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.
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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Purpose: Collaborative deliberation comprises personal engagement, recognition of alternative actions, comparative learning, preference elicitation, and preference integration. Collaborative deliberation may be improved by assisting preference elicitation during shared decision-making. This study proposes a framework for preference elicitation to facilitate collaborative deliberation in long-term care consultations. Methods: First, a literature overview was conducted comprising current models for the elicitation of preferences in health and social care settings. The models were reviewed and compared. Second, qualitative research was applied to explore those issues that matter most to clients in long-term care. Data were collected from clients in long-term care, comprising 16 interviews, 3 focus groups, 79 client records, and 200 online client reports. The qualitative analysis followed a deductive approach. The results of the literature overview and qualitative research were combined. Results: Based on the literature overview, five overarching domains of preferences were described: “Health”, “Daily life”, “Family and friends”, ”Living conditions”, and “Finances”. The credibility of these domains was confirmed by qualitative data analysis. During interviews, clients addressed issues that matter in their lives, including a “click” with their care professional, safety, contact with loved ones, and assistance with daily structure and activities. These data were used to determine the content of the domains. Conclusion: A framework for preference elicitation in long-term care is proposed. This framework could be useful for clients and professionals in preference elicitation during collaborative deliberation.
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)
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
