Using behavioral analytics to personalize learning experiences in digital medical education: a case study

Abstract

Today, digital medical education requires building an individual trajectory and considering the students’ needs. Personalized learning is proved to be an effective model as it concerns self-organization learning and customized instruction. The findings show that the implementation of personalized learning model depends on the behavioral analytics and the students’ learning styles. The present research is aimed to assess the learning styles among medical students and to substantiate the choice of effective teaching methods. To achieve the research objectives, we applied the case study method among 167 students from three institutions of higher medical education in Ukraine. The findings showed that most students belong to the converger and accommodator learning style groups. Considering the Cohen’s approach, we found that implementation of personalized learning model in digital medical education based on behavioral analytics increased the efficiency of learning process in all three institutions. The average efficiency index amounts 3,9 %. This proves the idea of positive impact of personalized learning model based on behavioral analytics upon the enhancement of digital medical education. As a conclusion, we elaborated a set of teaching methods suitable for different groups to provide high-quality digital medical education. In addition, certain recommendations were developed for heterogeneous groups.