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Academics' intention to sustain new teaching practices after the COVID-19 pandemic: Examined through the theory of planned behaviour.

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Title: Academics' intention to sustain new teaching practices after the COVID-19 pandemic: Examined through the theory of planned behaviour.
Authors: Broadbent, J.1 (AUTHOR) jaclyn.broadbent@deakin.edu.au, Bearman, M.1 (AUTHOR), Boud, D.1,2,3 (AUTHOR), Dawson, P.1 (AUTHOR)
Source: Higher Education (00181560). Aug2025, Vol. 90 Issue 2, p371-386. 16p.
Subject Terms: *Educational change, *Effective teaching, *Online education, *Higher education, *Educational planning, *Attitude (Psychology), COVID-19, Planned behavior theory
Abstract: Following the COVID-19 pandemic, higher education teachers, having transitioned to new teaching methodologies, including online learning and modified assessment strategies, face the question: Do they intend to revert to pre-pandemic ways of operating or retain their new practices? A university-wide invitation with an incentive resulted in 63 academics from all four faculties of the university participating in this study. Using the Theory of Planned Behaviour, this exploratory research assessed the roles of attitudes, subjective norms, and perceived control in determining academics' intentions to maintain instructional shifts. Collectively, these elements robustly predicted intention, explaining 38% of the variance. Notably, our findings revealed that fostering a positive attitude towards these changes was the sole unique driver for wanting to maintain them. Without a conducive attitude to teaching, the efforts of others and perceived control become inconsequential in effecting educational change. Within the limitations of a small sample size, our study offers an understanding of the variables influencing academics' intentions to sustain shifts in teaching practices. Such knowledge can guide future decision-making in higher education environments, especially when substantial transformation is required. [ABSTRACT FROM AUTHOR]
Copyright of Higher Education (00181560) is the property of Springer Nature 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.)
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  Data: Following the COVID-19 pandemic, higher education teachers, having transitioned to new teaching methodologies, including online learning and modified assessment strategies, face the question: Do they intend to revert to pre-pandemic ways of operating or retain their new practices? A university-wide invitation with an incentive resulted in 63 academics from all four faculties of the university participating in this study. Using the Theory of Planned Behaviour, this exploratory research assessed the roles of attitudes, subjective norms, and perceived control in determining academics' intentions to maintain instructional shifts. Collectively, these elements robustly predicted intention, explaining 38% of the variance. Notably, our findings revealed that fostering a positive attitude towards these changes was the sole unique driver for wanting to maintain them. Without a conducive attitude to teaching, the efforts of others and perceived control become inconsequential in effecting educational change. Within the limitations of a small sample size, our study offers an understanding of the variables influencing academics' intentions to sustain shifts in teaching practices. Such knowledge can guide future decision-making in higher education environments, especially when substantial transformation is required. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Higher Education (00181560) is the property of Springer Nature 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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