This research involved the design of a task-based dialogue system and evaluation of its learning effectiveness. Dialogue training still heavily depends on human communication with instant feedback or correction. However, it is not possible to provide a personal tutor for every English learner. With the rapid development of information technology, digitized learning and voice communication is a possible solution. The goal of this research was to develop an innovative model to refine the task-based dialogue system, including natural language understanding, disassembly intention, and dialogue state tracking. To enable the dialogue system to find the corresponding sentence accurately, the dialogue system was designed with machine learning algorithms to allow users to communicate in a task-based fashion. Past research has pointed out that computer-assisted instruction has achieved remarkable results in language reading, writing, and listening. Therefore, the direction of the discussion is to use the task-oriented dialogue system as a speaking teaching assistant. To train the speaking ability, the proposed system provides a simulation environment with goal-oriented characteristics, allowing learners to continuously improve their language fluency in terms of speaking ability by simulating conversational situational exercises. To evaluate the possibility of replacing the traditional English speaking practice with the proposed system, a small English speaking class experiment was carried out to validate the effectiveness of the proposed system. Data of 28 students with three assigned tasks were collected and analyzed. The promising results of the collected students’ feedback confirm the positive perceptions toward the system regarding user interface, learning style, and the system’s effectiveness.
The study uses augmented reality (AR) technology to integrate virtual objects into the real learning environment for language learning. The English AR classroom is constructed using the system prototyping method and evaluated by semi-structured in-depth interviews. According to the flow theory by Csikszenmihalyi in 1975 along with the immersive factors from Trevino and Webster in 1992, the proposed system uses the computer information systems with augmented reality technology to create flow experiences and embodied cognition. The effects of the English AR classroom are evaluated by semi-structured in-depth interviews. Based on the opinions of the domain experts, the AR prototype system validates the possibility of carrying out digital immersive language learning and embodied cognition. In addition, the concerns of the curriculum designs based on this system are discussed to show the intension of the practical uses.
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