Proceedings of the 2018 3rd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2018)

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

Authors
Tuanji Gong, Xuefeng Zheng
Corresponding Author
Tuanji Gong
Available Online July 2018.
DOI
https://doi.org/10.2991/icesame-18.2018.2How to use a DOI?
Keywords
Speaker Diarization, Speaker Recognition, Style of Teachers, State of students' Learning, Deep Learning, Classification, K12
Abstract
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%.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Cite this article

TY  - CONF
AU  - Tuanji Gong
AU  - Xuefeng Zheng
PY  - 2018/07
DA  - 2018/07
TI  - A Study of Classifying Style of Teachers & State of Students' Learning based on K12 Online Education
BT  - 2018 3rd International Conference on Education, Sports, Arts and Management Engineering (ICESAME 2018)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/icesame-18.2018.2
DO  - https://doi.org/10.2991/icesame-18.2018.2
ID  - Gong2018/07
ER  -