How Can We Teach Our Students if We Do Not Know How they Learn? - Medical students' learning styles and academic performance - - Nataša M. Milić; Andrija R. Pavlović; Valerija B. Janićijević (University of Belgrade, Teacher Education Faculty, 2023)
Circumstances arising after the proclamation of the COVID-19 pandemic indicate the need for a permanent change in the access to education in medicine, the use of online tools and flexibility in the application of innovative learning solutions. This study aimed to determine medical students' learning styles and to use this information to improve distance learning platforms in order to promote personalized learning performance. A prospective cohort study was conducted among medical students attending the Faculty of Medicine, University of Belgrade, who were enrolled in the obligatory Medical statistics and informatics (MSI) course during 2017-18 school year. The Index of Learning Styles (ILS) questionnaire was used to measure the dimensions of learning styles: Sensing/Intuitive, Visual/Verbal, Active/Reflective, and Sequential/Global. Additional data included demographic information and formal evaluation of student achievements. The existing online teaching approach supported by Moodle LMS was upgraded for upcoming 2020-21 school year to cover all student learning preferences. Four hundred sixty-two medical students were enrolled. Most students were female (64.5%); average age 21.4±1.1 years. The average problem solving and final statistics scores were 16.8±2.6 and 82.8±12.4, respectively. The dominant learning styles on the Active/Reflective and Sensing/Intuitive scales were active (74.9%) and sensing (50%). On the Visual/Verbal and Sequential/Global scales main learning preferences were neutral to visual (48.5% and 41.3%, respectively) and neutral to sequential (72.3% and 18.4%, respectively). The strong sensing learning style and age were significant predictors in multivariate regression models, with problem solving and final statistics score as dependent variables. Based on these findings, the existing learning platform has been upgraded to cover all learning preferences and personalize learning for students with learning styles other than sensing. Students with a strong sensing learning preference have a better academic performance in MSI. Better knowledge and understanding of students learning styles can aid instructors and curriculum designers to adjust teaching methods in order to help students gain their full academic potential during COVID-19 pandemic.