Inferring the Climate in Classrooms from Audio and Video Recordings: A Machine Learning Approach
Inferring the Climate in Classrooms from Audio and Video Recordings: A Machine Learning Approach
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从音频和视频记录推断教室气氛：一种机器学习方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Dauwels
中科院分区：
文献类型：
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作者：
Anusha James;M. Kashyap;Y. H. V. Chua;Tomasz Maszczyk;Ana Moreno Núñez;R. Bull;J. Dauwels
课堂气氛是由教师的做法和同伴关系的组合。课堂评估评分系统（CLASS）旨在观察和编码学生和教师之间的课堂互动，以便提供有关教学实践的形成性反馈并改进教师教学。但是培训、观察和编码的周转时间使得很难产生即时反馈。由于很少有自动评估工具，旨在衡量课堂气氛，我们提出了一个新的系统，自动评估课堂气氛，语音，行为线索和视频功能的基础上，应用机器学习技术。本文阐述了一个用于预测课堂气氛的音视频分析平台的设计和验证。采用机器学习分类器而不是主观测量可以简化和加快编码。我们认为我们的系统可以使教育系统不断审查和改进教学策略，从而在未来促进智能课堂。
The classroom climate is shaped by a combination of teacher practices and peer relationships. The Classroom Assessment Scoring System (CLASS) has been designed to observe and code classroom interactions between students and teachers in order to provide formative feedback on teaching practices and improve teacher instruction. But the turnover time for training, observing and coding makes it hard to generate instant feedback. Since there are few automated assessment tools designed to measure the classroom climate, we propose a novel system for automatic assessment of classroom climate, based on speech, behavioral cues and video features by applying machine learning techniques. This paper elaborates on the design and validation of an audio-video analytics platform for predicting classroom climate. Employing machine learning classifiers instead of subjective measures can ease and expedite the coding. We presume our system can empower education systems to continuously review and improve teaching strategies thus promoting smart classroom in the future.