Classroom sound can be used to classify teaching practices in college science courses
| Autor: | Steven B. Waters, Rachel Small, Amy S. Edwards, Wilfred F. Denetclaw, Segal M. Boaz, Yee-Hung M Chan, Sara K. Krause, Jeffrey N. Schinske, Gloriana Trujillo, Loretta A Kelley, Diana W. Wright, Susan F. Akana, Lance Lund, Mike Wong, Greg S. Spicer, Kristine M. Okimura, Pleuni S. Pennings, Natalia Caporale, Paul Z. Hankamp, Zahur-Saleh Subedar, L. Jeanette Green, Dana T. Byrd, Linda J. McPheron, Kathleen E. Duncan, Holly E Harris, Karen D. Crow, Joseph R. Perez, J R Blair, Stephen B Ingalls, Shannon B. Seidel, Katharyn E. Boyer, Bryan K. Clarkson, Amy Chovnick, Joseph J. Gorga, Peter Ingmire, Diana S Chu, Lori E. Krueger, Terrye L Light, Paul H. Nagami, Brinda Govindan, Lisa M. Schultheis, Andrea Swei, Lily Chen, Robert Patterson, Jonathan D. Knight, Scott William Roy, Joseph M Romeo, Shangheng Sit, Jonathon H. Stillman, Leticia Márquez-Magaña, Sara E. Cooper, Colin D Harrison, Hilary P Benton, Gloria Nusse, Mark Kamakea, J. Rebecca Jacobs, Sally G. Pasion, Carmen R. Domingo, Laura W. Burrus, Rhea R. Kimpo, Zheng-Hui He, Kimberly D. Tanner, Vanessa C Miller-Sims, José R. de la Torre, Susanne Lietz, Jennifer M. Wade, Travis E. Bejines, Tatiane Russo-Tait, Gigi N. Acker, Julia K. Willsie, Steven L. Weinstein, Christopher A. Moffatt, Melinda T. Owens, Lakshmikanta Sengupta, Brad Balukjian, Karen L. Erickson, Jason B. Bram, Edward J. Carpenter, Megumi Fuse, Briana K. McCarthy, Pamela C. Muick, Blake Riggs, Catherine Creech |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Technology
Universities Computer science Teaching method Science Social Sciences computer.software_genre 01 natural sciences 0103 physical sciences Mathematics education ComputingMilieux_COMPUTERSANDEDUCATION Humans 010306 general physics Students Class (computer programming) Multidisciplinary Multimedia Teaching 05 social sciences 050301 education Variance (accounting) Problem-Based Learning Clicker Sound Active learning 0503 education computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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