Team-based learning (TBL) is an emerging teaching and learning strategy being employed in medical schools. The College of Medicine at Alfaisal University has adopted a TBL approach as an instructional method for first-year medical students. The aim of the present study was to describe the TBL method employed at Alfaisal University College of Medicine and to assess first-year medical students' perceptions of this learning modality for the anatomy- and physiology-based blocks/courses in organ systems form of curriculum. A five-point Likert scale questionnaire was structured based on Kirkpatrick's theory and assessed three major domains: reaction, learning, and behavior. Confirmatory factor analysis (CFA) and Cronbach's α-coefficient tests were used to assess the validity and reliability of the construct, respectively. CFA showed an adequate validity of the survey and Cronbach's α revealed an acceptable internal uniformity (0.69). A total of 185 respondents rated reaction, learning, and behavior toward introduction of TBL as 3.53 ± 1.01, 3.59 ± 1.12, and 3.57 ± 1.12, respectively. Excellent students rated TBL highly in all major domains compared with borderline students (reaction, behavior, and learning domains with P values of <0.049, <0.035, and <0.031, respectively). Students who had prior teamwork experience rated TBL higher in terms of their learning experience compared with those who were rarely involved in team work. This study demonstrated that Alfaisal University first-year medical students perceived TBL positively as a teaching and learning strategy for functional anatomy, and prior involvement in teamwork and academic performance correlates with higher ratings of TBL.

IntroductionMedical students often adopt different learning strategies and motives that guide them during studying. Preclinical students often face difficulties coping with new learning environments hence impacting their learning styles. The literature shows that studies examining the influence of surface and deep learning approaches on the academic performance of preclinical students are limited. Hence, this study aims to measure the impact of superficial and deep learning approaches on their academic performance.MethodsThe revised two‐factor version of the Study Process Questionnaire (R‐SPQ‐2F) was distributed among first, second and third year medical students at Alfaisal University. Each learning approach, surface or deep, is composed of motives and strategies. Exploratory factor analysis was conducted to validate the tool and Cronbach's alpha to assess its reliability. GPA was grouped into four groups: excellent (>3.5), good (3.0–3.5), average (2.5 – 2.9) and poor (<2.5). Regression analysis explored the prediction of academic performance by different learning styles. One‐way Analysis of Variance (ANOVA) and independent samples t‐test were used to examine the differences between learning styles, different study resources and average study time.ResultsA total of 159 students were enrolled in the study and the tool was tested to be valid and reliable. Deep learning approach predicted higher academic performance among the students (β 0.26, p=0.001). Excellent students showed higher ratings of deep learning strategies than good (p=0.025) and average students (p=0.001). Students who study for more than 8 hours daily also ranked higher in deep learning strategies than those who study for 4–6 hours (0.024) and 2–4 hours (p=0.01). Additionally, students who read books demonstrated higher self‐rating in deep motives and strategies (p=0.008 & p=0.005, respectively). Those who watch videos and utilize internet to expand medical knowledge tend to have higher ratings in deep motives with p values of 0.038 and 0.019, respectively.ConclusionThis study demonstrates adequate validity and reliability of R‐SPQ‐2F model among the study subjects. Students with deep approach style tend to study for a longer time, use different resources and achieve a higher GPA compared to students with surface approach. This may help students with surface approach shift their learning motivations to deep motives in order to achieve a transition to deep learning approach.