Unlabelled: COVID-19 pandemic has affected the entire world in many ways. It has sparked a prominent pedagogical shift for university level students, as it has changed the way students learn, attend classes, or communicate with teachers. Globally, every student is forced to adopt Emergency Remote Learning (ERL) as a result of immediate transformation of physical classes into remote education. This two-fold study investigated the differences between traditional distance, online, and virtual learning solutions and the new Emergency Remote Learning (ERL) method for the university level education. Furthermore, a pragmatic mix-method study is conducted in the form of surveys, semi-structured interviews, and diary study spanning across 10 months of pandemic, to examine self-reported insights on ERL challenges, experiences, and learning engagement of the students from Finland and India. Cumulative findings suggest that scheduling, distractions, pessimistic emotions, longer durations, and concentration were the highest challenges faced by the students which impacted their learning experiences and engagement. The study also found that the ERL specific factors like low-interactivity, technical limitations, non-structured, and non-standardized methods had a prominent impact on the effectiveness of remote education. Furthermore, the study has suggested guidelines for improving remote learning experience as a futuristic solution beyond COVID-19 pandemic.
Supplementary Information: The online version contains supplementary material available at 10.1007/s10639-021-10747-1.
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http://dx.doi.org/10.1007/s10639-021-10747-1 | 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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