de Haas Mirjam, Vogt Paul, Krahmer Emiel
Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands.
Department of Communication and Cognition, Tilburg Center for Cognition and Communication, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands.
Front Robot AI. 2020 Aug 4;7:101. doi: 10.3389/frobt.2020.00101. eCollection 2020.
To investigate how a robot's use of feedback can influence children's engagement and support second language learning, we conducted an experiment in which 72 children of 5 years old learned 18 English animal names from a humanoid robot tutor in three different sessions. During each session, children played 24 rounds in an "I spy with my little eye" game with the robot, and in each session the robot provided them with a different type of feedback. These feedback types were based on a questionnaire study that we conducted with student teachers and the outcome of this questionnaire was translated to three within-design conditions: (teacher) preferred feedback, (teacher) dispreferred feedback and no feedback. During the preferred feedback session, among others, the robot varied his feedback and gave children the opportunity to try again (e.g., "Well done! You clicked on the horse.", "Too bad, you pressed the bird. Try again. Please click on the horse."); during the dispreferred feedback the robot did not vary the feedback ("Well done!", "Too bad.") and children did not receive an extra attempt to try again; and during no feedback the robot did not comment on the children's performances at all. We measured the children's engagement with the task and with the robot as well as their learning gain, as a function of condition. Results show that children tended to be more engaged with the robot and task when the robot used preferred feedback than in the two other conditions. However, preferred or dispreferred feedback did not have an influence on learning gain. Children learned on average the same number of words in all conditions. These findings are especially interesting for long-term interactions where engagement of children often drops. Moreover, feedback can become more important for learning when children need to rely more on feedback, for example, when words or language constructions are more complex than in our experiment. The experiment's method, measurements and main hypotheses were preregistered.
为了研究机器人使用反馈如何影响儿童的参与度并支持第二语言学习,我们进行了一项实验,72名5岁儿童在三个不同的环节中从一个人形机器人导师那里学习18个英语动物名称。在每个环节中,孩子们与机器人玩24轮“我用小眼睛来找”游戏,并且在每个环节中机器人为他们提供不同类型的反馈。这些反馈类型基于我们对实习教师进行的一项问卷调查研究,该问卷的结果被转化为三种设计内条件:(教师)偏好反馈、(教师)非偏好反馈和无反馈。在偏好反馈环节中,机器人会改变他的反馈,并给孩子们再次尝试的机会(例如,“做得好!你点击了马。”,“太糟糕了,你按了鸟。再试一次。请点击马。”);在非偏好反馈环节中,机器人不改变反馈(“做得好!”,“太糟糕了。”),孩子们没有得到额外的再次尝试机会;在无反馈环节中,机器人根本不对孩子们的表现进行评价。我们测量了孩子们对任务和机器人的参与度以及他们的学习收获,作为条件的函数。结果表明,当机器人使用偏好反馈时,孩子们往往比在其他两种条件下更愿意与机器人互动并参与任务。然而,偏好或非偏好反馈对学习收获没有影响。在所有条件下,孩子们平均学到的单词数量相同。这些发现对于长期互动尤其有趣,因为在长期互动中孩子们的参与度往往会下降。此外,当孩子们需要更多地依赖反馈时,例如当单词或语言结构比我们实验中的更复杂时,反馈对于学习可能会变得更加重要。该实验的方法、测量和主要假设都进行了预先注册。