ABSTRACT

Mobile augmented reality (AR) technology creates realistic learning situations and a strong sense of immersion, which is conducive to enhance learning experience and stimulate learning motivation. However, existing mobile outdoor augmented reality applications generally have a complicated operation process and a mismatch between learning resources and corresponding scenes, which leads to a poor learning experience. Therefore, we propose a lightweight mobile outdoor AR method that combines deep learning and knowledge modeling to perceive learning scenes with a goal to improve learning experience. This method improves the accuracy of scene perception and resources retrieval and provides a convenient mobile AR technology solution for outdoor learning. To evaluate the proposed method, we provide objective criteria to assess the effectiveness of the lightweight object detection model and the learning resources retrieval approach. Simultaneously, we investigate the evaluation of participants majoring in teacher education on the usability of the proposed method by the modified system usability scale questionnaire and net promoter score. Experimental results demonstrate that our method achieves high detection accuracy, good usability, and is of great significance to improve outdoor learning experience.

摘要

移动增强现实（AR）技术可创建逼真的学习情况和强烈的沉浸感，这有助于增强学习体验并激发学习动机。然而，现有的移动户外增强现实应用通常具有复杂的操作过程以及学习资源与相应场景之间的不匹配，导致学习体验较差。因此，我们提出了一种轻量级的移动户外AR方法，该方法结合了深度学习和知识建模以感知学习场景，旨在改善学习体验。该方法提高了场景感知和资源检索的准确性，为户外学习提供了方便的移动AR技术解决方案。要评估提出的方法，我们提供客观的标准来评估轻量级对象检测模型和学习资源检索方法的有效性。同时，我们通过改进的系统可用性量表和净启动子评分，调查了教师教育专业参与者对所提出方法的可用性的评估。实验结果表明，该方法具有较高的检测精度，良好的可用性，对改善户外学习体验具有重要意义。