Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Online learning is prevalent among nursing students, but the effect of online learning seems not as good as expected. Deep learning, as a learning approach that could help people solve complex problems and make innovative decisions, is associated with individual behavior and psychology. However, from the perspective of individual behavior and psychology to explore the potential influence mechanism of deep learning in online courses is little, in China or indeed internationally.
Objectives: The purpose of this study is to explore the relationship between online learning engagement, academic self-concept and deep learning in online courses for Chinese nursing students, and the mediating effect of academic self-concept on the relationship between online learning engagement and deep learning in online courses of Chinese nursing students.
Design: A cross-sectional electronic survey.
Settings And Participants: The study was conducted using a convenience sample of 617 nursing students in five schools in eastern, central, and western China from September 2021 to October 2021 (the number of eligible students in the five schools was 2065).
Methods: The data were collected with the College students' learning engagement scale in cyberspace, Academic self-concept scale and Deep learning scale in online courses, and analyzed by correlation analysis, univariate analysis, multiple linear regression and PROCESS macro.
Results: 594 valid questionnaires were collected (effective response rate: 96.2 %). High online learning engagement and high academic self-concept were correlated with a high level of deep learning in online courses (correlation coefficient: 0.731 to 0.800). Part of the influence of online learning engagement on deep learning in online courses was mediated by academic self-concept, and the indirect effect accounts for 39.75 % of the total effect.
Conclusions: Chinese nursing students' online learning engagement may partially influence deep learning in online courses through academic self-concept.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.nedt.2022.105481 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multimodal self-supervised retinal vessel segmentation.
Neural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFSelf-supervised representation learning with continuous training data improves the feel and performance of myoelectric control.
Comput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFAutomated Literature Screening for Hepatocellular Carcinoma Treatment Through Integration of 3 Large Language Models: Methodological Study.
JMIR Med Inform
September 2025
Department of Hepatobiliary and Vascular Surgery, First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background: Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.
Objective: This study proposes an automated literature screening workflow powered by large language models (LLMs) to accelerate evidence synthesis for HCC treatment guidelines.
Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification.
PLoS One
September 2025
Department of Information Technology, Uppsala University, Uppsala, Sweden.
For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of single-cell bacterial growth collected from microfluidic chip traps, also known as a "mother machine".
View Article and Find Full Text PDFImprovement of mask R-CNN and deep learning for defect detection and segmentation in electronic products.
PLoS One
September 2025
Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products.
View Article and Find Full Text PDF