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

The Efficacy of Digital Inductive Models in Enhancing Linguistic Thinking Among Pre-Service FFL Student-Teachers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Efficacy of Digital Inductive Models in Enhancing Linguistic Thinking Among Pre-Service FFL Student-Teachers Mohamed Mekheimer, Fatma Abdelaal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7720531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Social science/Education Humanities/Language and linguistics Social science/Language and linguistics Humanities/Literature digital inductive model linguistic thinking pre-service teacher education French as a Foreign Language (FFL) mixed-methods research data-driven learning Figures Figure 1 Figure 2 Figure 3 Introduction The widespread incorporation of digital technology into education has fundamentally transformed the realm of teacher preparation, necessitating a transition from cultivating basic digital literacy to enhancing advanced digital pedagogical skills (Aleksieva, 2025 ; Reisoğlu & Çebi, 2020 ). For pre-service language teachers, this evolution entails not only the acquisition of new tools but also the comprehension of how technology can be utilized to enhance and enrich language learning environments (Choi, 2024 ). While extensive research has examined the integration of digital technologies in teacher education, a notable deficiency persists in comprehending how particular digitally-enhanced pedagogical frameworks can foster the advanced cognitive skills requisite for proficient language instruction (Tunjera, 2019 ). The difficulty is in going beyond using technology just to deliver information and instead using it to get people to think more deeply about the subject. This challenge is especially important for learning "linguistic thinking," which is the ability to critically look at language structures, find patterns, and come up with logical guesses about how language works. This kind of thinking is important for teachers because they have to help their students move from memorizing things to really knowing how to use the language. Traditional deductive teaching methods don't always work well for building this skill (Kharade & Peese, 2014 ), which is why more and more people are interested in constructivist methods like inductive learning, where students find rules and patterns in real data (Ma et al., 2024 ). This method of teaching is in line with modern linguistic methods, like corpus-based pedagogy, which uses technology to look at large language datasets inductively (Kurt, 2025 ). This dynamic is most apparent in Egypt's current educational landscape, which is experiencing a unified national initiative to modernize its teacher training programs, especially in French as a Foreign Language (FFL). The Franco-Egyptian TrèFLE project is an example of a high-level initiative that will provide new curricula and digital resources to more than 13,000 FFL teachers and inspectors. This shows that Egypt is making a big investment in building digital capacity (Egyptian Ministry of Education, 2024). At the same time, national standards like the ICDL "Teachers" certification are setting a standard for digital skills (ICDL Arabia, n.d.). These reforms are important for building basic skills, but they don't automatically close the gap between knowing how to use technology and knowing how to use it for deep pedagogical inquiry. The merging of inductive methods and digital platforms makes it possible to create strong "digital inductive models" of learning. These models can furnish pre-service teachers with dynamic, data-rich environments to investigate linguistic concepts, thereby augmenting their metacognitive and linguistic competencies prior to their classroom engagement (Ana & López-Medina, 2021 ). Even though these models look good and Egypt's policies are moving in the right direction, there is still a big gap in the research. There is a lack of research that specifically assesses the influence of digital inductive pedagogies on the linguistic cognition of pre-service FFL teachers in the Egyptian context. This study examines the effectiveness of digital inductive models, seeking to elucidate how focused, technology-enhanced strategies can more effectively equip the forthcoming generation of language educators for the intricacies of 21st-century instruction. Context of the Study The widespread incorporation of digital technology has established a new necessity in teacher education: to progress beyond the development of fundamental digital literacy towards the enhancement of advanced digital pedagogical competence (Aleksieva, 2025 ; Reisoğlu & Çebi, 2020 ). For pre-service language teachers, this evolution necessitates more than mere proficiency with new tools; it requires a profound comprehension of how technology can be coordinated to cultivate richer, more effective learning environments (Choi, 2024 ). This challenge is especially pronounced in cultivating "linguistic thinking"—the capacity to critically evaluate language structures, discern patterns, and formulate evidence-based hypotheses regarding language functionality. Teachers who want to help their students learn real proficiency instead of just memorizing facts need to be able to analyze things, but traditional, deductive teaching methods often don't help them do this (Kharade & Peese, 2014 ). This dynamic is most apparent in Egypt's current educational landscape, which is experiencing a concerted national initiative to modernize its teacher training programs, especially in French as a Foreign Language (FFL). The Franco-Egyptian TrèFLE project is an example of a high-level initiative that will provide new curricula and digital resources to more than 13,000 FFL teachers and inspectors. This shows that Egypt is making a big investment in building digital capacity (Egyptian Ministry of Education, 2024). At the same time, national standards like the ICDL "Teachers" certification are setting a minimum level for digital skills (ICDL Arabia, n.d.). These reforms are important for building basic skills, but they don't automatically close the gap between knowing how to use technology and knowing how to use it for deep pedagogical inquiry. This gap underscores the urgent necessity for pedagogical frameworks that utilize Egypt's emerging digital infrastructure to develop the advanced cognitive skills required for contemporary language education. This research responds to this necessity by examining the effectiveness of a digital inductive model tailored for pre-service foreign language teachers. Based on constructivist ideas, this model uses digital tools like corpora and concordancers to get trainees involved in a guided discovery process, where they look at real language data to figure out grammatical rules and usage patterns on their own (Ma et al., 2024 ). This method not only improves their own linguistic thinking, but it also shows them how to use a powerful, student-centered teaching style that they can use in their own classrooms in the future. Even though these models look promising and there is a lot of support for them in Egypt, there is still a big gap in the research locally and globally. Although studies on general digital competence are emerging, there is a paucity of research specifically assessing the influence of digital inductive pedagogies on the linguistic thinking of pre-service FFL teachers in the Egyptian context. This study seeks to address this deficiency by offering a mixed-methods analysis of the model's effects. It aims to (1) quantitatively assess the enhancement of pre-service teachers' linguistic thinking skills, (2) qualitatively investigate their perceptions regarding the model's influence on their analytical and pedagogical practices, and (3) delineate the primary opportunities and challenges they face. The findings aim to guide the development of enhanced teacher training programs that can fully leverage Egypt's digital transformation in language education. Research Questions To fill this gap in the literature and conduct a focused investigation into the efficacy of the digital inductive model, this study is guided by the following research questions: To what extent does engagement with a digital inductive learning model impact the development of linguistic thinking skills among pre-service teachers? How do pre-service teachers perceive the role of the digital inductive model in shaping their approach to language analysis and their future pedagogical practices? What are the key affordances and challenges encountered by pre-service teachers when using digital inductive models for linguistic inquiry? Literature Review The Shift from Digital Literacy to Digital Pedagogical Competence For technology to be successfully used in teacher education, we need to make a big change from teaching basic digital literacy to teaching deep digital pedagogical competence. Frameworks like TPACK (Technological Pedagogical Content Knowledge) and DigCompEdu stress that effective teaching isn't just about knowing how to use digital tools; it's also about being able to use them in a planned way to meet certain teaching goals and content areas. Research shows that even though many pre-service teachers are comfortable with technology, they don't feel ready to use their skills to make big changes in the classroom (Lu et al., 2025 ; Reisoğlu & Çebi, 2020 ). There is a clear need for training models that go beyond teaching people how to use tools and instead focus on how to use technology to encourage analytical, inquiry-based learning (Huang et al., 2024 ). Digital Policy and Capacity-Building in Egyptian Language Education This global trend is especially important in Egypt's current education system, which is going through a big digital change, especially in how it teaches foreign languages. Recent national initiatives have made it possible to study how advanced digital training models affect people. For example, the Franco-Egyptian TrèFLE project wants to train thousands of French as a Foreign Language (FFL) teachers and use new, digitally-supported curricula in the 2024–2025 school year. Because of this policy push, it is now more important than ever to make sure that digital training helps people develop higher-order teaching skills, not just basic tool use. At the same time, the Egyptian Ministry of Education's decision to use the ICDL "Teachers" certification as a general standard for digital competence does not give teachers the specific skills they need to use technology to improve language analysis. This makes a gap between basic digital literacy and the advanced teaching skills needed to use data-rich tools like corpora and concordancers. To close this gap, universities are working together in new ways. For example, the French section of Ain Shams University and Aix-Marseille University are working together to modernize FFL degree programs and promote new teaching methods. These converging initiatives highlight the urgency of investigating digitally-enabled, inquiry-based pedagogical models for the forthcoming generation of FFL teachers in Egypt. Inductive Pedagogy and Data-Driven Learning for Linguistic Thinking This study examines inductive learning methodologies to connect tool utilization with pedagogical application. Inductive methodologies, wherein learners discern rules and patterns from genuine examples, are exceptionally effective for language instruction as they reflect naturalistic pattern recognition and promote the hypothesis-testing fundamental to linguistic cognition. Data-Driven Learning (DDL) exemplifies a technology-enhanced inductive methodology. Using tools like concordancers to look at real language data, learners can find collocations, grammatical structures, and pragmatic conventions on their own. The DDL literature substantiates that these tasks cultivate essential analytical skills and metalinguistic awareness—competencies that are particularly advantageous for educators who must ultimately facilitate analogous discovery processes for their students. The Intersection: Digital Inductive Models in Teacher Education This study is positioned at the intersection of these three trends, examining the effectiveness of digital inductive models. These are organized learning spaces that use digital corpora, concordancers, and guided inquiry sequences to help students learn through inductive discovery. For pre-service teachers, such models provide a dual advantage: they simultaneously improve their personal linguistic cognition (pattern recognition, metalinguistic reflection) while exemplifying a robust pedagogical strategy that can be applied in their future classrooms. Although research on corpus-based pedagogy is expanding, the evidence supporting the effectiveness of structured digital inductive models in pre-service teacher education, particularly within the Egyptian FFL context, is still insufficient. This study seeks to fill this gap by investigating how these models can adequately equip teachers to address the challenges of a digitally evolving educational system. Digital Technologies in Language Teacher Education In the specific area of language education, this digital change brings both new chances and new problems. Teacher preparation programs are using more and more technologies to make language learning experiences that are real, interactive, and full of data. These include practices like digital storytelling to improve narrative skills and digital collaboration (Yu & Wang, 2025 ; Spaliviero & Serragiotto, 2025 ), using video to reflect on and analyze classroom practice (Sun et al., 2025 ), and using Generative AI to help with lesson planning and content creation (Moorhouse et al., 2024 ; Kurt, 2025 ). A significant emphasis in the literature is on enhancing pre-service teachers' capacity to integrate technological tools with effective pedagogical principles, commonly known as Technological Pedagogical Content Knowledge (TPACK) (Huang, Zhang, & Lv, 2024 ). However, research indicates that pre-service teachers frequently perceive themselves as inadequately equipped to utilize these tools effectively, attributing this to insufficient training in advancing from superficial application to transformative pedagogy (Yetkin & Özer-Altınkaya, 2024 ). This emphasizes the necessity for educational frameworks that not only familiarize trainees with technology but also explicitly instruct them in its utilization to cultivate advanced cognitive skills in their prospective students. The Role of Inductive Learning in Developing Linguistic Thinking As digital tools become more popular, so does the interest in inductive learning methods in education. Inductive teaching, on the other hand, encourages students to find patterns, rules, and ideas in real-life examples (Kharade & Peese, 2014 ). In traditional deductive methods, rules are given first. This method is especially good for teaching languages because it mimics how people naturally learn languages and encourages deeper analytical thinking (Ana & López-Medina, 2021 ). Through the analysis of authentic language data, learners cultivate "linguistic thinking," which encompasses the capacity to hypothesize, test, and enhance their comprehension of grammatical and lexical frameworks (Naranjo Chimborazo, 2024 ). Studies indicate that inductive methodologies can substantially improve pre-service teachers' critical thinking and metacognitive awareness (Ömeroğlu & Hakkoymaz, 2025 ). Studies indicate that inductive methodologies can markedly improve pre-service teachers' critical thinking, problem-solving abilities, and metacognitive awareness (Ana & Beatriz, 2021; Ömeroğlu & Hakkoymaz, 2025 ). Teachers are better able to help their students learn through discovery when they have this kind of learning experience themselves. Inductive reasoning is essential for recognizing grammatical patterns, comprehending semantic subtleties, and valuing the contextual aspects of language use—all crucial skills for an effective language educator. The Convergence: Digital Inductive Models The real potential for new ways of teaching comes from the combination of digital technology and inductive learning. "Digital inductive models" use technology to make inquiry-based learning more effective, efficient, and available to everyone. For example, digital corpora and concordancing tools let pre-service teachers look at a lot of real language data, which helps them find grammatical and collocational patterns in a way that would be impossible with regular textbooks (Kurt, 2025 ; Ma et al., 2024 ). These models are based on a constructivist philosophy, which sees the pre-service teacher as an active builder of knowledge instead of a passive receiver (Choi, 2024 ). By working on digital inductive tasks, trainees can improve their language skills and analytical skills while also creating a mental model for how to use technology to support inquiry-based learning in their future work. These models adhere to a constructivist philosophy, viewing the pre-service teacher as an active creator of knowledge instead of a passive receiver (Choi, 2025). By participating in digital inductive tasks, trainees can improve their language awareness and analytical abilities while concurrently developing a conceptual framework for the implementation of technology-enhanced, inquiry-based learning in their future practice. Nonetheless, although the theoretical advantages are evident, a substantial deficiency exists in the literature concerning the empirical effectiveness of these integrated models. A significant portion of the research concentrates on either the integration of digital tools broadly or on non-digital inductive methodologies. A comprehensive examination of the influence of structured digital inductive models on the cultivation of linguistic cognition in pre-service teachers is essential to affirm their significance in contemporary teacher education. Theoretical Framework The theoretical framework for this study is envisioned as a cohesive model that integrates three fundamental domains to elucidate the enhancement of linguistic thinking within a particular educational context. This framework is based on the pillars of Digital Pedagogical Competence, Constructivist and Socio-Cultural Learning, and Inductive Pedagogy, as shown in Fig. 1 . These fundamental theories underpin a series of essential pedagogical processes that coalesce to create the Digital Inductive Model, subsequently contextualized within the Egyptian FFL Teacher Education Framework. The framework is fundamentally based on Constructivism and Socio-Cultural Learning, emphasizing that knowledge is actively constructed by learners through mediated tools and social interaction. Digital Pedagogical Competence (TPACK and DigCompEdu) adds another layer to this. This framework focuses on how to combine technology, pedagogy, and content into a coherent way of teaching (Reisoğlu & Çebi, 2020 ). The third strand is Inductive Pedagogy, which is put into practice through methods like Data-Driven Learning (DDL), which helps students find patterns and come up with hypotheses based on real data (Kharade & Peese, 2014 ; Ma et al., 2024 ). Figure 1 shows that these domains and their related processes come together to make the Digital Inductive Model. This model is a structured system that uses digital tools in a collaborative setting to get pre-service teachers to think more deeply about language. The entire model functions within the Egyptian FFL Teacher Education Context, where national policies serve as the structural impetus for innovation. Inductive Pedagogy is the third pillar, and it is put into action through techniques like Data-Driven Learning (DDL). Inductive teaching prompts students to extract rules and principles from genuine examples, thereby enhancing their conceptual comprehension (Kharade & Peese, 2014 ; Ma et al., 2024 ). This teaching method directly helps with the cognitive processes of Pattern Discovery and Hypothesis Testing. When pre-service teachers utilize digital tools such as concordancers, they participate in genuine linguistic investigation, thereby enhancing their analytical abilities and metalinguistic awareness. Figure 1 shows that these processes come together to make the Digital Inductive Model. This model is a structured way for pre-service teachers to learn. They use inductive methods to analyze language, guided by constructivist principles and their growing digital skills. The main goal of this model is to help people develop Enhanced Linguistic Thinking, which is the ability to critically look at language structures, come up with evidence-based hypotheses, and think about how they use language. By engaging in this model, pre-service teachers enhance their analytical skills and encounter a pedagogical methodology they can subsequently implement in their own classrooms. Finally, this whole framework is based on the Egyptian FFL Teacher Education Context. National policies such as the TrèFLE project and initiatives for university modernization create the structural impetus for these innovative models. But there is also a conflict between these digital pedagogies that focus on reform and long-standing methods of teaching grammar through deduction. This framework serves as a lens for the design and evaluation of the Digital Inductive Model's effectiveness within this distinctive and evolving context. Hypotheses Based on the theoretical framework and the existing literature, this study proposes the following hypotheses, which correspond directly to the research questions: There are statistically significant differences in pre-service teachers’ linguistic thinking skills before and after participation in the digital inductive model, specifically in: (a) identifying grammatical and lexical patterns in authentic French texts, (b) formulating evidence-based hypotheses about language use, and (c) engaging in metalinguistic reflection. There are statistically significant differences in pre-service teachers’ pedagogical perceptions before and after participation in the digital inductive model, such that they shift from favoring deductive approaches toward valuing constructivist, inquiry-based pedagogy. There are statistically significant differences between traditional instruction and the digital inductive model in terms of perceived affordances, with the model providing greater access to authentic data, stronger learner autonomy, and more collaborative knowledge construction. There are statistically significant differences between expected ease of implementation and actual experiences with the digital inductive model, with participants reporting higher cognitive load and tension with prior deductive learning traditions. Methods Research Design This study utilized a mixed-methods sequential explanatory design to examine the effectiveness of a digital inductive model on the linguistic cognition of pre-service FFL teachers. The design was selected to initially conduct a quantitative evaluation of the intervention's impact (QUAN), followed by the utilization of qualitative data to elucidate and expand upon those results (qual). The quantitative phase employed a single-group, pre-test/post-test design to assess alterations in linguistic thinking skills. The next qualitative phase used semi-structured focus groups and reflective journals to look into how participants perceived the model in terms of its affordances and challenges. Participants The study involved 56 pre-service French as a Foreign Language (FFL) student teachers enrolled in a third-year course, FFL Didactics and Pedagogy , at a public university in Beni-Suef, Egypt. Participants were purposively selected to ensure a solid foundation in French grammar and pedagogy, but without prior training in data-driven or corpus-based methodologies. All 56 completed the quantitative phase, which comprised a pre-test and post-test. The cohort included 48 females and 8 males, with a mean age of 20.5 years ( SD = 1.2), and all reported a CEFR proficiency level of B2 or higher. Participation was voluntary, informed consent was obtained, and ethical approval was granted by the university’s Institutional Review Board (Ref. No. BSU-FoE-003-01-3-2025). From this cohort, a purposive subsample of 17 participants was selected for qualitative analysis to capture diverse perspectives. This subsample comprised two non-overlapping groups: (a) ten participants who submitted weekly reflective journals, representing a range of pre-test scores and engagement levels, and (b) seven participants who contributed to a focus group discussion, providing interactive insights into their experiences. The Intervention: The Digital Inductive Model Over a period of 10 weeks, the digital inductive model was integrated into the participants' regular FFL didactics course. The intervention was designed to shift learning from a deductive, rule-based approach to an inductive, inquiry-based process. Each weekly module consisted of a 90-minute session and was structured around the following components: Linguistic Phenomenon : Participants were introduced to a complex area of French grammar or lexis (e.g., the subjunctive mood, pronominal verbs, use of prepositions). Digital Tools : Participants were trained to use a web-based concordancing tool and a custom-built digital corpus of authentic French texts (including news articles, blog posts, and literary excerpts). Guided Inquiry Tasks : In small groups, participants used the concordancer to search the corpus for authentic examples of the target linguistic phenomenon. They were guided by worksheets that prompted them to observe patterns, formulate hypotheses about rules and usage, and find counter-examples. Collaborative Reflection : Groups posted their findings, hypotheses, and newly derived "rules" to a shared online forum (Moodle). Each session concluded with a whole-class discussion, moderated by the instructor, to compare findings and consolidate understanding. Data Collection and Instruments To address the three research questions, both quantitative and qualitative data were collected. Linguistic Analysis Task (LAT) (Quantitative) : To measure the development of linguistic thinking (RQ1), a pre-test/post-test instrument was designed and administered before and after the 10-week intervention. The LAT consisted of two parts: (a) a pattern recognition section, where participants identified and explained grammatical nuances in authentic sentences, and (b) a hypothesis-testing section, where they were given a small data set and asked to formulate a rule governing its usage. The instrument was validated for content validity by three senior FFL pedagogy experts. Reflective Journals (Qualitative) : Throughout the intervention, participants were required to maintain weekly digital journals. Prompts encouraged them to reflect on their learning process, their changing perceptions of grammar instruction (RQ2), and any difficulties or "aha" moments they experienced while using the digital tools (RQ3). Semi-Structured Focus Groups (Qualitative) : In the final week of the intervention, eight focus groups (each with 7 participants) were conducted. The discussions were guided by a protocol designed to elicit detailed insights into participants’ perceptions of the model's effectiveness, its influence on their future teaching plans (RQ2), and the specific affordances and challenges they encountered (RQ3). Each focus group session lasted approximately 60 minutes and was audio-recorded and transcribed verbatim. Data Analysis The collected data were analyzed in two distinct phases corresponding to the sequential explanatory research design. 1. Quantitative Analysis The quantitative data from all 56 participants, consisting of pre-test and post-test scores from the Linguistic Analysis Task (LAT), were analyzed using SPSS (Version 28). Descriptive statistics (means and standard deviations) were calculated for both the pre-test and post-test scores. A paired-samples t-test was performed to evaluate the primary hypothesis (H1) by assessing whether a statistically significant difference existed in the mean linguistic thinking scores prior to and following the intervention. The level of significance was p < .05. 2. Qualitative Analysis Qualitative data were gathered from a purposefully chosen subsample of 17 participants to facilitate a comprehensive understanding of their experiences. The dataset consisted of two sources: (a) a verbatim transcript from one of eight focus groups with seven participants, and (b) weekly reflective journals from ten additional participants chosen to reflect diverse levels of initial engagement and pre-test performance. The data were analyzed thematically according to Braun and Clarke’s (2006) six-phase framework: familiarization, inductive coding, theme development, theme review, theme definition, and report generation. To guarantee reliability, a second researcher independently coded 20% of the dataset, resulting in a high level of inter-coder agreement (Cohen’s Kappa = .88). The analysis sought to elucidate the quantitative findings and to tackle Research Questions 2 and 3, which concentrated on participants' perceptions, affordances, and challenges. Results Quantitative Findings To address Research Question 1 and test the main hypothesis (H1), we looked at quantitative data from the Linguistic Analysis Task (LAT). Fifty-six pre-service teachers took the pre-test, but one of them did not take the post-test and was not included in the inferential analyses. This left a final paired sample of N = 55. Table 1 shows that after the intervention, participants' LAT performance improved a lot. The average Total Linguistic Thinking Score rose from M = 9.14 (SD = 1.66) at the pre-test to M = 16.98 (SD = 1.90) at the post-test. Paired-samples t-tests confirmed this gain was highly significant, t(54) = 21.43, p < .001, with a very large effect size (d = 2.89). There were also big improvements in all of the subcomponents: pattern recognition, t(54) = 17.26, p < .001, d = 2.33; hypothesis formulation, t(54) = 17.26, p < .001, d = 2.33; and metalinguistic reflection, t(54) = 16.04, p < .001, d = 2.16 (Table 2). These findings furnish compelling evidence that the digital inductive model significantly augmented both overall linguistic cognition and its fundamental cognitive elements. Table 1 Descriptive Statistics for the Linguistic Analysis Task (LAT) Scores Measure N (Pre-Test) M (Pre-Test) SD (Pre-Test) N (Post-Test) M (Post-Test) SD (Post-Test) Total Linguistic Thinking Score (Max 20) 56 9.14 1.66 55 16.98 1.90 Pattern Recognition Score (Part A, Q1) 56 2.25 0.78 55 4.25 0.78 Hypothesis Formulation Score (Part A, Q2) 56 2.20 0.82 55 4.20 0.82 Metalinguistic Reflection Score (Part B) 56 4.69 1.06 55 8.53 1.05 Note. N refers to the number of participants. Post-test statistics are based on the paired sample (N = 55) for consistency with inferential tests. The descriptive statistics in Table 1 reveal a clear and substantial increase in performance across all measures of linguistic thinking following the intervention. The average Total Linguistic Thinking Score improved dramatically, rising from 9.14 to 16.98, representing a shift from moderate baseline performance to a high level of proficiency. This pattern of growth is mirrored across all sub-components, with scores for Pattern Recognition, Hypothesis Formulation, and Metalinguistic Reflection nearly doubling. The relatively small and consistent standard deviations suggest that the improvement was a uniform phenomenon across the entire cohort. These descriptive results provide strong preliminary evidence that the intervention was highly effective, a finding that is further tested for statistical significance in Table 2. Table 2 Paired-Samples t-Tests Comparing Pre-Test and Post-Test LAT Scores (N = 55) Comparison (Pre-Test vs. Post-Test) M Difference SD Difference t p Cohen’s d Total Linguistic Thinking Score 7.84 2.71 21.43 < .001 2.89 Pattern Recognition Score 2.00 0.82 17.26 < .001 2.33 Hypothesis Formulation Score 2.00 0.82 17.26 < .001 2.33 Metalinguistic Reflection Score 3.84 1.67 16.04 < .001 2.16 Note. df = 54 for all t-tests. All p values are two-tailed. Cohen’s d values represent the standardized mean difference for paired samples. The results of the paired-samples t-tests presented in Table 2 confirm that the observed improvements are highly statistically significant. For the Total Linguistic Thinking Score, the analysis revealed a significant increase ( t (54) = 21.43, p < .001). Crucially, the magnitude of this change, as measured by Cohen’s d , was exceptionally large ( d = 2.89), indicating that the digital inductive model had a profound and practically significant impact on the participants' overall analytical abilities. This powerful effect was consistent across all measured skills. These inferential statistics provide robust, unequivocal support for H1, demonstrating that the intervention was not just effective, but transformative in developing the multifaceted components of linguistic thinking. Qualitative Findings The qualitative data from reflective journals (n=10) and a focus group (n=7) were analyzed to explain the quantitative improvement in linguistic thinking and to understand the participants' experience. A preliminary analysis of the data in NVivo revealed a focus on dynamic, process-oriented language, as illustrated in the word cloud (Figure 2). Key terms such as changed, give, process, think, and active highlight a journey of transformation rather than a static state. The thematic analysis revealed a clear developmental trajectory, which is visualized as a process model in Figure 3. Participants did not experience the themes as isolated categories but as stages in a transformative journey, moving from initial apprehension to the formation of a new pedagogical identity. This journey is presented below in four interconnected stages. The journey began with an Initial Emotional Response of anxiety and uncertainty, which served as a catalyst for engagement. The breakthrough to Cognitive Engagement—or "detective work"—was directly facilitated by the core affordances of authenticity and collaboration. This deep engagement led to a Shift in Pedagogical Perception, where participants' teaching philosophies evolved toward a student-centered, facilitative stance. The culmination of this process was a Transformed Pedagogical Identity, characterized by confidence and a new vision for their role as language educators. Stage 1: Initial Emotional Response (Anxiety and Uncertainty) As depicted in Figure 3, the participants' journey began not with cognitive engagement, but with an emotional hurdle. Accustomed to deductive instruction, the uncertainty of the inductive model provoked anxiety. This initial resistance was a critical first step. P3 described the feeling as "Terrifying at first. I kept thinking, 'What if my rule is wrong?' I just wanted the teacher to give me the answer." This conceptual anxiety was compounded by technical barriers, with P3 adding, "The first two weeks, I felt like I was fighting the software more than learning French." This phase of arousal and discomfort served as the necessary catalyst for motivating a new approach to learning. Stage 2: Cognitive Engagement through Digital Affordances The breakthrough from anxiety to engagement was directly facilitated by the core affordances of the digital tools: authenticity and collaboration . Access to real-world language data was a revelation. As P53 stated, "The authenticity. Full stop. We are seeing the language as it is actually used by native speakers, not the sanitized version from textbooks." This authentic context enabled the deep Cognitive Engagement that participants described as "detective work." Grammar became a puzzle to be solved rather than a list to be memorized. P15 explained, "Before, if a rule had exceptions, it was frustrating. Now, the exceptions are the most interesting part. They are clues." This active process of pattern recognition led to a deeper, more tangible understanding. P49 noted, "Seeing it used after verbs of emotion and opinion in hundreds of real articles made that idea tangible, not just an abstract definition in a book." Collaboration was essential in this phase, scaffolding both technical and conceptual challenges. P9 affirmed, "Having four different perspectives meant we saw patterns that one person alone would have missed. The debates were the most productive part." Stage 3: The Shift in Pedagogical Perception As participants gained a deeper understanding through their own cognitive engagement, their perceptions of teaching began to change. The experience of discovering rules for themselves directly influenced their pedagogical beliefs, causing a shift from a teacher-centered to a student-centered perspective . P4’s reflection captured this evolution powerfully: "I now feel that the deductive way is passive and almost disrespectful to the learner's intelligence. This inductive approach treats students like capable thinkers." However, this emerging identity was tempered by practical considerations. Participants demonstrated sophisticated pedagogical reasoning by acknowledging the need for adaptation . They recognized that the model required scaffolding for different levels and contexts. P26 explained , "I'm 100% going to use it, but I'll adapt it. I wouldn't just give the concordancer to my A2 students. That would be chaos." This highlights a move toward becoming reflective practitioners who can orchestrate pedagogy, rather than simply replicating a method. Stage 4: A Transformed Pedagogical Identity The culmination of this process was a form of self-actualization, resulting in a Transformed Pedagogical Identity . Having moved from anxiety to confidence as learners, participants began to envision themselves as confident, student-centered teachers. They no longer saw their role as the sole authority on grammar but as a facilitator of discovery. The emotional journey concluded with a sense of empowerment and purpose. As P26 shared, "It genuinely changed the way I see my future career. I feel more like a language coach or guide now, and less like a grammar police officer." This final stage represents the full integration of the cognitive, emotional, and pedagogical shifts experienced throughout the intervention. Figure 3 presents a process model that visually synthesizes the qualitative findings of the study. It moves beyond a simple categorization of themes to illustrate the dynamic, developmental journey that pre-service teachers underwent as they engaged with the digital inductive model. The model depicts a sequential yet interconnected pathway where emotional responses, cognitive engagement, and pedagogical perceptions culminate in a transformed professional identity. As shown in Figure 3, the process begins with the Initial Emotional Response , characterized by anxiety and uncertainty. This initial state acts as a catalyst, creating an "Arousal & Motivation" that propels participants toward Cognitive Engagement . This central phase, described as "Detective Work" and "Pattern Recognition," is directly enabled by the Digital Tool Affordances of authenticity and collaboration. The model shows these affordances having two distinct effects: they facilitate the cognitive work itself (dashed arrow) and directly influence pedagogical thinking (solid arrow). The successful cognitive engagement leads to a "Deeper Understanding," which in turn fosters a new Pedagogical Perception . This perception is defined by a shift toward facilitation and an awareness of the need for adaptation. Finally, this new pedagogical mindset undergoes a process of "Self-Actualization," leading to the ultimate outcome: a Transformed Pedagogical Identity , characterized by confidence and a student-centered approach. More than a summary of themes, Figure 3 interprets the participants' experience as a story of transformation, highlighting key cause-and-effect relationships. Emotion as a Catalyst: The model posits that the transformative journey does not begin with cognition, but with emotion. The initial anxiety and uncertainty were not merely obstacles but a necessary motivational force. This "Arousal & Motivation" created a state of cognitive dissonance that pushed participants to resolve their uncertainty by actively engaging with the new learning method, rather than passively resisting it. Affordances as the Engine of Engagement: The model emphasizes the critical role of the digital tool affordances in making the cognitive leap possible. Authenticity and collaboration were not just features of the model; they were the essential scaffolding that allowed participants to move past their initial anxiety and engage in meaningful "detective work." The authenticity of the data made the inquiry feel relevant and powerful, while collaboration provided the peer support needed to navigate both technical and conceptual challenges. From Understanding to Identity: The model illustrates that a change in pedagogical identity is not a direct result of learning a new technique. Instead, it is the outcome of a deeper, multi-stage process. It is the "Deeper Understanding" —gained through personal cognitive struggle and discovery—that makes a shift in pedagogical perception possible. This new perception then solidifies through a process of "Self-Actualization," where participants begin to see themselves as capable of enacting this new pedagogy. The final transformation is therefore not just a change in belief, but a change in professional identity—a shift from being a "grammar police officer" to a "language coach or guide." In summary, Figure 3 provides a theoretical explanation for the qualitative findings, illustrating how the digital inductive model facilitates a journey where initial emotional and cognitive challenges, when supported by the right technological affordances, can lead to a profound and lasting transformation in a pre-service teacher's professional identity. To consolidate the thematic analysis presented above, the developmental trajectory of the pre-service teachers' experience is summarized in Table 1. The table presents the core finding for each theme, a representative quote from the dataset—specifying its source as either a reflective journal or the focus group—and the coding coverage. The "Coding Coverage (Weighted %)" was calculated in NVivo and indicates the proportion of the total coded text attributed to each theme, highlighting the relative prominence of each dimension in the participants' experience. This quantitative representation of the qualitative data corroborates the narrative finding that Cognitive Engagement was the most extensively discussed aspect of the participants' journey. Table 3 Summary of Thematic Analysis Findings Theme Core Finding / Description Illustrative Quote & Source Coding Coverage (Weighted %) Cognitive Engagement The shift from passive memorization to active 'detective work' through pattern recognition and hypothesis testing. “Now, the exceptions are the most interesting part. They are clues.” (P15, Focus Group) 35% Emotional Response A developmental arc from initial anxiety and frustration (“terrified”) to eventual confidence and empowerment. “The most empowering part is the confidence I feel. I can now investigate any grammar question I have on my own.” (P2, Journal, Week 8) 25% Pedagogical Perception A transformation in teaching philosophy toward facilitation, balanced by a pragmatic awareness of the need for adaptation and scaffolding. “This inductive approach treats students like capable thinkers. My entire view of grammar teaching has shifted.” (P4, Journal, Week 8) 20% Digital Tool Affordances The dual role of authenticity (real data) and collaboration (group work) as critical enablers, despite an initial technical learning curve. “The authenticity. Full stop. We are seeing the language as it is actually used by native speakers, not the sanitized version from textbooks.” (P53, Focus Group) 20% Integration of Quantitative and Qualitative Findings This study employed a sequential explanatory design, where the qualitative findings were used to explain and elaborate upon the quantitative results. This section integrates both data sources to provide a comprehensive answer to the research questions, demonstrating not only that the digital inductive model was effective, but also how and why it facilitated a transformation in the pre-service teachers' linguistic thinking and pedagogical identity. The relationship between the quantitative and qualitative findings is presented in the joint display in Table 3 . The quantitative results provided conclusive evidence of the intervention's efficacy. The qualitative data illuminates the process behind this dramatic statistical improvement. The theme of Cognitive Engagement reveals that participants moved from passive memorization to active analysis. As P15 explained, "When you spend an hour debating and looking at evidence to arrive at a conclusion, you don't forget it." This "detective work" is the cognitive mechanism that explains the substantial gains measured by the LAT. Furthermore, the qualitative findings answer the second and third research questions by detailing the participants' transformative journey. While the quantitative data showed what skills were developed, the qualitative data captured the shift in perception and identity . The process model (Figure 3) illustrates that this was not a simple, linear progression. Participants began with an Initial Emotional Response of anxiety, but the key Digital Tool Affordances —authenticity and collaboration—provided the necessary scaffolding to overcome these barriers. This journey culminated in a Transformed Pedagogical Identity , with participants envisioning their future role not as a purveyor of rules, but as a "language coach or guide" (P26) who facilitates discovery. The joint display in Table 4 visually connects these threads, providing a holistic view of the study's findings. Table 4 Joint Display Integrating Quantitative and Qualitative Findings Research Question Quantitative Findings (The "What") Qualitative Findings (The "How & Why") Illustrative Quote RQ1: Impact on Linguistic Thinking Statistically significant improvement in Total Linguistic Thinking Score ( p < .001, d = 2.89) and all sub-skills. The improvement was driven by a shift to active Cognitive Engagement , where participants acted as "language detectives," using pattern recognition to achieve a deeper, more memorable understanding. “We found adjectives that didn't fit and tried to figure out why. It felt like we were co-creating the knowledge.” (P15, Focus Group) RQ2: Perceptions of the Model's Role N/A (Primarily qualitative). However, the strong positive outcomes provide context for the perceptions. The model catalyzed a Transformation in Pedagogical Identity . Participants moved from a teacher-centered to a student-centered philosophy, balanced by a pragmatic awareness of the need for adaptation. “This inductive approach treats students like capable thinkers. My entire view of grammar teaching has shifted.” (P4, Journal, Week 8) RQ3: Affordances & Challenges N/A (Primarily qualitative). Challenges: The journey began with an Initial Emotional Response of anxiety and technical frustration. “Terrifying at first… I felt like I was fighting the software more than learning French.” (P3, Focus Group) Affordances: The key Digital Tool Affordances of authenticity (real data) and collaboration (group work) were crucial for overcoming challenges and enabling cognitive engagement. “The authenticity. Full stop. We are seeing the language as it is actually used by native speakers, not the sanitized version from textbooks.” (P53, Focus Group) Discussion This study sought to determine the efficacy of a digital inductive model in enhancing the linguistic thinking of pre-service FFL teachers in Egypt. The findings provide a resounding affirmation of the model's effectiveness, not only in developing measurable analytical skills but also in catalyzing a profound transformation in pedagogical identity. This discussion integrates the results, corroborates them with the existing literature, and explores the implications for theory and practice. From Inductive Theory to Empirical Reality: Explaining the Growth in Linguistic Thinking The quantitative results unequivocally confirmed H1, demonstrating that participants' linguistic thinking skills improved dramatically. This finding provides robust empirical evidence for the theoretical claims made in the literature regarding the power of inductive pedagogy and DDL (Kharade & Peese, 2014 ; Ma et al., 2024 ). The qualitative data explains how this growth was achieved: participants' journey from passive rule-receivers to active "language detectives" illustrates the cognitive mechanism at play. This "co-creating of knowledge," as P15 described it, is the lived experience behind the statistical gains, confirming that the digital inductive model successfully operationalized constructivist principles to foster deep, durable understanding. The Transformative Journey: From Digital Competence to Pedagogical Identity The qualitative findings illuminate a journey of professional transformation that supports Propositions 2 and 3. This journey corroborates literature that highlights the challenge of moving pre-service teachers from basic digital skills to sophisticated digital pedagogical competence (Lu et al., 2025 ; Reisoğlu & Çebi, 2020 ). Our study shows that this is not merely a technical transition but an emotional and philosophical one. Participants began with anxiety and uncertainty, but the key affordances of authenticity and collaboration served as a critical scaffold. This process led not only to cognitive gains but to a profound shift in pedagogical identity, as participants transitioned from a "grammar police officer" to a "language coach or guide" (P26). This finding enriches the TPACK framework by demonstrating that true pedagogical orchestration is not just a skill, but an identity that emerges from personal, transformative learning experiences. Implications for Theory, Policy, and Practice in the Egyptian Context This study makes several important contributions. Theoretically, it validates the integrated framework proposed in Fig. 1 , confirming that combining constructivist principles, digital pedagogical competence, and inductive methods creates a powerful engine for developing higher-order thinking. For policy and practice in Egypt, the implications are direct and timely. As national initiatives like the TrèFLE project and ICDL certification roll out, this study provides a clear directive: providing access to digital tools is not enough. To truly enhance the quality of FFL instruction, teacher education programs must adopt pedagogical models that guide pre-service teachers through inquiry-based learning themselves. The challenges identified by participants—the initial technical curve and the tension with deductive traditions—are not failures of the model but critical data points for implementation. Training programs should include dedicated technical workshops and explicitly address the philosophical shift from deductive to inductive teaching. Limitations and Future Research This study, while providing strong evidence, has several limitations. The single-group, pre-test/post-test design does not allow for a direct comparison with a control group. The qualitative sample, while rich, was limited to 17 participants from a single institution, which may limit generalizability. Furthermore, the study measured perceptions and skills within a training context; a longitudinal study is needed to determine whether this transformed pedagogical identity translates into sustained changes in classroom practice. Future research should address these limitations through experimental designs and explore the adaptability of this model for in-service teacher professional development. Conclusion In conclusion, this study provides compelling mixed-methods evidence for the efficacy of digital inductive models in not only enhancing the linguistic thinking of pre-service FFL teachers but also in profoundly transforming their pedagogical identity. The quantitative data demonstrated a statistically significant and exceptionally large improvement in analytical skills, while the qualitative findings illuminated the process behind this growth: a transformative journey from apprehensive learners to confident, inquiry-driven pedagogical thinkers. By strategically combining digital tools with inductive pedagogy within a supportive, collaborative framework, this study confirms that teacher education can move beyond the superficial application of technology and cultivate the deep, analytical, and student-centered competence required of 21st-century educators. The findings offer several critical pedagogical implications. First, pedagogy must drive technology, not the other way around. The success of this intervention was rooted not in the novelty of the tools, but in their purposeful orchestration within an inductive methodology. Second, the affective and cognitive journey of the pre-service teacher must be explicitly scaffolded. Teacher education programs must anticipate initial resistance and build in robust support structures. Finally, the dual outcome of the digital inductive model—enhancing both content mastery and pedagogical skill—is its most powerful advantage. Participants did not just learn about French grammar; they learned how to think like a linguist and, in doing so, acquired a transferable model for their own future teaching. For a nation like Egypt, poised at the cusp of a major digital transformation in education, this study offers a research-validated pathway for ensuring that its investment in technology yields a profound and lasting return. The adoption of models that foster inquiry and empower future educators as autonomous, analytical thinkers will enable Egypt to prepare a new generation of FFL teachers who are not only digitally competent but also analytically confident, pedagogically adaptive, and ready to meet the demands of modern language education. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Human Ethics and Consent to Participate Ethical approval for this study was obtained from the Institutional Review Board of the Faculty of Education, Beni-Suef University (Reference No. BSU-FoE-003-01-3-2025). The study was conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the study. AI Usage Declaration AI including QuillBot, Chatgpt and Gemini was only used in polishing the language, editing where this was deemed necessary, but was not used in data generation or analysis tasks. Author Contribution MM conceived the research, contrived the research design; MM & FA designed and validated the instruments. FA conducted the field investigation collected the data and organized it. MM analyzed the data and wrote the first draft. Both authors revised the final draft. References Aleksieva, L. (2025). 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Supplementary Files AppendicesFA.docx Book1LATTest.xlsx DataFile1FAQualData.docx WordFrequencyQuerywordlist.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:39:41","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116113,"visible":true,"origin":"","legend":"","description":"","filename":"7edc4cbab05845f3a4902ed1ec58954c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/ade2db03ce58ac7a47c233df.xml"},{"id":92480081,"identity":"75ea1160-cbb3-4516-b0ae-6474e5e251a6","added_by":"auto","created_at":"2025-09-30 07:39:41","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130448,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/48175f76bb06879e84d93ff7.html"},{"id":92481201,"identity":"93eaf891-8e26-4494-9c14-36cb2c60976a","added_by":"auto","created_at":"2025-09-30 07:47:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102304,"visible":true,"origin":"","legend":"\u003cp\u003eThe Theoretical Framework of the StudyNote. This diagram shows the integration of three foundational domains: Digital Pedagogical Competence (TPACK, DigCompEdu), Constructivist and Socio-Cultural Learning, and Inductive Pedagogy (Data-Driven Learning). These domains inform the core processes of the Digital Inductive Model, which is designed to enhance linguistic thinking.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/3830a3f29b2ae0ac9565b8ff.png"},{"id":92480067,"identity":"bb704fa4-7c61-4027-93e8-6645a4d01d46","added_by":"auto","created_at":"2025-09-30 07:39:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":798849,"visible":true,"origin":"","legend":"\u003cp\u003eWord Cloud of Most Frequent Terms in Qualitative Data\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The size of the word corresponds to its frequency in the dataset. Words like give, abstract, changed, time, and active were central to participants' descriptions of their experience.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/51849b92b5796060052d8a80.png"},{"id":92482575,"identity":"bc50423c-60e1-4819-8950-2a99061824c1","added_by":"auto","created_at":"2025-09-30 07:55:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176537,"visible":true,"origin":"","legend":"\u003cp\u003eA Process Model of the Pre-Service Teachers' Transformative Experience\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e This model illustrates the developmental pathway identified in the qualitative data, moving from an initial emotional response to a transformed pedagogical identity, facilitated by cognitive engagement and the core affordances of the digital inductive model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/627273567d9d3b1a631869f7.png"},{"id":101630545,"identity":"694ad7a5-e106-4163-bb94-6507cded5d4e","added_by":"auto","created_at":"2026-02-02 05:25:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2556862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/e72cfdf2-9520-4384-8dd3-9c6dff5ced8d.pdf"},{"id":92480059,"identity":"d7db7514-4bce-4613-b7a6-3fad968b2333","added_by":"auto","created_at":"2025-09-30 07:39:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19819,"visible":true,"origin":"","legend":"","description":"","filename":"AppendicesFA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/ca6e97bc22b476aa75f5a9ba.docx"},{"id":92480061,"identity":"5c013f63-302b-4cc8-89cc-dd863d7dd296","added_by":"auto","created_at":"2025-09-30 07:39:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11827,"visible":true,"origin":"","legend":"","description":"","filename":"Book1LATTest.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/ef37a06adc6e63d6eeb6dfc0.xlsx"},{"id":92481203,"identity":"6a8f990d-a204-440f-b5b6-d397bce52433","added_by":"auto","created_at":"2025-09-30 07:47:41","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31574,"visible":true,"origin":"","legend":"","description":"","filename":"DataFile1FAQualData.docx","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/0358f323aa7c4a6dd06cfdc2.docx"},{"id":92482574,"identity":"99e07b0f-9b8f-4e2d-bac0-9ffe2d967ccd","added_by":"auto","created_at":"2025-09-30 07:55:42","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":40250,"visible":true,"origin":"","legend":"","description":"","filename":"WordFrequencyQuerywordlist.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7720531/v1/698786cf970d5d6c94bbbf4a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Efficacy of Digital Inductive Models in Enhancing Linguistic Thinking Among Pre-Service FFL Student-Teachers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe widespread incorporation of digital technology into education has fundamentally transformed the realm of teacher preparation, necessitating a transition from cultivating basic digital literacy to enhancing advanced digital pedagogical skills (Aleksieva, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Reisoğlu \u0026amp; \u0026Ccedil;ebi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For pre-service language teachers, this evolution entails not only the acquisition of new tools but also the comprehension of how technology can be utilized to enhance and enrich language learning environments (Choi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While extensive research has examined the integration of digital technologies in teacher education, a notable deficiency persists in comprehending how particular digitally-enhanced pedagogical frameworks can foster the advanced cognitive skills requisite for proficient language instruction (Tunjera, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The difficulty is in going beyond using technology just to deliver information and instead using it to get people to think more deeply about the subject.\u003c/p\u003e\u003cp\u003eThis challenge is especially important for learning \"linguistic thinking,\" which is the ability to critically look at language structures, find patterns, and come up with logical guesses about how language works. This kind of thinking is important for teachers because they have to help their students move from memorizing things to really knowing how to use the language. Traditional deductive teaching methods don't always work well for building this skill (Kharade \u0026amp; Peese, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which is why more and more people are interested in constructivist methods like inductive learning, where students find rules and patterns in real data (Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This method of teaching is in line with modern linguistic methods, like corpus-based pedagogy, which uses technology to look at large language datasets inductively (Kurt, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis dynamic is most apparent in Egypt's current educational landscape, which is experiencing a unified national initiative to modernize its teacher training programs, especially in French as a Foreign Language (FFL). The Franco-Egyptian Tr\u0026egrave;FLE project is an example of a high-level initiative that will provide new curricula and digital resources to more than 13,000 FFL teachers and inspectors. This shows that Egypt is making a big investment in building digital capacity (Egyptian Ministry of Education, 2024). At the same time, national standards like the ICDL \"Teachers\" certification are setting a standard for digital skills (ICDL Arabia, n.d.). These reforms are important for building basic skills, but they don't automatically close the gap between knowing how to use technology and knowing how to use it for deep pedagogical inquiry.\u003c/p\u003e\u003cp\u003eThe merging of inductive methods and digital platforms makes it possible to create strong \"digital inductive models\" of learning. These models can furnish pre-service teachers with dynamic, data-rich environments to investigate linguistic concepts, thereby augmenting their metacognitive and linguistic competencies prior to their classroom engagement (Ana \u0026amp; L\u0026oacute;pez-Medina, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Even though these models look good and Egypt's policies are moving in the right direction, there is still a big gap in the research. There is a lack of research that specifically assesses the influence of digital inductive pedagogies on the linguistic cognition of pre-service FFL teachers in the Egyptian context. This study examines the effectiveness of digital inductive models, seeking to elucidate how focused, technology-enhanced strategies can more effectively equip the forthcoming generation of language educators for the intricacies of 21st-century instruction.\u003c/p\u003e\n\u003ch3\u003eContext of the Study\u003c/h3\u003e\n\u003cp\u003eThe widespread incorporation of digital technology has established a new necessity in teacher education: to progress beyond the development of fundamental digital literacy towards the enhancement of advanced digital pedagogical competence (Aleksieva, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Reisoğlu \u0026amp; \u0026Ccedil;ebi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For pre-service language teachers, this evolution necessitates more than mere proficiency with new tools; it requires a profound comprehension of how technology can be coordinated to cultivate richer, more effective learning environments (Choi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This challenge is especially pronounced in cultivating \"linguistic thinking\"\u0026mdash;the capacity to critically evaluate language structures, discern patterns, and formulate evidence-based hypotheses regarding language functionality. Teachers who want to help their students learn real proficiency instead of just memorizing facts need to be able to analyze things, but traditional, deductive teaching methods often don't help them do this (Kharade \u0026amp; Peese, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis dynamic is most apparent in Egypt's current educational landscape, which is experiencing a concerted national initiative to modernize its teacher training programs, especially in French as a Foreign Language (FFL). The Franco-Egyptian Tr\u0026egrave;FLE project is an example of a high-level initiative that will provide new curricula and digital resources to more than 13,000 FFL teachers and inspectors. This shows that Egypt is making a big investment in building digital capacity (Egyptian Ministry of Education, 2024). At the same time, national standards like the ICDL \"Teachers\" certification are setting a minimum level for digital skills (ICDL Arabia, n.d.). These reforms are important for building basic skills, but they don't automatically close the gap between knowing how to use technology and knowing how to use it for deep pedagogical inquiry. This gap underscores the urgent necessity for pedagogical frameworks that utilize Egypt's emerging digital infrastructure to develop the advanced cognitive skills required for contemporary language education.\u003c/p\u003e\u003cp\u003eThis research responds to this necessity by examining the effectiveness of a digital inductive model tailored for pre-service foreign language teachers. Based on constructivist ideas, this model uses digital tools like corpora and concordancers to get trainees involved in a guided discovery process, where they look at real language data to figure out grammatical rules and usage patterns on their own (Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This method not only improves their own linguistic thinking, but it also shows them how to use a powerful, student-centered teaching style that they can use in their own classrooms in the future.\u003c/p\u003e\u003cp\u003eEven though these models look promising and there is a lot of support for them in Egypt, there is still a big gap in the research locally and globally. Although studies on general digital competence are emerging, there is a paucity of research specifically assessing the influence of digital inductive pedagogies on the linguistic thinking of pre-service FFL teachers in the Egyptian context. This study seeks to address this deficiency by offering a mixed-methods analysis of the model's effects. It aims to (1) quantitatively assess the enhancement of pre-service teachers' linguistic thinking skills, (2) qualitatively investigate their perceptions regarding the model's influence on their analytical and pedagogical practices, and (3) delineate the primary opportunities and challenges they face. The findings aim to guide the development of enhanced teacher training programs that can fully leverage Egypt's digital transformation in language education.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Questions\u003c/h2\u003e\u003cp\u003eTo fill this gap in the literature and conduct a focused investigation into the efficacy of the digital inductive model, this study is guided by the following research questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo what extent does engagement with a digital inductive learning model impact the development of linguistic thinking skills among pre-service teachers?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do pre-service teachers perceive the role of the digital inductive model in shaping their approach to language analysis and their future pedagogical practices?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the key affordances and challenges encountered by pre-service teachers when using digital inductive models for linguistic inquiry?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eThe Shift from Digital Literacy to Digital Pedagogical Competence\u003c/h2\u003e\u003cp\u003eFor technology to be successfully used in teacher education, we need to make a big change from teaching basic digital literacy to teaching deep digital pedagogical competence. Frameworks like TPACK (Technological Pedagogical Content Knowledge) and DigCompEdu stress that effective teaching isn't just about knowing how to use digital tools; it's also about being able to use them in a planned way to meet certain teaching goals and content areas. Research shows that even though many pre-service teachers are comfortable with technology, they don't feel ready to use their skills to make big changes in the classroom (Lu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Reisoğlu \u0026amp; Çebi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There is a clear need for training models that go beyond teaching people how to use tools and instead focus on how to use technology to encourage analytical, inquiry-based learning (Huang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDigital Policy and Capacity-Building in Egyptian Language Education\u003c/h3\u003e\n\u003cp\u003eThis global trend is especially important in Egypt's current education system, which is going through a big digital change, especially in how it teaches foreign languages. Recent national initiatives have made it possible to study how advanced digital training models affect people. For example, the Franco-Egyptian TrèFLE project wants to train thousands of French as a Foreign Language (FFL) teachers and use new, digitally-supported curricula in the 2024–2025 school year. Because of this policy push, it is now more important than ever to make sure that digital training helps people develop higher-order teaching skills, not just basic tool use.\u003c/p\u003e\u003cp\u003eAt the same time, the Egyptian Ministry of Education's decision to use the ICDL \"Teachers\" certification as a general standard for digital competence does not give teachers the specific skills they need to use technology to improve language analysis. This makes a gap between basic digital literacy and the advanced teaching skills needed to use data-rich tools like corpora and concordancers. To close this gap, universities are working together in new ways. For example, the French section of Ain Shams University and Aix-Marseille University are working together to modernize FFL degree programs and promote new teaching methods. These converging initiatives highlight the urgency of investigating digitally-enabled, inquiry-based pedagogical models for the forthcoming generation of FFL teachers in Egypt.\u003c/p\u003e\n\u003ch3\u003eInductive Pedagogy and Data-Driven Learning for Linguistic Thinking\u003c/h3\u003e\n\u003cp\u003eThis study examines inductive learning methodologies to connect tool utilization with pedagogical application. Inductive methodologies, wherein learners discern rules and patterns from genuine examples, are exceptionally effective for language instruction as they reflect naturalistic pattern recognition and promote the hypothesis-testing fundamental to linguistic cognition. Data-Driven Learning (DDL) exemplifies a technology-enhanced inductive methodology. Using tools like concordancers to look at real language data, learners can find collocations, grammatical structures, and pragmatic conventions on their own. The DDL literature substantiates that these tasks cultivate essential analytical skills and metalinguistic awareness—competencies that are particularly advantageous for educators who must ultimately facilitate analogous discovery processes for their students.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eThe Intersection: Digital Inductive Models in Teacher Education\u003c/h2\u003e\u003cp\u003eThis study is positioned at the intersection of these three trends, examining the effectiveness of digital inductive models. These are organized learning spaces that use digital corpora, concordancers, and guided inquiry sequences to help students learn through inductive discovery. For pre-service teachers, such models provide a dual advantage: they simultaneously improve their personal linguistic cognition (pattern recognition, metalinguistic reflection) while exemplifying a robust pedagogical strategy that can be applied in their future classrooms. Although research on corpus-based pedagogy is expanding, the evidence supporting the effectiveness of structured digital inductive models in pre-service teacher education, particularly within the Egyptian FFL context, is still insufficient. This study seeks to fill this gap by investigating how these models can adequately equip teachers to address the challenges of a digitally evolving educational system.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDigital Technologies in Language Teacher Education\u003c/h3\u003e\n\u003cp\u003eIn the specific area of language education, this digital change brings both new chances and new problems. Teacher preparation programs are using more and more technologies to make language learning experiences that are real, interactive, and full of data. These include practices like digital storytelling to improve narrative skills and digital collaboration (Yu \u0026amp; Wang, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Spaliviero \u0026amp; Serragiotto, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), using video to reflect on and analyze classroom practice (Sun et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and using Generative AI to help with lesson planning and content creation (Moorhouse et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kurt, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA significant emphasis in the literature is on enhancing pre-service teachers' capacity to integrate technological tools with effective pedagogical principles, commonly known as Technological Pedagogical Content Knowledge (TPACK) (Huang, Zhang, \u0026amp; Lv, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, research indicates that pre-service teachers frequently perceive themselves as inadequately equipped to utilize these tools effectively, attributing this to insufficient training in advancing from superficial application to transformative pedagogy (Yetkin \u0026amp; Özer-Altınkaya, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This emphasizes the necessity for educational frameworks that not only familiarize trainees with technology but also explicitly instruct them in its utilization to cultivate advanced cognitive skills in their prospective students.\u003c/p\u003e\n\u003ch3\u003eThe Role of Inductive Learning in Developing Linguistic Thinking\u003c/h3\u003e\n\u003cp\u003eAs digital tools become more popular, so does the interest in inductive learning methods in education. Inductive teaching, on the other hand, encourages students to find patterns, rules, and ideas in real-life examples (Kharade \u0026amp; Peese, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In traditional deductive methods, rules are given first. This method is especially good for teaching languages because it mimics how people naturally learn languages and encourages deeper analytical thinking (Ana \u0026amp; López-Medina, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Through the analysis of authentic language data, learners cultivate \"linguistic thinking,\" which encompasses the capacity to hypothesize, test, and enhance their comprehension of grammatical and lexical frameworks (Naranjo Chimborazo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies indicate that inductive methodologies can substantially improve pre-service teachers' critical thinking and metacognitive awareness (Ömeroğlu \u0026amp; Hakkoymaz, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies indicate that inductive methodologies can markedly improve pre-service teachers' critical thinking, problem-solving abilities, and metacognitive awareness (Ana \u0026amp; Beatriz, 2021; Ömeroğlu \u0026amp; Hakkoymaz, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Teachers are better able to help their students learn through discovery when they have this kind of learning experience themselves. Inductive reasoning is essential for recognizing grammatical patterns, comprehending semantic subtleties, and valuing the contextual aspects of language use—all crucial skills for an effective language educator.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eThe Convergence: Digital Inductive Models\u003c/h2\u003e\u003cp\u003eThe real potential for new ways of teaching comes from the combination of digital technology and inductive learning. \"Digital inductive models\" use technology to make inquiry-based learning more effective, efficient, and available to everyone. For example, digital corpora and concordancing tools let pre-service teachers look at a lot of real language data, which helps them find grammatical and collocational patterns in a way that would be impossible with regular textbooks (Kurt, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These models are based on a constructivist philosophy, which sees the pre-service teacher as an active builder of knowledge instead of a passive receiver (Choi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By working on digital inductive tasks, trainees can improve their language skills and analytical skills while also creating a mental model for how to use technology to support inquiry-based learning in their future work.\u003c/p\u003e\u003cp\u003eThese models adhere to a constructivist philosophy, viewing the pre-service teacher as an active creator of knowledge instead of a passive receiver (Choi, 2025). By participating in digital inductive tasks, trainees can improve their language awareness and analytical abilities while concurrently developing a conceptual framework for the implementation of technology-enhanced, inquiry-based learning in their future practice. Nonetheless, although the theoretical advantages are evident, a substantial deficiency exists in the literature concerning the empirical effectiveness of these integrated models. A significant portion of the research concentrates on either the integration of digital tools broadly or on non-digital inductive methodologies. A comprehensive examination of the influence of structured digital inductive models on the cultivation of linguistic cognition in pre-service teachers is essential to affirm their significance in contemporary teacher education.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical Framework\u003c/h2\u003e\u003cp\u003eThe theoretical framework for this study is envisioned as a cohesive model that integrates three fundamental domains to elucidate the enhancement of linguistic thinking within a particular educational context. This framework is based on the pillars of Digital Pedagogical Competence, Constructivist and Socio-Cultural Learning, and Inductive Pedagogy, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These fundamental theories underpin a series of essential pedagogical processes that coalesce to create the Digital Inductive Model, subsequently contextualized within the Egyptian FFL Teacher Education Framework.\u003c/p\u003e\u003cp\u003eThe framework is fundamentally based on Constructivism and Socio-Cultural Learning, emphasizing that knowledge is actively constructed by learners through mediated tools and social interaction. Digital Pedagogical Competence (TPACK and DigCompEdu) adds another layer to this. This framework focuses on how to combine technology, pedagogy, and content into a coherent way of teaching (Reisoğlu \u0026amp; Çebi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The third strand is Inductive Pedagogy, which is put into practice through methods like Data-Driven Learning (DDL), which helps students find patterns and come up with hypotheses based on real data (Kharade \u0026amp; Peese, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that these domains and their related processes come together to make the Digital Inductive Model. This model is a structured system that uses digital tools in a collaborative setting to get pre-service teachers to think more deeply about language. The entire model functions within the Egyptian FFL Teacher Education Context, where national policies serve as the structural impetus for innovation.\u003c/p\u003e\u003cp\u003eInductive Pedagogy is the third pillar, and it is put into action through techniques like Data-Driven Learning (DDL). Inductive teaching prompts students to extract rules and principles from genuine examples, thereby enhancing their conceptual comprehension (Kharade \u0026amp; Peese, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This teaching method directly helps with the cognitive processes of Pattern Discovery and Hypothesis Testing. When pre-service teachers utilize digital tools such as concordancers, they participate in genuine linguistic investigation, thereby enhancing their analytical abilities and metalinguistic awareness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that these processes come together to make the Digital Inductive Model. This model is a structured way for pre-service teachers to learn. They use inductive methods to analyze language, guided by constructivist principles and their growing digital skills. The main goal of this model is to help people develop Enhanced Linguistic Thinking, which is the ability to critically look at language structures, come up with evidence-based hypotheses, and think about how they use language. By engaging in this model, pre-service teachers enhance their analytical skills and encounter a pedagogical methodology they can subsequently implement in their own classrooms.\u003c/p\u003e\u003cp\u003eFinally, this whole framework is based on the Egyptian FFL Teacher Education Context. National policies such as the TrèFLE project and initiatives for university modernization create the structural impetus for these innovative models. But there is also a conflict between these digital pedagogies that focus on reform and long-standing methods of teaching grammar through deduction. This framework serves as a lens for the design and evaluation of the Digital Inductive Model's effectiveness within this distinctive and evolving context.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHypotheses\u003c/h2\u003e\u003cp\u003eBased on the theoretical framework and the existing literature, this study proposes the following hypotheses, which correspond directly to the research questions:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThere are statistically significant differences in pre-service teachers’ linguistic thinking skills before and after participation in the digital inductive model, specifically in:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(a) identifying grammatical and lexical patterns in authentic French texts,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(b) formulating evidence-based hypotheses about language use, and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(c) engaging in metalinguistic reflection.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThere are statistically significant differences in pre-service teachers’ pedagogical perceptions before and after participation in the digital inductive model, such that they shift from favoring deductive approaches toward valuing constructivist, inquiry-based pedagogy.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThere are statistically significant differences between traditional instruction and the digital inductive model in terms of perceived affordances, with the model providing greater access to authentic data, stronger learner autonomy, and more collaborative knowledge construction.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThere are statistically significant differences between expected ease of implementation and actual experiences with the digital inductive model, with participants reporting higher cognitive load and tension with prior deductive learning traditions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e"},{"header":"Methods","content":"\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis study utilized a mixed-methods sequential explanatory design to examine the effectiveness of a digital inductive model on the linguistic cognition of pre-service FFL teachers. The design was selected to initially conduct a quantitative evaluation of the intervention's impact (QUAN), followed by the utilization of qualitative data to elucidate and expand upon those results (qual). The quantitative phase employed a single-group, pre-test/post-test design to assess alterations in linguistic thinking skills. The next qualitative phase used semi-structured focus groups and reflective journals to look into how participants perceived the model in terms of its affordances and challenges.\u003c/p\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eThe study involved 56 pre-service French as a Foreign Language (FFL) student teachers enrolled in a third-year course, \u003cem\u003eFFL Didactics and Pedagogy\u003c/em\u003e, at a public university in Beni-Suef, Egypt. Participants were purposively selected to ensure a solid foundation in French grammar and pedagogy, but without prior training in data-driven or corpus-based methodologies. All 56 completed the quantitative phase, which comprised a pre-test and post-test. The cohort included 48 females and 8 males, with a mean age of 20.5 years (\u003cem\u003eSD\u003c/em\u003e = 1.2), and all reported a CEFR proficiency level of B2 or higher. Participation was voluntary, informed consent was obtained, and ethical approval was granted by the university’s Institutional Review Board (Ref. No. BSU-FoE-003-01-3-2025).\u003c/p\u003e\u003cp\u003eFrom this cohort, a purposive subsample of 17 participants was selected for qualitative analysis to capture diverse perspectives. This subsample comprised two non-overlapping groups: (a) ten participants who submitted weekly reflective journals, representing a range of pre-test scores and engagement levels, and (b) seven participants who contributed to a focus group discussion, providing interactive insights into their experiences.\u003c/p\u003e\u003ch2\u003eThe Intervention: The Digital Inductive Model\u003c/h2\u003e\u003cp\u003eOver a period of 10 weeks, the digital inductive model was integrated into the participants' regular FFL didactics course. The intervention was designed to shift learning from a deductive, rule-based approach to an inductive, inquiry-based process. Each weekly module consisted of a 90-minute session and was structured around the following components:\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLinguistic Phenomenon\u003c/b\u003e: Participants were introduced to a complex area of French grammar or lexis (e.g., the subjunctive mood, pronominal verbs, use of prepositions).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDigital Tools\u003c/b\u003e: Participants were trained to use a web-based concordancing tool and a custom-built digital corpus of authentic French texts (including news articles, blog posts, and literary excerpts).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGuided Inquiry Tasks\u003c/b\u003e: In small groups, participants used the concordancer to search the corpus for authentic examples of the target linguistic phenomenon. They were guided by worksheets that prompted them to observe patterns, formulate hypotheses about rules and usage, and find counter-examples.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCollaborative Reflection\u003c/b\u003e: Groups posted their findings, hypotheses, and newly derived \"rules\" to a shared online forum (Moodle). Each session concluded with a whole-class discussion, moderated by the instructor, to compare findings and consolidate understanding.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003ch2\u003eData Collection and Instruments\u003c/h2\u003e\u003cp\u003eTo address the three research questions, both quantitative and qualitative data were collected.\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLinguistic Analysis Task (LAT) (Quantitative)\u003c/b\u003e: To measure the development of linguistic thinking (RQ1), a pre-test/post-test instrument was designed and administered before and after the 10-week intervention. The LAT consisted of two parts: (a) a pattern recognition section, where participants identified and explained grammatical nuances in authentic sentences, and (b) a hypothesis-testing section, where they were given a small data set and asked to formulate a rule governing its usage. The instrument was validated for content validity by three senior FFL pedagogy experts.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eReflective Journals (Qualitative)\u003c/b\u003e: Throughout the intervention, participants were required to maintain weekly digital journals. Prompts encouraged them to reflect on their learning process, their changing perceptions of grammar instruction (RQ2), and any difficulties or \"aha\" moments they experienced while using the digital tools (RQ3).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSemi-Structured Focus Groups (Qualitative)\u003c/b\u003e: In the final week of the intervention, eight focus groups (each with 7 participants) were conducted. The discussions were guided by a protocol designed to elicit detailed insights into participants’ perceptions of the model's effectiveness, its influence on their future teaching plans (RQ2), and the specific affordances and challenges they encountered (RQ3). Each focus group session lasted approximately 60 minutes and was audio-recorded and transcribed verbatim.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe collected data were analyzed in two distinct phases corresponding to the sequential explanatory research design.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1. Quantitative Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe quantitative data from all 56 participants, consisting of pre-test and post-test scores from the Linguistic Analysis Task (LAT), were analyzed using SPSS (Version 28). Descriptive statistics (means and standard deviations) were calculated for both the pre-test and post-test scores. A paired-samples t-test was performed to evaluate the primary hypothesis (H1) by assessing whether a statistically significant difference existed in the mean linguistic thinking scores prior to and following the intervention. The level of significance was p \u0026lt; .05.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2. Qualitative Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQualitative data were gathered from a purposefully chosen subsample of 17 participants to facilitate a comprehensive understanding of their experiences. The dataset consisted of two sources: (a) a verbatim transcript from one of eight focus groups with seven participants, and (b) weekly reflective journals from ten additional participants chosen to reflect diverse levels of initial engagement and pre-test performance.\u003c/p\u003e\u003cp\u003eThe data were analyzed thematically according to Braun and Clarke’s (2006) six-phase framework: familiarization, inductive coding, theme development, theme review, theme definition, and report generation. To guarantee reliability, a second researcher independently coded 20% of the dataset, resulting in a high level of inter-coder agreement (Cohen’s Kappa = .88). The analysis sought to elucidate the quantitative findings and to tackle Research Questions 2 and 3, which concentrated on participants' perceptions, affordances, and challenges.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eQuantitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address Research Question 1 and test the main hypothesis (H1), we looked at quantitative data from the Linguistic Analysis Task (LAT). Fifty-six pre-service teachers took the pre-test, but one of them did not take the post-test and was not included in the inferential analyses. This left a final paired sample of N = 55.\u003c/p\u003e\n\u003cp\u003eTable 1 shows that after the intervention, participants\u0026apos; LAT performance improved a lot. The average Total Linguistic Thinking Score rose from M = 9.14 (SD = 1.66) at the pre-test to M = 16.98 (SD = 1.90) at the post-test. Paired-samples t-tests confirmed this gain was highly significant, t(54) = 21.43, p \u0026lt; .001, with a very large effect size (d = 2.89). There were also big improvements in all of the subcomponents: pattern recognition, t(54) = 17.26, p \u0026lt; .001, d = 2.33; hypothesis formulation, t(54) = 17.26, p \u0026lt; .001, d = 2.33; and metalinguistic reflection, t(54) = 16.04, p \u0026lt; .001, d = 2.16 (Table 2). These findings furnish compelling evidence that the digital inductive model significantly augmented both overall linguistic cognition and its fundamental cognitive elements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics for the Linguistic Analysis Task (LAT) Scores\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN (Pre-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM (Pre-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD (Pre-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN (Post-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM (Post-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD (Post-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Linguistic Thinking Score (Max 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePattern Recognition Score (Part A, Q1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypothesis Formulation Score (Part A, Q2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMetalinguistic Reflection Score (Part B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e N refers to the number of participants. Post-test statistics are based on the paired sample (N = 55) for consistency with inferential tests.\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics in Table 1 reveal a clear and substantial increase in performance across all measures of linguistic thinking following the intervention. The average Total Linguistic Thinking Score improved dramatically, rising from 9.14 to 16.98, representing a shift from moderate baseline performance to a high level of proficiency. This pattern of growth is mirrored across all sub-components, with scores for Pattern Recognition, Hypothesis Formulation, and Metalinguistic Reflection nearly doubling. The relatively small and consistent standard deviations suggest that the improvement was a uniform phenomenon across the entire cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese descriptive results provide strong preliminary evidence that the intervention was highly effective, a finding that is further tested for statistical significance in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePaired-Samples t-Tests Comparing Pre-Test and Post-Test LAT Scores (N = 55)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComparison (Pre-Test vs. Post-Test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM Difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSD Difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal Linguistic Thinking Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePattern Recognition Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypothesis Formulation Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMetalinguistic Reflection Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003edf\u003c/em\u003e = 54 for all t-tests. All \u003cem\u003ep\u003c/em\u003e values are two-tailed. Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e values represent the standardized mean difference for paired samples.\u003c/p\u003e\n\u003cp\u003eThe results of the paired-samples t-tests presented in Table 2 confirm that the observed improvements are highly statistically significant. For the\u0026nbsp;Total Linguistic Thinking Score, the analysis revealed a significant increase (\u003cem\u003et\u003c/em\u003e(54) = 21.43, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Crucially, the magnitude of this change, as measured by Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e, was exceptionally large (\u003cem\u003ed\u003c/em\u003e = 2.89), indicating that the digital inductive model had a profound and practically significant impact on the participants\u0026apos; overall analytical abilities. This powerful effect was consistent across all measured skills. These inferential statistics provide robust, unequivocal support for H1, demonstrating that the intervention was not just effective, but transformative in developing the multifaceted components of linguistic thinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe qualitative data from reflective journals (n=10) and a focus group (n=7) were analyzed to explain the quantitative improvement in linguistic thinking and to understand the participants\u0026apos; experience. A preliminary analysis of the data in NVivo revealed a focus on dynamic, process-oriented language, as illustrated in the word cloud (Figure 2). Key terms such as\u0026nbsp;changed,\u0026nbsp;give,\u0026nbsp;process,\u0026nbsp;think, and\u0026nbsp;active\u0026nbsp;highlight a journey of transformation rather than a static state.\u003c/p\u003e\n\u003cp\u003eThe thematic analysis revealed a clear developmental trajectory, which is visualized as a process model in Figure 3. Participants did not experience the themes as isolated categories but as stages in a transformative journey, moving from initial apprehension to the formation of a new pedagogical identity. This journey is presented below in four interconnected stages.\u003c/p\u003e\n\u003cp\u003eThe journey began with an\u0026nbsp;Initial Emotional Response\u0026nbsp;of anxiety and uncertainty, which served as a catalyst for engagement. The breakthrough to\u0026nbsp;Cognitive Engagement\u0026mdash;or \u0026quot;detective work\u0026quot;\u0026mdash;was directly facilitated by the core affordances of\u0026nbsp;authenticity and collaboration. This deep engagement led to a\u0026nbsp;Shift in Pedagogical Perception, where participants\u0026apos; teaching philosophies evolved toward a student-centered, facilitative stance. The culmination of this process was a\u0026nbsp;Transformed Pedagogical Identity, characterized by confidence and a new vision for their role as language educators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 1: Initial Emotional Response (Anxiety and Uncertainty)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs depicted in Figure 3, the participants\u0026apos; journey began not with cognitive engagement, but with an emotional hurdle. Accustomed to deductive instruction, the uncertainty of the inductive model provoked anxiety. This initial resistance was a critical first step. P3 described the feeling as \u003cem\u003e\u0026quot;Terrifying at first. I kept thinking, \u0026apos;What if my rule is wrong?\u0026apos; I just wanted the teacher to give me the answer.\u0026quot;\u003c/em\u003e This conceptual anxiety was compounded by technical barriers, with P3 adding, \u003cem\u003e\u0026quot;The first two weeks, I felt like I was fighting the software more than learning French.\u0026quot;\u003c/em\u003e This phase of arousal and discomfort served as the necessary catalyst for motivating a new approach to learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 2: Cognitive Engagement through Digital Affordances\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe breakthrough from anxiety to engagement was directly facilitated by the core affordances of the digital tools: \u003cem\u003eauthenticity\u0026nbsp;and\u0026nbsp;collaboration\u003c/em\u003e. Access to real-world language data was a revelation. As P53 stated, \u003cem\u003e\u0026quot;The authenticity. Full stop. We are seeing the language as it is actually used by native speakers, not the sanitized version from textbooks.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis authentic context enabled the deep \u003cem\u003eCognitive Engagement\u003c/em\u003e that participants described as \u0026quot;detective work.\u0026quot; Grammar became a puzzle to be solved rather than a list to be memorized. P15 explained, \u003cem\u003e\u0026quot;Before, if a rule had exceptions, it was frustrating. Now, the exceptions are the most interesting part. They are clues.\u0026quot;\u003c/em\u003e This active process of \u003cem\u003epattern recognition\u003c/em\u003e led to a deeper, more tangible understanding. P49 noted, \u003cem\u003e\u0026quot;Seeing it used after verbs of emotion and opinion in hundreds of real articles made that idea tangible, not just an abstract definition in a book.\u0026quot;\u003c/em\u003e Collaboration was essential in this phase, scaffolding both technical and conceptual challenges. P9 affirmed, \u003cem\u003e\u0026quot;Having four different perspectives meant we saw patterns that one person alone would have missed. The debates were the most productive part.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 3: The Shift in Pedagogical Perception\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs participants gained a deeper understanding through their own cognitive engagement, their perceptions of teaching began to change. The experience of discovering rules for themselves directly influenced their pedagogical beliefs, causing a \u003cem\u003eshift from a teacher-centered to a student-centered perspective\u003c/em\u003e. P4\u0026rsquo;s reflection captured this evolution powerfully: \u003cem\u003e\u0026quot;I now feel that the deductive way is passive and almost disrespectful to the learner\u0026apos;s intelligence. This inductive approach treats students like capable thinkers.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHowever, this emerging identity was tempered by practical considerations. Participants demonstrated sophisticated pedagogical reasoning by acknowledging the need for \u003cem\u003eadaptation\u003c/em\u003e. They recognized that the model required scaffolding for different levels and contexts. P26 explained\u003cem\u003e,\u0026nbsp;\u0026quot;I\u0026apos;m 100% going to use it, but I\u0026apos;ll adapt it. I wouldn\u0026apos;t just give the concordancer to my A2 students. That would be chaos.\u0026quot;\u003c/em\u003e This highlights a move toward becoming reflective practitioners who can orchestrate pedagogy, rather than simply replicating a method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStage 4: A Transformed Pedagogical Identity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe culmination of this process was a form of self-actualization, resulting in a \u003cem\u003eTransformed Pedagogical Identity\u003c/em\u003e. Having moved from anxiety to confidence as learners, participants began to envision themselves as confident, student-centered teachers. They no longer saw their role as the sole authority on grammar but as a facilitator of discovery. The emotional journey concluded with a sense of empowerment and purpose. As P26 shared, \u003cem\u003e\u0026quot;It genuinely changed the way I see my future career. I feel more like a language coach or guide now, and less like a grammar police officer.\u0026quot;\u003c/em\u003e This final stage represents the full integration of the cognitive, emotional, and pedagogical shifts experienced throughout the intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e presents a process model that visually synthesizes the qualitative findings of the study. It moves beyond a simple categorization of themes to illustrate the dynamic, developmental journey that pre-service teachers underwent as they engaged with the digital inductive model. The model depicts a sequential yet interconnected pathway where emotional responses, cognitive engagement, and pedagogical perceptions culminate in a transformed professional identity.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 3, the process begins with the \u003cem\u003eInitial Emotional Response\u003c/em\u003e, characterized by anxiety and uncertainty. This initial state acts as a catalyst, creating an \u0026quot;Arousal \u0026amp; Motivation\u0026quot; that propels participants toward \u003cem\u003eCognitive Engagement\u003c/em\u003e. This central phase, described as \u0026quot;Detective Work\u0026quot; and \u0026quot;Pattern Recognition,\u0026quot; is directly enabled by the \u003cem\u003eDigital Tool Affordances\u003c/em\u003e of authenticity and collaboration. The model shows these affordances having two distinct effects: they facilitate the cognitive work itself (dashed arrow) and directly influence pedagogical thinking (solid arrow).\u003c/p\u003e\n\u003cp\u003eThe successful cognitive engagement leads to a \u0026quot;Deeper Understanding,\u0026quot; which in turn fosters a new \u003cem\u003ePedagogical Perception\u003c/em\u003e. This perception is defined by a shift toward facilitation and an awareness of the need for adaptation. Finally, this new pedagogical mindset undergoes a process of \u0026quot;Self-Actualization,\u0026quot; leading to the ultimate outcome: a \u003cem\u003eTransformed Pedagogical Identity\u003c/em\u003e, characterized by confidence and a student-centered approach.\u003c/p\u003e\n\u003cp\u003eMore than a summary of themes,\u0026nbsp;Figure 3\u0026nbsp;interprets the participants\u0026apos; experience as a story of transformation, highlighting key cause-and-effect relationships.\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eEmotion as a Catalyst:\u003c/strong\u003e The model posits that the transformative journey does not begin with cognition, but with emotion. The initial anxiety and uncertainty were not merely obstacles but a necessary motivational force. This \u0026quot;Arousal \u0026amp; Motivation\u0026quot; created a state of cognitive dissonance that pushed participants to resolve their uncertainty by actively engaging with the new learning method, rather than passively resisting it.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAffordances as the Engine of Engagement:\u003c/strong\u003e The model emphasizes the critical role of the digital tool affordances in making the cognitive leap possible. \u003cem\u003eAuthenticity\u0026nbsp;and\u0026nbsp;collaboration\u003c/em\u003e were not just features of the model; they were the essential scaffolding that allowed participants to move past their initial anxiety and engage in meaningful \u0026quot;detective work.\u0026quot; The authenticity of the data made the inquiry feel relevant and powerful, while collaboration provided the peer support needed to navigate both technical and conceptual challenges.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFrom Understanding to Identity:\u003c/strong\u003e The model illustrates that a change in pedagogical identity is not a direct result of learning a new technique. Instead, it is the outcome of a deeper, multi-stage process. It is the \u003cem\u003e\u0026quot;Deeper Understanding\u0026quot;\u003c/em\u003e\u0026mdash;gained through personal cognitive struggle and discovery\u0026mdash;that makes a shift in pedagogical perception possible. This new perception then solidifies through a process of \u003cem\u003e\u0026quot;Self-Actualization,\u0026quot;\u003c/em\u003e where participants begin to see themselves as capable of enacting this new pedagogy. The final transformation is therefore not just a change in belief, but a change in professional identity\u0026mdash;a shift from being a \u0026quot;grammar police officer\u0026quot; to a \u0026quot;language coach or guide.\u0026quot;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn summary, Figure 3 provides a theoretical explanation for the qualitative findings, illustrating how the digital inductive model facilitates a journey where initial emotional and cognitive challenges, when supported by the right technological affordances, can lead to a profound and lasting transformation in a pre-service teacher\u0026apos;s professional identity.\u003c/p\u003e\n\u003cp\u003eTo consolidate the thematic analysis presented above, the developmental trajectory of the pre-service teachers\u0026apos; experience is summarized in Table 1. The table presents the core finding for each theme, a representative quote from the dataset\u0026mdash;specifying its source as either a reflective journal or the focus group\u0026mdash;and the coding coverage. The \u0026quot;Coding Coverage (Weighted %)\u0026quot; was calculated in NVivo and indicates the proportion of the total coded text attributed to each theme, highlighting the relative prominence of each dimension in the participants\u0026apos; experience. This quantitative representation of the qualitative data corroborates the narrative finding that \u003cem\u003eCognitive Engagement\u003c/em\u003e was the most extensively discussed aspect of the participants\u0026apos; journey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSummary of Thematic Analysis Findings\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCore Finding / Description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIllustrative Quote \u0026amp; Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoding Coverage (Weighted %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive Engagement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe shift from passive memorization to active \u0026apos;detective work\u0026apos; through pattern recognition and hypothesis testing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;Now, the exceptions are the most interesting part. They are clues.\u0026rdquo; (P15, Focus Group)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEmotional Response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA developmental arc from initial anxiety and frustration (\u0026ldquo;terrified\u0026rdquo;) to eventual confidence and empowerment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;The most empowering part is the confidence I feel. I can now investigate any grammar question I have on my own.\u0026rdquo; (P2, Journal, Week 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePedagogical Perception\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA transformation in teaching philosophy toward facilitation, balanced by a pragmatic awareness of the need for adaptation and scaffolding.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;This inductive approach treats students like capable thinkers. My entire view of grammar teaching has shifted.\u0026rdquo; (P4, Journal, Week 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDigital Tool Affordances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe dual role of authenticity (real data) and collaboration (group work) as critical enablers, despite an initial technical learning curve.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;The authenticity. Full stop. We are seeing the language as it is actually used by native speakers, not the sanitized version from textbooks.\u0026rdquo; (P53, Focus Group)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of Quantitative and Qualitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a sequential explanatory design, where the qualitative findings were used to explain and elaborate upon the quantitative results. This section integrates both data sources to provide a comprehensive answer to the research questions, demonstrating not only \u003cem\u003ethat\u003c/em\u003e the digital inductive model was effective, but also \u003cem\u003ehow\u003c/em\u003e and \u003cem\u003ewhy\u003c/em\u003e it facilitated a transformation in the pre-service teachers\u0026apos; linguistic thinking and pedagogical identity. The relationship between the quantitative and qualitative findings is presented in the joint display in Table \u003cstrong\u003e3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe quantitative results provided conclusive evidence of the intervention\u0026apos;s efficacy. The qualitative data illuminates the process behind this dramatic statistical improvement. The theme of \u003cstrong\u003eCognitive Engagement\u003c/strong\u003e reveals that participants moved from passive memorization to active analysis. As P15 explained, \u0026quot;When you spend an hour debating and looking at evidence to arrive at a conclusion, you don\u0026apos;t forget it.\u0026quot; This \u0026quot;detective work\u0026quot; is the cognitive mechanism that explains the substantial gains measured by the LAT.\u003c/p\u003e\n\u003cp\u003eFurthermore, the qualitative findings answer the second and third research questions by detailing the participants\u0026apos; transformative journey. While the quantitative data showed \u003cem\u003ewhat\u003c/em\u003e skills were developed, the qualitative data captured the \u003cem\u003eshift in perception and identity\u003c/em\u003e. The process model (Figure 3) illustrates that this was not a simple, linear progression. Participants began with an \u003cem\u003eInitial Emotional Response\u003c/em\u003e of anxiety, but the key \u003cem\u003eDigital Tool Affordances\u003c/em\u003e\u0026mdash;authenticity and collaboration\u0026mdash;provided the necessary scaffolding to overcome these barriers. This journey culminated in a \u003cem\u003eTransformed Pedagogical Identity\u003c/em\u003e, with participants envisioning their future role not as a purveyor of rules, but as a \u0026quot;language coach or guide\u0026quot; (P26) who facilitates discovery. The joint display in Table \u003cstrong\u003e4\u003c/strong\u003e visually connects these threads, providing a holistic view of the study\u0026apos;s findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eJoint Display Integrating Quantitative and Qualitative Findings\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"3\" cellpadding=\"0\" width=\"690\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eResearch Question\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQuantitative Findings (The \u0026quot;What\u0026quot;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQualitative Findings (The \u0026quot;How \u0026amp; Why\u0026quot;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIllustrative Quote\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRQ1: Impact on Linguistic Thinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistically significant improvement in Total Linguistic Thinking Score (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e = 2.89) and all sub-skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe improvement was driven by a shift to active \u003cstrong\u003eCognitive Engagement\u003c/strong\u003e, where participants acted as \u0026quot;language detectives,\u0026quot; using pattern recognition to achieve a deeper, more memorable understanding.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;We found adjectives that didn\u0026apos;t fit and tried to figure out why. It felt like we were co-creating the knowledge.\u0026rdquo; (P15, Focus Group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRQ2: Perceptions of the Model\u0026apos;s Role\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A (Primarily qualitative). However, the strong positive outcomes provide context for the perceptions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe model catalyzed a \u003cstrong\u003eTransformation in Pedagogical Identity\u003c/strong\u003e. Participants moved from a teacher-centered to a student-centered philosophy, balanced by a pragmatic awareness of the need for adaptation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;This inductive approach treats students like capable thinkers. My entire view of grammar teaching has shifted.\u0026rdquo; (P4, Journal, Week 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRQ3: Affordances \u0026amp; Challenges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A (Primarily qualitative).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChallenges:\u003c/strong\u003e The journey began with an \u003cstrong\u003eInitial Emotional Response\u003c/strong\u003e of anxiety and technical frustration.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;Terrifying at first\u0026hellip; I felt like I was fighting the software more than learning French.\u0026rdquo; (P3, Focus Group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAffordances:\u003c/strong\u003e The key \u003cstrong\u003eDigital Tool Affordances\u003c/strong\u003e of \u003cstrong\u003eauthenticity\u003c/strong\u003e (real data) and \u003cstrong\u003ecollaboration\u003c/strong\u003e (group work) were crucial for overcoming challenges and enabling cognitive engagement.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;The authenticity. Full stop. We are seeing the language as it is actually used by native speakers, not the sanitized version from textbooks.\u0026rdquo; (P53, Focus Group)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to determine the efficacy of a digital inductive model in enhancing the linguistic thinking of pre-service FFL teachers in Egypt. The findings provide a resounding affirmation of the model's effectiveness, not only in developing measurable analytical skills but also in catalyzing a profound transformation in pedagogical identity. This discussion integrates the results, corroborates them with the existing literature, and explores the implications for theory and practice.\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eFrom Inductive Theory to Empirical Reality: Explaining the Growth in Linguistic Thinking\u003c/h2\u003e\u003cp\u003eThe quantitative results unequivocally confirmed H1, demonstrating that participants' linguistic thinking skills improved dramatically. This finding provides robust empirical evidence for the theoretical claims made in the literature regarding the power of inductive pedagogy and DDL (Kharade \u0026amp; Peese, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The qualitative data explains \u003cem\u003ehow\u003c/em\u003e this growth was achieved: participants' journey from passive rule-receivers to active \"language detectives\" illustrates the cognitive mechanism at play. This \"co-creating of knowledge,\" as P15 described it, is the lived experience behind the statistical gains, confirming that the digital inductive model successfully operationalized constructivist principles to foster deep, durable understanding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003eThe Transformative Journey: From Digital Competence to Pedagogical Identity\u003c/h2\u003e\u003cp\u003eThe qualitative findings illuminate a journey of professional transformation that supports Propositions 2 and 3. This journey corroborates literature that highlights the challenge of moving pre-service teachers from basic digital skills to sophisticated digital pedagogical competence (Lu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Reisoğlu \u0026amp; \u0026Ccedil;ebi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our study shows that this is not merely a technical transition but an emotional and philosophical one. Participants began with anxiety and uncertainty, but the \u003cem\u003ekey affordances\u003c/em\u003e of authenticity and collaboration served as a critical scaffold. This process led not only to cognitive gains but to a profound shift in pedagogical identity, as participants transitioned from a \"grammar police officer\" to a \"language coach or guide\" (P26). This finding enriches the TPACK framework by demonstrating that true pedagogical orchestration is not just a skill, but an identity that emerges from personal, transformative learning experiences.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImplications for Theory, Policy, and Practice in the Egyptian Context\u003c/h3\u003e\n\u003cp\u003eThis study makes several important contributions. Theoretically, it validates the integrated framework proposed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, confirming that combining constructivist principles, digital pedagogical competence, and inductive methods creates a powerful engine for developing higher-order thinking. For policy and practice in Egypt, the implications are direct and timely. As national initiatives like the Tr\u0026egrave;FLE project and ICDL certification roll out, this study provides a clear directive: providing access to digital tools is not enough. To truly enhance the quality of FFL instruction, teacher education programs must adopt pedagogical models that guide pre-service teachers through inquiry-based learning themselves. The challenges identified by participants\u0026mdash;the initial technical curve and the tension with deductive traditions\u0026mdash;are not failures of the model but critical data points for implementation. Training programs should include dedicated technical workshops and explicitly address the philosophical shift from deductive to inductive teaching.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Research\u003c/h3\u003e\n\u003cp\u003eThis study, while providing strong evidence, has several limitations. The single-group, pre-test/post-test design does not allow for a direct comparison with a control group. The qualitative sample, while rich, was limited to 17 participants from a single institution, which may limit generalizability. Furthermore, the study measured perceptions and skills within a training context; a longitudinal study is needed to determine whether this transformed pedagogical identity translates into sustained changes in classroom practice. Future research should address these limitations through experimental designs and explore the adaptability of this model for in-service teacher professional development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides compelling mixed-methods evidence for the efficacy of digital inductive models in not only enhancing the linguistic thinking of pre-service FFL teachers but also in profoundly transforming their pedagogical identity. The quantitative data demonstrated a statistically significant and exceptionally large improvement in analytical skills, while the qualitative findings illuminated the process behind this growth: a transformative journey from apprehensive learners to confident, inquiry-driven pedagogical thinkers. By strategically combining digital tools with inductive pedagogy within a supportive, collaborative framework, this study confirms that teacher education can move beyond the superficial application of technology and cultivate the deep, analytical, and student-centered competence required of 21st-century educators.\u003c/p\u003e\u003cp\u003eThe findings offer several critical pedagogical implications. First, pedagogy must drive technology, not the other way around. The success of this intervention was rooted not in the novelty of the tools, but in their purposeful orchestration within an inductive methodology. Second, the affective and cognitive journey of the pre-service teacher must be explicitly scaffolded. Teacher education programs must anticipate initial resistance and build in robust support structures. Finally, the dual outcome of the digital inductive model\u0026mdash;enhancing both content mastery and pedagogical skill\u0026mdash;is its most powerful advantage. Participants did not just learn \u003cem\u003eabout\u003c/em\u003e French grammar; they learned \u003cem\u003ehow to think like a linguist\u003c/em\u003e and, in doing so, acquired a transferable model for their own future teaching.\u003c/p\u003e\u003cp\u003eFor a nation like Egypt, poised at the cusp of a major digital transformation in education, this study offers a research-validated pathway for ensuring that its investment in technology yields a profound and lasting return. The adoption of models that foster inquiry and empower future educators as autonomous, analytical thinkers will enable Egypt to prepare a new generation of FFL teachers who are not only digitally competent but also analytically confident, pedagogically adaptive, and ready to meet the demands of modern language education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Institutional Review Board of the Faculty of Education, Beni-Suef University (Reference No. BSU-FoE-003-01-3-2025). The study was conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Usage Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI including QuillBot, Chatgpt and Gemini was only used in polishing the language, editing where this was deemed necessary, but was not used in data generation or analysis tasks.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMM conceived the research, contrived the research design; MM \u0026amp; FA designed and validated the instruments. FA conducted the field investigation collected the data and organized it. MM analyzed the data and wrote the first draft. Both authors revised the final draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAleksieva, L. (2025). Preparing pre-service teachers for the digital transformation of education: Exploring university teacher educators\u0026rsquo; views and practical strategies. \u003cem\u003eEducation Sciences, 15\u003c/em\u003e(4), 404. https://doi.org/10.3390/educsci15040404\u003c/li\u003e\n\u003cli\u003eAna, O., \u0026amp; L\u0026oacute;pez-Medina, B. M. (2021). Promoting metacognitive and linguistic skills: Digital learning logs in pre-service teacher training. \u003cem\u003eJournal of Language \u0026amp; Education, 7\u003c/em\u003e(4), 113\u0026ndash;122. https://doi.org/10.17323/jle.2021.11680 \u003c/li\u003e\n\u003cli\u003eChoi, L. J. (2024). 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Using digital storytelling to promote language learning, digital skills and digital collaboration among English pre-service teachers. \u003cem\u003eSystem, 129\u003c/em\u003e, 102866. https://doi.org/10.1016/j.system.2024.103577\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital inductive model, linguistic thinking, pre-service teacher education, French as a Foreign Language (FFL), mixed-methods research, data-driven learning","lastPublishedDoi":"10.21203/rs.3.rs-7720531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7720531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis 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 \u0026lt; .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.\u003c/p\u003e","manuscriptTitle":"The Efficacy of Digital Inductive Models in Enhancing Linguistic Thinking Among Pre-Service FFL Student-Teachers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:39:36","doi":"10.21203/rs.3.rs-7720531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89657cad-5469-41b5-b625-2466645375b9","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55385563,"name":"Social science/Education"},{"id":55385564,"name":"Humanities/Language and linguistics"},{"id":55385565,"name":"Social science/Language and linguistics"},{"id":55385566,"name":"Humanities/Literature"}],"tags":[],"updatedAt":"2026-02-02T05:24:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:39:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7720531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7720531","identity":"rs-7720531","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}