Reliability and validity of the instrument.

The COVID-19 pandemic disrupted global education systems, forcing rapid shifts in teaching practices, technology integration, and assessment methods. However, little is known about how teacher efficacy, job satisfaction, and digital adoption vary across economic contexts. Insufficient research examines how income levels influence these factors, hindering equitable support for educators in post-pandemic recovery. This study examines variations in teacher efficacy (TE), job satisfaction (JS), assessment practices (AP), and technology adoption (UIT/UDT) across low-, upper-middle-, and high-income countries (LMICs, UMICs, HICs) before and after the COVID-19 pandemic. Guided by Bandura’s Social Cognitive Theory, the analysis explores how these factors interact and shift in response to pandemic-related disruptions in educational systems. The study utilizes PISA 2018 (pre-pandemic) and 2022 (post-pandemic) data from 128,866 teachers across 24 countries, employing structural equation modeling and machine learning as primary analytical techniques. Results indicate that job satisfaction significantly affects teacher efficacy but has minimal direct impact on the use of instructional technology tools. Teacher efficacy demonstrates a significant positive effect on both technology adoption (UIT) and assessment practices, while the use of digital learning and communication tools similarly influences assessment practices. These findings suggest that teacher efficacy and digital tool integration are key determinants of assessment practices. The study highlights how economic contexts shape teacher development, proposing targeted approaches for equitable post-pandemic education. HICs benefit from institutional support reinforcing the JS-TE relationship, while LMICs require solutions addressing resource gaps that impede consistent technology implementation. These evidence-based findings support context-specific policy interventions to enhance teacher support and digital integration globally.新冠疫情（COVID-19 pandemic）对全球教育体系造成了显著冲击，迫使教学实践、技术整合与评价方式快速转型。然而，学界对教师效能感、工作满意度及技术应用在不同经济环境下的差异仍缺乏充分认知。现有关于收入水平如何影响上述因素的研究不足，这一现状阻碍了后疫情时代对教育工作者的公平支持。 本研究聚焦新冠疫情前后，低收入、中上等收入及高收入国家（LMICs、UMICs、HICs）的教师效能感（teacher efficacy, TE）、工作满意度（job satisfaction, JS）、教学评价实践（assessment practices, AP）与技术应用（UIT/UDT）的差异展开分析。本研究以班杜拉社会认知理论（Bandura’s Social Cognitive Theory）为指导框架，探究上述因素如何相互作用，并随教育体系遭遇疫情冲击而发生变化。 本研究采用来自24个国家的128866名教师的国际学生评估项目（PISA）2018年（疫情前）与2022年（疫情后）数据，以结构方程模型（structural equation modeling）与机器学习（machine learning）作为主要分析手段。 研究结果显示，工作满意度对教师效能感具有显著正向影响，但对教学技术工具的使用仅存在微弱直接作用；教师效能感对技术应用（UIT）与教学评价实践均存在显著正向影响，而数字学习与沟通工具的使用同样会对教学评价实践产生积极作用。上述发现表明，教师效能感与数字工具整合是教学评价实践的关键决定因素。 本研究揭示了经济环境如何塑造教师发展路径，并提出了后疫情时代实现教育公平的针对性策略。高收入国家可借助制度性支持强化工作满意度与教师效能感间的关联，而低收入国家则需出台解决方案以弥补阻碍技术稳定应用的资源缺口。这些基于实证的研究结论，可为全球范围内制定针对性政策干预措施以提升教师支持水平与数字教育整合程度提供有力支撑。