Document Type

Book Chapter

Publication Date

2024

DOI

10.1093/oso/9780198882077.003.0020

Publication Title

Uses of Artificial Intelligence in STEM Education

Pages

439-466

Abstract

Classroom videos have become an integral part of classroom observation and instructional quality studies. Researchers encounter substantial financial and time-consuming costs gathering trained personnel to catalogue and analyze a large quantity (i.e., hundreds of hours) of classroom videos. In this chapter, we argue that deep-learning neural networks have potential to help with these tasks by detecting features of instruction in videos. We evaluated the performance of three neural networks in detecting instructional activities in classroom videos. The video dataset included a total of forty-six hours, which was evenly split between elementary mathematics and English language arts lesson recordings. Results showed that the three neural networks detected instructional activities with a moderate degree of accuracy. We discuss potential opportunities and challenges in automating classroom observations with neural networks for educational research and teaching practice.

Rights

© 2024 Oxford University Press.

This is an open access publication, available online and distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) License.

Subject to this license, all rights are reserved. The moral rights of the authors have been asserted.

Comments

Editors: Xiaoming Zhai, Joseph Krajcik.

Original Publication Citation

Foster, J. K., Korban, M., Youngs, P., Watson, G. S., & Acton, S. T. (2024). Classification of instructional activities in classroom videos using neural networks. X. Zhai & J. Krajcik (Eds.), Uses of artificial intelligence in STEM education. Oxford University Press. https://doi.org/10.1093/oso/9780198882077.003.0020

ORCID

0000-0001-7197-1654 (Watson)

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