Inferring the climate in classrooms from audio and video recordings: a machine learning approach

Anusha James, Mohan Kashyap, Yi Han Victoria Chua, Tomasz Maszczyk, Ana Moreno Nunez, Rebecca Bull, Justin Dauwels

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationTALE 2018
Subtitle of host publicationProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering
EditorsMark J.W. Lee, Sasha Nikolic, Montserrat Ros, Jun Shen, Leon C. U. Lei, Gary K.W. Wong, Neelakantam Venkatarayalu
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages983-988
Number of pages6
ISBN (Electronic)9781538665220
DOIs
Publication statusPublished - 16 Jan 2019
Externally publishedYes
Event2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 - Wollongong, Australia
Duration: 4 Dec 20187 Dec 2018

Publication series

NameIEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)
ISSN (Electronic)2470-6698

Conference

Conference2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
CountryAustralia
CityWollongong
Period4/12/187/12/18

Keywords

  • classroom climate prediction
  • machine learning
  • audio-video analytics
  • social behavior
  • educational research

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