Here is the premise: Classroom sound can be used to classify teaching practices in college science courses. Sounds interesting. Sound is interesting.
Using teaching techniques beyond lecture, such as pair discussions and reflective writing, has been shown to boost student learning, but it is unknown what proportion of STEM faculty use these active-learning pedagogies.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373389/
That needs unpicking and qualifying.
This team have developed a machine learning-based algorithm (Decibel Analysis for Research in Teaching or DART) that can rapidly analyse thousands of audio-recorded class sessions per day to measure the use of teaching strategies beyond traditional lecture.
I understand the ability to predict the “activity” – if not the quality or depth of the learning nor the ability to develop or modify the teachers craft. This is outline by the researchers themselves. “DART is not intended to measure the quality of teaching.”
Set aside their global findings and predictions, I found the data illuminating (1,720 hours of recordings from 67 courses). Note there were six sound annotations, (Lecture with Q/A, Video Discussion, Transition, Silent, Other)
linking to three sound conditions (Single voice, Multiple voice and No voice).
All courses used ‘single voice’ the majority of the time, 69 – 100%
In individual class sessions (n = 1,486), time spent in ‘single voice’ ranged from 15 – 100%
Some instructors that had no ‘multiple’ or ‘no voice’ in some class sessions nevertheless spent up to 37% of the time in these categories in another class session within the same course. Highlighting significant variability between lessons with the same instructor.
The promise of sound?
According to the research team:
- At this level of accuracy, ease, and time efficiency it is possible to analyze and draw broad conclusions about millions of hours of class sessions at periodic intervals over time.
- DART only analyses sound levels – it therefore protects the anonymity of instructors and students.
- DART promises to reveal differences among types of courses, instructors, disciplines, and institutions.
- DART holds promise as a tool for individual instructor professional development.
Scale, absolutely. Anonymity, yes, to a degree, though I am not sure that it is an imperative. Revealing, yes, providing instant or catalytic feedback. Instructional development? Somewhat.
Still fascinating stuff.