Hoi Ki Tang, Matthijs H. J. Smakman, M. De Haas, Rianne van den Berghe
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引用次数: 0
Abstract
Vocabulary is a crucial part of second language (L2) learning. Children learn new vocabulary by forming mental lexicon relations with their existing knowledge. This is called lexical inferencing: using the available clues and knowledge to guess the meaning of the unknown word. This study explored the potential of second language vocabulary acquisition through lexical inferencing in child-robot interaction. A storytelling robot read a book to Dutch kindergartners (N = 36, aged 4-6 years) in Dutch in which a few key words were translated into French (L2), and with a robot providing additional word explanation cues or not. The results showed that the children learned the key words successfully as a result of the reading session with the storytelling robot, but that there was no significant effect of additional word explanation cues by the robot. Overall, it seems promising that lexical inferencing can act as a new and different way to teach kindergartners a second language.
期刊介绍：
ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain.
THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.
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