Human motion has multifarious meanings that can be recognized using a facial detection machine. This article aims to explore body motion recognition to explain the relationship between students’ motions and their achievement, as well as teachers’ responses to students’ motions, and especially to negative ones. Students’ motions can be identified according to three categories; facial expression, hand gestures, and body position and movement. Facial expression covers four categories, namely, contempt, fear, happiness, and sadness. Contempt is used to express conflicted feelings, fear to express unpleasantness, happiness to express satisfaction, and sadness to express that the environment is uncomfortable. Hand gestures can likewise be grouped into four categories: conversational gestures, controlling gestures, manipulative gestures, and communicative gestures. Conversational gestures refer to communicative gestures. Controlling gestures refer to vision-based interface communications, like the ones popular in current technology. Manipulative gestures refer to ones used in human interaction with virtual objects. Communicative gestures relate to human interaction, and therefore involve the field of psychology. Body position and movement also can be classified into four categories, namely: leaning forward, leaning backward, correct posture, and physical relocation. Leaning forward happens when a user is working with a high level of concentration. Leaning backward occurs when a user has been highly concentrated on work for several hours, and needs a break or change. Correct posture is the sign of an enjoyable working position which involves sitting in a free and relaxed manner. Movement refers to a change to the student’s sitting location, reflecting some inadequacy of the learning environment. Teachers can anticipate changes of students’ emotions by good learning design, teaching metacognitive skills, self-regulated performance, exploratory talks, mastery approach/avoidance, using hybrid learning environments, and controlling space within classrooms. Teachers’ responses to students’ motions will be explored in this article

This research aims to apply a depth sensor to create a human-body-sensing context for outdoor learning paths; it is conducted by incorporating both quasiexperiment and survey to compare students' cognitive learning outcome within the context and understand students' attitudes toward the context created. The result of ANCOVA indicates that the context of an introductory film activated by the depth sensor produces better cognitive learning outcome. Furthermore, the survey of students' attitudes also shows positive attitudes from both experimental and control groups toward the additional use of a human-body-sensing system activated by the depth sensor. The experimental results show the depth sensor can be used to improve conventional learning paths, i.e. with information boards only, for it enables students to participate in wireless sensing learning with their hands free from holding any device.