Using Learning Analytics to Identify Medical Student Misconceptions in an Online Virtual Patient Environment

This study aimed to identify misconceptions in medical student knowledge by mining user interactions in the MedU online learning environment. Data from 13000 attempts at a single virtual patient case were extracted from the MedU MySQL database. A subgroup discovery method was applied to identify patterns in learner-generated annotations and responses to multiple-choice items on the diagnosis and management of acute myocardial infarction (i.e., heart attack). First, the algorithm generated rules where single terms from the learner annotations were used to predict incorrect answers to the multiple-choice items. Second, the possible combinations of terms and their relevant synonyms were used to determine whether their inclusion led to better rates of prediction. The second step was found to significantly increase prediction precision and weighted relative accuracy, uncovering four misconceptions at a rate greater than 70%. These findings serve to inform the design of an adaptive system that tailors the delivery of formative feedback to promote better learning outcomes in the domain of clinical reasoning.