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An Automated Framework for Swift Lecture Evaluation Using Speech Recognition and NLP

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Information and Communication Technology and Applications (ICTA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1350))

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Abstract

Feedback produced by students can give useful insights on lecturer’s performance and ability of teaching. Many institutions use the feedbacks from students efficiently to improve the education quality. In this study. A novel framework for collection and swift processing of students’ feedbacks about lecture is proposed in this paper, which addresses the shortcomings of traditional scale-rated surveys and open-end comments. The automated framework uses speech recognition and NLP tools to produce frequency graph of mostly used words, which can help to identify the topics need to be revised. An experiment was successfully conducted to test the framework among 3rd year undergraduate students.

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Author information

Authors and Affiliations

  1. Nile University of Nigeria, Plot 681, Cadastral Zone C-OO, Research and Institution Area, Jabi, Abuja, Nigeria

    Ochilbek Rakhmanov

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  1. Ochilbek Rakhmanov
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Correspondence to Ochilbek Rakhmanov .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. Federal University of Technology Minna, Minna, Nigeria

    Bilkisu Muhammad-Bello

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Rakhmanov, O. (2021). An Automated Framework for Swift Lecture Evaluation Using Speech Recognition and NLP. In: Misra, S., Muhammad-Bello, B. (eds) Information and Communication Technology and Applications. ICTA 2020. Communications in Computer and Information Science, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-69143-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-69143-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69142-4

  • Online ISBN: 978-3-030-69143-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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