Background: The nationwide coronavirus (COVID-19) pandemic and ensuing lockdown has enforced institutions crosswise India to provisionally close to inhibit the spread of the virus and started online learning for students. To measure the level of satisfaction of nursing students with online learning and to identify the barriers which restrict to online learning.
Materials And Methods: The current study adopted quantitative research approach with an online survey research design and carried out during May-June 2020. Participants were selected through a web-based survey (Google form), in which 219 students enrolled. Self-structured questionnaire with the Likert scale was used to measure the level of satisfaction of nursing students with online learning and identify the barriers which restrict online learning. The descriptive and inferential statistics were used for the analysis in which 219 participants were enrolled in the study of data with IBM SPSS version 20.
Results: Majority of student's participants 148 (67.57%) were extremely satisfied with online learning. The findings suggest that the highest barriers which restrict to online learning among nursing students is low voice and language clarity (2.16 ± 0.593), physical health barriers such as eye strain (2.43 ± 0.613), reliability and connectivity problem (2.26 ± 0.534). Among all demographic data, age is significantly associated with the level of satisfaction of online learning.
Conclusions: The study data indicated that maximum students were extremely satisfied the with online learning and among barriers which effect online learning is low voice and language clarity, reliability and connectivity problem, physical health barriers such as eye strain.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719575 | PMC |
http://dx.doi.org/10.4103/jehp.jehp_1221_20 | DOI Listing |
Bioinformatics
January 2025
Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, 1004, Philippines.
Motivation: Recent computational approaches for predicting phage-host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity.
Results: We present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera.
Bioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.
Chem Biodivers
January 2025
Department of Horticultural Science, Faculty of Agriculture, Jahrom University, Jahrom, Iran.
The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root.
View Article and Find Full Text PDFJ Exp Psychol Gen
January 2025
Centre for Perception and Cognition, School of Psychology, University of Southampton.
It has been claimed that deliberately making errors while studying, even when the correct answers are provided, can enhance memory for the correct answers, a phenomenon termed the derring effect. Such deliberate erring has been shown to outperform other learning techniques, including copying and underlining, elaborative studying with concept mapping, and synonym generation. To date, however, the derring effect has only been demonstrated by a single group of researchers and in a single population of participants.
View Article and Find Full Text PDFMedEdPublish (2016)
January 2025
Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Background: According to the Association of American Medical Colleges (AAMC) Year 2 questionnaire, the percentage of students using online medical education videos (Boards and Beyond®Sketchy Medical®, Youtube) at least once per week increased from 47.7% (2015) to 70.1% (2022).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!