San Francisco State University
Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains compared with lecture-based pedagogies. Here, we describe the machine-learning–derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze audio recordings of STEM courses quickly, cheaply, and without human observers to estimate the frequency of active learning. DART analyzes the volume and variance of classroom recordings to predict the quantity of time spent on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities. Therefore, DART has the potential to systematically inventory the presence of active learning with ∼90% accuracy across thousands of courses in diverse settings with minimal effort.