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Investigating the Relationship between Learning Styles Based on the VARK Model with Gender, Performance, and Academic Semester of Anesthesia Student
Shaqayeq Taghizadeh, Shahnam Sedigh Mroufi, Kimia Khonakdar, Atiyeh Sadat Sajadi, Alireza Babajani,
Volume 19, Issue 2 (7-2025)
Abstract
Background and Aim: Learning style refers to each learner’s preferred approach to receiving, processing, and retaining information. It is considered one of the key factors influencing the effectiveness of teaching and learning processes. This study aimed to examine the relationship between learning styles based on the VARK model and gender, academic performance, and academic semester among undergraduate anesthesia students at Iran University of Medical Sciences (IUMS).
Materials and Methods: This descriptive-analytical (cross-sectional) study was conducted during the 2022–2023 academic year using a census sampling method on 65 undergraduate anesthesia students enrolled in the 2nd, 4th, and 6th semesters at IUMS. Data were collected using the validated VARK questionnaire with a Cronbach’s alpha reliability coefficient of 98.6 Statistical analysis was performed using SPSS software, employing descriptive statistics (mean and frequency) and inferential tests (Chi-square and ANOVA). A significance level of P<0.05 was considered.
Results: Out of 60 fully completed questionnaires (32 female and 28 male students), 86.7% of students reported using a single (unimodal) learning style, while 13.3% used multiple (multimodal) styles. The predominant learning style was auditory (46.7%), followed by kinesthetic (23.3%), while visual style was the least frequent (5%). Among multimodal learners, the most common combination was auditory–kinesthetic (6.7%). In all academic semesters (2nd, 4th, and 6th), auditory style remained dominant, with no significant differences found across semesters (P=0.094). Similarly, no significant association was observed between learning styles and gender (P=0.229). ANOVA results indicated no significant relationship between learning styles and academic performance, with a mean GPA of 17.05 (P=0.345).
Conclusion: The findings indicated that most students preferred a single learning style, particularly the auditory modality, with no significant differences based on gender, academic semester, or academic performance. Identifying students’ learning styles and aligning teaching methods accordingly may enhance the teaching–learning process. It is recommended that learning style assessments be conducted at the beginning of academic programs and considered in curriculum planning. Further studies with larger and more diverse samples are suggested to evaluate the impact of learning style-based instruction on student satisfaction and academic achievement.
 

Applications and Role of Artificial Intelligence in the Management of Cardiac Diseases: A Systematic Review of Persian Research Studies
Mozhgan Farazmand, Mandana Asgari, Hamid Bouraghi, Taleb Khodaveisi, Ali Mohammadpour, Soheila Saeedi,
Volume 19, Issue 3 (9-2025)
Abstract
Background and Aim: Cardiovascular diseases have a very high prevalence globally and are recognized as one of the main causes of mortality worldwide. Artificial intelligence, as a novel technology, has garnered attention in recent years in Iran and other parts of the world for the management of a wide variety of diseases. The present study aimed to systematically review research studies conducted in the field of applying artificial intelligence in cardiovascular diseases.
Materials and Methods: To investigate research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence, the Persian language databases SID, Google Scholar, and Magiran were searched. This search was conducted without time limitations on April 3, 2024 and included all research studies that, up to this date, had used various artificial intelligence methods in the field of cardiovascular diseases in the present systematic review.
Results: The results of the search in the aforementioned three databases led to the retrieval of 17,819 research studies, of which 46 research studies met the inclusion and exclusion criteria of the study. These research studies had used artificial intelligence in three areas: prediction, treatment, and diagnosis. Neural networks (n=22), support vector machines (n=20), and decision trees (n=16) were the algorithms that were used more than other techniques. The data sources of the included research studies were mainly patient medical records and the UCI database. Additionally, MATLAB software was used more than other software. The most frequently mentioned limitations in the research studies included not considering all factors, limited access to data, insufficient data, the presence of noise in signals or images, and the presence of outliers, missing values, and non-normality of data.
Conclusion: The systematic review of research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence showed that this technology has been used in a wide range of cardiovascular diseases, and most of the conducted research studies confirmed its effectiveness and successful performance.

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