BACKGROUND: In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes . Evaluation of such courses has thus far been on a small scale at single institutions . Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming . This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods .
METHOD: This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery . Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules . Quantitative data were analysed using simple descriptive statistics . Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA).
RESULTS: One thousand six hundred and eleventh collaborators from 24 countries completed the e-learning course; 1396 (86.7 %) were medical students; 1067 (66.2 %) entered feedback . 1031 (96.6 %) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was + 1.54/5 (5: most positive; SD 1.19) and + 0.287/1 (1: most positive; SD 0.390) using syuzhet . LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity .
CONCLUSIONS: E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.
Index: Computer-assisted instruction, Education, Machine learning, Methods, Research design