Distribution and academic significance of learning approaches among pre-clinical medical students at Trinity School of Medicine, St Vincent and the Grenadines

• Main Authors: Keshab Raj Paudel, Hari Prasad Nepal, Binu Shrestha, Raju Panta, Stephen Toth
• Format: Article
• Language: English
• Published: Korea Health Insurance Licensing Examination Institute 2018-04-01
• Series: Journal of Educational Evaluation for Health Professions
• Subjects: academic performance; learning; medical students; st. vincent and the grenadines
• Online Access: http://www.jeehp.org/upload/jeehp-15-9.pdf

Purpose Different students may adopt different learning approaches: namely, deep and surface. This study aimed to characterize the learning strategies of medical students at Trinity School of Medicine and to explore potential correlations between deep learning approach and the students’ academic scores. Methods The study was a questionnaire-based, cross-sectional, observational study. A total of 169 medical students in the basic science years of training were included in the study after giving informed consent. The Biggs’s Revised Two-Factor Study Process Questionnaire in paper form was distributed to subjects from January to November 2017. For statistical analyses, the Student t-test, 1-way analysis of variance followed by the post-hoc t-test, and the Pearson correlation test were used. The Cronbach alpha was used to test the internal consistency of the questionnaire. Results Of the 169 subjects, 132 (response rate, 78.1%) completely filled out the questionnaires. The Cronbach alpha value for the items on the questionnaire was 0.8. The score for the deep learning approach was 29.4± 4.6, whereas the score for the surface approach was 24.3± 4.2, which was a significant difference (P< 0.05). A positive correlation was found between the deep learning approach and students’ academic performance (r= 0.197, P< 0.05, df= 130). Conclusion Medical students in the basic science years at Trinity School of Medicine adopted the deep learning approach more than the surface approach. Likewise, students who were more inclined towards the deep learning approach scored significantly higher on academic tests.