This study examined absolute and relative judgment accuracies of German early childhood (EC) teachers with respect to the mathematical skills of the children under their supervision. The two types of judgment accuracies are crucial prerequisites for pacing activities in EC education and offering differentiated educational activities adapted to individual skill levels of children. Data from 39 EC teachers and 268 children were analyzed using multilevel modeling. Teachers rated the skills of children on a structured observation instrument ("Kinder Diagnose Tool," KiDiT). Children were assessed on their mathematical skills with a standardized test ("Mathematische Basiskompetenzen im Kindesalter," MBK-0). On average, 65% of the variation in judgments of teachers on the KiDiT could be explained by MBK-0 scores of children, which suggest that teachers are-on average-able to rank children within their groups. Teachers were also able to judge the mathematical level of skills of children as assessed by the MBK-0. Neither mathematical content knowledge (MCK) of teachers nor their mathematics pedagogical content knowledge (MPCK) or general pedagogical knowledge (GPK) moderated the relationship between judgments of teachers and test scores of children or the relationship between the level of the judgments and the level of test scores. Conclusions for future research and practice are drawn.
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http://dx.doi.org/10.3389/fpsyg.2021.701730 | DOI Listing |
J Intellect Disabil
January 2025
Pro Vice Chancellor, Staffordshire University, UK.
Background: Autism spectrum disorder poses challenges in social communication and behavior, while Intellectual disabilities are characterized by deficits in cognitive, social, and adaptive skills, frequently accompanied by stereotypies and challenging behaviors. Despite the progress made in autism spectrum disorder research, there is often a lack of research focusing on individuals with co-occurring autism spectrum disorder and intellectual disability. Robot-assisted autism therapies are effective in addressing these needs.
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December 2024
Psychology, University of Bath, Bath, GBR.
Artificial intelligence (AI) and mixed reality (MR), within human-computer interaction (HCI), are rapidly redefining areas of healthcare by introducing new approaches to patient care and clinical education. This editorial explores how these technologies, through Extended Mind Theory, enhance mental health treatment and medical training. AI-powered virtual therapists, using natural language processing and predictive analytics, provide accessible, personalized mental health support, allowing for remote and immersive therapy.
View Article and Find Full Text PDFLight Sci Appl
January 2025
State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing, 100871, China.
Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. Here, we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks, including electromagnetic (EM) field manipulations and interactions with robots and humans.
View Article and Find Full Text PDFCPT Pharmacometrics Syst Pharmacol
December 2024
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Patients with recurrent high-grade glioma (rHGG) have a poor prognosis with median progression-free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow-up imaging studies to adaptively predict the risk of progression.
View Article and Find Full Text PDFFront Psychiatry
December 2024
Departamento de Personalidad, Evaluación y Tratamiento Psicológicos, Universidad de Salamanca, Salamanca, Spain.
Introduction: It is crucial to provide a quality educational response to the needs of autistic children across various mathematical domains. However, there is no consensus on which of the early skills have the greatest predictive effect in the short and long term within these domains. Therefore, this research aimed to a) compare early numerical skills and mathematics domains, and 2) analyze the predictive value of early numerical skills into mathematics domains.
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