What’s in the Noise? Classifying Teaching Practices with DART (Decibel Analysis for Research in Teaching)

Melinda Owens
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.

Infusing Technology Through Equitable Access | The Stanford Teacher Education Program & iPads for Learning

Christine Bywater
Kim Vinh
Antero Garcia
Cary Kelly
Nan Li
Stanford University

The STEP program used iPads for every student and partnered with instructors to infuse more creative and collaborative activities and assignments to encourage a larger use of technology in the classroom. The Literacies class, Science class, Seminar class all used a version of a technology component (backchanneling via Twitter chats or Padlet, creative assignments, student choice (more options than a paper).