Dynamic Teacher's Technology Adoption During the COVID-19 Pandemic

Understanding the teacher's technology adoption process is essential to comprehend and narrow the digital divide in the post-epidemic age. During the pandemic, the stay-at-home orders not only intervened schooling and teaching but also increased digital accessibility to teachers. This research studies teacher heterogeneity and adoption controls in the epidemic to simultaneously affect teacher's underlying intention and adoption behavior based on a dynamic framework under the theory of planned behavior. We present a quantitative framework for modeling the teachers' adoption behavior of a technology conditioned on intention, defined as latent dynamic processes via a hidden Markov model. This model allows us to examine the effects of three concerned adoption controls: epidemic, community, and experience. We also explicitly characterized teachers' digital traits as the estimated results accounts for teacher's heterogeneity. The implicit quality of digital teaching artifacts is examined to correlate the dynamic analyses with the qualitative supports. We collected data from four primary schools in Shanghai over 173 weeks, using an after-school activity management system. The data collection spanned periods both before and after the school closure caused by the epidemic, providing us with a dynamic view of technology adoption patterns under different circumstances. Our results suggest that the interventions derived from the controls of the epidemic did not significantly narrow the digital gap. In particular, well-prepared teachers may be more sensitive to adjusting their usage to meet the evolving standards. The inexperienced teacher struggles to maintain a high level of adoption once the passive external pressure is eliminated. Even the compulsory policy can temporarily change their adoption behavior. These implications highlight the second-order digital divide problem.