IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14006-d955271.html
   My bibliography  Save this article

Exploring Useful Teacher Roles for Sustainable Online Teaching in Higher Education Based on Machine Learning

Author

Listed:
  • Yanni Shi

    (Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
    Department of Electronic Information, Henan University, Kaifeng 475001, China)

  • Fucheng Guo

    (Department of Artificial Intelligence, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

The COVID-19 emergency necessitated a rapid transition to online teaching by university lecturers. Hence, lecturers need to develop and reorganize their instructions and adjust their teaching roles and activities to the technological demands so as to further facilitate their continuous usage of technological systems after the crisis. Based on the behaviors of lecturers who utilized a particular teaching system—Rain Classroom—during school closure, this study aimed to predict their retention of online teaching beyond lockdown. Classical machine-learning classifiers were adopted to make predictions, most of which had an accuracy greater than 73%. Moreover, through a byproduct of these algorithms—feature scoring—we also aimed to determine the prime activities and roles that have strong relationships with lecturers’ retention dispositions. The domain meaning of feature scoring was revealed based on a specific conceptualization of perceived usefulness and the TAM model, which further enlightened system devisers about strategies to improve technological quality. A coevolution mechanism was thus formed, both providing guidance for lecturers in changing their overt behaviors with respect to online teaching and supporting the customization of system functionalities, so as to foster the mutual adaption of teachers’ pedagogies and artifact affordances. The findings, concerning useful teaching roles (namely, learning assessment, guiding technology usage, and learning support) and activities (such as in-class exercises, monitoring of students’ attendance, formal testing, etc.), are corroborated by evidence from other reports in the literature.

Suggested Citation

  • Yanni Shi & Fucheng Guo, 2022. "Exploring Useful Teacher Roles for Sustainable Online Teaching in Higher Education Based on Machine Learning," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14006-:d:955271
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14006/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14006/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chuan-Yu Mo & Te-Hsin Hsieh & Chien-Liang Lin & Yuan Qin Jin & Yu-Sheng Su, 2021. "Exploring the Critical Factors, the Online Learning Continuance Usage during COVID-19 Pandemic," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    2. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    3. Tinggui Chen & Lijuan Peng & Bailu Jing & Chenyue Wu & Jianjun Yang & Guodong Cong, 2020. "The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China," Sustainability, MDPI, vol. 12(18), pages 1-31, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eun-Jung Kim & Jinkyung Jenny Kim & Sang-Ho Han, 2021. "Understanding Student Acceptance of Online Learning Systems in Higher Education: Application of Social Psychology Theories with Consideration of User Innovativeness," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    2. Saeideh Sharifi fard & Ezhar Tamam & Md Salleh Hj Hassan & Moniza Waheed & Zeinab Zaremohzzabieh, 2016. "Factors affecting Malaysian university students’ purchase intention in social networking sites," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1182612-118, December.
    3. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    4. Philippe Cohard, 2020. "Information Systems Values: A Study of the Intranet in Three French Higher Education Institutions," Post-Print hal-02987225, HAL.
    5. Melih Engin & Fatih Gürses, 2019. "Adoption of Hospital Information Systems in Public Hospitals in Turkey: An Analysis with the Unified Theory of Acceptance and Use of Technology Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1-19, October.
    6. Morosan, Cristian, 2016. "An empirical examination of U.S. travelers’ intentions to use biometric e-gates in airports," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 120-128.
    7. Abdesamad Zouine & Pierre Fenies, 2014. "The Critical Success Factors Of The ERP System Project: A Meta-Analysis Methodology," Post-Print hal-01419785, HAL.
    8. Talukder, Md. Shamim & Sorwar, Golam & Bao, Yukun & Ahmed, Jashim Uddin & Palash, Md. Abu Saeed, 2020. "Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    9. Sarv Devaraj & Robert F. Easley & J. Michael Crant, 2008. "Research Note ---How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use," Information Systems Research, INFORMS, vol. 19(1), pages 93-105, March.
    10. Claudio Vitari & Elisabetta Raguseo, 2016. "Big data value and financial performance: an empirical investigation [Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data]," Post-Print halshs-01923271, HAL.
    11. Chen-Yuan Chen & Bih-Yaw Shih & Shih-Hsien Yu, 2012. "Disaster prevention and reduction for exploring teachers’ technology acceptance using a virtual reality system and partial least squares techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(3), pages 1217-1231, July.
    12. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.
    13. Fatima Zahra Barrane & Gahima Egide Karuranga & Diane Poulin, 2018. "Technology Adoption and Diffusion: A New Application of the UTAUT Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-19, December.
    14. Mary D. Adu & Usman H. Malabu & Aduli E. O. Malau-Aduli & Aaron Drovandi & Bunmi S. Malau-Aduli, 2020. "Efficacy and Acceptability of My Care Hub Mobile App to Support Self-Management in Australians with Type 1 or Type 2 Diabetes," IJERPH, MDPI, vol. 17(7), pages 1-18, April.
    15. Ofir Turel & Catherine E. Connelly, 2012. "Team Spirit: The Influence of Psychological Collectivism on the Usage of E-Collaboration Tools," Group Decision and Negotiation, Springer, vol. 21(5), pages 703-725, September.
    16. Baicu Claudia Gabriela & Gârdan Iuliana Petronela & Gârdan Daniel Adrian & Epuran Gheorghe, 2020. "The impact of COVID-19 on consumer behavior in retail banking. Evidence from Romania," Management & Marketing, Sciendo, vol. 15(s1), pages 534-556, October.
    17. Joan Torrent-Sellens & Cristian Salazar-Concha & Pilar Ficapal-Cusí & Francesc Saigí-Rubió, 2021. "Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    18. Zamrudi Zakky & Wicaksono Teguh, 2018. "Promoting the Use of Social Commerce on SME in the Context of Logistics: UTAUT Model Examination," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 9(2), pages 73-82, November.
    19. Yuting Zhang & Xiaofen Yu & Ning Cai & Yong Li, 2020. "Analyzing the Employees’ New Media Use in the Energy Industry：The Role of Creative Self-Efficacy, Perceived Usefulness and Leaders’ Use," Sustainability, MDPI, vol. 12(3), pages 1-13, January.
    20. Cliff R. Kikawa & Charity Kiconco & Moses Agaba & Dimas Ntirampeba & Amos Ssematimba & Billy M. Kalema, 2022. "Social Media Marketing for Small and Medium Enterprise Performance in Uganda: A Structural Equation Model," Sustainability, MDPI, vol. 14(21), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14006-:d:955271. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.