Abstract. While the use of location-based augmented reality (AR) for education has demonstrated benefits on participants' motivation, engagement, and on their physical activity, geolocation data inaccuracy causes augmented objects to jitter or drift, which is a factor in downgrading user experience. We developed a free and open source web AR application and conducted a comparative user test (n = 54) in order to assess the impact of geolocation data on usability, exploration, and focus. A control group explored biodiversity in nature using the system in combination with embedded GNSS data, and an experimental group used an external module for RTK data. During the test, eye tracking data, geolocated traces, and in-app user-triggered events were recorded. Participants answered usability questionnaires (SUS, UEQ, HARUS).We found that the geolocation data the RTK group was exposed to was less accurate in average than that of the control group. The RTK group reported lower usability scores on all scales, of which 5 out of 9 were significant, indicating that inaccurate data negatively predicts usability. The GNSS group walked more than the RTK group, indicating a partial effect on exploration. We found no significant effect on interaction time with the screen, indicating no specific relation between data accuracy and focus. While RTK data did not allow us to better the usability of location-based AR interfaces, results allow us to assess our system's overall usability as excellent, and to define optimal operating conditions for future use with pupils.

翻译：摘要：尽管在教学中使用基于位置的增强现实技术已被证明能提升学习者的动机、参与度及身体活动水平，但地理定位数据的不准确性会导致增强物体出现抖动或漂移，从而降低用户体验。我们开发了一款免费开源的网络增强现实应用，并进行了比较性用户测试（n=54），以评估地理定位数据对可用性、探索行为及专注度的影响。对照组使用结合嵌入式全球导航卫星系统数据的系统探索自然界的生物多样性，实验组则采用外部模块获取实时动态差分数据。测试过程中记录了眼动数据、地理定位轨迹及应用内用户触发事件。参与者完成了可用性问卷（系统可用性量表、用户体验问卷、手持增强现实可用性量表）。研究发现，实验组所接触的地理定位数据平均精度低于对照组。实验组在所有量表上的可用性得分均较低，其中9个维度中有5个达到显著差异，表明不准确的数据会负向预测可用性。对照组行走距离显著多于实验组，说明数据精度对探索行为存在部分影响。未发现数据精度与屏幕交互时间存在显著关联，表明数据准确性与专注度之间无特定关系。虽然实时动态差分数据未能提升基于位置的增强现实界面的可用性，但研究结果将我们系统的整体可用性评估为优秀，并为未来学生使用场景定义了最佳运行条件。