Background: The benefit of online learning materials in medical education is not well defined.
Aim: The study correlated certain self-identified learning styles with the use of self-selected online learning materials.
Methods: First-year osteopathic medical students were given access to review and/or summary materials via an online course management system (CMS) while enrolled in a pre-clinical course. At the end of the course, students completed a self-assessment of learning style based on the Index of Learning Styles and a brief survey regarding their usage and perceived advantage of the online learning materials.
Results: Students who accessed the online materials earned equivalent grades to those who did not. However, the study found that students who described their learning styles as active, intuitive, global, and/or visual were more likely to use online educational resources than those who identified their learning style as reflective, sensing, sequential, and/or verbal.
Conclusions: Identification of a student's learning style can help medical educators direct students to learning resources that best suit their individual needs.
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http://dx.doi.org/10.3109/0142159X.2011.542209 | DOI Listing |
Sci Rep
December 2024
College of Furnishings and Industrial Design, Nanjing Forestry University, No.159 Longpan Road, Nanjing, 210037, Jiangsu, China.
The widespread use of mobile applications (apps) offers a new platform for sustaining traditional culture, yet insufficient focus on interface design has hindered user experience. This paper focuses on traditional Chinese medicine (TCM) apps, examining user preferences for interface design elements and their combinations across four dimensions: visual effects, functional attributes, layout, and interaction modes. Utilizing Conjoint Analysis Method (CAM), this study quantitatively explores user preferences for the combination schemes of 18 orthogonal designs.
View Article and Find Full Text PDFIn unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Mathematics, Universität Innsbruck, Technikerstraße 13, A-6020 Innsbruck, Austria.
Medical image processing has been highlighted as an area where deep-learning-based models have the greatest potential. However, in the medical field, in particular, problems of data availability and privacy are hampering research progress and, thus, rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy but also allows the drawing of new patients with specific characteristics, enabling the development of data-driven models on a much larger scale.
View Article and Find Full Text PDFCurr Pharm Teach Learn
December 2024
Sunrais Health, 2909 Loma Vista Rd, Ventura, CA 93003, United States of America.
Despite representing over 20 % of pharmacy students, Asian and Pacific Islander (API) remain underrepresented in leadership positions in the United States. This article examines barriers API pharmacy students face in pursuing leadership roles and offers strategies to navigate these challenges. Stereotypical assumptions that API students are "quiet and passive" may hinder their advancement to leadership and create a bamboo ceiling that limits their progress.
View Article and Find Full Text PDFAccid Anal Prev
December 2024
Nanjing Hurys Intelligence Technology Co., LTD, Nanjing 210000, China.
To deepen the understanding of the impact of car-following driving style (CFDS) on traffic conflict risk and address the lack of clear CFDS evaluation metrics, this study proposes an improved CFDS metric based on the Asymmetric Behavior (AB) theory. Interpretable machine learning models were utilized for regression analysis to examine the relationship between CFDS and conflict risk. The generalized AB model calculates the difference between vehicle trajectories and the Newell trajectory, constructing the driving style evaluation metric, which quantifies driver aggressiveness in a manner that is both computationally straightforward and easily interpretable.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!