Two groups of adolescents with learning difficulties in mathematics were compared on their ability to generate solutions to a contextualized problem after being taught problem-solving skills under two conditions, one involving standard word problems, the other involving a contextualized problem on videodisc. All problems focused on adding and subtracting fractions in relation to money and linear measurement. Both groups of students improved their performance on solving word problems, but students in the contextualized problem group did significantly better on the contextualized problem posttest and were able to use their skills in two transfer tasks that followed instruction.
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http://dx.doi.org/10.1177/001440299305900608 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Public Health Policy and Systems, University of Liverpool, Brownlow Street, Liverpool, L69 3GF, UK.
Background: Adversity in childhood is increasing in the United Kingdom. Complex health and social problems affecting children cluster in families where adults also have high need, but services are rarely aligned to support the whole family. Household level segmentation can help identify households most needing integrated support.
View Article and Find Full Text PDFComput Biol Med
January 2025
College of Electronic Information, Xijing University, Xi'an, China. Electronic address:
Accurate and efficient drug-drug interaction extraction (DDIE) from the medical corpus is essential for pharmacovigilance, drug therapy and drug development. To solve the problems of unbalance dataset and lack of accurate manual annotations in DDIE, a cross-attention guided Siamese quantum BiGRU (CA-SQBG) is constructed to improve feature representation learning ability for DDIE. It mainly consists of two quantum BiGRUs (QBiGRUs) and a cross-attention, where two QBiGRUs are Siamese implemented in a variational quantum environment to learn the contextual semantic feature representation of drug pairs, cross-attention is employed to learn mutual information from the Siamese QBiGRUs, which in turn allows the two modules to extract DDI more collaboratively.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Electronic and Information Engineering, Ankang University, Ankang 725000, China.
Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, Rhode Island, United States of America.
Negotiating social dynamics among allies and enemies is a complex problem that often requires individuals to tailor their behavioral approach to a specific situation based on environmental and/or social factors. One way to make these contextual adjustments is by arranging behavioral output into intentional patterns. Yet, few studies explore how behavioral patterns vary across a wide range of contexts, or how allies might interlace their behavior to produce a coordinated response.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Suzhou Key Lab of Multi-modal Data Fusion and Intelligent Healthcare, No. 1188 Wuzhong Avenue, Wuzhong District Suzhou, Suzhou 215004, China.
The automatic and accurate extraction of diverse biomedical relations from literature constitutes the core elements of medical knowledge graphs, which are indispensable for healthcare artificial intelligence. Currently, fine-tuning through stacking various neural networks on pre-trained language models (PLMs) represents a common framework for end-to-end resolution of the biomedical relation extraction (RE) problem. Nevertheless, sequence-based PLMs, to a certain extent, fail to fully exploit the connections between semantics and the topological features formed by these connections.
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