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Children's mathematics skills undergo extensive development during the preschool years. Opportunities for engaging in mathematics in the preschool classroom, however, are limited, and activities and instruction are often targeted below children's skill levels. Although researchers have developed general learning trajectories of children's mathematics skills, no fine-grained trajectories across a broad range of mathematics skills exist. Such a fine-grained trajectory of when specific numeracy skills develop would allow preschool and kindergarten instruction to more appropriately match and scaffold children's mathematics capabilities. The current study examined preschool children's item-level performance on eight numeracy subtests at half-year age points throughout the preschool period. Data were compiled across six studies, and participants comprised 801 preschool children (age range = 3.12-5.99 years, M = 4.63, SD = 0.68). Children were grouped into six age categories (3, 3.5, 4, 4.5, 5, and 5.5 years). Linear regression analyses were used to investigate the number of children who correctly answered each item of a specific subtest within a particular age group. Findings provide clear trajectories of children's early mathematics skills that can be used to inform preschool classroom practices and facilitate the design of intervention studies.
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http://dx.doi.org/10.1016/j.jecp.2020.104846 | DOI Listing |
Chaos
December 2024
Department of Electrical and Computer Engineering, the Clarkson Center for Complex Systems Science, Clarkson University, Potsdam, New York 13699, USA.
Artificial Neural Networks (ANNs) have proven to be fantastic at a wide range of machine learning tasks, and they have certainly come into their own in all sorts of technologies that are widely consumed today in society as a whole. A basic task of machine learning that neural networks are well suited to is supervised learning, including when learning orbits from time samples of dynamical systems. The usual construct in ANN is to fully train all of the perhaps many millions of parameters that define the network architecture.
View Article and Find Full Text PDFEnviron Geochem Health
December 2024
Department of Chemistry, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Cocoa-growing areas in Ghana have experienced a rise in mining activities affecting cocoa cultivation and increased concentrations of potentially toxic metals in the soil, which can accumulate in cocoa beans. This study evaluated potential toxic metal contamination in cocoa beans and soils from cocoa farms in mining and non-mining areas in Ghana. We used X-ray fluorescence and an ICP-MS to determine metal concentrations, and a Zeeman mercury analyzer to determine mercury.
View Article and Find Full Text PDFMed Sci Monit
December 2024
Department of Paediatric Intensive Care, Emma Children's Hospital, Amsterdam UMC, Meibergdreef, Netherlands.
BACKGROUND Patient monitoring systems (PMSs) are essential for monitoring and managing the condition of critically ill patients. In low-resource settings, limited access to technology, low-level digital literacy, and power outage challenges are usability concerns. The main aim of this study was to evaluate the usability of the IMPALA (Innovative Monitoring in Paediatrics in Low-resource settings: an Aid to save lives) PMS optimized for use in low-resource settings by assessing the opinions and experiences of 24 healthcare professionals.
View Article and Find Full Text PDFJ Sch Psychol
February 2025
Department of Education and Human Services, Lehigh University, United States.
Within multi-tiered systems of support (MTSS) practice and research, students' need for academic intervention is often determined by comparison of students' screening scores to cut scores. We examined the degree to which the relationship between students' fall screening data (i.e.
View Article and Find Full Text PDFNPJ Digit Med
December 2024
Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Formative verbal feedback during live surgery is essential for adjusting trainee behavior and accelerating skill acquisition. Despite its importance, understanding optimal feedback is challenging due to the difficulty of capturing and categorizing feedback at scale. We propose a Human-AI Collaborative Refinement Process that uses unsupervised machine learning (Topic Modeling) with human refinement to discover feedback categories from surgical transcripts.
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