Teacher self-efficacy (TSE) significantly influences teacher well-being and pedagogical quality, thereby impacting student learning outcomes. Despite the recognized effectiveness of Lesson Study (LS) in shaping teachers' practices and beliefs, there is a research gap in TSE, particularly in higher education. To address this, we conducted a longitudinal study at a northeastern Chinese university, tracking TSE evolution in three English as a Foreign Language (EFL) teachers, encompassing both novices and experienced educators, during a six-month LS program. We collected both quantitative and qualitative data, encompassing interviews, surveys, and observations. Underpinned by Bandura and Wyatt's self-efficacy theory, the impact of LS on teachers' TSE development was explored and interpreted. Our findings shed light on the many ways in which LS effects individual teachers' TSE. Notably, both novice and experienced teachers improved significantly in TSE, especially in the domains of instructional techniques and student engagement. However, in the context of classroom management, there was considerably less growth. Furthermore, our research identifies a number of challenges that can have a negative impact on teachers' TSE, such as adapting to students' changing needs, dealing with limited resources, managing time constraints, navigating the school examination system, and overcoming initial resistance to the LS approach.

COVID-19 has been extremely difficult to control. The lack of understanding of key aspects of pandemics has affected virus transmission. On the other hand, there is a demand to incorporate Computational Thinking (CT) in the curricula with applications in STEM. However, there are still no exemplars in the curriculum that apply CT to real-world problems such as controlling a pandemic or other similar global crises. In this paper, we fill this gap by proposing exemplars of CT for modeling the pandemic. We designed exemplars following the three pillars of the APEC InMside framework for CT: algorithmic thinking, computational modeling, and machine learning. For each pillar, we designed a progressive sequence of activities that covers from elementary to high school. In an experimental study with elementary and middle school students from 2 schools of high vulnerability, we found that the computational modeling exemplar can be implemented by teachers and correctly understood by students. We conclude that it is feasible to introduce the exemplars at all grade levels, and that this is a powerful example of STEM integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.