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Developing a digital psychosocial support program for men with low-risk prostate cancer during active surveillance
No full textIeder Kind Alle Kansen: Interprofessioneel Samenwerken in de Pedagogisch-Educatieve Sector
No full textMet dit rondetafelgesprek wordt een vierjarig onderzoek naar interprofessionele samenwerking in de pedagogisch-educatieve sector afgetrapt. Zowel de manier waarop (aanstaande) professionals ondersteund kunnen worden bij het interprofessioneel samenwerken als de manier waarop zij op hun beurt kinderen kunnen ondersteunen met een inclusief aanbod worden onderzocht
Determinants of Heavy Episodic Drinking Among University Students, an Application of the I - Change Model
No full textA systematic mapping review at the intersection of artificial intelligence and self-regulated learning
No full textRecently, artificial intelligence (AI) has increasingly been integrated into self-regulated learning (SRL), presenting novel pathways to support SRL. While AI-SRL research has experienced rapid growth, there remains a significant gap in understanding the intersection between AI and SRL, resulting in oversight when identifying critical areas necessitating additional research or practical attention. Building upon a well-established framework, from Chatti and colleagues, this systematic mapping review identified 84 studies through the Web of Science, Scopus, IEEE Xplore, ACM Digital, EBSCOHost, Google Scholar, and Open Alex, to explore the intersection of AI and SRL within the four key aspects—Who (stakeholders), What (theory), How (methods), and Why (objectives). The main results revealed that AI-SRL research predominantly focuses on higher education students, with minimal attention to primary education and educators. AI is primarily implemented as an intervention—through adaptive systems and personalization, prediction and profiling, intelligent tutoring systems, and assessment and evaluation—to support students' SRL and learning processes. The direct impact of AI on SRL was primarily focused on the metacognitive and cognitive aspects of SRL, while the motivational aspect of SRL remains underexplored. While over one-third of the AI-SRL studies did not specify an SRL theory, Zimmerman’s model of SRL was the most frequently applied among those that did. The use of AI in supporting SRL has extended beyond just focusing on and supporting SRL itself; it has also aimed to enhance various educational and learning activities as end outcomes such as improving academic performance, motivation and emotions, engagement, and collaborative learning. The results of this study extend our understanding of the effective application of AI in supporting SRL and optimizing educational outcomes. Suggestions for further research and practice are provided
Naar een Europese Unie die beter bij de burger past
No full textDe EU moet meer rekening houden met de zeggenschap van nationale parlementen’, is een veelgehoord standpunt. Dit boek draagt bij aan het debat hierover. Is een grotere rol voor het nationale parlement noodzakelijk om burgers meer te betrekken bij de Europese politiek? Dit betreft het attributiebeginsel, het subsidiariteits- en proportionaliteitsbeginsel. Is er sprake van competence creep van de EC en is er behoefte aan een ‘nationale kop’ op Europese richtlijnen? Het draagvlak voor Europa wordt er wel door beïnvloed. Door groeiende onenigheid binnen Europa is er een honger naar Europese variaties voor draagvlak. Er wordt een nieuwe manier van werken aan de orde gesteld met versterking van subsidiariteit en proportionaliteit voor betere regelgeving en er wordt aandacht besteed aan het wetsvoorstel voor een Europawet. Wat is de eurokloof en hoe kan die worden beslecht? Hoe bruggen te bouwen tussen de burger en Europa? Dit onderzoek probeert die vraag te beantwoorden. Lisanne Henneveld studeerde Nederlands recht aan de Universiteit Utrecht en Europees- en internationaal recht aan de Vrije Universiteit Amsterdam. Zij breidde haar onderzoek voor deze laatste studie naar de Mediationrichtlijn uit vanwege discussies in het Nederlandse parlement over subsidiariteit en proportionaliteit en over het draagvlak voor Europa. Dit leidde tot een promotieonderzoek aan de Open Universiteit te Heerlen
Living Group Climate and Treatment Motivation in Adolescent Girls in Youth Detention - The Importance of Gender Specific Treatment
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Quantification and mapping of tyre wear emissions:from EU regional analysis to global projections
No full textTyre wear particle (TWP) emissions are a major source of microplastic pollution. This study analyzes the variability in national TWP emissions estimated through different methodologies and provides guidance for optimizing these estimations. Findings show substantial discrepancies in per capita emissions across European countries, with variations up to 2 kg/y*cap, particularly between Southern and Eastern European countries, as well as Estonia and Finland. In contrast, Western European countries exhibit minimal variation despite diverse methodologies. We predicted annual TWP loads reaching environmental compartments such as air, soil, sewers, and surface waters. Germany, France, and Italy were identified as major emitters, each exceeding 100,000 tons annually. Notably, only a small fraction of these emissions, approximately 13,000 tons per year, reaches surface waters due to varying efficiencies of wastewater treatment facilities. Based on the country-specific emission estimates, we developed predictive models for emission estimation based on socioeconomic variables such as Gross Domestic Product. While a simplified Ordinary Least Squares (OLS) model identified key linear drivers, a more advanced Random Forest (RF) model demonstrated significantly higher predictive accuracy (LOOCV R2 > 0.7 for major vehicle types), revealing the complex, non-linear nature of emission drivers. This dual-modeling approach provides a robust framework for global projections, highlighting Luxembourg and the United States as leaders in per capita emissions (3.56 and 3.12 kg/cap*y, respectively), while China and India exhibit the highest total emissions. Our predictive models reduce data requirements and facilitate a preliminary, data-driven estimation of the global distribution of TWP emissions