Study Notes on VR Technology in English Learning

Article Overview

  • Title: Exploring the Acceptance of Learning English Through VR Technology Amongst Secondary School Students: Intrinsic and Extrinsic Motivation as Key Determinants

  • Authors: Siu Shing Man, Yizhen Fang, Alan Hoi Shou Chan, Jiayan Han

  • Published in: Journal of Educational Computing Research, 2026, Vol. 64(2), pp. 243–274

  • DOI: 10.1177/07356331251390717

Abstract

  • Virtual reality (VR) technology is transforming English learning through immersive experiences.

  • Limited quantitative studies have looked into acceptance factors regarding VR in English learning for secondary students.

  • This research integrated the Technology Acceptance Model (TAM), intrinsic motivation (IM), and extrinsic motivation (EM), with self-efficacy (SE) and learning engagement (LE)mediating the relationship.

  • Participants: 524 secondary school students completed a structured questionnaire analyzed using structural equation modeling.

  • Key findings:

    • EM and IM positively influence SE and LE.

    • SE enhances perceived usefulness (PU) and perceived ease of use (PEOU).

    • PU positively influences attitudes toward using VR technology (AT), but PEOU does not.

    • PU and LE affect students’ behavioral intentions to use VR.

  • Practical implications for VR developers and educators to enhance learning efficacy.

Keywords

  • English learning

  • Secondary education

  • VR in education

  • Motivation in language learning

  • Technology acceptance model (TAM)

  • Self-efficacy (SE)

  • Learning engagement (LE)

Introduction

  • English learning has evolved from teacher-centric methods to multimedia-rich approaches.

  • VR technology offers immersive learning, addressing challenges like passive engagement and disconnect between theory and practice.

  • Secondary students are actionable candidates for VR interventions due to their cognitive maturity and language development stage, enhancing vocabulary retention, pronunciation, and cultural understanding.

  • VR redefines learning environments, promising more effective education in the digital age.

VR Technology

  • Definition: VR is a simulated 3D environment where users can interact using devices like VR headsets.

  • Key characteristics per Heim (1994):

    • Interaction

    • Telepresence

    • Simulation

    • Immersion

    • Artificiality

    • Network communication

    • Full-body immersion

  • Benefits: Enhances motivation and satisfaction in language learning, as shown by studies (Peixoto et al. 2021; Chen et al. 2021).

Challenges

  • Scarcity of quality instructional content and technology integration in existing curricula hinder the widespread use of VR in education.

  • Concerns about the usefulness and accessibility of VR contribute to its limited acceptance.

Motivation Theory

  • Definition: Motivation is crucial for second language acquisition; it can be intrinsic (IM) for enjoyment and growth, or extrinsic (EM) for external rewards (Ryan & Deci, 2020).

  • Both IM and EM influence students' willingness to adopt new educational tools, including VR.

Self-Efficacy (SE)

  • Definition: SE refers to students' belief in their capabilities; it significantly affects their adaptation to new technologies (Wang & Wu, 2008).

  • High SE enhances resilience and positive attitudes in learning.

Learning Engagement (LE)

  • Definition: LE is students’ active involvement in learning tasks, influencing educational technology usage.

  • LE is driven by motivation levels, impacting learning effectiveness.

Proposed Research Framework

  • Integrated model includes TAM along with IM, EM, SE, and LE to understand acceptance of VR in English learning among secondary school students.

  • 12 Hypotheses developed from literature and define relationships among SE, LE, PU, PEOU, AT, and BI.

Methods

Research Design

  • Multi-stage sampling across four secondary schools in Guangdong, China.

  • 524 students, randomly sampled to mitigate bias.

  • Instruction: Students participated in VR-based English instruction using immersive platforms in real-life scenarios.

Data Collection

  • Questionnaire Sections:

    • Demographics

    • VR learning status

    • TAM constructs: PU, PEOU, AT, BI

    • Expanding factors: IM, EM, SE, LE

  • Pilot study confirms validity and clarity of the questionnaire.

Measurement Instruments

  • Five-point Likert scale employed for reliability measurement of constructs, with specified thresholds set for validity.

Statistical Techniques

  • Confirmatory factor analysis (CFA) for reliability and validity of the scales.

  • Structural equation modeling (SEM) for hypothesis testing.

  • Shapiro-Wilk test used for normality assessment.

Results

Measurement Model

  • Descriptive statistics showcasing mean values and distribution for each construct.

  • Reliability confirmed via Cronbach’s alpha, and convergence assessed.

Structural Model

  • SEM results affirm all 12 hypotheses.

  • Findings: IM and EM positively impact SE; SE enhances PU and PEOU.

  • PU affects AT; PEOU positively relates to PU.

  • LE also influences BI, but to a lesser extent compared to AT and PU.

Discussion

  • Importance of TAM in understanding technology acceptance; PU found to fundamental influence both on AT and BI.

  • PEOU’s link to AT not significant among secondary students, indicating greater concern for efficacy over ease of use.

  • LE and motivational aspects critical for effective use of VR technology.

Practical Implications

  • Recommendations for educators and VR developers to create user-friendly and engaging VR learning environments aligned with students' interests and needs.

  • Strategies should focus on enhancing both intrinsic and extrinsic motivation while providing mechanisms for feedback and support to build SE.

Limitations

  • Cross-sectional nature restricts understanding of longitudinal acceptance patterns.

  • Lacks differentiation based on developmental stages of sample population.

  • Personality traits may also influence technological acceptance but were not included.

Conclusion

  • Develops a comprehensive acceptance model of VR in English learning, elucidating the mediating roles played by SE and LE in enhancing secondary students’ acceptance of VR technology.

  • Findings contribute significantly to the domains of educational technology and motivation in language learning contexts.

Author Contributions

  • Outlined roles of each author in conducting research, from conceptualization to analysis and writing.

Funding

  • List of various funding sources for the research.

Ethical Approval

  • Informed consent obtained from all participants; Research Ethics Committee approval noted.

References

  • Extensive list of scholarly articles cited, providing depth to arguments and findings presented, featuring works related to TAM, motivation theories, and VR in education.