| Literature DB >> 28265087 |
Melinda T Owens1, Shannon B Seidel2, Mike Wong3, Travis E Bejines2, Susanne Lietz1, Joseph R Perez2, Shangheng Sit1, Zahur-Saleh Subedar1, Gigi N Acker4,5, Susan F Akana6, Brad Balukjian7, Hilary P Benton1,8, J R Blair1, Segal M Boaz9, Katharyn E Boyer1,10, Jason B Bram4, Laura W Burrus1, Dana T Byrd1, Natalia Caporale11, Edward J Carpenter1,10, Yee-Hung Mark Chan1, Lily Chen1, Amy Chovnick9, Diana S Chu1, Bryan K Clarkson12, Sara E Cooper8, Catherine Creech13, Karen D Crow1, José R de la Torre1, Wilfred F Denetclaw1, Kathleen E Duncan8, Amy S Edwards8, Karen L Erickson8, Megumi Fuse1, Joseph J Gorga14, Brinda Govindan1, L Jeanette Green15, Paul Z Hankamp16, Holly E Harris1, Zheng-Hui He1, Stephen Ingalls1, Peter D Ingmire1,17, J Rebecca Jacobs8, Mark Kamakea18, Rhea R Kimpo1,19, Jonathan D Knight1, Sara K Krause20, Lori E Krueger21,22, Terrye L Light1, Lance Lund1, Leticia M Márquez-Magaña1, Briana K McCarthy23, Linda J McPheron24, Vanessa C Miller-Sims1, Christopher A Moffatt1, Pamela C Muick21,25, Paul H Nagami1,7,26, Gloria L Nusse1, Kristine M Okimura27, Sally G Pasion1, Robert Patterson1, Pleuni S Pennings1, Blake Riggs1, Joseph Romeo1, Scott W Roy1, Tatiane Russo-Tait28, Lisa M Schultheis8, Lakshmikanta Sengupta16, Rachel Small29, Greg S Spicer1, Jonathon H Stillman1,10, Andrea Swei1, Jennifer M Wade30, Steven B Waters23, Steven L Weinstein1, Julia K Willsie12, Diana W Wright5,31, Colin D Harrison32, Loretta A Kelley33, Gloriana Trujillo34, Carmen R Domingo1, Jeffrey N Schinske4,8, Kimberly D Tanner35.
Abstract
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.Entities:
Keywords: active learning; assessment; evidence-based teaching; lecture; science education
Mesh：
Year: 2017 PMID： 28265087 PMCID： PMC5373389 DOI： 10.1073/pnas.1618693114
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN： 0027-8424 Impact factor: 11.205