field of cultural tourism and heritage. They have also designed and implemented a master’s programme in the WB countries and created an online platform for communication between stakeholders, industry leaders, managers, workforce, and academia.PartnersHochschule Heibronn, FH Joanneum Gesellschaft, World University Service - Österreichisches Komitee (WUS Austria), Dzemal Bijedic University of Mostar (UNMO), University of East Sarajevo (UES), The University of Banja Luka (UBL), University of NIS (UNI), University of Montenegro (UoM), Sarajevo Meeting of Cultures (SMOC), rovincial Institute for the Protection of Cultural Monuments (PZZZSK), Tourism Organisation of Kotor Municipality (TO Kotor)
Dutch Cycling Intelligence (DCI) embodies all Dutch cycling knowledge to enhances customer-oriented cycling policy. Based on the data-driven cycle policy enhancement tools and knowledge of the Breda University of Applied Sciences, DCI is the next step in creating a learning community between road authorities, consultants, cycling industry, and knowledge institutes with their students. The DCI consists of three pilars:- Connecting- Accelerating knowledge- Developing knowledgeConnecting There are many stakeholders and specialists in the cycling domain. Specialists with additional knowledge about socio-cultural impacts, geo-special knowledge, and technical traffic solutions. All of these specialists need each other to ensure a perfect balance between the (electric) bicycle, the cyclist and the cycle path in its environment. DCI connects and brings together all kind of different specialists.Accelerating knowledge Many bicycle innovations take place in so-called living labs. Within the living lab, the triple helix collaboration between road authorities the industry and knowledge institutes is key. Being actively involved in state-of-the-art innovations creates an inspiring work and learning environment for students and staff. A practical example of a successful living lab is the cycle superhighway F261 between Tilburg and Waalwijk, where BUAS tested new cycle route signage. Next, the Cycling Lab F58 is created, where the road authorities Breda and Tilburg opened up physical cycling infrastructure for entrepreneurs in the bicycle domain and knowledge institutes to develop e-cycling innovation. The living labs are test environments where pilots can be carried out in practice and an excellent environment for students to conduct scientifically applied research.Developing knowledge Ultimately, data and information must be translated into knowledge. With a team of specialists and partners Breda University of applied sciences developed knowledge and tools to monitor and evaluate cycling behavior. By participating in (inter)national research programs BUAS has become one of the frontrunners in data-driven cycle policy enhancement. In close collaboration with road authorities, knowledge institutes as well as consultants, new insights and answers are developed in an international context. By an active knowledge contribution to the network of the Dutch Cycling Embassy, BUAS aims to strengthen its position and add to the global sustainability challenges. Partners: Province Noord-Brabant, Province Utrecht, Vervoerregio Amsterdam, Dutch Cycling Embassy, Tour de Force, University of Amsterdam, Technical University Eindhoven, Technical University Delft, Utrecht University, DTV Capacity building, Dat.mobility, Goudappel Coffeng, Argaleo, Stratopo, Move.Mobility Clients:Province Noord-Brabant, Province Utrecht, Province Zuid-Holland, Tilburg, Breda, Tour de Force
