A longitudinal qualitative study on Iranian university lecturers' identity reconstruction during the COVID-19 pandemic through text-mining: a dynamic approach

Abstract

AbstractThis research addressed the critical gap in understanding the procedure of professional identity reconstruction among university lecturers in Iran during the pandemic. Over the course of two academic semesters, this study utilized a dynamic theoretical framework to investigate the intricacies of how 41 Iranian university English teachers reconstructed their professional identities. Data were collected through a dynamic methodological framework, comprising: semi-structured interviews conducted monthly, focus group interviews held every 3 months, and participants' reflective journal entries collected every 2 weeks. Text mining techniques and content analysis, through KH Coder 3.0, were employed to explore the nuanced aspects of teachers' identity reconstruction, focusing on contextual elements, agency, and pedagogical practices. The use of KH Coder 3.0 software for word co-occurrence and word association analysis revealed that university lecturers reconstructed their professional identities in response to the challenges posed by two main factors: (1) adapting to new educational contexts and (2) overcoming difficulties in teacher–student communication caused by the abrupt shift from traditional in-person education to online schooling. Through their practical experiences, educators emerged as proactive problem solvers, receptive adapters, and confident professionals. These findings carry implications for educational institutions and teacher educators, emphasizing the importance of prioritizing pedagogical systems that encourage teachers to excel in hybrid teaching contexts, fostering proficiency in online communication and virtual teaching methods.