Existing research on EFL learners' attitudes towards English language textbooks primarily investigates metaphors at the level of mental spaces, limiting insights into embodied cognition and experience. This study extends the analysis of metaphors to a more schematic level of domains/frames. We analyzed 163 metaphors from 123 Chinese university students' perceptions of English language textbooks under the guidance of Conceptual Metaphor Theory and the meta-functions of metaphors in language education. Findings reveal textbooks' three primary roles in learning English as i) a guide in a journey, ii) a cornerstone of a building, and iii) an appetizer in eating. The Chi-Square Test of Independence showed a moderate association between metaphor sources and emotional valence, with nature and container metaphors associated with negative evaluations. The combination of discourse analysis and statistical analysis highlights learners' physical and emotional engagement with English language textbooks. Pedagogical implications are discussed.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11882096PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0315292PLOS

Publication Analysis

Top Keywords

english language
16
language textbooks
16
chinese university
8
university students'
8
students' perceptions
8
perceptions english
8
english
5
language
5
metaphors
5
guide cornerstone
4

Similar Publications

Risk Prediction Models for Sentinel Node Positivity in Melanoma: A Systematic Review and Meta-Analysis.

JAMA Dermatol

March 2025

Department of Surgery, Arthur J.E. Child Comprehensive Cancer Centre, University of Calgary, Calgary, Alberta, Canada.

Importance: There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma.

Objective: To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma.

Data Sources: Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles.

View Article and Find Full Text PDF

Research on brain plasticity, particularly in the context of deafness, consistently emphasizes the reorganization of the auditory cortex. But to what extent do all individuals with deafness show the same level of reorganization? To address this question, we examined the individual differences in functional connectivity (FC) from the deprived auditory cortex. Our findings demonstrate remarkable differentiation between individuals deriving from the absence of shared auditory experiences, resulting in heightened FC variability among deaf individuals, compared to more consistent FC in the hearing group.

View Article and Find Full Text PDF

Introduction: There is a move towards engaging people with lived experience and families (PWLE/F)-also referred to as PWLE/F engagement-in mental health and/or substance use research. However, PWLE/F engagement is inadequately reported on in mental health and/or substance use research papers.

Objective: To understand what PWLE/F and researchers perceive are important components to report on related to engagement in mental health and/or substance use research.

View Article and Find Full Text PDF

Enhancing Large Language Models with Retrieval-augmented Generation: A Radiology-specific Approach.

Radiol Artif Intell

March 2025

Department of Radiology & Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, Calif.

Retrieval-augmented generation (RAG) is a strategy to improve performance of large language models (LLMs) by providing the LLM with an updated corpus of knowledge that can be used for answer generation in real-time. RAG may improve LLM performance and clinical applicability in radiology by providing citable, up-to-date information without requiring model fine-tuning. In this retrospective study, a radiology-specific RAG was developed using a vector database of 3,689 articles published from January 1999 to December 2023.

View Article and Find Full Text PDF

Purpose To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights language models (LMs) and retrieval augmented generation (RAG) and to assess the effects of model configuration variables on extraction performance. Materials and Methods This retrospective study utilized two datasets: 7,294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2,154 pathology reports annotated for mutation status (January 2017 to July 2021). An automated pipeline was developed to benchmark the performance of various LMs and RAG configurations for structured data extraction accuracy from reports.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!