IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p2228-d502019.html
   My bibliography  Save this article

Home-Based Learning (HBL) Teacher Readiness Scale: Instrument Development and Demographic Analysis

Author

Listed:
  • Azlin Norhaini Mansor

    (Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Nur Hidayah Zabarani

    (Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Khairul Azhar Jamaludin

    (Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Mohamed Yusoff Mohd Nor

    (Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Bity Salwana Alias

    (Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Ahmad Zamri Mansor

    (Faculty of Education, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

Abstract

The unprecedented disruption in education due to the COVID-19 pandemic has forced teachers worldwide to adapt to online teaching and the immediate implementation of home-based learning (HBL). However, little is known regarding teacher readiness for HBL. Thus, there is a pressing need to develop an instrument to measure teachers’ readiness for online teaching, which can provide feedback to guide policymakers and school leaders in planning strategic interventions and support for implementing HBL. This study aimed to refine and validate the HBL Teacher Readiness Scale and to ascertain the view of secondary school teachers on aspects of their readiness to implement HBL. A total of 931 from a population of 3826 secondary school teachers were selected using random sampling, from the state of Selangor, Malaysia. The validity and reliability of the HBL Teacher Readiness instrument were tested using exploratory factor analysis and reliability analysis. As a result of the analysis, the scale remained at 26 items across four factors, namely efficacy in technology, attitude, perceived behaviour control, and subjective norms. The Cronbach Alpha coefficient for the entire scale was 0.94. Demographic analysis revealed that, overall, the in-service teachers’ level of readiness was at a high level across all dimensions, although the highest was in attitude and the lowest was in subjective norms. Based on this initial sample, the HBL Teacher Readiness Scale was shown to be a suitable instrument to measure teacher readiness for change in the context of the implementation of HBL, although further testing should be conducted on more diverse groups.

Suggested Citation

  • Azlin Norhaini Mansor & Nur Hidayah Zabarani & Khairul Azhar Jamaludin & Mohamed Yusoff Mohd Nor & Bity Salwana Alias & Ahmad Zamri Mansor, 2021. "Home-Based Learning (HBL) Teacher Readiness Scale: Instrument Development and Demographic Analysis," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2228-:d:502019
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/2228/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/4/2228/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernando Ferri & Patrizia Grifoni & Tiziana Guzzo, 2020. "Online Learning and Emergency Remote Teaching: Opportunities and Challenges in Emergency Situations," Societies, MDPI, vol. 10(4), pages 1-18, November.
    2. Wahab Ali, 2020. "Online and Remote Learning in Higher Education Institutes: A Necessity in light of COVID-19 Pandemic," Higher Education Studies, Canadian Center of Science and Education, vol. 10(3), pages 1-16, September.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. 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.
    5. Shirley Taylor & Peter A. Todd, 1995. "Understanding Information Technology Usage: A Test of Competing Models," Information Systems Research, INFORMS, vol. 6(2), pages 144-176, June.
    6. Henry Kaiser, 1970. "A second generation little jiffy," Psychometrika, Springer;The Psychometric Society, vol. 35(4), pages 401-415, December.
    7. Henry Kaiser, 1974. "An index of factorial simplicity," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 31-36, March.
    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. Muhammad Ali & Syed Ali Raza & Chin-Hong Puah & Mohd Zaini Abd Karim, 2017. "Islamic home financing in Pakistan: a SEM-based approach using modified TPB model," Housing Studies, Taylor & Francis Journals, vol. 32(8), pages 1156-1177, November.
    2. Hart O. Awa & Ojiabo Ukoha & Bartholomew C. Emecheta, 2016. "Using T-O-E theoretical framework to study the adoption of ERP solution," Cogent Business & Management, Taylor & Francis Journals, vol. 3(1), pages 1196571-119, December.
    3. Konietzny, Jirka & Caruana, Albert & Cassar, Mario L., 2018. "Fun and fair, and I don’t care: The role of enjoyment, fairness and subjective norms on online gambling intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 91-99.
    4. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    5. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    6. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    7. Cowan, Kelly R. & Daim, Tugrul U., 2011. "Review of technology acquisition and adoption research in the energy sector," Technology in Society, Elsevier, vol. 33(3), pages 183-199.
    8. Chia-Chien Hsu & Brian Sandford & Chia-Ju Ling & Ching-Torng Lin, 2021. "Can the Unified Theory of Acceptance and Use of Technology (UTAUT) Help Explain Subjective Well-Being in Senior Citizens due to Gateball Participation?," IJERPH, MDPI, vol. 18(17), pages 1-15, August.
    9. Francisco Rejón-Guardia & Juán Sánchez-Fernández & Francisco Muñoz-Leiva, 2011. "Motivational Factors that influence the Acceptance of Microblogging Social Networks: The µBAM Model," FEG Working Paper Series 06/11, Faculty of Economics and Business (University of Granada).
    10. Sanjeev Verma, 2015. "Harnessing the Benefit of Social Networking Sites for Intentional Social Action: Determinants and Challenges," Vision, , vol. 19(2), pages 104-111, June.
    11. Yiming Shao & Zhugen Wang & Zhiwei Zhou & Haojing Chen & Yuanlong Cui & Zhenghuan Zhou, 2022. "Determinants Affecting Public Intention to Use Micro-Vertical Farming: A Survey Investigation," Sustainability, MDPI, vol. 14(15), pages 1-26, July.
    12. Al-Qeisi, Kholoud & Dennis, Charles & Alamanos, Eleftherios & Jayawardhena, Chanaka, 2014. "Website design quality and usage behavior: Unified Theory of Acceptance and Use of Technology," Journal of Business Research, Elsevier, vol. 67(11), pages 2282-2290.
    13. Chua Chang Jin & Lim Chee Seong & Aye Aye Khin, 2019. "Factors Affecting the Consumer Acceptance towards Fintech Products and Services in Malaysia," International Journal of Asian Social Science, Asian Economic and Social Society, vol. 9(1), pages 59-65, January.
    14. Yang, Byunghwa & Kim, Youngchan & Yoo, Changjo, 2013. "The integrated mobile advertising model: The effects of technology- and emotion-based evaluations," Journal of Business Research, Elsevier, vol. 66(9), pages 1345-1352.
    15. Guych Nuryyev & Yu-Ping Wang & Jennet Achyldurdyyeva & Bih-Shiaw Jaw & Yi-Shien Yeh & Hsien-Tang Lin & Li-Fan Wu, 2020. "Blockchain Technology Adoption Behavior and Sustainability of the Business in Tourism and Hospitality SMEs: An Empirical Study," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    16. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    17. Hossain, Akram & Quaresma, Rui & Rahman, Habibur, 2019. "Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study," International Journal of Information Management, Elsevier, vol. 44(C), pages 76-87.
    18. Deborah Compeau & Barbara Marcolin & Helen Kelley & Chris Higgins, 2012. "Research Commentary ---Generalizability of Information Systems Research Using Student Subjects---A Reflection on Our Practices and Recommendations for Future Research," Information Systems Research, INFORMS, vol. 23(4), pages 1093-1109, December.
    19. Mohammad Khasawneh, 2017. "Promoting the Higher Education Excellence in Jordan: Factors Influencing Learner Attitude toward E-Learning Environment Based on the Integrated Platform," Journal of Social Sciences (COES&RJ-JSS), , vol. 6(1), pages 139-155, January.
    20. Kasilingam, Dharun Lingam, 2020. "Understanding the attitude and intention to use smartphone chatbots for shopping," Technology in Society, Elsevier, vol. 62(C).

    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:13:y:2021:i:4:p:2228-:d:502019. 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.