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Machine learning system to guide teacher reflection on behavior management skills

  • Christopher Dann
  • , Shirley O'Neill
  • , Seyum Getenet
  • , Khaled Aboufarw
  • , Navdeep Verma
  • , Subrata Chakraborty
  • , Kun Yu
  • , Shawn Edmondson
  • , Nigel Quirke-Bolt
  • , Dalit Levy
  • , Stephen McFarlane
  • , Carol Quadrelli
  • , Molly Daly
  • , Tami Seifert

Research output: Contribution to conference › Paper

Abstract

This paper presents a classroom behavior management skills classification system based on machine learning to assist teachers to develop their classroom behavior management skills through guided reflection. Such a system would enable more cost-effective application of demonstrably successful approaches to having expert observers identify suggestible moments for reflection. The proposed system accepts input videos from teachers and provides classification results of specific behavior management skills that occurred on those videos. The classification results, together with relevant additional information will be provided to teachers as suggestions for reflection. The proposed approach relies on deep learning and computer vision techniques to provide the classification results. Additionally, the proposed approach has been evaluated on videos containing four of the essential teaching skills and has achieved an average F1-score of 84.75%.

Original languageEnglish
Pages302-314
Publication statusPublished - 30 Nov 2021
EventInnovate Learning Summit 2021 - Online Event, Online Event
Duration: 9 Nov 2021 → 11 Nov 2021

Conference

ConferenceInnovate Learning Summit 2021
CityOnline Event
Period9/11/21 → 11/11/21

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