Profiling students via clustering in a flipped clinical skills course using learning analytics

Abstract

Flipped classrooms have become popular as a student-centered approach in medical education because they allow students to improve higher-order thinking skills and problem-solving applications during in-class activities. However, students are expected to study videos and other class materials before class begins. Learning analytics and unsupervised machine learning algorithms (clustering) can be used to examine the pre-class activities of these students to identify inadequate student preparation before the in-class stage and make appropriate interventions. Furthermore, the students’ profiles, which provide their interaction strategies towards online materials, can be used to design appropriate interventions. This study investigates student profiles in a flipped classroom. The learning management system interactions of 375 medical students are collected and preprocessed. The k-means clustering algorithms examined in this study show a two-cluster structure: ‘high interaction’ and ‘low-interaction.’ These results can be used to help identify low-engaged students and give appropriate feedback.