Context: Adapting web-based (WB) instruction to learners' individual differences may enhance learning. Objectives This study aimed to investigate aptitude-treatment interactions between learning and cognitive styles and WB instructional methods.
Methods: We carried out a factorial, randomised, controlled, crossover, post-test-only trial involving 89 internal medicine residents, family practice residents and medical students at 2 US medical schools. Parallel versions of a WB course in complementary medicine used either active or reflective questions and different end-of-module review activities ('create and study a summary table' or 'study an instructor-created table'). Participants were matched or mismatched to question type based on active or reflective learning style. Participants used each review activity for 1 course module (crossover design). Outcome measurements included the Index of Learning Styles, the Cognitive Styles Analysis test, knowledge post-test, course rating and preference.
Results: Post-test scores were similar for matched (mean +/- standard error of the mean 77.4 +/- 1.7) and mismatched (76.9 +/- 1.7) learners (95% confidence interval [CI] for difference - 4.3 to 5.2l, P = 0.84), as were course ratings (P = 0.16). Post-test scores did not differ between active-type questions (77.1 +/- 2.1) and reflective-type questions (77.2 +/- 1.4; P = 0.97). Post-test scores correlated with course ratings (r = 0.45). There was no difference in post-test subscores for modules completed using the 'construct table' format (78.1 +/- 1.4) or the 'table provided' format (76.1 +/- 1.4; CI - 1.1 to 5.0, P = 0.21), and wholist and analytic styles had no interaction (P = 0.75) or main effect (P = 0.18). There was no association between activity preference and wholist or analytic scores (P = 0.37).
Conclusions: Cognitive and learning styles had no apparent influence on learning outcomes. There were no differences in outcome between these instructional methods.
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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 PDFBMC 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.
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 PDFBrain 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 PDFFront 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.
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