Learning analytics can help higher educational institutions improve learning. Its adoption, however, is a complex undertaking. The Learning Analytics Capability Model describes what 34 organizational capabilities must be developed to support the successful adoption of learning analytics. This paper described the first iteration to evaluate and refine the current, theoretical model. During a case study, we conducted four semi-structured interviews and collected (internal) documentation at a Dutch university that is mature in the use of student data to improve learning. Based on the empirical data, we merged seven capabilities, renamed three capabilities, and improved the definitions of all others. Six capabilities absent in extant learning analytics models are present at the case organization, implying that they are important to learning analytics adoption. As a result, the new, refined Learning Analytics Capability Model comprises 31 capabilities. Finally, some challenges were identified, showing that even mature organizations still have issues to overcome.
The instrument called Families Importance in Nursing Care-Nurses' Attitudes (FINC-NA) is used to measure nurses' attitudes toward involving families in their nursing care. The aim of this study is to evaluate the FINC-NA scale in a population of Dutch nurses and add new psychometric information to existing knowledge about this instrument. Using a cross-sectional design, 1,211 nurses received an online application in 2015. Psychometric properties were based on polychoric correlations and the Generalized Partial Credit Model. A total of 597 (49%) nurses responded to the online application. Results confirmed a four-subscale structure. All response categories were utilized, although some ceiling effects occurred. Most items increase monotonically, and the majority of items discriminate well between different latent trait scores of nurses with some items providing more information than others. This study reports the psychometric properties of the Dutch language FINC-NA instrument. New insights into the construct and content of items enable the possibility of a more generic instrument that could be valid across several cultures.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
In order to achieve much-needed transitions in energy and health, systemic changes are required that are firmly based on the principles of regard for others and community values, while at the same time operating in market conditions. Social entrepreneurship and community entrepreneurship (SCE) hold the promise to catalyze such transitions, as they combine bottom-up social initiatives with a focus on financially viable business models. SCE requires a facilitating ecosystem in order to be able to fully realize its potential. As yet it is unclear in which way the entrepreneurial ecosystem for social and community entrepreneurship facilitates or hinders the flourishing and scaling of such entrepreneurship. It is also unclear how exactly entrepreneurs and stakeholders influence their ecosystem to become more facilitative. This research programme addresses these questions. Conceptually it integrates entrepreneurial ecosystem frameworks with upcoming theories on civic wealth creation, collaborative governance, participative learning and collective action frameworks.This multidisciplinary research project capitalizes on a unique consortium: the Dutch City Deal ‘Impact Ondernemen’. In this collaborative research, we enhance and expand current data collection efforts and adopt a living-lab setting centered on nine local and regional cases for collaborative learning through experimenting with innovative financial and business models. We develop meaningful, participatory design and evaluation methods and state-of-the-art digital tools to increase the effectiveness of impact measurement and management. Educational modules for professionals are developed to boost the abovementioned transition. The project’s learnings on mechanisms and processes can easily be adapted and translated to a broad range of impact areas.
The valorization of biowaste, by exploiting side stream compounds as feedstock for the sustainable production of bio-based materials, is a key step towards a more circular economy. In this regard, chitin is as an abundant resource which is accessible as a waste compound of the seafood industry. From a commercial perspective, chitin is chemically converted into chitosan, which has multiple industrial applications. Although the potential of chitin has long been established, the majority of seafood waste containing chitin is still left unused. In addition, current processes which convert chitin into chitosan are sub-optimal and have a significant impact on the environment. As a result, there is a need for the development of innovative methods producing bio-based products from chitin. This project wants to contribute to these challenges by performing a feasibility study which demonstrates the microbial bioconversion of chitin to polyhydroxyalkanoates (PHAs). Specifically, the consortium will attempt to cultivate and engineer a recently discovered bacterium Chi5, so that it becomes able to directly produce PHAs from chitin present in solid shrimp shell waste. If successful, this project will provide a proof-of-concept for a versatile microbial production platform which can contribute to: i) the valorization of biowaste from the seafood industry, ii) the efficient utilization of chitin as feedstock, iii) the sustainable and (potentially low-cost) production of PHAs. The project consortium is composed of: i) Van Belzen B.V., a Dutch shrimp trading company which are highly interested in the valorization of their waste streams, hereby making their business model more profitable and sustainable. ii) AMIBM, which have recently isolated and characterized the Chi5 marine-based chitinolytic bacterium and iii) Zuyd, which will link aforementioned partners with students in creating a novel collaboration which will stimulate the development of students and the translation of academic knowledge to a feasible application technology for SME’s.
