Social robots for education: A review <s> REVIEW <s> This meta‐analysis reviews the findings of 81 studies (N=24,474) examining the relationship between teachers' verbal or nonverbal immediacy (or both) and students' learning outcomes. The cumulative evidence indicates meaningful correlations between teachers' nonverbal immediacy and student reports of perceived learning (r=.51) and affective learning (r=.49), but only a slight correlation with students' performance on cognitive learning measures (r=.17). Teachers' verbal immediacy was found to correlate with students' perceived learning (r=.49) and affective learning (r=.49), but again the relationship with performed cognitive learning was notably smaller (r=.06). Average effect sizes were generally smaller in experimental research designs than in survey‐type research, and generally larger when verbal/nonverbal immediacy was measured as a single construct. The cumulative results of this meta‐analysis confirm that teacher immediacy has a substantial relationship with certain attitudes and perceptions of stu... <s> BIB001 </s> Social robots for education: A review <s> REVIEW <s> We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors. <s> BIB002
To support our review, we used a meta-analysis of the literature on robots for education. In this, three key questions framed the meta-analysis and dictated which information was extracted: 1. Efficacy. What are the cognitive and affective outcomes when robots are used in education? 2. Embodiment. What is the impact of using a physically embodied robot when compared with alternative technologies? 3. Interaction role. What are the different roles the robot can take in an educational context? For the meta-analysis, we used published studies extracted from the Google Scholar, Microsoft Academic Search, and CiteSeerX databases by using the following search terms: robot tutor, robot tutors, socially assistive robotics (with manual filtering of those relevant to education), robot teacher, robot assisted language learning, and robot assisted learning. The earliest published work appeared in 1992, and the survey cutoff date was May 2017. In addition, proceedings of prominent social HRI journals and conferences were manually searched for relevant material: International Conference on Human-Robot Interaction, International Journal of Social Robotics, Journal of Human-Robot Interaction, International Conference on Social Robotics, and the International Symposium on Robot and Human Interactive Communication (RO-MAN). The selection of papers was based on four additional criteria: 1) Novel experimental evaluations or analyses should be presented. 2) The robot should be used as the teacher (i.e., the robot is an agent in the interaction) rather than the robot being used as an educational prop or a learner with no intention to educate [e.g., (49)]. 3) The work must have included a physical robot, with an educative intent. For example, studies considering "coaches" that sought to improve motivation and compliance, but did not engage in education [e.g., (50)], were not included, whereas those that provided tutoring and feedback were included [e.g., BIB002 ]. 4) Only full papers were included. Extended abstracts were omitted because these often contained preliminary findings, rather than complete results and full analyses. We withheld 101 papers for analysis and excluded 12 papers for various reasons (e.g., the paper repeated results from an earlier publication). The analyzed papers together contain 309 study results  . To compare outcomes of the different studies, we first divided the outcomes of an intervention into either affective or cognitive. Cognitive outcomes focus on one or more of the following competencies: knowledge, comprehension, application, analysis, synthesis, and evaluation    . Affective outcomes refer to qualities that are not learning outcomes per se, for example, the learner being attentive, receptive, responsive, reflective, or inquisitive  . The metaanalysis contained 99 (33.6%) data points on cognitive learning outcomes and 196 (66.4%) data points on affective learning outcomes; 14 study results did not contain a comparative experiment on learning outcomes. Cognitive outcomes are typically measured through pre-and posttests of student knowledge, whereas affective outcomes are more varied and can include self-reported measures and observations by the experimenters. Table 1 contains the most common methods for measuring cognitive and affective outcomes reported in the literature. Most studies focused on children (179 data points; 58% of the sample; mean age, 8.2 years; SD, 3.56), whereas adults (≥18 years old) were a lesser focus of research in robot tutoring (98 data points; 32% of the sample; mean age, 30.5; SD, 17.5). For 29 studies (9%), both children and adults were used, or the age of the participants was not specified. If the results reported an effect size expressed as Cohen's d, then this was used unaltered. In cases where the effect size was not reported or if it was expressed in a measure other than Cohen's d, then an online calculator (55) [see also (56) ] was used if enough statistical information was present in the paper (typically participant numbers, means, and SDs are sufficient). We captured the following data gleaned from the publications: the study design, the number of conditions, the number of participants per condition, whether participants were children or adults, participant ages (mean and SD), the robot used, the country in which the study was run, whether the study used a within or between design, the reported outcomes (affective or cognitive, with details on what was measured exactly), the descriptive statistics (where available mean, SD, t, and F values), the effect size as Cohen's d, whether the study involved one robot teaching one person or one robot teaching many, the role of the robot (presenter, teaching assistant, teacher, peer, or tutor), and the topic under study (embodiment of the robot, social character of the robot, the role of the robot, or other). The studies in our sample reported more on affective outcomes than cognitive outcomes (Fig. 1A ). This is due to the relative ease with which a range of affective outcomes can be assessed by using questionnaires and observational studies, whereas cognitive outcomes require administering a controlled knowledge assessment before and after the interaction with the robot, of which typically only one is reported per study. Figure 2B shows the countries where studies were run. Robots for learning research, perhaps unsurprisingly, happen predominantly in East Asia (Japan, South Korea, and Taiwan), Europe, and the United States. An exception is the research in Iran on the use of robots to teach English in class settings. Coding emotional expressions of the learner, can be automated using face analysis software (47) Godspeed questionnaire, measuring the user's perception of robots (78) Tripod survey, measuring the learner's perspective on teaching, environment, and engagement (79) Immediacy, measuring psychological availability of the robot teacher  BIB001 Evolution of time between answers, e.g., to indicate fatigue (31)