Integrating Cognitive Grammar and Educational Technology: A Cognitive-Data Synergy Framework for Chinese L1 Learners' English Grammar Acquisition

This study proposes a Cognitive-Data Synergy Framework (CDSF) that integrates Cognitive Grammar theory with adaptive educational technologies to address the challenges of English grammar acquisition among Chinese L1 learners. Traditional grammar instruction, constrained by structuralist paradigms and delayed feedback mechanisms, often fails to foster deep conceptual understanding or accommodate individual cognitive differences. By combing Langacker's conceptualization theory and Talmy's force dynamics, the CDSF encodes cognitive schemas (e.g., container and path schemas) into computational models, enabling the real-time error diagnosis and personalized interventions. A mixed-methods design was employed, involving 1,850 participants divided into three groups: Group A (full CDSF), Group B (basic CDSF), and Group C (traditional instruction). Quantitative data (behavioral logs, test scores, NASA-TLX cognitive load assessments) and qualitative insights (learner interviews, teacher journals) were analyzed by machine learning and structural equation modeling. Results demonstrated that Group A achieved significant improvements in grammar accuracy (82.4% in tense consistency vs. 53.2% for Group C, p<0.001, η²= 0.43), reduced cognitive load (NASA-TLX scores decreased by 32%), and enhanced transferability. Neurocognitive data revealed strengthened activation in brain networks (N400 amplitude reduction: 31%). By bridging cognitive linguistics with adaptive technologies, CDSF offers a replicable model for transforming grammar instruction from rule-based memorization to cognitively enriched, data-driven pedagogy. Future directions could address its efficiency on K-12 learners and multilingual contexts.