This study is intended to create an innovative contextual English learning environment making use of the widely used communication software, LINE ChatBot, based on the Artificial Intelligence Markup Language, in order to improve speaking and listening ability among learners. A total of 73 students were invited to participate in learning activities involving a 4-week English conversation exercise including both speaking and listening. Additionally, in order to explore the influence of competition on language acquisition, we added competition characteristics to the learning (...) activities in the experimental group to enhance learning motivation and learning outcomes. The results showed that with the help of the LINE ChatBot contextual learning environment, the performance of both groups of students was slightly enhanced, but no significant differences were found. Meanwhile, extrinsic motivation in both the experimental and control group was improved if they spoke anonymously. That is, the contextual learning environment based on the LINE ChatBot significantly improved the learners’ English speaking and listening ability. In addition, the results showed that the addition of a competition element effectively enhanced the learners’ intrinsic motivation to learn English on the LINE ChatBot. (shrink)

Maker education mainly involves “hands-on” as the core concept and combines various educational theories to redefine interactions between learners and teachers in a learning environment. Identification of meaningful “hands-on” behaviors is crucial to evaluate students’ learning performance, although an instructor’s observation of every student is not feasible. However, such observation is possible with the aid of the artificial intelligence image processing technique; the AI learning behavior recognition system can serve as the second eyes of teachers, thus accounting for individual differences. (...) However, in previous studies, learning behavior recognition was applied to the traditional or static classroom. A behavior recognition system for identifying “hands-on” actions in the learning context has still not been developed. Therefore, this study designed a human posture evaluation system, obtained human articulation node information from learning field images, and built a learning behavior recognition model suitable for maker education based on the AI convolutional neural network. A learning behavior model was defined, along with a number of student behavior indexes. Subsequently, the effectiveness of the model and behavior indexes was verified through practical learning activities. The model evaluation results indicated that the proposed model achieved a training accuracy of 0.99 and a model accuracy of 0.83. Thus, the model can be applied to dynamic maker activity learning environments. (shrink)

Students often face difficulties and experience negative emotions toward second language learning. The affective tutoring system is a next-generation learning approach that can detect the affective status of learning to increase performance. Therefore, for the purposes of this study, an innovative affective mobile language tutoring system was designed to support Japanese language learning. The effects of AMLTS, along with asynchronous discussion, that were intended to improve performance, were examined using a triangulation method. To investigate the effect on emotion, the proposed (...) AMLTS provides a virtual emotion agent that can interact with users and record emotional events, learning assessments, and the results of the interaction into a database. Learning effectiveness evaluations were conducted via two experiments: prototype evaluation and final evaluation. Sixty-three students, all beginners, were invited to use the AMLTS to learn Japanese. The research results show that the proposed AMLTS affective interaction design significantly improves learner engagement and performance. In the emotion feedback analysis and learning process, AMLTS helped students deepen their understanding of the content, enabled them to clearly understand the content, and to engage in peer interaction and experience positive emotions. In the evaluation of system usability, AMLTS reveals good usability for foreign language acquisition. (shrink)