The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech

Abstract

Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students’ attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.