Predicting At-Risk Students in an Online Flipped Anatomy Course Using Learning Analytics

Abstract

When using the flipped classroom method, students are required to come to the lesson after having prepared the basic concepts. Thus, the effectiveness of the lecture depends on the students’ preparation. With the ongoing COVID-19 pandemic, it has become difficult to examine student preparations and to predict student course failures with limiting variables. Learning analytics can overcome this limitation. In this study, we aimed to develop a predictive model for at-risk students who are at risk of failing their final exam in an introductory anatomy course. In a five-week online flipped anatomy course, students’ weekly interaction metrics, quiz scores, and pretest scores were used to design a predictive model. We also compared the performances of different machine learning algorithms. According to the results, the Naïve Bayes algorithm showed the best performance for predicting student grades with an overall classification accuracy of 68% and with at-risk prediction accuracy of 71%. These results can be used as a traffic light project wherein the “at-risk” group will receive the red light, and thus, will require more effort to engage with the content and they might need to solve the quiz tests after an individual study period.

Keywords

• flipped classrooms
• learning analytics
• early warning
• machine learning
• at-risk students