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  4. Innovating Grammar Instruction: Cognitive Linguistics and Bayesian Modeling for Effective Learning = 創新語言教育: 結合認知語言學與貝葉斯建模的文法教學新方向
 
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Innovating Grammar Instruction: Cognitive Linguistics and Bayesian Modeling for Effective Learning = 創新語言教育: 結合認知語言學與貝葉斯建模的文法教學新方向

Principal Investigator
Dr. WONG Man Ho, Ivy  
Department
Department of English Language and Literature  
Grant Awarding Body
Research Grants Council
Grant Type
Faculty Development Scheme
Project Code
UGC/FDS15/H30/25
Amount Awarded
HK$765,101
Funding Year
2025
Duration of the Project
24 months
Status
Ongoing
Abstract
If-conditionals pose significant learning challenges for second language (L2) learners due to their complex form-meaning relationships and traditional grammar instruction's limitations. Existing pedagogical materials often rely on simplified conditional types (Type 0–3) that do not fully align with authentic language use. This project addresses these gaps by leveraging cognitive linguistics (CL) to develop innovative instructional approaches that enhance learners’ conceptual understanding and real-world application of conditionals.
The current study will first analyse learner challenges in if-conditionals through qualitative evaluations of student writings and surveys. This phase aims to identify areas where L2 learners struggle and the extent to which current teaching methods contribute to these difficulties. Based on these findings, the study will design and implement two CL-informed instructional frameworks: Cognitive-Linguistics Inspired Pedagogy (CLIP) and Concept-Based Language Instruction (CBLI). These approaches emphasize conceptual links between grammar and meaning, providing learners with conceptual tools—such as visual diagrams—to internalize conditionals more effectively.
To assess the efficacy of these instructional models, the study will adopt a Bayesian mixed-effects modeling approach, analysing both fixed (instructional type, proficiency level) and random (individual learner variability, item difficulty) factors. Data will be gathered through acceptability judgment tests, metalinguistic knowledge tests, and syntactic priming tasks to measure improvements in both explicit and implicit knowledge. Additionally, qualitative insights from teacher and student interviews will supplement the findings, ensuring pedagogical feasibility and engagement.
By integrating computational modeling and open science practices, this project aims to advance evidence-based language education, offering scalable, replicable, and innovative teaching strategies for L2 classrooms. Findings will be shared through academic publications, open-access materials, and teacher training workshops, contributing to theoretical, methodological, and practical advancements in instructed second language acquisition.
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