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Predicting the Intention to Use Audi and Video Teaching Styles: An Empirical Study with PLS-SEM and Machine Learning Models

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The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022) (AMLTA 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 113))

Abstract

Lately, there has been an increasing trend of using audio and video content on different online platforms. This trend of incorporation of audio and video content in online interaction was specifically beneficial for teachers and students who experienced better teaching and learning owing to the audio and visual data that offered them a richer interactive environment. The educational domain was positively affected by this practice. It is important to note that there is a dearth of empirical studies that use a conceptual model based on the factor of acceptance to offer insight into the acceptance of this video and audio content. Fortunately, this dearth in literature has been addressed by the current study that contributes in exploring the factors like speed and vividness, perceived concentration, perceived ease of use, perceived usefulness and sheds light on the impact of these factors on the acceptance of audio-video material. This study used the survey method for data collection and subsequently evaluated the research model. The Partial least squares-structural equation modelling (PLS-SEM) and machine learning models were used for this study. The study used a conceptual model to reveal the intentions of the students for the incorporation of audio-visual content on online platforms. The acceptance of technology by students was investigated concerning TAM constructs as well as the predictors like perceived concentration, speed, and vividness. Lastly, the study suggested theoretical and practical implications in the recommendations section. The recommendations were specifically associated with technology users, designers, and change managers.

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Author information

Authors and Affiliations

  1. Student Services, Rabdan Academy, Abu Dhabi, UAE

    Khadija Alhumaid

  2. Faculty of Art, Computing and Creative Industries, Universiti Pendidikan Sultan Idris, Perak, Malaysia

    Raghad Alfaisal

  3. Computer Science and Engineering Department, Yanbu University College, Royal Commission for Jubail and Yanbu, Yanbu Industrial City, Yanbu, Kingdom of Saudi Arabia

    Noha Alnazzawi

  4. Department of Languages and Translation, Arar Community College, Northern Border University, Arar, Kingdom of Saudi Arabia

    Aseel Alfaisal

  5. Educational Administration Department School of Education, Taibah University, Al Madinah Al Munawwarah, Medina, Kingdom of Saudi Arabia

    Naimah Nasser Alhumaidhi

  6. Industrial Engineering, The Hashemite University, Zarqa, Jordan

    Mohammad Alamarin

  7. School of Science, Engineering, and Environment, University of Salford, Salford, UK

    Said A. Salloum

Authors
  1. Khadija Alhumaid
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  2. Raghad Alfaisal
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  3. Noha Alnazzawi
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  4. Aseel Alfaisal
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  5. Naimah Nasser Alhumaidhi
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  6. Mohammad Alamarin
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  7. Said A. Salloum
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Corresponding author

Correspondence to Said A. Salloum .

Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Port Said University, Port Fouad, Egypt

    Rawya Y. Rizk

  3. Department of Computer Science, VÅ B-TUO, Ostrava-Poruba, Czech Republic

    Václav Snášel

  4. Faculty of Engineering, Port Said University, Port Fouad, Egypt

    Rehab F. Abdel-Kader

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Alhumaid, K. et al. (2022). Predicting the Intention to Use Audi and Video Teaching Styles: An Empirical Study with PLS-SEM and Machine Learning Models. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_23

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