Concept Maps as a Conceptual Modelling Device in L2 Abstract Vocabulary Acquisition

The goal of this paper is to demonstrate the role of conceptual metaphor and concept maps in SLL by exploring the employment of conceptual metaphor theory in the design of concept maps as an instrument enhancing the effectiveness of abstract vocabulary acquisition in L2. It claims that the ubiquity of audio—and visual stimuli in contemporary society, which often renders students 'insensitive' to linguistically coded information, requires more stimulating tools for teaching vocabulary. Concept maps—a blend of visual and textual information that introduces concepts and their connections—can serve as a suitable tool for the introduction, revision, and systematic presentation of abstract vocabulary. What brings this opportunity to the fore in the second language classroom is the fact that abstract concepts and abstract vocabulary are commonly rooted in concrete conceptual domains through metaphor—a pattern of thought and a mechanism of experiential grounding of abstractions. Therefore, the methodology of the present research is the intersection between cognitive semantics, metaphor theory, and the constructivist approach to learning. To prove the effectiveness of concept maps transposing conceptual into conventional metaphors in vocabulary acquisition, an inquiry is made into their symbolic isomorphism with conceptual mapping viewed as the process aspect of metaphorical conceptualization. Using the method of structural and functional modelling, an invariant model is created for the design of such concept maps and their transformation into a didactic tool for abstract vocabulary acquisition. The educational adequacy of the resulting didactic Cmaps is yet to be validated experimentally.

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Publication Details

Journal
Forum for Linguistic Studies
Published
2025-09-28
DOI
https://doi.org/10.30564/fls.v7i10.10642
Primary Topic
Natural Language Processing Techniques
Type
article
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article

Concept Maps as a Conceptual Modelling Device in L2 Abstract Vocabulary Acquisition

Milena Levunlieva
Forum for Linguistic Studies
Natural Language Processing Techniques
article

Concept Maps as a Conceptual Modelling Device in L2 Abstract Vocabulary Acquisition

Milena Levunlieva
article en

Abstract

The goal of this paper is to demonstrate the role of conceptual metaphor and concept maps in SLL by exploring the employment of conceptual metaphor theory in the design of concept maps as an instrument enhancing the effectiveness of abstract vocabulary acquisition in L2. It claims that the ubiquity of audio—and visual stimuli in contemporary society, which often renders students 'insensitive' to linguistically coded information, requires more stimulating tools for teaching vocabulary. Concept maps—a blend of visual and textual information that introduces concepts and their connections—can serve as a suitable tool for the introduction, revision, and systematic presentation of abstract vocabulary. What brings this opportunity to the fore in the second language classroom is the fact that abstract concepts and abstract vocabulary are commonly rooted in concrete conceptual domains through metaphor—a pattern of thought and a mechanism of experiential grounding of abstractions. Therefore, the methodology of the present research is the intersection between cognitive semantics, metaphor theory, and the constructivist approach to learning. To prove the effectiveness of concept maps transposing conceptual into conventional metaphors in vocabulary acquisition, an inquiry is made into their symbolic isomorphism with conceptual mapping viewed as the process aspect of metaphorical conceptualization. Using the method of structural and functional modelling, an invariant model is created for the design of such concept maps and their transformation into a didactic tool for abstract vocabulary acquisition. The educational adequacy of the resulting didactic Cmaps is yet to be validated experimentally.

Forum for Linguistic StudiesVol. 7(10)
South-West University "Neofit Rilski" (BG)
Openalex Percentile: Top 86%
Natural Language Processing Techniques
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