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Will teachers continue to teach online post-COVID-19?

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Τίτλος: Will teachers continue to teach online post-COVID-19?
Συγγραφείς: Arora, Jaskiran, Sahi, Gurjeet Kaur, Yates, Nicholas
Πηγή: Behaviour & Information Technology; Jun2023, Vol. 42 Issue 8, p1110-1126, 17p, 1 Diagram, 6 Charts
Θεματικοί όροι: Online education, Structural equation modeling, Teacher-student relationships, Research, Pilot projects, Teaching methods, Cognitive dissonance, Motivation (Psychology), Work, College teacher attitudes, Interviewing, Cronbach's alpha, Interpersonal relations, Descriptive statistics, Experiential learning, Questionnaires, Chi-squared test, Factor analysis, Statistical correlation, COVID-19 pandemic
Γεωγραφικοί όροι: India
Περίληψη: Numerous studies have captured the experiences of teachers teaching online, but the current 'emergency' to teach online is unprecedented and has been challenging. Grounded in the theory of cognitive dissonance, this paper attempts to recapitulate the experiences of university teachers and analyses whether they have developed the consonant cognitions to teach online during the pandemic period or would they prefer switching back to 'normal' teaching as soon as the circumstances permit. Technology-enabled teaching has been found to be complex as it mandates teaching in a computerised setting and lacks an element of social interaction, which is at the heart of face-to-face teaching. Using Structural Equation Modelling, this study presents the determining factors that motivate teachers to embrace technology-driven teaching more convincingly. The study finds that in the absence of adequate training imparted to the teachers for developing technological and pedagogical knowledge (TPK), high psychological capital and facilitating conditions are the two most important factors ensuring teaching proficiency, creating positive online experiences and a continued intention to teach online. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Behaviour & Information Technology; Jun2023, Vol. 42 Issue 8, p1110-1126, 17p, 1 Diagram, 6 Charts
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  Data: Numerous studies have captured the experiences of teachers teaching online, but the current 'emergency' to teach online is unprecedented and has been challenging. Grounded in the theory of cognitive dissonance, this paper attempts to recapitulate the experiences of university teachers and analyses whether they have developed the consonant cognitions to teach online during the pandemic period or would they prefer switching back to 'normal' teaching as soon as the circumstances permit. Technology-enabled teaching has been found to be complex as it mandates teaching in a computerised setting and lacks an element of social interaction, which is at the heart of face-to-face teaching. Using Structural Equation Modelling, this study presents the determining factors that motivate teachers to embrace technology-driven teaching more convincingly. The study finds that in the absence of adequate training imparted to the teachers for developing technological and pedagogical knowledge (TPK), high psychological capital and facilitating conditions are the two most important factors ensuring teaching proficiency, creating positive online experiences and a continued intention to teach online. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0144929X.2022.2060861
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      – SubjectFull: COVID-19 pandemic
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              Text: Jun2023
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