Designing for Engagement: A Mixed-Methods Study of AR and Concept Mapping Method in Mobile Vocabulary Learning

This study investigates the effectiveness of integrating augmented reality (AR) and concept mapping method in English vocabulary learning and proposes a design thinking-based development framework for learning systems. Grounded in mobile learning and situated learning theories, the study applies a user-centered design approach through the design thinking process. Initially, semi-structured interviews with eight experienced users were conducted to identify key design requirements, including interface simplicity, visual clarity, thematic relevance, and AR interactivity. These insights informed the development of a concrete interface prototype. Subsequently, a quasi-experimental study involving 30 participants was conducted, employing vocabulary tests, questionnaires, and interviews to enable mixed-methods analysis. The results indicate that the immersive environment created by AR enhances learners’ motivation and learning outcomes, while concept maps support the structured understanding of vocabulary. However, the digital representation of concept maps requires further optimization. The study suggests future research explore variations in digital representation and content depth of concept maps, and incorporate gamification and immersive technologies to enrich learning experiences. The main contribution of this research lies in embedding design thinking into educational technology experimentation, establishing an evidence-based, user-centered development model that offers methodological insights for future learning technology research.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3582899
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Designing for Engagement: A Mixed-Methods Study of AR and Concept Mapping Method in Mobile Vocabulary Learning

Shuo Fang Liu, Yi Wu, A.W.Y. Su, Sheng-Fei Chien
IEEE Access
Augmented Reality Applications
article

Designing for Engagement: A Mixed-Methods Study of AR and Concept Mapping Method in Mobile Vocabulary Learning

Shuo Fang Liu, Yi Wu, A.W.Y. Su, Sheng-Fei Chien
article en

Abstract

This study investigates the effectiveness of integrating augmented reality (AR) and concept mapping method in English vocabulary learning and proposes a design thinking-based development framework for learning systems. Grounded in mobile learning and situated learning theories, the study applies a user-centered design approach through the design thinking process. Initially, semi-structured interviews with eight experienced users were conducted to identify key design requirements, including interface simplicity, visual clarity, thematic relevance, and AR interactivity. These insights informed the development of a concrete interface prototype. Subsequently, a quasi-experimental study involving 30 participants was conducted, employing vocabulary tests, questionnaires, and interviews to enable mixed-methods analysis. The results indicate that the immersive environment created by AR enhances learners’ motivation and learning outcomes, while concept maps support the structured understanding of vocabulary. However, the digital representation of concept maps requires further optimization. The study suggests future research explore variations in digital representation and content depth of concept maps, and incorporate gamification and immersive technologies to enrich learning experiences. The main contribution of this research lies in embedding design thinking into educational technology experimentation, establishing an evidence-based, user-centered development model that offers methodological insights for future learning technology research.

IEEE AccessVol. 13
National Cheng Kung University (TW)
National Science and Technology Council
Quality Education
Openalex Percentile: Top 87%
Augmented Reality Applications
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Designing for Engagement: A Mixed-Methods Study of AR and Concept Mapping Method in Mobile Vocabulary Learning — Shuo Fang Liu, Yi Wu, et al. · IEEE Access (2025) | TGRS Research Map | TGRS