A study of the improvement of the quality of student teachers’ lessons in interactive (story)book reading through the use of data-feedback on observed lessons. Variables regarding the optimal time use, the quality of instruction and the student teachers’ pedagogical relation with pupils were included in a one group pre-test post-test design.
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Dit project poogt een bijdrage te leveren aan het versterken van “de kennisketen van de gastvrijheidseconomie” middels de volgende projectdoelstellingen: • SWOT-analyse van huidige situatie, vanuit verschillende stakeholderperspectieven: kijkend vanuit de ontwikkelopgaves die men ziet, aan welke data over de customer journey is behoefte (inventarisatie)? Wat zijn de bijbehorende sterktes, zwaktes, kansen en bedreigingen (analyse)? • Versterken van de kennisketen via: hoe kunnen we kennisketen versterken met nieuwe technieken en door slim organiseren? • Een overzicht van strategische opties: welke strategische opties zijn er om 1.) sterktes te benutten om kansen te pakken en bedreigingen af te wenden en 2.) zwaktes op te lossen door kansen te pakken en gevaren te voorkomen die met bedreigingen meekomen • Input leveren voor 2.0 versie van het manifest van Gastvrij Overijssel en de beoogde oprichting van een “Data Hub” (waarvoor nog geen officiële werktitel) In de opvolgende hoofdstukken en paragrafen gaan we in op de aanpak (hoofdstuk 2) en de uitkomsten (hoofdstuk 3).
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Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.
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During the COVID-19 pandemic, the bidirectional relationship between policy and data reliability has been a challenge for researchers of the local municipal health services. Policy decisions on population specific test locations and selective registration of negative test results led to population differences in data quality. This hampered the calculation of reliable population specific infection rates needed to develop proper data driven public health policy. https://doi.org/10.1007/s12508-023-00377-y
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Rook is playful data visualization device aiming at the dissemination of air quality data. The Rook unit accesses the database of the Hollandse Luchten (henceforth HL) project, which collects data on air quality in Nord-Holland via a network of sensor kits distributed to the local population and displays it in an interactive way using lights and mist. The eerie and mysterious effect of the colored mist aims at a playful and enticing effect on its users, while, at the same time, spreading awareness on one of the key sustainability issues of the region. People can interact freely with the machine so to explore its working and the data it conveys.This paper aims to contextualize and describe the design process behind the creation of Rook and to propose some reflections on playfulness, citizenship and data visualization.
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The Technical Manual for the digital evaluation tool QualiTePE supports users of the QualiTePE tool in creating, conducting and analysing evaluations to record the quality of teaching in physical education. The information on the General Data Protection Regulation (GDPR) instructs users on how to anonymise the data collection of evaluations and which legal bases apply with regard to the collection of personal data. The technical manual for the digital evaluation tool QualiTePE and the information on the General Data Protection Regulation (GDPR) are available in English, German, French, Italian, Spanish, Dutch, Swedish, Slovenian, Czech and Greek.
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from the article: Abstract Based on a review of recent literature, this paper addresses the question of how urban planners can steer urban environmental quality, given the fact that it is multidimensional in character, is assessed largely in subjective terms and varies across time. The paper explores three questions that are at the core of planning and designing cities: ‘quality of what?’, ‘quality for whom?’ and ‘quality at what time?’ and illustrates the dilemmas that urban planners face in answering these questions. The three questions provide a novel framework that offers urban planners perspectives for action in finding their way out of the dilemmas identified. Rather than further detailing the exact nature of urban quality, these perspectives call for an approach to urban planning that is integrated, participative and adaptive. ; ; sustainable urban development; trade-offs; quality dimensions
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In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.
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Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.
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