Abstract

PurposeThe purpose of this study was to examine the relationship between factors in the extended technology acceptance model (TAM) model and teachers' self-efficacy in remote teaching during the COVID-19 pandemic. In addition, the authors sought to listen to classroom teachers as they expressed their unbiased views of the advantages, disadvantages and challenges of teaching remotely during the COVID-19 pandemic.Design/methodology/approachA survey was employed to examine the relationship between factors in the extended TAM model and teachers' self-efficacy in remote teaching during the COVID-19 pandemic using the 49-item questionnaire. A multiple regression analysis using a stepwise procedure was used to examine the relationship between factors in the extended TAM model and teachers' self-efficacy. Three open-ended questions closely examined remote teaching during the pandemic, related to challenges, advantages and disadvantages.FindingsQualitative findings challenges included Internet connection, lack of interaction and communication and challenges with motivation and student engagement. Disadvantages included teachers’ level of self-efficacy in using technology to teach, lack of support and resources to teach online and the struggle to motivate and engage students. Perceived benefits included flexibility for the teacher and differentiation, rich resources and a way to support learners when in-person instruction is not possible.Research limitations/implicationsThe data suggest that instead, during COVID-19, many teachers were learning about the platforms simultaneously as they were instructing students.Practical implicationsTo ensure quality remote instruction and that students receive the support to make instruction equitable, teachers need to perceive that their instructional technology needs are met to focus on teaching, learning and needs of their students.Social implicationsTeachers need opportunities to explore the platforms and to experience success in this environment before they are exposed to the high stakes of preparing students to meet K-12 standards.Originality/valueInstructional delivery has not explored teacher motivational and instructional teaching self-efficacy related to satisfaction with the learning management system (LMS).

Highlights

  • The purpose of this study was to examine the relationship between factors in the extended technology acceptance model (TAM) model and teachers’ self-efficacy in remote teaching during the COVID-19 pandemic

  • This teaching selfefficacy during pandemic information was especially critical during the COVID-19 pandemic, when teachers were thrust into remote instruction without preparation, as they scrambled to use various remote learning platforms

  • Purpose of the study The purpose of this study was to examine the relationship between factors in the extended TAM model and teachers’ self-efficacy in remote teaching during the COVID-19 pandemic

Read more

Summary

Introduction

The purpose of this study was to examine the relationship between factors in the extended technology acceptance model (TAM) model and teachers’ self-efficacy in remote teaching during the COVID-19 pandemic. Design/methodology/approach – A survey was employed to examine the relationship between factors in the extended TAM model and teachers’ self-efficacy in remote teaching during the COVID-19 pandemic using the 49-item questionnaire. Developed the extended technology acceptance model (TAM) We used this model to examine the relationship between perceived usefulness and teachers’ remote teaching self-efficacy. Vankatesh and Davis (2000) examined how the perceived usefulness and usage intention construct changed with continued information system (IS) usage This teaching selfefficacy during pandemic information was especially critical during the COVID-19 pandemic, when teachers were thrust into remote instruction without preparation, as they scrambled to use various remote learning platforms. Recent studies employing the TAM model have shown the perceived usefulness and ease of use to be significant predictors of technology (Cheung and Huang, 2005; Teo et al, 2008)

Objectives
Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.