BackgroundThe world’s population is aging, and with aging population comes an increase of chronic diseases and multimorbidity. At the same time a shortfall of trained health care professionals is anticipated. This raises questions on how to provide the best possible care. The use of Information and communication technology (ICT) and e-health has the potential to address the challenges that healthcare is facing. ICT applications and e-health, such as videophones, telemedicine and mobile devices, can benefit the healthcare system. Nonetheless, ICT is not used to its full potential. One of the key factors is the low adoption rate by nursing professionals. The nursing profession is characterized by teamwork and interdisciplinary collaboration. Nurses often work in nursing teams and collaboration between different disciplines is necessary for providing health care. Thus, collaboration is necessary when implementing ICT innovations.MethodsA systematic literature review was conducted in online databases PubMEd, CINAHL and IEEE, using key words related to innovation, nursing teams and adoption.ResultsThe result of the systematic review is that little is known about the relation between ICT adoption by nurses and the nature of collaboration by nurses in teams and in interdisciplinary networks. This leads to further research questions and a need for further research in this subject.
Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
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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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Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
Flying insects like dragonflies, flies, bumblebees are able to couple hovering ability with the ability for a quick transition to forward flight. Therefore, they inspire us to investigate the application of swarms of flapping-wing mini-drones in horticulture. The production and trading of agricultural/horticultural goods account for the 9% of the Dutch gross domestic product. A significant part of the horticultural products are grown in greenhouses whose extension is becoming larger year by year. Swarms of bio-inspired mini-drones can be used in applications such as monitoring and control: the analysis of the data collected enables the greenhouse growers to achieve the optimal conditions for the plants health and thus a high productivity. Moreover, the bio-inspired mini-drones can detect eventual pest onset at plant level that leads to a strong reduction of chemicals utilization and an improvement of the food quality. The realization of these mini-drones is a multidisciplinary challenge as it requires a cross-domain collaboration between biologists, entomologists and engineers with expertise in robotics, mechanics, aerodynamics, electronics, etc. Moreover a co-creation based collaboration will be established with all the stakeholders involved. With this approach we can integrate technical and social-economic aspects and facilitate the adoption of this new technology that will make the Dutch horticulture industry more resilient and sustainable.
Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
