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Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children's learning and emotive engagement

机译:与儿童一起进行的教与学:通过社交机器人进行的同伴相互学习对儿童的学习和情绪参与的影响

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Pedagogical agents are typically designed to take on a single role: either as a tutor who guides and instructs the student, or as a tutee that learns from the student to reinforce what he/she knows. While both agent-role paradigms have been shown to promote student learning, we hypothesize that there will be heightened benefit with respect to students' learning and emotional engagement if the agent engages children in a more peer-like way - adaptively switching between tutor/tutee roles. In this work, we present a novel active role-switching (ARS) policy trained using reinforcement learning, in which the agent is rewarded for adapting its tutor or tutee behavior to the child's knowledge mastery level. To investigate how the three different child-agent interaction paradigms (tutee, tutor, and peer agents) impact children's learning and affective engagement, we designed a randomized controlled between-subject experiment. Fifty-nine children aged 5-7 years old from a local public school participated in a collaborative word-learning activity with one of the three agent-role paradigms. Our analysis revealed that children's vocabulary acquisition benefited from the robot tutor's instruction and knowledge demonstration, whereas children exhibited slightly greater affect on their faces when the robot behaves as a tutee of the child. This synergistic effect between tutor and tutee roles suggests why our adaptive peer-like agent brought the most benefit to children's vocabulary learning and affective engagement, as compared to an agent that interacts only as a tutor or tutee for the child. This work sheds light on how fixed role (tutor/tutee) and adaptive role (peer) agents support children's cognitive and emotional needs as they play and learn. It also contributes to an important new dimension of designing educational agents - actively adapting roles based on the student's engagement and learning needs.
机译:教学人员通常被设计为扮演一个角色:作为指导和指导学生的导师,或者作为向学生学习以强化他/她所学知识的学生。虽然两种代理人角色范例均已证明可促进学生学习,但我们假设,如果代理人以更像同伴的方式与孩子互动,则会在学生的学习和情感投入方面带来更大的好处-自适应地在导师/学徒之间切换角色。在这项工作中,我们提出了一种使用强化学习训练的新型主动角色切换(ARS)策略,在该策略中,代理将其导师或学员的行为适应儿童的知识掌握水平而受到奖励。为了研究三种不同的儿童-个体互动模式(学生,导师和同伴)如何影响儿童的学习和情感投入,我们设计了一个随机对照的受试者间实验。来自当地一所公立学校的59名5-7岁的儿童参加了与三个特工角色范式之一合作的词汇学习活动。我们的分析表明,机器人导师的指导和知识展示使孩子的词汇习得受益,而当机器人充当孩子的学徒时,孩子的脸部表情受到的影响更大。导师和受训者角色之间的这种协同作用表明,为什么与仅作为孩子的导师或受训者进行交互的代理人相比,我们的适应性同伴状代理人能为儿童的词汇学习和情感投入带来最大的好处。这项工作揭示了固定角色(导师/学员)和适应性角色(同伴)代理人如何在儿童玩耍和学习时支持他们的认知和情感需求。它还有助于设计教育代理人的一个重要的新方面-根据学生的参与度和学习需求积极地适应角色。

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