Development and initial validation of an online engagement metric using virtual patients

Background: Considerable evidence in the learning sciences demonstrates the importance of engagement in online learning environments. The purpose of this work was to demonstrate feasibility and to develop and collect initial validity evidence for a computer-generated dynamic engagement score based on student interactions in an online learning environment, in this case virtual patients used for clinical education. Methods: The study involved third-year medical students using virtual patient cases as a standard component of their educational program at more than 125 accredited US and Canadian medical schools. The engagement metric algorithm included four equally weighted components of student interactions with the virtual patient. We developed a self-report measure of motivational, emotional, and cognitive engagement and conducted confirmatory factor analysis to assess the validity of the survey responses. We gathered additional validity evidence through educator reviews, factor analysis of the metric, and correlations between student use of the engagement metric and self-report measures of learner engagement. Results: Confirmatory factor analysis substantiated the hypothesized four-factor structure of the survey scales. Educator reviews demonstrated a high level of agreement with content and scoring cut-points (mean Pearson correlation 0.98; mean intra-class correlation 0.98). Confirmatory factor analysis yielded an acceptable fit to a one-factor model of the engagement score components. Correlations of the engagement score with self-report measures were statistically significant and in the predicted directions. Conclusions: We present initial validity evidence for a dynamic online engagement metric based on student interactions in a virtual patient case. We discuss potential uses of such an engagement metric including better understanding of student interactions with online learning, improving engagement through instructional design and interpretation of learning analytics output.