A Survey of Robot-Assisted Language Learning (RALL) <s> Personalization <s> The current paper presents a case study conducted to evaluate the robot rabbit Nabaztag functioning as a vocabulary trainer for beginners of English. Hedonic aspects and motivating function, as well as the general usability and overall impression evoked by rabbit, were tested. A group of 18 5th grade students of an English class at a German junior high school were instructed to practice 20 pairs of vocabulary. Ten students were assisted by the speaking robot rabbit, while eight learned by applying an ordinary paper-and-pencil method. Results show that after one week, students who had learned with the Nabaztag had, on average, a higher recall than the control group. Moreover, the evaluation of the hedonic and pragmatic quality of interacting with the rabbit was high, as was the evaluation of both ease of use and perceived usefulness. Students learning with the rabbit were in a better mood afterwards than those who learned by means of the traditional method. <s> BIB001 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Personalization <s> Recently, language education has a great demand from elementary school to adults. Robots are used as teaching assistants in Robot-Assisted Language Learning. It is very effective to use robots for language education. However, the robots may have some problems. One of the problems is to get bored when interacting with robots. This paper deals with this issue by using a method based on social cognitive theory. We discuss the role of robots based on mutual learning in language education. Next, we explain the concept of self-efficacy for evaluating the learning condition of robots. We propose a method to express self-efficacy using fuzzy control. The essence of the proposed method is to adapt to human's state. The experimental results show the effectiveness of the proposed method for long-term communication between a human and a robot. <s> BIB002 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Personalization <s> Children's oral language skills in preschool can predict their success in reading, writing, and academics in later schooling. Helping children improve their language skills early on could lead to more children succeeding later. As such, we examined the potential of a sociable robotic learning/teaching companion to support children's early language development. In a microgenetic study, 17 children played a storytelling game with the robot eight times over a two-month period. We evaluated whether a robot that "leveled" its stories to match the child's current abilities would lead to greater learning and language improvements than a robot that was not matched. All children learned new words, created stories, and enjoyed playing. Children who played with a matched robot used more words, and more diverse words, in their stories than unmatched children. Understanding the interplay between the robot's and the children's language will inform future work on robot companions that support children's education through play. <s> BIB003 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Personalization <s> Though substantial research has been dedicated towards using technology to improve education, no current methods are as effective as one-on-one tutoring. A critical, though relatively understudied, aspect of effective tutoring is modulating the student's affective state throughout the tutoring session in order to maximize long-term learning gains. We developed an integrated experimental paradigm in which children play a second-language learning game on a tablet, in collaboration with a fully autonomous social robotic learning companion. As part of the system, we measured children's valence and engagement via an automatic facial expression analysis system. These signals were combined into a reward signal that fed into the robot's affective reinforcement learning algorithm. Over several sessions, the robot played the game and personalized its motivational strategies (using verbal and non-verbal actions) to each student. We evaluated this system with 34 children in preschool classrooms for a duration of two months. We saw that (1) children learned new words from the repeated tutoring sessions, (2) the affective policy personalized to students over the duration of the study, and (3) students who interacted with a robot that personalized its affective feedback strategy showed a significant increase in valence, as compared to students who interacted with a non-personalizing robot. This integrated system of tablet-based educational content, affective sensing, affective policy learning, and an autonomous social robot holds great promise for a more comprehensive approach to personalized tutoring. <s> BIB004 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Personalization <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> BIB005 </s> A Survey of Robot-Assisted Language Learning (RALL) <s> Personalization <s> Social robots represent a fruitful enhancement of intelligent tutoring systems that can be used for one-to-one tutoring. The role of affective states during learning has so far only scarcely been considered in such systems, because it is unclear which cues should be tracked, how they should be interpreted, and how the system should react to them. Therefore, we conducted expert interviews with preschool teachers, and based on these results suggest a conceptual model for tracing and managing the affective state of preschool children during robot-child tutoring. <s> BIB006
Besides using an individual's name during the interaction, personalization can occur in three discrete areas. First is the customization of content to one's ability. Yorita, Botzheim, and Kubota used the concept of self-efficacy as one component in an algorithm designed to aid in learning BIB002 . That is, depending on how likely the robot was to get a response, the level of complexity of its speech would vary. The potential viability of this approach can be reflected in the results of Westlund & Breazeal BIB003 . They found that "[c]hildren who played with a matched robot used more words, and more diverse words, in their stories than unmatched children" [109:65] . Though Schodde, Bergmann, and Kopp did not find a significant effect of difficulty level adaptation on learning BIB005 , their study was likely too short to detect an effect (one short interaction vs eight interactions in the aforementioned study). In CALL, adapting content to the user's progress has been shown to aid learning  . This is consistent with the use of scaffolding and the belief that people learn in their zone of proximal development (ZPD)-while performing tasks that are at a challenging but achievable level of difficulty-concepts that were originated by Vygotsky and are often echoed in education today. Additionally, feedback can be customized based on one's progress BIB001 . Instead of only considering responses to a given question, feedback can be adaptive to the overall trajectory of right or wrong answers. Last, the robot's affect or engagement strategy may be modified based on its partner's emotional state, resulting in individualized verbal and non-verbal responses BIB004 BIB006 . It has yet to be determined whether these latter two adaptations correlate with enhanced outcomes in RALL, though they are strategies human instructors often employ.