Wednesday, November 10
12:45 PM-1:05 PM
Room 4

Machine learning system to guide teacher reflection on behavior management skills

Full Paper: Journal (Live Presentation) ID: 59102
  1. aaa
    Christopher Dann
    University of Southern Queensland, School of Education
  2. Shirley O'Neill
    University of Southern Queensland, School of Education
  3. Seyum Getenet
    University of Southern Queensland, School of Education
  4. Khaled Aboufarw
    University of Technology Sydney
  5. Navdeep Verma
    University of Southern Queensland, School of Education
  6. Subrata Chakraborty
    University of Technology Sydney
  7. Kun Yu
    University of Technology Sydney
  8. Shawn Edmondson
    IRIS Connect
  9. aaa
    Nigel Quirke-Bolt
    Mary Immaculate College Limerick
  10. aaa
    Dalit Levy
    CSEd & Educational Technology, Kibbutzim College of Education
  11. Stephen McFarlane
    Mary Immaculate College Limerick
  12. Carol Quadrelli
    University of Southern Queensland
  13. aaa
    Molly Daly
    Mary Immaculate College Limerick
  14. aaa
    Tami Seifert
    Kibbutzim College of Education

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%.

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