In non-English-speaking countries, students learning EFL (English as a Foreign Language) without a “real” learning environment mostly shows poor English-learning performance. In order to improve the English-learning effectiveness of EFL students, we propose the use of augmented reality (AR) to support situational classroom learning and conduct teaching experiments for situational English learning. The purpose of this study is to examine whether the learning performance of EFL students can be enhanced using augmented reality within a situational context. The learning performance of the experimental student group is validated by means of the attention, relevance, confidence, and satisfaction (ARCS) model. According to statistical analysis, the experimental teaching method is much more effective than that of the control group (i.e., the traditional teaching method). The learning performance of the experimental group students is obviously enhanced and the feedback of using AR by EFL students is positive. The experimental results reveal that (1) students can concentrate more on the practice of speaking English as a foreign language; (2) the real-life AR scenarios enhanced student confidence in learning English; and (3) applying AR teaching materials in situational context classes can provide near real-life scenarios and improve the learning satisfaction of students.

ABSTRACT：Thermal infrared data become more popular in remote sensing investigation, for it could be acquired both in day and night. The change of temperature has special characteristic in natural environment, so the thermal infrared images could be used in monitoring volcanic landform, the urban development, and disaster prevention. Heat shadow is formed by reflecting radiating capacity which followed the objects. Because of poor spatial resolution of thermal infrared images in satellite sensor, shadow effects were usually ignored. This research focus on discussing the shadow effects of various features, which include metals and nonmetallic materials. An area-based thermal sensor, FLIR-T360 was selected to acquire thermal images. Various features with different emissivity were chosen as reflective surface to obtain thermal shadow in normal atmospheric temperature. Experiments found that the shadow effects depend on the distance between sensors and features, depression angle, object temperature and emissivity of reflective surface. The causes of shadow effects have been altered in the experiment for analyzing the variance in thermal infrared images. The result shows that there were quite different impacts by shadow effects between metals and nonmetallic materials. The further research would be produced a math model to describe the shadow effects of different features in the future work.