Engagement and learning approaches among medical students in an online surgical teaching programme: A cross-sectional study
Year: 2024
Type: article
Study design: Observational Study
Abstract: Background The COVID-19 pandemic prompted the transition of all teaching and learning of final-year General Surgery students to an online platform. Despite the utility of online methods, challenges exist such as a sense of impersonal learning, and poor student engagement. Student engagement with course content is important for deep learning. An Online Student Engagement Scale (OSE) and a revised Biggs Two-Factor Study Process Questionnaire (R-SPQ-2F) were used to evaluate student engagement and learning approaches respectively. Methods A cross-sectional study was conducted in 2021 at a South African university. The OSE and R-SPQ-2F online survey tools were administered to all final-year students ( n = 325) enrolled in the surgical online module. Quantitative data was collected, and the data was analysed statistically using R-Statistical computing software. Results are presented in the form of descriptive and inferential statistics. The reliability of the tools was evaluated by Cronbach's alpha. Results The survey response rate was 35.4 % (115/325). Students were engaged at a high level, and the median (IQR) scores of the OSE tool were 71.0 (63.0–78.0). Overall, students adopted a deep approach (DA) to learning, with median (IQR) scores of 34.0 (30.0–39.0) on the R-SPQ-2F tool. There was a moderate positive correlation between the total OSE score and DA (0.53, p < 0.001). Both the OSE and R-SPQ-2F tools showed an acceptable level of internal consistency of 0.893 and 0.806 respectively. Conclusions Student engagement was associated with deep learning approaches. The OSE and R-SPQ-2F tools were reliable tools to measure student engagement and learning approaches among medical students.
Source: Surgery Open Science
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,000
OpenAlex ID: W4403935371
