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this research combines the artificial intelligence technique RNN/LSTM to develop a dialogue system, which mainly used in the bargaining unit of a negotiation course. The chatbot was applied in the dialogue of student drills in bargaining situations and investigates the feasibility of the dialogue system used in the course. The two-hour course has 48 second-year university students with different colleges and departments. The results show that most students have a positive view of instant messaging conducts dialogue exercises, and feel interesting. The groups targeting dialogue robots, students prefer courses. For groups with real people, students think it is more helpful for learning, and they have higher satisfaction with the results of bargaining negotiations.

In this study, the effectiveness of the application of speech-to-text recognition (STR) technology on enhancing learning and concentration in a calm state of mind, hereafter referred to as meditation (An intentional and self-regulated focusing of attention in order to relax and calm the mind), was investigated. This effectiveness was further explored with regard to foreign language ability and gender. Finally, students’ perceptions towards STR-texts were surveyed. 60 non-native English speaking undergraduates participated in this study. All students were randomly assigned into either a control or an experimental group, with 30 students in each group. Two lectures, both in English but at different levels of difficulty, were given in a classroom environment. Students in the control group received a lecture containing only a video of the instructor and slides; students in the experimental group received the video of the instructor and slides as well as STR-texts of the lecture. The following main findings were obtained: First, STR-texts had a positive effect on the learning performance, attention and meditation of students. In addition, most students had positive perceptions regarding the usefulness of STR-texts for learning. This is because students in the experimental group received instructional content in both verbal (i.e., speech) and visual (i.e., STR-texts) forms, which made the content more comprehensible and easier to process. Second, during lectures with STR, high ability and female students had higher levels of attention and meditation in most cases compared to their counterparts. This finding can be explained by the difference in learning motivation and in the use of learning strategies. That is, high ability and female students are more interested in learning and display greater use of various learning strategies. Based on these results, it is suggested that educators and researchers integrate STR-texts during lectures in English in order to enhance learning and to increase the level of attention and meditation of non-native English speaking students.

Satisfying students’ different needs for learning is an important issue in the education domain. However, teachers used to take an exam to understanding students’ learning progress often in the past, resulting in increasing students’ stress and frustration. Owing to the advent of sensor technology, it provides educators a new way to conveniently assess every student’s learning status in diverse dimensions. For example, a cushion with pressure sensors can find the variety of learners’ sitting postures. With the experiences in the development of sensor and educational technologies, we have seen an emerging trend in the application of multi-sensor technologies to educational environments. Therefore, this article aims to investigate the suitability of different kinds of sensors for the educational scenario, and the potential benefits of integrating multi-sensor technology in schools. It is hoped to suggest readers a further research blueprint for this topic.

With the development of information technology, Cloud computing looks promising in support of e-learning, it could provide a variety of learning services and resources to facilitate a ubiquitous learning environment to preview and review the course content. Meanwhile, the Cloud technology supports the social networking sites (SNSs) that provide a virtual interactive learning platform to communicate with each other and enhance online cooperative learning. In addition, recently the sensor technology-based applications for e-learning has become a popular research issue, which includes context awareness, augmented reality, motion learning, data acquisition. Therefore, this chapter tends to highlight the possible application of Cloud technology, SNSs and sensing technology on e-learning, and explore the pedagogical development with these technologies. By the objective above, it is hoped the content addressed in this chapter can inspire readers reshaping the relationship between education and latest information technology so as to broaden their horizon on forthcoming development of e-learning.

Modern life has a strong tie with ICT technology, so as to the sensing technology which has played an important role than before. Its application is somewhat more than we have learned, such as education. Meanwhile, with the advance of software/hardware and the popularity of the Internet, e-books have drawn much of attention. With the introduction of the sensing technology to e-readers, it instills e-books the functions of reading or learning behavior. In self-learning situation, learners often suffer from detraction problem which cannot be easily detected and amended. In this study, we proposed a sensor-assisted learning system to monitor a learner's retention during the reading or learning process. The system has been incorporated into an e-book platform, or e-reader to become a personalized e-reader system. We proceed to conduct two experiments for sensor-assisted model (SAM) and e-book systems, respectively. The ultimate objective is to establish an adaptive personalized learning system by the feedback of data collected by sensors.

Modern lifestyle is closely associated with information technology, and sensor technology is particularly used, especially in learning and education. This study proposed a sensor-assisted learning system using sensor technology, in order to determine the learning retention of learners in the learning process, and further provide assistance or feedback. The identification rule of this system is constructed based on decision tree algorithm ID3 (C4.5). The system determined the learning retention according to the learners' visual attention recognition, sitting position variability, and physiological signals analysis.

In this study, we implemented a digital game-based learning (DGBL) system over Face book platform. For better understanding the impact of DGBL applied to social networking, the technology acceptance model (TAM) was employed for the evaluation. Results of this study showed that the DGBL incorporated into a social network website is a feasible and sound model for teaching. This study investigated the potential important design issues of the application over a social network website. It is believed that this study could bring teachers a better understanding how those characteristics of social networks can attribute to the success of users' learning.