A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> This research explores feasibility of using intellectual robots as a language instruction tool for young children. Since the intellectual robots have several sensors which can recognize status of the other parties, it can perform various bi-directional interaction strategies. This study verified how such bi-directional interaction using intellectual robots affects the improvement of linguistic ability. The subjects were 34 4-year-old children, 17 of whom were in the traditional media-assisted reading program, and 17 of whom were in the Robot-assisted reading program. The results indicated that the children in the robot-assisted groups improved significantly compared with media-assisted groups in linguistic ability (story making, understanding, word recognition, PPVT (Peabody Picture Vocabulary Test)). Furthermore, the benefits and limitations of using Intelligent Robot in linguistic education are discussed. <s> BIB001 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> We report results of a study in which a low cost sociable robot was immersed at an Early Childhood Education Center for a period of 2 weeks. The study was designed to investigate whether the robot, which operated fully autonomously during the intervention period, could improve target vocabulary skills of 18-24 month of age toddlers. The results showed a 27 % improvement in knowledge of the target words taught by the robot when compared to a matched set of control words. The results suggest that sociable robots may be an effective and low cost technology to enrich Early Childhood Education environments. <s> BIB002 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> The main aim of the modern popular teaching method of authentic learning has been to provide students with everyday-life challenges that develop knowledge and skills through problem solving in different situations. Many emerging information technologies have been used to present authentic environment in pedagogical purpose. However, there are few studies that have been discussed the sense of authenticity and characters in scene and how students interact with the characters involved in the task. We designed a system, RoboStage, with authentic scenes by using mixed-reality technology and robot to investigate the difference in learning with either physical or virtual characters and learning behaviors and performance through the system. Robots were designed to play real interactive characters in the task. The experiment of the study conducted with 36 junior high students. The results indicated that RoboStage significantly improved the sense of authenticity of the task and also positively affected learning motivation. Learning performance was conditionally affected by RoboStage. <s> BIB003 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> This paper presents the effect of robotics assisted language learning (RALL) on the vocabulary learning and retention of Iranian English as foreign language (EFL) junior high school students in Tehran, Iran. After taking a vocabulary pre-test, 46 beginner level female students at the age of 12, studying in their first year of junior-high participated in two groups of RALL (30 students) and non-RALL (16 students) in this study. The textbook used was the English book (Prospect-1) devised by the Iranian Ministry of Education for 7th graders, and the vocabulary taught and tested (pre-test and post-test) were taken from this book. Moreover, the treatment given by a teacher accompanied by a humanoid robot assistant in the RALL group took about five weeks in which half of the book was covered, and the non-RALL group was taught in a traditional method. Finally, the teacher administered the post-test and delayed post-test whose results of repeated measures ANOVA and Two Ways ANOVA indicated that there was a significant difference regarding participants' vocabulary gain and retention in RALL group comparing to non-RALL group. In addition, the teacher reported the students' positive reaction to RALL in learning vocabulary. Overall, the results revealed that RALL has been very influential in creating an efficient and pleasurable English learning environment. This study has some implications for technology-based education, language teaching, and social robotics fields. <s> BIB004 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> Effective tutoring requires personalization of the interaction to each student. Continuous and efficient assessment of the student's skills are a prerequisite for such personalization. We developed a Bayesian active-learning algorithm that continuously and efficiently assesses a child's word-reading skills and implemented it in a social robot. We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot. The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child's word-reading skills as well as empowering the child. We show that our algorithm results in an accurate representation of the child's word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range. We also show that employing child-specific assessment-based tutoring results in an age- and initial reading skill-independent learning, compared to random tutoring. Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot's reading skills results in knowledge that is comparable to what the child thinks the robot has learned. The child's perception of the robot's knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind. <s> BIB005 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> Introduction This research project deals with a robot (MecWilly) designed to help preschool children (4-6 years) in their learning of a second language (English). The main innovation is the experimental setting chosen in order to obtain an improvement in the children's English language skills. In previous studies of educational contexts, robots have typically been used as "teachers" to "structure" the learning process (Fridin, 2014). This also means that children viewed the robot-teacher as "other" (in the case of humanoid robots) or as an "artifact" like a computer or a book (in the case of robots with non-human features). The link between Robotics and Psychology has presented a very interesting challenge for many scholars over the last fifty years (Kelley & Cassenti, 2011). Starting from the development of the first types of computer (the first Turingcomplete machine, ENIAC, created by John Von Neumann in the middle of the 20th century), the simulation of human mental processes, and, later, of human decisions and actions, has lead robotics to become one of the most exciting fields for the evolution of human behavioral models. Throughout this process, many of the most important models and theories of Psychology have been examined, in particular Piaget's Constructivism and the related processes of assimilation and accommodation, Skinner's operant conditioning, the Vygotskian Zone of Proximal Development, Cognitivism's Human-Information Processing (HIP) model, as well as the latest models of artificial intelligence, Connectionism and Neural Networks (Dautenhahn & Billard, 1999a; 1999b; Ziemke, 2001). Even though all these models have been looked at as part of the broad field of Human-Robot Interactions (HRI) or in Robot Development/Evolution, each model has also been analyzed in more specific and well-defined scopes of application. We can briefly summarize five areas of application for psychological models in robotics: (a) The evolution of robot prototypes which simulate human mental and physical behavior; (b) Robot-robot interaction in complex environments for simulating human social behavior; (c) Human[right arrow]robot and robots[right arrow]robot interaction in which the human or one of the robots acts as the tutor|teacher|expert and the (other) robot is a learner; (d) Human[left arrow]robot interaction in which the robot acts as a mechanical "partner" which allows the human to try out (and/or train) certain abilities or social skills so that he/she may make progress; (e) And finally a situation in which the human-robot interaction is characterized by a human who acts as a "tutor|teacher" for the robot and a robot acting as a counterpart in the learning process which, in the end, sees the convergence of improvement and knowledge acquisition influencing the human's cognitive development. The first two areas have been principally influenced by Skinner's Operant Conditioning, Cognitivism, and more recently by Artificial Intelligence (AI), Connectionism and neural networks (including their recent evolutions). Behaviorism played an important role in the development of early robots, as this psychological model has simple rules which can be easily fed into a machine. Thanks to these characteristics, Skinner's teaching machines were very successful in learning environments and demonstrated the importance of feedback (reinforcement) and also of coherence and repeated reinforcement over time in maximizing the learning process. Applying behavioral rules to robotics (and informatics), i.e., determining the behavior of a machine on the basis of the effects of a particular type of behavior and its related reinforcement, helped to produce a set of "mechanical" machines that were able to react to their environment by simulating certain types of human behavior. Robots capable of more sophisticated actions (and quasi-decisions) have been developed using behavioral models derived from AI connected to Behaviorism, the socalled Behavior-Based Robotics (Arkin, 1998; Brooks, 1986a). … <s> BIB006 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> An increasing amount of research is being conducted to determine how a robot tutor should behave socially in educational interactions with children. Both human-human and human-robot interaction literature predicts an increase in learning with increased social availability of a tutor, where social availability has verbal and nonverbal components. Prior work has shown that greater availability in the nonverbal behaviour of a robot tutor has a positive impact on child learning. This paper presents a study with 67 children to explore how social aspects of a tutor robot's speech influences their perception of the robot and their language learning in an interaction. Children perceive the difference in social behaviour between `low' and `high' verbal availability conditions, and improve significantly between a pre- and a post-test in both conditions. A longer-term retention test taken the following week showed that the children had retained almost all of the information they had learnt. However, learning was not affected by which of the robot behaviours they had been exposed to. It is suggested that in this short-term interaction context, additional effort in developing social aspects of a robot's verbal behaviour may not return the desired positive impact on learning gains. <s> BIB007 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Vocabulary <s> Abstract Social robots are innovative new technologies that have considerable potential to support children’s education as tutors and learning companions. Given this potential, it behooves us to study the mechanisms by which children learn from social robots, as well as the similarities and differences between children’s learning from robots as compared to human partners. In the present study, we examined whether young children will attend to the same nonverbal social cues from a robot as from a human partner during a word learning task, specifically gaze and bodily orientation to an unfamiliar referent. Thirty-six children viewed images of unfamiliar animals with a human and with a robot. The interlocutor (human or robot) oriented toward, and provided names for, some of the animals, and children were given a posttest to assess their recall of the names. We found that children performed equally well on the recall test whether they had been provided with names by the robot or by the human. Moreover, in each case, their performance was constrained by the spatial distinctiveness of nonverbal orientation cues available to determine which animal was being referred to during naming. <s> BIB008
The ability of robots to teach vocabulary is well documented in RALL (e.g., References BIB004 BIB005 BIB007 ). For example, Movellan et al. showed a 27% improvement in vocabulary as compared to control words among toddlers BIB002 . Further, attesting to the usefulness of robots in learning, students working in child-robot pairs have been shown to stimulate more learning compared with childchild pairs BIB006 . That robots can improve students' vocabulary is perhaps unsurprising. A more interesting question is: How do robots compare with other technologies in their ability to do so? Studies suggest that, at least for simple vocabulary teaching, robots perform on par with iPads  , and for that matter, human teachers  BIB008 . All three served equally well for transferring knowledge of rudimentary vocabulary. However, children had a strong preference for learning with the robot over both the iPad and person  , though this may be due to a novelty effect. Hyun, Kim, Jang, and Park also compared robots with computers and found they were both capable of increasing children's vocabulary, but there were no significant differences in their ability to do so BIB001 . Yet, the robot did significantly better than the computer at imparting other skills, such as reading, story comprehension, and general communicative ability. To belabor the point, the results of Chang, Lee, Wang, and Chen showed no difference in vocabulary learning when using a co-present robot versus a robot in virtual space; however, there were differences in reported motivation and engagement BIB003 . More nuanced results were obtained by Köse et al. when looking at sign recognition  . When signs were performed by a virtual or physical Robovie, there was no difference in recognition rates among adults with no previous sign experience and children with advanced knowledge of sign. Children with beginner's level knowledge, however, were better able to recognize signs when performed by the physical robot. They suggest that "prior knowledge. . . has an influence on the significance of physical presence" and conclude that young children in the beginning stages of language learning may benefit more from embodiment [60:545] . This idea that physical embodiment plays a more prominent role in learning for children and beginners is worth further exploration. These results reveal that, at least for most groups, the use of robots will not likely present an advantage in vocabulary learning over other technologies, at least in the short-term (as measured in days or weeks). The true benefit their use presents over 2D technologies may be in increasing the desire for continued training-by increasing motivation, interest, and engagement-and this may be important in long-term success. They may also contribute to content retention. In a fiveweek study by Alemi, Meghdari, and Ghazisaedy, students in the RALL condition were able to cover lesson material much faster than the non-RALL/teacher only group. They also exhibited greater retention of the material when tested two weeks later BIB004 . However, that the two groups covered material at different rates may suggest they were not equivalent. Further research should be undertaken to investigate the long-term effects of robots on vocabulary acquisition.