The Efficacy of Digital Inductive Models in Enhancing Linguistic Thinking Among Pre-Service FFL Student-Teachers

This study examined the effectiveness of a digital inductive model in improving the linguistic cognition and pedagogical identity of pre-service French as a Foreign Language (FFL) teachers in Egypt. In light of Egypt's national initiative for digital transformation in education, this research highlights the urgent necessity for pedagogical models that transcend fundamental digital literacy to cultivate advanced analytical skills. A mixed-methods sequential explanatory design was utilized with 56 pre-service FFL teachers. The intervention comprised a 10-week digital inductive model wherein participants utilized a web-based concordancer and a digital corpus to analyze authentic language data. Quantitative data were obtained through a pre-test/post-test Linguistic Analysis Task (LAT), whereas qualitative data were collected from reflective journals and focus groups. The quantitative findings indicated a statistically significant enhancement in participants' linguistic cognitive abilities, exhibiting a substantial effect size (t(54) = 21.43, p < .001, d = 2.89). The qualitative findings elucidated this growth, outlining a transformative progression from initial anxiety to a confident, student-centered pedagogical identity, a process enabled by the core tool affordances of authenticity and collaboration. The research findings indicate that pedagogically-driven, inquiry-based digital models are exceptionally effective in equipping language educators for the complexities of 21st-century education. The results have direct implications for teacher training programs that want to use technology to make long-lasting and deep changes in how they teach.