Semi-automatic detection of teacher questions from human-transcripts of audio in live classrooms


We investigate automatic detection of teacher questions from automatically segmented human-transcripts of teacher audio recordings collected in live classrooms. Using a dataset of audio recordings from 11 teachers across 37 class sessions, we automatically segment teacher speech into individual teacher utterances and code each as containing a teacher question or not. We trained supervised machine learning models to detect questions using high-level natural language features extracted from human transcriptions of a random subset of 1,000 segmented utterances. The models were trained and validated independently of the teacher to ensure generalization to new teachers. We are able to detect questions with a weighted F1 score of 0.66, suggesting the feasibility of question detection on automatically segmented audio from noisy classrooms. We discuss the possibility of using automatic speech recognition to replace the human transcripts with an eye towards providing automatic feedback to teachers.

Publication Title

Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016

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