Social robots for education: A review <s> Robot behavior <s> Based on a ‘learning by playing' concept, a basic arithmetic learning task was extended with an engaging game to achieve long-term educational interaction for children. Personalization was added to this learning task, to further support the child's motivation and success in learning. In an experiment, twenty children (aged 9-10) interacted three times, spread over days, with a robot using the combined imitation and arithmetic game to test this support. Two versions of the robot were implemented. In one implementation the complexity of the arithmetic progressed towards a predefined group target. In the other version the assignments increased in complexity until a personal end level was reached. A subsequent free-choice period showed that children's motivation to play (and learn) was high, particularly when the game progressed to a personal target. Furthermore results show that most children in the last condition reach higher levels compared to the predefined group level. <s> BIB001 </s> Social robots for education: A review <s> Robot behavior <s> Objective Assess the effects of personalised robot behaviours on the enjoyment and motivation of children (8–12) with diabetes, and on their acquisition of health knowledge, in educational play. Methods Children (N = 5) played diabetes quizzes against a personal or neutral robot on three occasions: once at the clinic, twice at home. The personal robot asked them about their names, sports and favourite colours, referred to these data during the interaction, and engaged in small talk. Fun, motivation and diabetes knowledge was measured. Child–robot interaction was observed. Results Children said the robot and quiz were fun, but this appreciation declined over time. With the personal robot, the children looked more at the robot and spoke more. The children mimicked the robot. Finally, an increase in knowledge about diabetes was observed. Conclusion The study provides strong indication for how a personal robot can help children to improve health literacy in an enjoyable way. Children mimic the robot. When the robot is personal, they follow suit. Our results are positive and establish a good foundation for further development and testing in a larger study. Practice implications Using a robot in health care could contribute to self-management in children and help them to cope with their illness. <s> BIB002 </s> Social robots for education: A review <s> Robot behavior <s> In social interaction, people draw on a large repertoire of social acts tailoring their use of these acts to meet the demands of the social situation and to achieve the goals of the interaction. This paper presents an approach to creating such a repertoire of social acts for robots and enabling designers to specify the social situation to which robots may adapt their behaviors. Drawing on principles of Activity Theory and social-scientific findings on human social behavior, this paper introduces an implementation of this approach---the Robot Behavior Toolkit---and two studies that use a limited, proof-of-concept repertoire of specifications for gaze cues to demonstrate the feasibility of this approach for controlling robot gaze behavior. The first study assessed the feasibility of the use of this repertoire, comparing it to alternative, baseline repertoires in two human-robot interaction tasks, and found that it enabled the robot to more effectively support the interaction goals. The second study investigated the feasibility of the robot adapting its use of the repertoire to a social situation by comparing different goal specifications in two human-robot interaction tasks. The results showed that these specifications enabled the robot to achieve some of its task and communicative goals, although participant gender strongly affected whether the robot elicited these interaction outcomes. <s> BIB003 </s> Social robots for education: A review <s> Robot behavior <s> In education research, there is a widely-cited result called “Bloom’s two sigma” that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction [1]. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot’s actions to an individual’s strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants’ puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot’s personalizations were less sophisticated than what a human tutor can do, we still witnessed a “one-sigma” improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics; J.4 [Computer Applications]: Social And Behavioral Sciences—Psychology General Terms Experimentation <s> BIB004 </s> Social robots for education: A review <s> Robot behavior <s> As a great number of robotic products are entering people’s lives, the question of how can they behave in order to sustain long-term interactions with users becomes increasingly more relevant. In this paper, we present an empathic model for social robots that aim to interact with children for extended periods of time. The application of this model to a scenario where a social robot plays chess with children is described. To evaluate the proposed model, we conducted a long-term study in an elementary school and measured children’s perception of social presence, engagement and social support. <s> BIB005 </s> Social robots for education: A review <s> Robot behavior <s> Social robots are finding increasing application in the domain of education, particularly for children, to support and augment learning opportunities. With an implicit assumption that social and adaptive behaviour is desirable, it is therefore of interest to determine precisely how these aspects of behaviour may be exploited in robots to support children in their learning. In this paper, we explore this issue by evaluating the effect of a social robot tutoring strategy with children learning about prime numbers. It is shown that the tutoring strategy itself leads to improvement, but that the presence of a robot employing this strategy amplifies this effect, resulting in significant learning. However, it was also found that children interacting with a robot using social and adaptive behaviours in addition to the teaching strategy did not learn a significant amount. These results indicate that while the presence of a physical robot leads to improved learning, caution is required when applying social behaviour to a robot in a tutoring context. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems <s> BIB006 </s> Social robots for education: A review <s> Robot behavior <s> The application of social robots to the domain of education is becoming more prevalent. However, there remain a wide range of open issues, such as the effectiveness of robots as tutors on student learning outcomes, the role of social behaviour in teaching interactions, and how the embodiment of a robot influences the interaction. In this paper, we seek to explore children’s behaviour towards a robot tutor for children in a novel guided discovery learning interaction. Since the necessity of real robots (as opposed to virtual agents) in education has not been definitively established in the literature, the effect of robot embodiment is assessed. The results demonstrate that children overcome strong incorrect biases in the material to be learned, but with no significant differences between embodiment conditions. However, the data do suggest that the use of real robots carries an advantage in terms of social presence that could provide educational benefits. <s> BIB007 </s> Social robots for education: A review <s> Robot behavior <s> In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the interrelations between the learner's skills, her observed behaviour in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner's knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words. CCS Concepts. Computing methodologies → Probabilistic reasoning; Cognitive robotics;. Applied computing → Interactive learning environments;. Human-centered computing → Empirical studies in HCI; <s> BIB008
To be effective educational agents, the behavior of social robots must be tailored to support various aspects of learning across different learners and diverse educational contexts. Several studies focused on understanding critical aspects of educational interactions to which robots should respond, as well as determining both what behaviors social robots can use and when to deliver these behaviors to affect learning outcomes. Our meta-review shows that almost any strategy or social behavior of the robot aimed at increasing learning outcomes has a positive effect. We identified the influence of robot behaviors on cognitive outcomes (d = 0.69; N = 12; 95% CI, 0.56 to 0.83) and affective outcomes (d = 0.70; N = 32; 95% CI, 0.62 to 0.77). Similar to findings in the ITS community, robots that personalize what content to provide based on user performance during an interaction can increase cognitive learning gains BIB004 BIB008 . In addition to the adaptive delivery of learning material, social robots can offer socially supportive behaviors and personalized support for learners within an educational context. Personalized social support, such as using a child's name or referring to previous interactions BIB001 BIB002 , is the low-hanging fruit of social interaction. More complex prosocial behavior, such as attention-guiding (11), displaying congruent gaze behavior BIB003 , nonverbal immediacy (3), or showing empathy with the learner BIB005 , not only has a positive impact on affective outcomes but also results in increased learning. However, just as human tutors must at times sit quietly and allow students the opportunity to concentrate on problem solving, robot tutors must also limit their social behavior at appropriate times based on the cognitive load and engagement of the student BIB006 . The social behavior of the robot must be carefully designed in conjunction with the interaction context and task at hand to enhance the educational interaction and avoid student distraction. It is possible that the positive cognitive and affective learning outcomes of robot tutors are not directly caused by the robot having a physical presence, but rather the physical presence of the robot promotes social behaviors in the learner that, in turn, foster learning and create a positive learning experience. Robots have been shown to have a positive impact on compliance (26), engagement (21-23), and conformity BIB007 , which, in turn, are conducive to achieving learning gains. Hence, a perhaps valuable research direction is to explore what it is about social robots that affects the first-order outcomes of engagement, persuasion, and compliance.