Learning style preferences of preclinical medical students in oman.

Oman Med J

Department of Human Function, Oman Medical College, Sohar, Oman.

Published: November 2014

AI Article Synopsis

  • The study aimed to evaluate the learning preferences of preclinical medical students at Oman Medical College using the VARK model.
  • Over one third of students preferred a single learning style, while a significant number favored a mix of two to four modalities.
  • The findings highlight the need for tailored educational strategies to accommodate diverse learning preferences among students.

Article Abstract

Objective: Our study sought to assess the learning preferences of students studying in the preclinical years of the medical degree program at Oman Medical College, Sohar. 

Methods: In this descriptive, cross-sectional study, we administered a learning style questionnaire (VARK model) to 140 students to assess their preferred mode of learning, specifically the sensory modality by which they prefer to take in information. 

Results: Over one third (35%) of the respondents expressed their preference for a single mode of learning, either visual (8%), auditory (9%), read/write (9%), or kinesthetic (9%). The remaining students preferred learning using a combination of either two (14%), three (19%), or four (32%) sensory modalities. 

Conclusion: The results of our study provide us with useful information to develop appropriate learning approaches to reach all types of learners at the college.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289496PMC
http://dx.doi.org/10.5001/omj.2014.120DOI Listing

Publication Analysis

Top Keywords

learning style
8
mode learning
8
learning
7
style preferences
4
preferences preclinical
4
preclinical medical
4
students
4
medical students
4
students oman
4
oman objective
4

Similar Publications

Background: Neurocognitive health is influenced by multiple modifiable and non-modifiable lifestyle factors. Machine learning tools offer a promising approach to better understand complex models of cognitive function. We used extreme gradient boosting (XG Boost), an algorithm of decision-tree modeling, to analyze the association between 15 late-life lifestyle and demographic factors with episodic memory performance.

View Article and Find Full Text PDF

Clinical Manifestations.

Alzheimers Dement

December 2024

Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka, India.

Background: In the early stages of typical Alzheimer's disease, there is a well-documented pattern of memory deficits, especially episodic memory, substantiated with evidence of medial temporal lobe atrophy, specifically of the hippocampus in line with the memory deficits. Studies have shown that several other demographic, biological, and lifestyle factors influence memory and there is a need for identifying early risk factors and for the development of clinical intervention programs to delay or prevent cognitive decline. Therefore, the objective of this study is to explore the impact of various factors on episodic memory decline in an urban cohort.

View Article and Find Full Text PDF

Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).

Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.

View Article and Find Full Text PDF

Anatomical Landmark detection in CT-Scan images is widely used in the identification of skeletal disorders. However, the traditional process of manually detecting anatomical landmarks, especially in three dimensions, is both time-consuming and prone to human errors. We propose a novel, deep-learning-based approach to automatic detection of 3D landmarks in CT images of the lower limb.

View Article and Find Full Text PDF

Want 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!