Abstract
Remote teaching has been used successfully with the evolution of videoconference solutions and broadband internet availability. Even several years before the global COVID 19 pandemic, Ceibal used this approach for different educational programs in Uruguay. As in face-to-face lessons, teaching evaluation is a relevant task in this context, which requires many time and human resources for classroom observation. In this work we propose automatic tools for the analysis of teaching practices, taking advantage of the lessons recordings provided by the videoconference system. We show that it is possible to detect with a high level of accuracy, relevant lessons metrics for the analysis, such as the teacher talking time or the language usage in English lessons.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Banegas, D.L.: ELT through videoconferencing in primary schools in Uruguay: first steps. Innov. Lang. Learn. Teach. 7(2), 179–188 (2013)
Blunt, P., Haskins, B.: A model for incorporating an automatic speech recognition system in a noisy educational environment. In: 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), pp. 1–7 (2019)
Bredin, H., et al.: pyannote.audio: neural building blocks for speaker diarization. In: IEEE ICASSP (2020)
Cosbey, R., Wusterbarth, A., Hutchinson, B.: Deep learning for classroom activity detection from audio. In: IEEE ICASSP, pp. 3727–3731 (2019)
Foil, J.: Language identification using noisy speech. In: IEEE ICASSP, vol. 11, pp. 861–864 (1986)
Guimarães, L.M., da Silva Lima, R.: A systematic literature review of classroom observation protocols and their adequacy for engineering education in active learning environments. Eur. J. Eng. Educ. 46(6), 908–930 (2021)
Kaplan, G.: Innovations in Education: Remote teaching. British Council, London, UK (2019)
Martinez, J., Perez, H., Escamilla, E., Suzuki, M.M.: Speaker recognition using Mel frequency cepstral coefficients (MFCC) and vector quantization (VQ) techniques. In: 22nd International Conference on Electrical Communications and Computers, pp. 248–251 (2012)
Millman, J., Darling-Hammond, L.: The New Handbook of Teacher Evaluation: Assessing Elementary and Secondary School Teachers. Corwin Press Inc., SAGE Publications (1990)
Owens, M., Seidel, S., Wong, M., Tanner, K.: Classroom sound can be used to classify teaching practices in college science courses. PNAS Psychol. Cogn. Sci. 114(12), 3035–3090 (2017)
Park, T.J., Kanda, N., Dimitriadis, D., Han, K.J., Watanabe, S., Narayanan, S.: A review of speaker diarization: recent advances with deep learning. Comput. Speech Lang. 72, 101317 (2022)
Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., Sutskever, I.: Robust speech recognition via large-scale weak supervision. arXiv CoRR abs/2212.04356 (2022)
Schlotterbeck, D., Uribe, P., Araya, R., Jimenez, A., Caballero, D.: What classroom audio tells about teaching: a cost-effective approach for detection of teaching practices using spectral audio features. In: 11th LAK Conference, pp. 132–140 (2021)
Slyman, E., Daw, C., Skrabut, M., Usenko, A., Hutchinson, B.: Fine-grained classroom activity detection from audio with neural networks. arXiv CoRR abs/2107.14369 (2021)
Wang, Q., Downey, C., Wan, L., Mansfield, P.A., Moreno, I.L.: Speaker diarization with LSTM. In: IEEE ICASSP, pp. 5239–5243 (2018)
Zissman, M.A., Berkling, K.M.: Automatic language identification. Speech Commun. 35(1), 115–124 (2001)
Zylich, B., Whitehill, J.: Noise-robust key-phrase detectors for automated classroom feedback. In: IEEE ICASSP, pp. 9215–9219 (2020)
Acknowledgements
This research was funded by Agencia Nacional de Investigación e Innovación (ANII) Uruguay, Grant Number FMV_1_2021_1_166660.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Springer Nature Switzerland AG
About this paper
Cite this paper
Ríos, B., Martínez, E., Silvera, D., Cancela, P., Capdehourat, G. (2024). Teaching Practices Analysis Through Audio Signal Processing. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-49018-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49017-0
Online ISBN: 978-3-031-49018-7
eBook Packages: Computer ScienceComputer Science (R0)
