A Study of Classifying Style of Teachers State of Students' Learning based on K12 Online Education

摘要

In recent years, online education has been advancing significantly. However there is a major challenge how to evaluate style of teachers and state of student learning. In this paper, we propose a novel method that combines speaker diarization, speaker recognition, feature selection to classify style of teachers and state of students learning based on audio data. We train speaker recognition model and learn embedding vector of teachers or students on online platform. We select 25 acoustic features and statistical features from audio recordings and train classification model to classify style of teachers and state of students' learning jointly. Experimental results show that the task of classifying style of teachers achieves 71.25% precision and precision of classifying state of students' learning is 83.71%.