The aim of the study on which this article reports was to identify parents' approaches to their children's remote education during the COVID-19 pandemic in April and May 2020. Additionally, this investigation sought to determine the role of parent perceptions of the barriers and benefits of remote education. The research draws on a survey of 421 parents of primary school students, in which a 66-item questionnaire (4 subscales) was used. Analysis revealed three main clusters that represent approaches adopted by parents: (1) the committed teacher approach, (2) the autonomy-supporting coach, and (3) the committed teacher and intervener. The parents in cluster 3 emphasised perceived barriers to remote learning more than parents in clusters 1 and 2. Regarding perceptions of the benefits, statistically significant differences were found in perceptions of child development facilitated by remote education (the parents in cluster 2 rated it most positively). The results can be used to support parents and schools in the provision of optimal remote learning.
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http://dx.doi.org/10.1111/ejed.12474 | DOI Listing |
NPJ Digit Med
January 2025
Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, M13 9PL, UK.
There is increasing use of digital tools to monitor people with psychosis and schizophrenia remotely, but using this type of data is challenging. This systematic review aimed to summarise how studies processed and analysed data collected through digital devices. In total, 203 articles collecting passive data through smartphones or wearable devices, from participants with psychosis or schizophrenia were included in the review.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
As a multivariate time series, the prediction of curling trajectories is crucial for athletes to devise game strategies. However, the wide prediction range and complex data correlations present significant challenges to this task. This paper puts forward an innovative deep learning approach, CasLSTM, by introducing integrated inter-layer memory, and establishes an encoder-predictor curling trajectory forecasting model accordingly.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
Interv Pain Med
March 2025
Department of Anesthesiology, Perioperative, and Pain Medicine, Weill Cornell Medicine, New York, NY, USA.
•: The AI-assisted VR module enables learners to engage in a 360-degree immersive environment, manipulating holographic anatomy models and simulating fluoroscopic guidance to perform the Gasserian ganglion block.•: Key anatomical landmarks, like the foramen ovale, are highlighted, and proper C-arm positioning is demonstrated, helping practitioners localize the target area for needle advancement.•: The module includes AI-driven multi-language options and AI-generated multiple-choice questions to enhance learning and retention.
View Article and Find Full Text PDFPlant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
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