Purpose: This study introduces a novel approach to enhance handheld mobile augmented reality (MAR) experiences through user mental models. By analyzing MAR characteristics, we construct a user mental model based on “execution" and “evaluation" dimensions alongside six attributes, guiding MAR interaction design. Methods: We analyze MAR traits like interactivity, information display, and physical comfort, deriving two dimensions (execution and evaluation) and six attributes (usage habits, information attention, interactive behavior, usage experience, cognitive experience, and workload). Through user interviews and affinity diagrams, we construct the mental model. To demonstrate its practical application, we utilize a MAR exhibition with a miniature model theme. Results: Through case application and user testing, we validate the effectiveness of the mental model-driven design in enhancing MAR interaction. Users perceive improvements in ease of use, learnability, and satisfaction with systems designed using this approach. Conclusion: This study underscores the value of leveraging user mental models rooted in “execution" and “evaluation" dimensions to guide MAR system design. Utilizing the affinity diagram method, our approach not only enhances system adaptability but also boosts user satisfaction, marking a significant advancement in MAR HCI research.

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Enhancing Augmented Reality (MAR) Interaction Experience: A Design Framework Grounded in User Mental Model Construction

  • Xiaozhan Liang,
  • Xiaona Ma

摘要

Purpose: This study introduces a novel approach to enhance handheld mobile augmented reality (MAR) experiences through user mental models. By analyzing MAR characteristics, we construct a user mental model based on “execution" and “evaluation" dimensions alongside six attributes, guiding MAR interaction design. Methods: We analyze MAR traits like interactivity, information display, and physical comfort, deriving two dimensions (execution and evaluation) and six attributes (usage habits, information attention, interactive behavior, usage experience, cognitive experience, and workload). Through user interviews and affinity diagrams, we construct the mental model. To demonstrate its practical application, we utilize a MAR exhibition with a miniature model theme. Results: Through case application and user testing, we validate the effectiveness of the mental model-driven design in enhancing MAR interaction. Users perceive improvements in ease of use, learnability, and satisfaction with systems designed using this approach. Conclusion: This study underscores the value of leveraging user mental models rooted in “execution" and “evaluation" dimensions to guide MAR system design. Utilizing the affinity diagram method, our approach not only enhances system adaptability but also boosts user satisfaction, marking a significant advancement in MAR HCI research.