IROS2019国际学术会议论文集 0656

Can a social robot encourage children s self study Risa Maeda1 Jani Even1 2and Takayuki Kanda1 2 Abstract This paper presents a robot behavioral model designed to support children during self study In particular we want to investigate how a robot could increase the time children keep concentration The behavioral model was developed by ob serving children during self study and by collecting information from experienced tutors through interviews After observing the children we decided to consider three states corresponding to different levels of concentration The child can be smoothly performing the task learning state encountering some diffi culties stuck state or distracted distracted state The behavioral model was designed to increase the time spent concentrating on the task by implementing adequate behaviors for each of these three states These behaviors were designed using the advices collected during the interview survey of the experienced tutors A self study system based on the proposed behavior model was implemented In this system a small robot sits on the table and encourages the child during self study An operator is in charge of determining the state of the child Wizard of Oz and the behavioral model triggers the appropriate behaviors for the different states To demonstrate the effectiveness of the proposed behavioral model a user study was conducted 22 children were asked to solve problems alone and to solve problems with the robot The children spent signifi cantly p 0 024 more time in the learning state when studying with the robot I INTRODUCTION In modern societies children are often required to study on their own However self study is diffi cult for children for example self regulating 1 They often loose concentration and stop studying Then they usually do not know how to regain their concentration Consequently situations like the ones depicted in Fig 1 are not uncommon With the recent advances in robotics it seems somehow realistic to imagine a robot helping the children to keep con centrating on studying A robot that observes the children s behavior and encourages them to study more when they loose their concentration Such a robot tutoring system should be benefi cial as we know that one on one interaction with a tutor is one of the most effective way of learning 2 A robot tutoring system is also needed as it is not possi ble to have human tutors provide on demand one on one tutoring to children Moreover for the children who do have access to tutoring scheduling is often an additional burden Thus the fl exibility offered by a robot tutoring system is also very appealing This research was supported by JST CREST Grant Number JP MJCR17A2 Japan 1Risa Maeda JaniEvenandTakayukiKandaarewiththe HumanRobotInteractionLaboratory UniversityofKyoto Japan maeda robot soc i kyoto u ac jp 2Jani Even and Takayuki Kanda are with the Intelligent Robotics and Communication Laboratories Advanced Telecommunications Research In stitute International Japan a b Fig 1 Examples of self study gone wrong One of the problem is that even if we know that one on one tutoring is one of the most effi cient way of learning we do not know exactly why 3 Moreover it is not clear how to encourage children to study In particular we do not know how effi cient such a robotic tutoring system would be Thus the goal of this paper is to answer that question by investigating if is it possible to use a robot to help the children to concentrate longer on the problem to solve The main contribution of the paper is the design and evaluation with a user study of a robot behavioral model that aims at increasing the duration of self study and the concentration time of the children during self study II RELATED WORK A Educational Resources on Tutoring Bloom compares the achievements of students following thee different types of instruction Conventional classroom education group instruction mastery learning group in struction with additional tests and feedbacks to measure the level of the students and tutoring one on one instruction with a tutor The result is that one on one interaction with a tutor is more effective than any of the two group instruction methods 2 VanLehn and his colleagues focus on determining what events occurring during tutoring contribute the most to the progress of the students 3 They show that reaching an impasse is a landmark as it teaches the students they need to learn something B Robots Tutoring Children Using modern infrastructure and information technologies researchers are trying to bring robots in home education Compared to traditional learning methods like books with audio tapes and web based interfaces learning English with a robot signifi cantly improves the concentration learning interest and achievements of the children 4 Robots have also been used in the classroom Kanda and his colleague show that the English level of japanese students 2019 IEEE RSJ International Conference on Intelligent Robots and Systems IROS Macau China November 4 8 2019 978 1 7281 4003 2 19 31 00 2019 IEEE1236 improved after interacting for two weeks with an English speaking robot that was introduced in their classroom 5 Moreover the presence of a robot has a positive infl uence on collaborative learning and the children show interest in using such system in the future 6 Saerbeck and his colleagues study how manipulating vari ables such as role model non verbal feedback attention building empathy and communicativeness has an effect on the perceived supportive role of the robot 7 In particular the tutor robot should not focus only on content transfer but should also consider the social aspect of the interaction Zaga and her colleagues are comparing the effect of a peer and tutor robot on the children 8 Ramachandran and his colleagues investigate how per sonalizing the break timing for each child has a positive effect on the learning 9 Note that personalization of affective reactions has also been investigated using tablets 10 Personalization is also the main focus of the work by Leysberg and his colleagues 11 Kennedy and his colleagues investigate the effect of the robot social behavior on the teaching performance They found that the presence of the robot itself has a strong effect 12 and that for short term interactions the social behavior effect is not as large as expected 13 In contrast with these previous works our study focuses on the way to encourage self learning from observation of children s learning behavior We do not try to have a personalized approach as in 7 11 10 9 but we rely on observing the children to implement a reactive behavior Our approach also differs from the system in 4 that considers e learning Considering the results in 12 13 the social behavior of our robot tutor was kept limited C Recognition of Student s State Understanding the state of a student is important for teach ing A common approach is to use a tablet for presenting the problem to the student and check the student s input to determine if she or he is actively studying This is the approach used for personalization in 9 10 Won and her colleagues predict the learning performance of a student during a dyadic interaction with a teacher from their non verbal behaviors 14 The prediction is based on a model that uses data captured by two Kinect sensors In this paper the state of the child interacting with the system plays an important role but contrary to the work in 14 10 9 the focus is on the behavioral model and not on the recognition of the state III BEHAVIOR MODEL OF SELF STUDY SUPPORT ROBOT To design the behavioral model we observed children during self study and conducted an interview survey of experienced tutors A Self Study Observation In a preliminary experiment 10 children from second to fourth grades 3 males and 7 females 8 3 0 78 years old were asked to sit at a desk alone in a room and solve TABLE I REPRESENTATIVE ACTIONS AND POSTURES DURING SELF STUDY Situation idDescriptionCount 1Looking at the problem Hands moving10 2Looking at the problem Hands still10 3Checking other problems8 4Day dreaming5 5Watching the surroundings7 6Lying on the desk2 7Resting on the chair4 mathematics and literacy problems while being fi lmed The diffi culty level of the problems was adjusted to the grades of the children After the experiment the video data was analysed The goal of the analysis was to determine in which state of mind the children were during their self study time In particular we were interested in fi nding noticeable actions or postures that could serve as a proxy for the level of concentration of the children Thus the focus was on the children interaction with the environment Table I contains the number of times each of the situations we judged as signifi cant occurred For example the situation Looking at the problem Hands moving is a good indicator that the child is smoothly solving the problem The situations Looking at the problem Hands still and Checking other problems may indicate some diffi culty in solving the current problem Situations like Day dreaming or Watching the surroundings may indicate an intermittent lack of concentration The child Lying on the desk or Resting on the chair would indicate a total loss of concentration or interest see Fig 1 B Interview Survey We conducted an interview survey of 10 tutors 7 males 3 females 23 3 1 7 years old who had one on one teaching experience The number of years of experience of teaching was one person for less than one year one person for one to two years and 8 persons for 3 to 4 years During the interviews the tutors were asked what they would do if confronted to the situations that we selected from the preliminary experiment analysis All the tutors told that they would not interfere when a child is smoothly solving a problem situation 1 in table I When the child is willing to solve the problem but is stuck situations 2 and 3 in table I the tutors would mainly talk about the problem with questions like What part of the problem you do not understand Is this problem diffi cult In situation 3 many tutors suggested to rest for a while and ask questions like What happened You cannot do it A tutor also suggested to skip the problem in this case if a fi xed time limit is imposed In situations like 4 to 7 the tutors told that light interaction is not enough It is necessary to refocus the child by doing 1237 something more drastic Half of the tutors suggested things like taking some time to refresh or stretch Four out of ten suggested to engage in a mundane conversation about what the child did on that day and mention the problem during this conversation C Establishing Behavior Model The interview survey indicated that tutors modulate their behavior according to Whether the child is willing to solve the problem or not Whether the child is able to solve the problem or not The preliminary experiment summarized in Table I also suggested a division following these two points Situations 1 to 3 indicates a will to solve the problem whereas the other situations clearly show that the child has no interest in solving the problem However situation 2 and 3 may indicate diffi culty to solve the current problem Consequently we decided to build our behavioral model using the following three states Learning state The child is smoothly solving the problem Stuck state The child is having diffi culties solving the problem Distracted state The child is not interested in solving the problem After defi ning the three states of our behavioral model we used the advices from the tutors to design the desired robot behaviors for each of the states Table II summarizes the robot behaviors for the different states For the Learning state situation 1 in Table I the child is concentrated on solving the problem and seems not to experience diffi culty Then the robot does not disturb the concentration of the child and stay quiet For the Stuck state situations 2 and 3 in Table I although attention is being given to the problem some minor problems have occurred such as a lower concentration or indecision in how to solve the current problem Therefore the robot starts by encouraging the child If the situation persists for a while then the robot suggests moving to the next problem For the Distracted state situations 4 to 7 in Table I concentration is lost and there is no motivation in solving the problem The interest of the child has shifted to things other than the problem In this case it is fi rst necessary to return the child s interest to the problem Therefore the robot once transfers interest to something else and then tries to return interest to the problem For example the robot fi rst encourages the child to stretch together and then tells about trying to solve the problem In the special case where the attention of the child is directed on the robot the robot does not try to shift the attention to something else fi rst but directly tells about solving the problem TABLE II BEHAVIORS OF ROBOT FOR THE DIFFERENT CHILD S STATES Child s stateBehavior of the robotUtterance examples Learning do nothingNone Stuck 1 Encourage the child Do your best You can do it 2 Suggest moving to the next problem If you can not solve it go ahead to the next prob lem Distracted 3 Ask about the child s day What did you do today 4 Propose to stretch to gether let s stretch together Fig 2 The Sota robot used by the study support system IV SELF STUDY SUPPORT SYSTEM A Robot Hardware To interact with the child we used the table top robot depicted in Fig 2 This robot is a Sota robot from Vstone It is 28 cm tall and has 8 degrees of freedom Body 1 Arm 2 2 Neck 3 The robot has a speaker to produce sound and comes with a voice synthesis software It is also equipped with a camera and is able to track and follow the face of a person with its gaze We used this functionality during the interaction with the children B System Overview The architecture of the system is described in Fig 3 There are two main modules solid line boxes State recognition module This module determines in which of the three concentration states the child is Behavior selection module This module selects the appropriate behavior for the robot 1 State recognition module In this research we pri oritized the evaluation of the proposed behavioral model Then during the experiments an operator performed the classifi cation Wizard of Oz 15 The decision fl owchart in fi gure 4 describes the labelling rules used by the operator to classify each state in real time Fig 5 shows a few examples of child s postures and how they were classifi ed using the fl owchart these examples are from the preliminary observation experiment 2 Behavior selection module After the state of the child is determined this module is in charge of selecting what is the most appropriate behavior to follow for the robot A 1238 Child State Recognition concentration state Behavior Selection Robot behaviors observation interaction Fig 3 Architecture of the system Looking the Problem Sitting straight Checking Next Problem Holding pen Hands not moving Writing Looking robots direction LearningStuckDistracted Start Fig 4 Posture labelling fl owchart general description of the behaviors for the different states is given in Table II Here a behavior refers to the robot talking and performing some movements These movements are either specifi c to the behavior or just movements used to make the robot looks more lively For each of the behaviors that appears in that table a predetermined list of utterances was created The robot uses one utterance at a time and sequentially goes through the lists In addition we also prepared a few utterances for the greetings instructions and to show that the robot is clueless about the problem solving if the child asks a question V USER STUDY We conducted a user study to check that the proposed behavioral model increases the time spent concentrating on problem solving We selected a within subject design meaning that each of the participants will have to experience the proposed model and a control condition A Hypothesis and Prediction We develop a behavioral model for a robot assisting children during self study that is based on advices from experienced tutors We know that peer tutoring is an effective Learning a Distracted c Stuck b b Fig 5 Posture labelling examples way to teach 2 and we believe that a social robot could bring similar effect than humans do if it behaves in a similar way the humans do Consequently if the developed model appropriately represents substantially important part of the human behavior we expect a robot implementing that model to be able to assist children during self study Therefore we formulate the following prediction Prediction Compared to studying alone a child is spending more time in the Learning state when using the self study support system that implements the proposed behavioral model B Method To verify our prediction the experiment has to investigate how much time a child spent in the Learning state for the following two conditions 1 A condition corresponding to the usual self studying condition a child would experience at home 2 A condition corresponding to the child self studying at home with the support of the proposed self study support system 1 Participants A recruitment agency was contacted to recruit 22 participants that agreed to participate in the experiment and to be fi lmed In order to be able to match the diffi culty of the problems to solve during self study all the participants were 9 years old students 6 females and 16 males At this age children should be familiar with self study but still have diffi culty to maintain concentration for an extended period and a self study support system should be benefi cial 2 Experiment environment The experimental setting is visible in Fig 6 A desk and a chair were placed in the center of an empty room The text book and pens used for self study were placed on the table The robot was installed