Enhancing dental and medical students’ self-regulated learning through multiple choice questions: An evaluative study using machine learning

Abstract

This article reports a mixed methods study that uses machine learning and thematic analysis to investigate student evaluation of multiple-choice questions (MCQ). The focus is on medical and dental students' experience of self-regulated learning and motivation. We evaluate two systems developed at Aarhus University: "MED MCQ" used by medical students and "MCQ anatomy" used by dental students. We evaluate through two surveys in SurveyXact with responses from 126 medical students and 70 dental students. We use topic modelling over free text responses. The machine-learning model identifies two groups of students who, in different ways, both experience the system as motivating and facilitating their learning process. The students experience increased self-regulated learning by being able to choose the form of presentation of questions and answer questions independently of the instructor. The article discusses how educators and developers can use MCQs to promote student learning and how to analyze open-ended questions. We discuss the potential for using machine learning and integrating MCQ systems into teaching.