Active-learning pedagogies have been repeatedly demonstrated to produce superior learning gains with large effect sizes compared with lecture-based pedagogies. Shifting large numbers of college science, technology, engineering, and mathematics (STEM) faculty to include any active learning in their teaching may retain and more effectively educate far more students than having a few faculty completely transform their teaching, but the extent to which STEM faculty are changing their teaching methods is unclear. Here, we describe the development and application of the machine-learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers. 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. Applying DART to 1,486 recordings of class sessions from 67 courses, a total of 1,720 h of audio, revealed varied patterns of lecture (single voice) and nonlecture activity (multiple and no voice) use. We also found that there was significantly more use of multiple and no voice strategies in courses for STEM majors compared with courses for non-STEM majors, indicating that DART can be used to compare teaching strategies in different types of courses. 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. active learning | evidence-based teaching | science education | lecture | assessment C urrent college STEM (science, technology, engineering, and mathematics) teaching in the United States continues to be lecture-based and is relatively ineffective in promoting learning (1, 2) . Undergraduate instructors continue to struggle to engage, effectively teach, and retain postsecondary students, both generally and particularly among women and students of color (3, 4) . Federal analyses suggest that a 10% increase in retention of undergraduate STEM students could address anticipated STEM workforce shortfalls (5). Replacing the standard lecture format with more active teaching strategies has been shown to increase
For examples, Owens et al. develop Decibel Analysis for Research in Teaching, i.e., DART, to analyzes the volume and variance of classroom recordings to predict the quantity of time spend on single voice (e.g., lecture), multiple voice (e.g., pair discussion), and no voice (e.g., clicker question thinking) activities REF .
Classroom sound can be used to classify teaching practices in college science courses
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Computer Science Medicine