Classroom sound can be used to classify teaching practices in college science courses

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.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

Collectively Improving Our Teaching: Attempting Biology Department–wide Professional Development in Scientific Teaching

“…In the second follow-up program, Talk Matters, faculty, in collaboration with postdoctoral fellows, collected direct evidence of teaching innovations by audio-recording all class sessions in one or more of their own courses. Transcripts and audio levels of the recordings were analyzed, and the results are being disseminated in conferences and peer-reviewed publications, with all participating faculty as coauthors ( Owens et al. , 2017 ).…”

“…To obtain an objective estimate of the amount of active learning in the classes of Biology FEST alumni, we used the Decibel Analysis for Research in Teaching (DART) tool, which is a machine-learning-based tool that can quickly analyze classroom audio recordings with near 90% accuracy ( Owens et al. , 2017 ).…”

“…Approval for this portion of the study was conferred by SFSU Human and Research Protections Protocols #E14-141a-d. Courses presented in this analysis were part of the aggregated data shown in Owens et al . (2017) but have been disaggregated for publication here.…”

Physical models can provide superior learning opportunities beyond the benefits of active engagements

“…Just as certain topics may be more or less challenging for undergraduate students to learn, certain pedagogies are more or less effective in promoting learning in the undergraduate science classroom. Although lecture‐only pedagogies have been shown to be relatively ineffective for student learning, many college STEM instructors still use a lecture‐only approach in their teaching . Numerous studies in the STEM education research literature have demonstrated that active‐engagement learning strategies result in higher learning gains and reduce the chance of failure compared with lecture‐only approaches .…”

Unsupervised Methods for Audio Classification from Lecture Discussion Recordings

“…In order to properly classify classroom audio into different categories, most previous classroom audio classification methods applied supervised machine learning. Owens et al [8] developed a supervised machine learning method to distinguish between different class conduct modes such as single voice, multiple voices and no voice. Anusha et al [9] used the LIUM speaker diarization toolkit and manually acquired ground-truth labels to automatically classify teacher's speech, children speech and their overlap with 77.3%, 71.6%, and 3.1% accuracies, respectively.…”