Learning approaches of undergraduate medical students at exit and entry of their medical schools.

Abstract

Objective: To Compare the learning approaches of medical students at the exit and entry of their medical school, to evaluate the influence of gender and to look for factors leading students towards a change in their learning approach, if any, through interviews. Study Design: Concurrent Mixed Method study. Setting: Ameer-ud-deen Medical College, Lahore. Period: March 2021 to March 2022. Methods: Sequential mixed method 100 first year and 100 Final year MBBS students were included in the study for quantitative analysis. ASSIST Questionnaire is used for identifying student’s approach to studying as either deep, surface or strategic and the correlation of gender with preference of study approach. Descriptive statistics wer`e performed. Student’s t-test was used for statistical comparisons. Qualitative data was collected through in depth semi structured interviews of final year students. Thematic analysis was done to interpret the data and triangulation method was used for validation. Results: There is an over-all predominance of deep approach in the both first and final year MBBS students. The number of first-year students employing a strategic approach were significantly in majority as compared to final year students doing the same. No significant difference is seen in the learning approaches of male and female students. Interviews indicated reasons of shifting away from strategic learning approach in the final year medical students. No significant difference was seen in the selection of approach between female and male students of both groups. Conclusion: Study suggests that there is a predominance of deep approach in the medical students both at exit and entry points. This is due to their clinical exposure and environment, assessment methodologies, curriculum planning, seniors and peer pressure and most importantly their immediate teachers. The more we understand these learning styles, the better we can guide our future generations towards deep learning and improve their attitude and professional skills.