Numerous learning styles, schemes, and models are described in the literature. Most common are VARK (visual, auditory, read/write, kinesthetic) model of learning style and Kolb's experiential learning. Since the concept of learning style was first described, educational psychologists and medical educators have debated its validity. Notwithstanding these disagreements, VARK model is the one most utilized by teachers and students. This article describes how medical students with different learning styles learn anatomy and integrate multiple learning styles (multimodal) to achieve the learning goals and focuses on the approach taken by kinesthetic learners. In addition to clay modeling, drawing, and sketching, kinesthetic learners adopted "crochet" to create a three-dimensional (3-D) conceptual model that helped them mentally visualize the structures in situ. From the lectures and cadaveric dissection, a kinesthetic learner could create a 3-D mental model. However, by "crochet" and clay modeling, kinesthetic learners are able to gain broader visuospatial understanding.
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http://dx.doi.org/10.1007/s40670-020-01049-1 | DOI Listing |
Phys Rev Lett
December 2024
CERN, Geneva, Switzerland.
Z boson events at the Large Hadron Collider can be selected with high purity and are sensitive to a diverse range of QCD phenomena. As a result, these events are often used to probe the nature of the strong force, improve Monte Carlo event generators, and search for deviations from standard model predictions. All previous measurements of Z boson production characterize the event properties using a small number of observables and present the results as differential cross sections in predetermined bins.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Introduction: Generating physician letters is a time-consuming task in daily clinical practice.
Methods: This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology.
Results: Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters.
Sci Rep
January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Background: The National Commission for Academic Accreditation and Assessment (NCAAA) in Saudi Arabia underscores the importance of assessing student satisfaction to ensure program quality. No previous studies have explored the satisfaction levels of dental students enrolled in clinical Periodontics courses at King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS). This study aims to assess dental students' satisfaction with clinical Periodontics courses and to explore potential differences in satisfaction based on gender and academic level.
View Article and Find Full Text PDFSensors (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.
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