Zoekresultaten

Evaluating living labs?

Living labs are complex multi-stakeholder collaborations that often employ a usercentred and design-driven methodology to foster innovation. Conventional management tools fall short in evaluating them. However, some methods and tools dedicated to living labs' special characteristics and goals have already been developed. Most of them are still in their testing phase. Those tools are not easily accessible and can only be found in extensive research reports, which are difficult to dissect. Therefore, this paper reviews seven evaluation methods and tools specially developed for living labs. Each section of this paper is structured in the following manner: tool’s introduction (1), who uses the tool (2), and how it should be used (3). While the first set of tools, namely “ENoLL 20 Indicators”, “SISCODE Self-assessment”, and “SCIROCCO Exchange Tool” assess a living lab as an organisation and are diving deeper into the organisational activities and the complex context, the second set of methods and tools, “FormIT” and “Living Lab Markers”, evaluate living labs’ methodologies: the process they use to come to innovations. The paper's final section presents “CheRRIes Monitoring and Evaluation Tool” and “TALIA Indicator for Benchmarking Service for Regions”, which assess the regional impact made by living labs. As every living lab is different regarding its maturity (as an organisation and in its methodology) and the scope of impact it wants to make, the most crucial decision when evaluating is to determine the focus of the assessment. This overview allows for a first orientation on worked-out methods and on possible indicators to use. It also concludes that the existing tools are quite managerial in their method and aesthetics and calls for designers and social scientists to develop more playful, engaging and (possibly) learning-oriented tools to evaluate living labs in the future. LinkedIn: https://www.linkedin.com/in/overdiek12345/ https://www.linkedin.com/in/mari-genova-17a727196/?originalSubdomain=nl

ICT research methods for machine learning engineering

The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.

The Animal Welfare Consequences and Moral Implications of Lethal and Non-Lethal Fox Control Methods

Control methods are applied worldwide to reduce predation on livestock by European red foxes (Vulpes vulpes). Lethal methods can inflict suffering; however, moral debate about their use is lacking. Non-lethal methods can also inflict suffering and can unintentionally lead to death, and yet both the welfare consequences and ethical perspectives regarding their use are rarely discussed. The aim of this study was to investigate the animal welfare consequences, the level of humaneness, the ethical considerations and the moral implications of the global use of fox control methods according to Tom Regan’s animal rights view and Peter Singer’s utilitarian view. According to Regan, foxes ought not to be controlled by either lethal or potentially harmful non-lethal methods because this violates the right of foxes not to be harmed or killed. According to Singer, if an action maximises happiness or the satisfaction of preferences over unhappiness or suffering, then the action is justified. Therefore, if and only if the use of fox control methods can prevent suffering and death in livestock in a manner that outweighs comparable suffering and death in foxes is one morally obligated t

Methods against methods

We De Try

We De Try: Creative Methods of Storylistening and Storytelling tells the story of a research journey the author underwent in trying to gain a better understanding of diversity and inclusion in higher education. Through insights generated by the exploration of methods of storytelling and storylistening, the research problematises using stories as tools for diversity and inclusion in higher educational institutions. In order to be transformed by that which emerges (storylistening) in a manner that is non-extractive, avoids reinforcing existing power imbalances, and recognises that stories transcend time and space asks for exploring questions relating to practicing care, generosity, and honouring and catering to different needs.

Stimulating students’ academic language

Mastering academic language (AL) by elementary school students is important for achieving school success. The extent to which teachers play a role in stimulating students’ AL development may differ. Two types of AL stimulating behavior are distinguished: aimed at students’ understanding and at triggering students’ production of AL. As mathematics requires abstract language use, AL occurs frequently. The instructional methods teachers use during mathematics instruction may offer different opportunities for AL stimulating behavior. In our first study, based on expert opinions, instructional methods were categorized according to opportunities they offer for stimulating students’ AL development. In the second study, video-observations of mathematics instruction of elementary school teachers were analyzed with respect to AL stimulating behavior and instructional methods used. Results showed that actual AL stimulating behavior of teachers corresponds to the expert opinions, except for behavior shown during task evaluation. Teachers differ in time and frequency of their use of instructional methods and therefore in opportunities for stimulating AL development. Four teaching profiles, reflecting different AL stimulating potential, were constructed: ‘teacher talking’, ‘balanced use of methods’, ‘getting students at work’ and ‘interactive teaching’. Teachers showed more types of behavior aimed at students’ AL understanding than at production.