Objectives: It has been identified that health science student groups may have distinctive learning needs. By university educators' and professional fieldwork supervisors' being aware of the unique learning style preferences of health science students, they have the capacity to adjust their teaching approaches to best fit with their students' learning preferences. The purpose of this study was to investigate the learning style preferences of a group of Australian health science students enrolled in 10 different disciplines.
Methods: The Kolb Learning Style Inventory was distributed to 2,885 students enrolled in dietetics and nutrition, midwifery, nursing, occupational therapy, paramedics, pharmacy, physiotherapy, radiation therapy, radiography, and social work at one Australian university. A total of 752 usable survey forms were returned (response rate 26%).
Results: The results indicated the converger learning style to be most frequently preferred by health science students and that the diverger and accommodator learning styles were the least preferred.
Conclusion: It is recommended that educators take learning style preferences of health science students into consideration when planning, implementing, and evaluating teaching activities, such as including more problem-solving activities that fit within the converger learning style.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview.
Sensors (Basel)
January 2025
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy.
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management.
View Article and Find Full Text PDFThe association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.
BMC Public Health
January 2025
Department of Health Management of Public Health, College of Public Health, Zhengzhou University, 100 Kexue Road, Gaoxin district, Zhengzhou, 450001, Henan, China.
Background: Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions. The aim of this study was to evaluate the effectiveness of machine learning-based lifestyle factors in predicting cardiovascular and all-cause mortality and compare the results obtained by traditional methods.
Method: A prospective cohort study was conducted using a nationally representative sample of adults aged 40 years or older, drawn from the US National Health and Nutrition Examination Survey from 2007 to 2010.
Impact of Ethical Leadership on Autonomy and Self-Efficacy in Virtual Work Environments: The Disintegrating Effect of an Egoistic Climate.
Behav Sci (Basel)
January 2025
Department of Economics and Business, Faculty of Business and Communication Studies, University of Vic-Central University of Catalonia, 08500 Vic, Spain.
Ethical management is key to ensuring organizational sustainability, through resources such as autonomy or self-efficacy. However, economic and social uncertainty occasionally leads to adaptive responses that prioritize profit as the primary interest, blurring the integrating role of ethical leadership. There are a number of studies that support this reality in a virtual work environment.
View Article and Find Full Text PDFNeuroscience Literacy and Academic Outcomes: Insights from a University Student Population.
Brain Sci
January 2025
Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
There is growing interest in neuroscience-informed education, as well as neuroscience-derived strategies that maximise learning. Studies on neuroscience literacy and neuromyths, i.e.
View Article and Find Full Text PDFAdvancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring.
Front Plant Sci
January 2025
College of Information Technology, Jilin Agricultural University, Changchun, China.
Introduction: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification.
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!