我一个人学得更好!与儿童和成人机器人合作和个人单词学习

Alireza M. Kamelabad, G. Skantze
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引用次数: 0

摘要

最近,社交机器人作为语言学习工具的使用得到了相当广泛的研究。虽然它们的有效性和与其他技术的比较已经得到了很好的研究,但机器人的外观和交互设置的影响却很少受到关注。随着教育机器人被设想出现在家庭或学校环境中,研究它们设计的角色或交互动态如何影响学习结果是很重要的。在这种环境中,儿童可能会一起或单独进行活动,或者在成人或其他儿童在场的情况下进行活动。在这方面,我们确定了两个新的因素来研究:机器人的感知年龄(成人或儿童)和同时与机器人互动的学习者数量(一个或两个)。我们用Furhat机器人设计了一个偶然的单词学习纸牌游戏,并在75名中学参与者中进行了一项受试者之间的实验。我们研究了儿童词汇学习成果、言语活动和机器人角色感知之间的相互作用和影响。结果显示,与成对玩耍的孩子相比,单独与机器人玩耍的孩子有更好的单词记忆能力,也更能将机器人拟人化。此外,与之前人类互动的研究结果不同,儿童在成人机器人和儿童机器人面前并没有表现出不同的行为。我们详细讨论了这些因素,并对协作学习与个体学习的直接比较以及机器人时代的新概念做出了新的贡献。
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I Learn Better Alone! Collaborative and Individual Word Learning With a Child and Adult Robot
The use of social robots as a tool for language learning has been studied quite extensively recently. Although their effectiveness and comparison with other technologies are well studied, the effects of the robot's appearance and the interaction setting have received less attention. As educational robots are envisioned to appear in household or school environments, it is important to investigate how their designed persona or interaction dynamics affect learning outcomes. In such environments, children may do the activities together or alone or perform them in the presence of an adult or another child. In this regard, we have identified two novel factors to investigate: the robot's perceived age (adult or child) and the number of learners interacting with the robot simultaneously (one or two). We designed an incidental word learning card game with the Furhat robot and ran a between-subject experiment with 75 middle school participants. We investigated the interactions and effects of children's word learning outcomes, speech activity, and perception of the robot's role. The results show that children who played alone with the robot had better word retention and anthropomorphized the robot more, compared to those who played in pairs. Furthermore, unlike previous findings from human-human interactions, children did not show different behaviors in the presence of a robot designed as an adult or a child. We discuss these factors in detail and make a novel contribution to the direct comparison of collaborative versus individual learning and the new concept of the robot's age.
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来源期刊
ACM Transactions on Human-Robot Interaction
ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence
CiteScore
7.70
自引率
5.90%
发文量
65
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.
期刊最新文献
Influence of Simulation and Interactivity on Human Perceptions of a Robot During Navigation Tasks Converging Measures and an Emergent Model: A Meta-Analysis of Human-Machine Trust Questionnaires Generating Pattern-Based Conventions for Predictable Planning in Human-Robot Collaboration Classification of Co-manipulation Modus with Human-Human Teams for Future Application to Human-Robot Systems Perceptions of a Robot that Interleaves Tasks for Multiple Users
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