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Developing Learning Scenarios to Foster Children's Handwriting Skills with the Help of Social Robots
Abstract
Social robots are being used to create better educational scenarios, boosting children's motivation and engagement. The focus of the research is to explore new ways to support children in acquisition of their handwriting skills with the help of a social robot. With this perspective, three studies are discussed to investigate aspects related to the learning modes of child-robot interaction, children's impression of a social robot and classification of children's common handwriting difficulties.
... On the other hand, in education contexts, Intelligent Tutoring Systems (ITS) are an example of agents that provide automated and personalized teaching and assessment to students [2]. In addition, robotic agents have been used to promote engagement in learning, specifically with children, targeting a diverse set of skills [8,43,47]. ...
Agents providing assistance to humans are faced with the challenge of automatically adjusting the level of assistance to ensure optimal performance. In this work, we argue that identifying the right level of assistance consists in balancing positive assistance outcomes and some (domain-dependent) measure of cost associated with assistive actions. Towards this goal, we contribute a general mathematical framework for structured tasks where an agent playing the role of a ‘provider’—e.g., therapist, teacher—assists a human ‘receiver’—e.g., patient, student. We specifically consider tasks where the provider agent needs to plan a sequence of actions over a fixed time horizon, where actions are organized along a hierarchy with increasing success probabilities, and some associated costs. The goal of the provider is to achieve a success with the lowest expected cost possible. We present OAssistMe, an algorithm that generates cost-optimal action sequences given the action parameters, and investigate several extensions of it, motivated by different potential application domains. We provide an analysis of the algorithms, including proofs for a number of properties of optimal solutions that, we show, align with typical human provider strategies. Finally, we instantiate our theoretical framework in the context of robot-assisted therapy tasks for children with Autism Spectrum Disorder (ASD). In this context, we present methods for determining action parameters based on a survey of domain experts and real child-robot interaction data. Our contributions unlock increased levels of flexibility for agents introduced in a variety of assistive contexts.
... In the educational context, Intelligent Tutoring Systems (ITS) [1] provide automated and personalized teaching and assessment to students. Robotic agents have also been used to promote engagement in learning, specifically with children, for a diverse set of skills [5,29,32]. ...
This work contributes an optimization framework in the context of structured interactions between an agent playing the role of a 'provider' and a human 'receiver'. Examples of provider/receiver interactions of interest include ones between occupational therapist and patient, or teacher and student. We specifically consider tasks where the provider agent needs to plan a sequence of actions with a fixed horizon, where actions are organized along a hierarchy with increasing probabilities of success and associated costs. The goal of the provider is to achieve a success with the lowest expected cost possible. In our application domains, a success may be for instance eliciting a desired behavior or a correct response from the receiver. We present a linear-time optimal planning algorithm that generates cost-optimal sequences for given action parameters. We also provide proofs for a number of properties of optimal solutions that align with typical human provider strategies. Finally, we instantiate our general formulation in the context of robot-assisted therapy tasks for children with Autism Spectrum Disorders (ASD). In this context, we present methods for determining action parameters , namely (1) an online survey with experts for determining action costs, and (2) a probabilistic model of child response based on data collected in a real child-robot interaction scenario. Our contributions may unlock increased levels of adaptivity for agents introduced in a variety of assistive contexts.
Research in education has long established how children mutually influence and support each other's learning trajectories, eventually leading to the development and widespread use of learning methods based on peer activities. In order to explore children's learning behavior in the presence of a robotic facilitator during a collaborative writing activity, we investigated how they assess their peers in two specific group learning situations: peer-tutoring and peer-learning. Our scenario comprises of a pair of children performing a collaborative activity involving the act of writing a word/letter on a tactile tablet. In the peer-tutoring condition, one child acts as the teacher and the other as the learner, while in the peer-learning condition, both children are learners without the attribution of any specific role. Our experiment includes 40 children in total (between 6 and 8 years old) over the two conditions, each time in the presence of a robot facilitator. Our results suggest that the peer-tutoring situation leads to significantly more corrective feedback being provided, as well as the children more disposed to self-disclosure to the robot.
In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based on the learning-by-teaching paradigm, one of the children acted as a teacher when the other acted as a learner. Our study involved 40 children between 6 and 8 years old, in two conditions (robot or human facilitator). The results suggest first that the child acting as a teacher feel more responsible when the facilitator is a robot, compared to a human ; they show then that the interaction between a (teacher) child and a robot facilitator can be characterized as being a reciprocity-based interaction, whereas a human presence fosters a compensation-based interaction.
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.
Takayuki Kanda is a computer scientist with interests in intelligent robots and human-robot interaction; he is a researcher in the Intelligent Robotics and Communication Laboratories at ATR (Advanced Telecommunications Re-search Institute), Kyoto, Japan. Takayuki Hirano is a computer scientist with an interest in human–robot interaction; he is an intern researcher in the Intelli-gent Robotics and Communication Laboratories at ATR, Kyoto, Japan. Daniel Eaton is a computer scientist with an interest in human–robot interaction; he is an intern researcher in the Intelligent Robotics and Communication Labora-tories at ATR, Kyoto, Japan. Hiroshi Ishiguro is a computer scientist with in-terests in computer vision and intelligent robots; he is Professor of Adaptive Machine Systems in the School of Engineering at Osaka University, Osaka, Ja-pan, and a visiting group leader in the Intelligent Robotics and Communication Laboratories at ATR, Kyoto, Japan. ABSTRACT Robots increasingly have the potential to interact with people in daily life. It is believed that, based on this ability, they will play an essential role in human society in the not-so-distant future. This article examined the proposition that robots could form relationships with children and that children might learn from robots as they learn from other children. In this article, this idea is studied in an 18-day field trial held at a Japanese elementary school. Two English-speak-ing "Robovie" robots interacted with first-and sixth-grade pupils at the perime-ter of their respective classrooms. Using wireless identification tags and sensors, these robots identified and interacted with children who came near them. The robots gestured and spoke English with the children, using a vocabulary of about 300 sentences for speaking and 50 words for recognition.
A random sample of primary grade teachers from across the United States was surveyed about their instructional practices in
handwriting. Nine out of every ten teachers indicated that they taught handwriting, averaging 70minutes of instruction per
week. Only 12% of teachers, however, indicated that the education courses taken in college adequately prepared them to teach
handwriting. Despite this lack of formal preparation, the majority of teachers used a variety of recommended instructional
practices for teaching handwriting. The application of such practices, though, was applied unevenly, raising concerns about
the quality of handwriting instruction for all children.
Handwriting currently has a low status and profile in literacy education. This paper examines the situation of current handwriting pedagogy in England and considers why handwriting efficiency has been neglected. The paper goes on to identify a number of studies located in the domains of special needs and psychology which re-evaluate the role of handwriting efficiency. These studies suggest that handwriting is more than just motor skill and may make a very important contribution to children's composing of text. Existing research into the way handwriting efficiency affects composing suggests that further research, more appropriate assessment and focused intervention could all make a significant contribution to children's writing progress and might positively affect the progress of the many boys who struggle with writing throughout the primary school years.
A large proportion of children referred to school-based occupational therapists experience difficulty with handwriting. Illegible handwriting is found to have secondary effects on school achievement and self-esteem. The purpose of this study was to identify handwriting error patterns of children with mild motor difficulties and to examine the relationship between handwriting error patterns and perceptual-motor abilities. Sixty-six children aged 7 to 12 years old with mild motor difficulties were involved in the study. The results indicated three possible handwriting error patterns: (1) Execution Factor; (2) Aiming Factor; (3) Visual-Spatial Factor. The Execution Factor was associated with visual-motor skill and sensory discrimination; the Aiming Factor was correlated with visual-motor and fine-motor performance. There were no significant associations with perceptual-motor abilities for the Visual-Spatial Factor.
A care-receiving robot (CRR) is a robot designed to receive care from its human user. We proposed the concept of the CRR, and assume that allowing children to learn tasks by teaching the CRR is beneficial for their education. So far, we have confirmed the feasibility of the CRR for educational purposes. In the present study, we verified whether the CRR's learning ability influenced the efficiency with which children acquire new knowledge. We conducted a field experiment in an actual classroom at an English language school for Japanese children. The target participants were children between 4-8 years old who attended the school. The participants attempted drawing various shapes along with the robot in order to enhance their knowledge of English words for these shapes (drawing game). The experiment was carried out under three conditions of the CRR's learning ability as follows: an excellent robot that could answer all the questions correctly; a CRR that was capable of learning; and a CRR that was not capable of learning at all. From the experiment, it was found that the CRR that was capable of learning helped children learn unknown English words for shapes. In addition, correct demonstrations by the robot had a greater impact than that had been expected on children's learning ability. The results obtained in this paper suggest that children benefited by teaching the robot and observing demonstrations by the robot.
There is a strong relationship between orthographic-motor integration related to handwriting and students' ability to produce creative and well-structured written text. This relationship is thought to be due to the cognitive load which results when attention is required by writers to write letters and words on the page. Lack of automaticity in orthographic-motor integration means that writers do not have sufficient cognitive resources to accomplish the more demanding aspects of text production such as ideation, text monitoring, and pragmatic awareness. A systematic handwriting program can significantly improve the quality of written text by young children experiencing problems with orthographic-motor integration. This study investigated the effectiveness of a handwriting program in remediating older students' problems in orthographic-motor integration and consequently enhancing their written language skills. Two groups of students in Grades 8 and 9 were provided with either practice in handwriting or daily completion of a written journal. There were no differences between the two groups at pre-test. However, at post-test, the handwriting group had significantly higher scores in orthographic-motor integration as well as for the length and quality of the text they wrote.
Synthesizing robotic handwriting motion by learning from human demonstrations
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