Objective: To determine the frequency of learning styles of medical students and their association with preferred teaching methodologies.
Methods: The cross-sectional study was conducted at Baqai Medical College, Gadap, Karachi, form July to October 2019, and comprised medical students regardless of age, gender and academic year. David Kolb's learning style questionnaire, along with another questionnaire, was used to collect data. Data was analysed using SPSS 23.
Results: Of the 523 students, 213(40.7%) were males and 310(59.3%) were females. The overall mean age was 21.5±1.69 years. Of the total, 268(51.7%) students were divergers, 118(22.8%) assimilators, 86(16.6%) accomodators and 46(8.9%) were convergers. There was a significant association between learning styles and selected teaching methodologies (p<0.05).
Conclusions: Majority students were found to be divergers and assimilators. Aligning instructional strategies with learning styles will improve learning and academic performance.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.47391/JPMA.1437 | DOI Listing |
J Contin Educ Nurs
February 2025
Background: Mobile microlearning (MML) provides concise and engaging educational activities that correspond with various learning preferences and styles. Microlearning is defined as bite-sized instruction, with modules ranging from approximately 90 seconds to 5 minutes. To consider MML as a form of continuing professional development it is essential first to identify the learning preferences of a new generation of nurses entering the professional field of health care.
View Article and Find Full Text PDFPhys 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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!