Background: Research findings have revealed that some students are not prepared to face the challenges associated with online learning, and attrition rates have been found to be a major concern. This review aimed to investigate the learning characteristics nursing students need before enrolling in online learning to ensure success.
Method: Electronic databases were searched to investigate the learning characteristics of students for the literature review.
Results: The search identified a final selection of 24 quantitative and qualitative studies. Readiness to learn, self-efficacy, motivation, persistence, and autonomy are learning characteristics for students to possess when enrolling in online learning environments. However, not all students have these learning characteristics; thus, their learning needs should be assessed.
Conclusion: More mixed-methods and randomized control trial studies are needed to examine online students' learning characteristics. Nurse educators must evaluate their students to investigate whether online learning fits their learning goals. .
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3928/01484834-20210322-05 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Presentation and Surgical Outcomes of Primary Hyperparathyroidism After Radioactive Iodine Therapy.
J Surg Res
January 2025
Department of Surgery, Washington University School of Medicine, Saint Louis, Missouri.
Background: Radioactive iodine (RAI) is a common treatment for various thyroid diseases. Previous studies have suggested susceptibility of parathyroid glands to the mutagenic effect of RAI and the development of primary hyperparathyroidism (PHPT). We tested the possible link between prior RAI treatment, disease presentation, and treatment outcomes.
View Article and Find Full Text PDFAI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFDelta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFBackground: With the increasing availability and use of digital tools such as virtual reality in medical education, there is a need to evaluate their impact on clinical performance and decision-making among healthcare professionals. The Trauma SimVR study is investigating the efficacy of virtual reality training in the context of traumatic in-hospital cardiac arrest.
Methods And Analysis: This study protocol (clinicaltrials.
Automating alloy design and discovery with physics-aware multimodal multiagent AI.
Proc Natl Acad Sci U S A
January 2025
Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
View Article and Find Full Text PDFWant 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!