Parents' views of natural learning environments were compared to those of practitioners having either considerable or little experience with the characteristics of everyday natural learning opportunities. 8 experienced practitioners' views were congruent with those of the parents, whereas the 8 inexperienced practitioners' views were incongruent with those of both parents and their experienced peers.

Download full-text PDF

Source
http://dx.doi.org/10.2466/pr0.94.1.251-256DOI Listing

Publication Analysis

Top Keywords

natural learning
12
everyday natural
8
learning environments
8
practitioners' views
8
parents' practitioners'
4
practitioners' perspectives
4
perspectives young
4
young children's
4
children's everyday
4
environments parents'
4

Similar Publications

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures.

View Article and Find Full Text PDF

Medical Visual Question Answering aims to assist doctors in decision-making when answering clinical questions regarding radiology images. Nevertheless, current models learn cross-modal representations through residing vision and text encoders in dual separate spaces, which inevitably leads to indirect semantic alignment. In this paper, we propose UnICLAM, a Unified and Interpretable Medical-VQA model through Contrastive Representation Learning with Adversarial Masking.

View Article and Find Full Text PDF

While radiation hazards induced by cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) can be reduced by sparse-view sampling, the image quality is inevitably degraded. We propose a deep learning-based multi-view projection synthesis (DLMPS) approach to improve the quality of sparse-view low-dose CBCT images. In the proposed DLMPS approach, linear interpolation was first applied to sparse-view projections and the projections were rearranged into sinograms; these sinograms were processed with a sinogram restoration model and then rearranged back into projections.

View Article and Find Full Text PDF

Swin-transformer for weak feature matching.

Sci Rep

January 2025

Department of Computer Science and Technology, Qilu University of Technology, No. 3501 Daxue Road, Jinan, 250300, Shandong, China.

Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes. Given the inherently significant local characteristics of image features, we employ a local self-attention mechanism to learn from weakly textured areas, maximally preserving the features of weak textures.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!