JP2017173546A - Learning support device, robot, learning support system, learning support method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a learning support device capable of supporting diversity-carrying learning.SOLUTION: A teacher robot 100 comprises: a biological information acquisition unit 120 for acquiring biological information which varies according to the state of a learner; and a learning guidance content determination unit 114 for determining a content of learning support to be carried out for the learner on the basis of the biological information acquired by the biological information acquisition unit 120.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a learning support device, a robot, a learning support system, a learning support method, and a program.

  Learning support robots that support user learning are widely used. For example, Patent Document 1 discloses an information communication robot apparatus that allows a user to learn in an interactive manner. The information communication robot apparatus disclosed in Patent Document 1 generates output information using voice information from a user and feedback information corresponding to education information stored in advance, and outputs the output information to the user.

JP 2001-242780 A

  However, the technique disclosed in Patent Document 1 recognizes a user's voice and executes a predetermined learning instruction corresponding to the converted character string. There is a possibility that.

  The present invention has been made in view of the above-described circumstances, and an object thereof is to provide a learning support apparatus that can support various kinds of learning.

In order to achieve the above object, a learning support apparatus according to the present invention provides:
Biometric information acquisition means for acquiring biometric information that changes depending on the state of the learner;
Learning instruction content determination means for determining the content of learning support to be performed for the learner based on the biological information acquired by the biological information acquisition means.

  ADVANTAGE OF THE INVENTION According to this invention, the learning assistance apparatus etc. which can support the learning with diversity can be provided.

It is a block diagram of the learning assistance system which concerns on embodiment of this invention. (A) is a figure which shows the functional structure of a teacher robot, (B) is a figure which shows the functional structure of a student robot. (A) is a figure explaining the classification | category of a pseudo personality, (B) is a figure explaining the classification | category and transition of a pseudo emotion. It is a figure which shows an example of the learning history table for teachers. It is a figure which shows an example of the learning related event table for teachers. It is a figure which shows the other example of the learning related event table for teachers. It is a figure which shows an example of the biometric event table for teachers. It is a figure which shows the other example of the biometrics related event table for teachers. It is a figure which shows an example of the learning guidance content change table. It is a figure which shows an example of the learning related event table for students. (A) is a flowchart which shows the flow of the learning instruction | indication process which a teacher robot performs, (B) is a flowchart which shows the flow of the learning assistance process which a student robot performs.

  Hereinafter, a learning support system according to an embodiment of the present invention will be described with reference to the drawings.

  The learning support system according to the embodiment of the present invention is a system for supporting learning of English conversation of a user (learner) U. As illustrated in FIG. 1, the learning support system 1 includes a teacher robot 100, a student robot 200, and a communication terminal 300.

  The teacher robot 100 is a robot that plays the role of a teacher who teaches the user U English conversation. The teacher robot 100 has a three-dimensional shape imitating a human body in appearance. The teacher robot 100 has a pseudo personality and a pseudo emotion, selects an appropriate learning curriculum according to the pseudo emotion that changes according to the biometric information of the user U and the learning situation, and performs learning support. Here, the pseudo personality is generated in a pseudo manner as if the teacher robot 100 generally has the personality of a person. The pseudo emotion is generated in a pseudo manner as if the teacher robot 100 had an emotion that a person who actually touched the user U would have.

  As shown in FIG. 3A, there are four types of pseudo personality of the teacher robot 100: “quiet”, “cool”, “angry”, and “disgust” according to the degree of sociality and stability. It is classified. The user U can select a desired pseudo personality via the communication terminal 300 when the teacher robot 100 is activated.

  The pseudo emotions of the teacher robot 100 are classified into four types of “joy”, “anger”, “sorrow”, and “easy” as shown in FIG. “Joy” indicates an emotional state of “joy” or “happy”. “Angry” indicates an emotional state of “angry” or “moody”. “Sad” indicates an emotional state such as “sad” or “anxiety”. “Easy” indicates an emotional state such as “calm” or “fun”. Each of these pseudo emotions shall transition to other pseudo emotions according to the occurrence state of the event. Further, the way of transition of the pseudo emotion differs depending on the pseudo personality selected by the user U.

  Functionally, the teacher robot 100 includes a control unit 110, a biological information acquisition unit 120, a storage unit 130, an expression unit 140, and a communication unit 150, as shown in FIG.

  The control unit 110 controls the entire operation of the teacher robot 100. The control unit 110 includes a microcomputer having a central processing unit (CPU), a read only memory (ROM), and a random access memory (RAM). The control unit 110 controls each unit of the teacher robot 100 when the CPU reads out an operation program stored in the ROM and executes it on the RAM.

  The control unit 110 includes a biological information analysis unit 111, a learning state determination unit 112, a pseudo emotion determination unit 113, a learning instruction content determination unit 114, and an expression determination unit 115.

  The biological information analysis unit 111 analyzes biological information acquired from the biological information acquisition unit 120 described later in time series, extracts changes in the biological state of the user U, etc., and identifies the state of the user U. For example, the biometric information analysis unit 111 analyzes the audio data acquired from the biometric information acquisition unit 120 and identifies whether the user U is concentrated or sleepy from the amplitude magnitude, the utterance content, the utterance frequency, and the like.

  The learning status determination unit 112 determines the learning status of the user U. Specifically, the learning status determination unit 112 determines whether or not the answer to the question is correct and measures the response speed (the time required for the answer), and learns the user U in consideration of various factors such as the correct answer rate and progress. Evaluate your grades.

  The pseudo emotion determination unit 113 determines the pseudo emotion of the teacher robot 100. For example, the pseudo emotion determination unit 113 determines pseudo emotions by referring to a teacher learning history table stored in the storage unit 130 described later at the start of learning instruction of the teacher robot 100. Further, for example, during execution of learning guidance, the pseudo emotion determination unit 113 refers to a teacher learning related event table and a teacher living body related event table stored in the storage unit 130, which will be described later, and generates new pseudo emotions. decide.

  The learning guidance content determination unit 114 determines the learning guidance content to be implemented for the user U. The control unit 110 determines a learning curriculum to be performed with reference to a teacher learning history table stored in the storage unit 130 described later. In addition, the learning instruction content determination unit 114 refers to a learning instruction content change table stored in the storage unit 130, which will be described later, in accordance with the timing when the pseudo emotion is changed by the pseudo emotion determination unit 113. Determine changes in order and progress. The learning instruction content determination unit 114 transmits the determination content to the student robot 200 via the communication unit 150.

  The expression determination unit 115 determines the content of the emotion expression executed by the teacher robot 100 for the user U according to the biological state of the user U, the learning situation, and the like. For example, the expression determining unit 115 refers to a teacher learning related event table or a teacher biometric related event table stored in the storage unit 130 to be described later, and executes the corresponding expression content each time a predetermined event occurs. Decide what to do. The expression determination unit 115 supplies instruction data based on the determined expression content to the expression unit 140. In addition, the expression determination unit 115 determines the voice to be output to the student robot 200 and the operation to be performed according to the biological state of the user U, the learning situation, and the like, and the determination content is transmitted to the student robot 200 via the communication unit 150. introduce.

  The biological information acquisition unit 120 acquires biological information that changes depending on the biological state of the user U. The biological information acquisition unit 120 includes, for example, a microphone and a voice input interface, converts the voice of the user U into voice data, and outputs the voice data to the biological information analysis unit 111.

  The storage unit 130 stores various data necessary for the control unit 110 to control each unit of the teacher robot 100. The storage unit 130 is configured by a non-volatile storage device such as a flash memory or an HDD (Hard Disc Drive). The storage unit 130 stores, for example, data indicating the pseudo personality selected by the user U via the communication terminal 300 in a predetermined storage area.

  The data stored in the storage unit 130 includes a teacher learning history table, a teacher learning related event table, a teacher biometric related event table, and a learning instruction content change table.

  The teacher learning history table is a table in which the history and learning results of the user U using the learning support system 1 are summarized. As shown in FIG. 4, the teacher learning history table is configured by associating each data of “start date and time”, “end date and time”, “learning time”, and “learning result”. Here, in this embodiment, it is assumed that the user U is an infant, and it is assumed that the learning curriculum is set so that the learning instruction performed by the teacher robot 100 is completed once in about 30 minutes. In addition, the learning curriculum mainly includes “words” that teach repetitive utterances of words and correction of pronunciation, “sentences” that mainly teach repetitive utterances of short sentences, and pronunciation by repeatedly uttering words and sentences according to rhythm. "Chants" that teaches how to learn and intonation, "conversations" that communicate in English on familiar topics, "story" that reads short stories, and "reviews" that check the level of understanding of learning content To do. The “learning results” are evaluated in three stages from “A” to “C” in consideration of the correct answer rate and the level of proficiency for each subject. Note that “-” means not implemented.

  The learning-related event table for teachers is a table that defines the correspondence when the occurrence of a learning-related event is detected, and is provided for each pseudo personality, and a different correspondence is defined for each. Here, the learning-related event refers to an event related to the learning situation such as correctness / incorrectness of answers to questions and learning progress. As shown in FIGS. 5 and 6, the teacher learning-related event table is configured by associating each data of “learning-related event”, “emotion point”, and “expression content” with respect to each current pseudo emotion, respectively. Has been. The “learning related event” is an item indicating the degree of proficiency of the user U determined by the learning status determination unit 112. Examples of the “learning related event” include “correct answer (incorrect answer)”, “learning progress is good (bad)”, and the like. The “emotion point” is a numerical value added to each pseudo emotion for each occurrence event. By setting different numerical values for the “emotion points” of each pseudo emotion, different weights are assigned to each pseudo emotion according to the importance of the occurrence event and the urgency of response. “Expression content” is an outline of output speech and execution operation for each occurrence event. The expression contents shown in FIGS. 5 and 6 mainly define the expression of the teacher robot 100 for each occurrence event, but also specify the expression to be executed by the student robot 200 in response to the occurrence event. Is done. The pseudo emotion determination unit 113 refers to the teacher learning related event table and calculates a cumulative value of each pseudo emotion according to the occurrence event. Moreover, the pseudo emotion determination unit 113 determines that a transition from the current pseudo emotion to the pseudo emotion exceeding the threshold is made when any of the cumulative values of the emotion points of each pseudo emotion exceeds a predetermined threshold. .

  The table shown in FIG. 5 is an example of a learning-related event table for teachers that is referred to when the pseudo personality is set to “cheerful”, and the table shown in FIG. 6 has the pseudo personality of “angry”. It is an example of the learning related event table for teachers referred when set.

  In the learning-related event table for teachers shown in FIG. 5, for example, when an event “learning progress is poor” indicating that learning is not progressing as planned in the current pseudo emotion “joy” is detected, The emotion point “2” is added to the “sorrow”, the emotion point “1” is added to the pseudo emotion “anger” and “easy”, and an operation that outputs a voice or encourages the user U is executed. It is stipulated in. On the other hand, in the learning-related event table for teachers shown in FIG. 6, for example, when the event “learning progress is bad” is detected in the current pseudo emotion “joy”, the emotion point “2” is added to the pseudo emotion “anger”. In addition, the emotion point “3” is added to the pseudo emotion “sorrow”, and a voice that speaks the user U is output. In this way, by making the “emotion points” and “expression contents” for learning-related events different according to the set pseudo-character, it is possible to cause the teacher robot 100 to carry out various learning instructions.

  The teacher-related bio-related event table is a table that defines the correspondence when the occurrence of the bio-related event is detected, and is provided for each pseudo personality, and a different correspondence is defined for each. Here, the bio-related event is an event caused by the state of the user U determined from the bio-information and the change thereof. As shown in FIGS. 7 and 8, the teacher's bio-related event table is configured by associating each data of “biological-related event”, “emotion point”, and “expression content” with respect to each current pseudo emotion. Has been. The “biological related event” is an item indicating the biological state of the user U detected from the biological information acquired by the biological information acquisition unit 120. Examples of “biological event” include “voice is loud (small)”, “response is fast (slow)”, and the like. The “emotion point” is a numerical value added to each pseudo emotion for each occurrence event, as in the case of the teacher learning related event table. “Expression content” is an outline of output speech and execution operation for each occurrence event. 7 and FIG. 8 mainly defines the expression of the teacher robot 100 for each occurrence event, but also defines the expression to be executed by the student robot 200 in response to the occurrence event. Is done. The pseudo emotion determination unit 113 refers to the biometric related event table for teachers and calculates a cumulative value of each pseudo emotion according to the occurrence event. Moreover, the pseudo emotion determination unit 113 determines that a transition from the current pseudo emotion to the pseudo emotion exceeding the threshold is made when any of the cumulative values of the emotion points of each pseudo emotion exceeds a predetermined threshold. .

  The table shown in FIG. 7 is an example of a biometric related event table for teachers that is referred to when the pseudo personality is set to “cheerful”, and the table shown in FIG. 8 has a pseudo personality of “angry”. It is an example of the biometric related event table for teachers referred when it is set.

  In the biometric event table for teachers shown in FIG. 7, for example, when an event “voice is loud” indicating that the voice of the answer of the user U is loud is detected in the current pseudo emotion “anger”, the pseudo emotion “ It is stipulated that an emotion point “1” is added to “joy”, “sorrow”, and “easy”, and a voice that compliments the user U is output. On the other hand, in the biometric event table for teachers shown in FIG. 8, for example, when the event “loud” is detected in the current pseudo emotion “anger”, the emotion points are expressed in the pseudo emotions “sorrow” and “easy”. It is specified to perform an operation of adding “1” and shaking the head vertically. In this way, by making the “emotion points” and “expression contents” for the biological related event different according to the set pseudo personality, the teacher robot 100 can perform various learning instructions. The pseudo emotion determination unit 113 refers to the biometric related event table for teachers and calculates a cumulative value of each pseudo emotion according to the occurrence event. Moreover, the pseudo emotion determination unit 113 determines that a transition from the current pseudo emotion to the pseudo emotion exceeding the threshold is made when any of the cumulative values of the emotion points of each pseudo emotion exceeds a predetermined threshold. .

  The learning instruction content change table is a table in which the changing contents of the learning instruction contents are specified in accordance with the transition of the pseudo emotion. The learning instruction content change table is configured by associating each data of “pseudo emotion after transition”, “pseudo emotion before transition”, and “learning instruction content”. In the learning instruction content change table shown in FIG. 9, for example, when the pseudo-emotion changes from “anger” to “joy”, the subject to be implemented is “change to a subject that user U is good at” or is being implemented. It is stipulated to selectively execute “Ask a difficult question” in the subject. This is intended to further draw out the motivation and challenge spirit from the user U in response to the improvement of the pseudo emotion of the teacher robot 100 accompanying the improvement in the learning result or biological state of the user U. Also, in the learning instruction content change table, for example, when the pseudo-emotion changes from “Easy” to “Sorrow”, “Lease the tempo” of learning or “ "Yes" is selectively executed. This is intended to give the user U a margin or change his mood when the pseudo-emotion of the teacher robot 100 is slightly deteriorated as the learning result or the biological state of the user U is lowered.

  The expression unit 140 performs pseudo emotion expression for the user U according to the determination content of the expression determination unit 115. The expression unit 140 includes an audio output unit 141 and a movable unit 142.

  The audio output unit 141 includes a speaker and an audio output interface, converts the audio data generated by the expression determination unit 115 into audio, and outputs the audio to the outside.

  The movable part 142 is a partial part of the teacher robot 100 and is displaced by driving of a driving member such as a motor. When the movable unit 142 is displaced according to the determination content of the expression determination unit 115, the teacher robot 100 performs, for example, an operation of moving the limbs, an operation of swinging the head up and down, left and right, and an eyelid opening and closing operation. Express emotions.

  The communication unit 150 includes a wireless communication module and an antenna, and performs data communication with the student robot 200 and the communication terminal 300. For example, the communication unit 150 transmits output data and instruction data related to the execution operation to the student robot 200. In addition, the communication unit 150 receives, for example, an operation instruction (such as a selection operation of the pseudo personality of the teacher robot 100) or information input (such as an answer input to a question) from the communication terminal 300.

  The student robot 200 is a robot that plays a role of student who receives English conversation instruction from the teacher robot 100 together with the user U. Similar to the teacher robot 100, the student robot 200 has a three-dimensional shape imitating a human body. In addition, the student robot 200 has the same pseudo personality and emotion as the teacher robot 100, and learning for improving the user's U willingness to learn according to the pseudo emotion that changes according to the biometric information and learning status of the user U. Provide support.

  The student robot 200 functionally includes a control unit 210, a biological information acquisition unit 220, a storage unit 230, an expression unit 240, and a communication unit 250, as shown in FIG.

  The control unit 210 controls the overall operation of the student robot 200. The control unit 210 is configured by a microcomputer having a CPU, a ROM, and a RAM. The control unit 210 controls each unit of the student robot 200 when the CPU reads out an operation program stored in the ROM and executes it on the RAM.

  The control unit 210 includes a biological information analysis unit 211, a learning state determination unit 212, a pseudo emotion determination unit 213, and an expression determination unit 215. These parts are configured in substantially the same manner as the biological information analysis unit 111, the learning status determination unit 112, the pseudo emotion determination unit 113, and the expression determination unit 115 of the teacher robot 100.

  The biological information analysis unit 211 analyzes the biological information acquired from the biological information acquisition unit 220 in time series, extracts changes in the biological state of the user U, etc., and identifies the state of the user U.

  The learning status determination unit 212 determines the learning status of the user U. Specifically, the learning status determination unit 212 determines whether the answer to the question is correct, measures the response speed, and the like, and evaluates the learning result of the user U in consideration of various factors such as the correct answer rate and the progress status.

  The pseudo emotion determination unit 213 determines the pseudo emotion of the student robot 200. For example, when starting the learning support of the student robot 200, the pseudo emotion determination unit 213 determines a pseudo emotion by referring to a student learning history table stored in the storage unit 230, which will be described later. In addition, for example, during execution of learning support, the pseudo emotion determination unit 213 refers to a student learning related event table and a student living body related event table stored in the storage unit 230 described later, and generates a new pseudo emotion. decide.

  The expression determination unit 215 determines the content of emotion expression executed by the student robot 200 for the user U according to the biological state of the user U, the learning situation, and the like. The expression determination unit 215 refers to, for example, a student learning related event table or a student biometric related event table stored in the storage unit 230 to be described later, and executes a corresponding expression content every time a predetermined event occurs. Decide what to do. The expression determination unit 215 supplies instruction data based on the determined expression content to the expression unit 240.

  The biometric information acquisition unit 220 acquires biometric information that changes depending on the state of the user U. The biological information acquisition unit 220 includes, for example, a microphone and a voice input interface, converts the voice of the user U into voice data, and outputs the voice data to the biological information analysis unit 211.

  The storage unit 230 stores various data necessary for the control unit 210 to control each unit of the student robot 200. The storage unit 230 is configured by a nonvolatile storage device such as a flash memory or an HDD, for example. The storage unit 230 stores, for example, data indicating the pseudo personality selected by the user U via the communication terminal 300 in a predetermined storage area.

  The data stored in the storage unit 230 includes a student learning history table, a student learning related event table, and a student biometric related event table. Each of these tables is configured in substantially the same manner as the above-described teacher learning history table, teacher learning related event table, and teacher biometric related event table. It should be noted that the “expression content” corresponding to each occurrence event in the student learning related event table and the student living body related event table is preferably content suitable for students and friends.

  In the learning related event table for students referred to when the pseudo personality shown in FIG. 10 is “lively”, for example, when an event “wrong answer” is detected in the current pseudo emotion “joy”, the user U is notified. It is stipulated to output a sound that gives hints or encourages them. Further, in the current pseudo emotion “angry”, when an event “learning progress is good” is detected, a voice envying the user U is output.

  The expression unit 240 performs pseudo emotion expression for the user U according to the determination content of the expression determination unit 215. The expression unit 240 includes an audio output unit 241 and a movable unit 242.

  The audio output unit 241 includes a speaker and an audio output interface. The audio output unit 241 converts the audio data generated by the expression determination unit 215 into audio and outputs the audio to the outside.

  The movable part 242 is a partial part of the student robot 200 and is displaced by driving of a driving member such as a motor. The student robot 200 displaces the movable unit 242 according to the determination content of the expression determining unit 215, and performs, for example, an operation of moving the limbs, an operation of shaking the head up and down, left and right, and an eyelid opening and closing operation. Express emotions.

  The communication unit 250 includes a wireless communication module and an antenna, and performs data communication with the teacher robot 100 and the communication terminal 300. For example, the communication unit 250 transmits output data and instruction data related to the execution operation to the student robot 200. In addition, the communication unit 250 receives, for example, an operation instruction (such as a selection operation of a pseudo personality of the student robot 200) or information input (such as an answer input to a question) from the communication terminal 300.

  The communication terminal 300 includes, for example, a smartphone or a tablet terminal, and communicates with the teacher robot 100 and the student robot 200. For example, the communication terminal 300 displays a selection image for answering a question according to transmission data from the teacher robot 100, and displays an image serving as a hint for answering a question according to transmission data from the student robot 200. In addition, the communication terminal 300 receives, for example, a selection input of pseudo characters of the teacher robot 100 and the student robot 200 by the user U, and transmits input data to each robot.

  Next, the learning instruction process executed by the teacher robot 100 will be described with reference to the flowchart shown in FIG. The learning instruction process is a process for appropriately determining the contents of learning instruction to be performed on the user U based on the learning result and biological information of the user U. The control unit 110 starts a learning instruction process in response to receiving an operation signal instructing to start learning via the communication terminal 300 by the operation of the user U.

  When starting the learning instruction process, the controller 110 sets the pseudo personality of the teacher robot 100 (step S101). The control unit 110 selects the pseudo-character of the teacher robot 100 among “quiet”, “cool”, “angry”, and “disgust” according to the selection by the operation of the user U or the setting content at the previous execution. Decide on either.

  Next, the control unit 110 (pseudo emotion determination unit 113) determines the pseudo emotion of the teacher robot 100 (step S102). For example, the control unit 110 refers to the learning history table stored in the storage unit 130 and determines the pseudo emotion of the teacher robot 100 according to the learning frequency and learning result of the user U. For example, when several days have passed since the previous use and the learning frequency is low, the control unit 110 determines the pseudo emotion of the teacher robot 100 as “sorrow”. In addition, when the learning result at the previous use is good, the control unit 110 determines the pseudo emotion as “joy”. Note that the control unit 110 may determine the pseudo emotion of the teacher robot 100 in consideration of the pseudo personality determined in step S101. For example, if the pseudo personality is determined to be “angry”, the pseudo emotion may be determined to be “easy” even if the learning result at the previous use is good.

  Subsequently, the control unit 110 (learning instruction content determination unit 114) determines a learning curriculum to be performed this time (step S103). The control unit 110 refers to the student learning history table stored in the storage unit 130 and determines the learning curriculum to be implemented this time according to the learning frequency of the user U and the learning results up to the previous time. For example, when the learning frequency is low, the control unit 110 performs a relatively familiar course “Chants” for a longer time than other courses in order to improve the user U's willingness to learn, Determine the learning curriculum to be implemented prior to the subject.

  After executing the process of step S103, the control unit 110 starts learning guidance according to the determined learning curriculum (step S104). The control unit 110 controls the expression unit 140 so as to execute voice output and operation corresponding to the learning content for each subject.

  Subsequently, the control unit 110 determines whether or not to end the learning instruction (step S105). The control unit 110 ends the learning instruction depending on whether an operation signal instructing the end of learning is received by the operation of the user U or whether all subjects of the current learning curriculum have been implemented. It is determined whether or not. When it determines with complete | finishing learning instruction | indication (step S105; YES), the control part 110 complete | finishes learning instruction processing.

  When it determines with continuing learning instruction | indication (step S105; NO), the control part 110 determines whether the learning related event or the biological body related event generate | occur | produced (step S106). Based on the analysis result of the biometric information analysis unit 111 and the determination result of the learning status determination unit 112, the control unit 110 is defined in any of the teacher learning related event table and the teacher biometric related event table stored in the storage unit 130. It is determined whether or not such an event has occurred. When it determines with the learning related event or the biological body related event not having generate | occur | produced (step S106; NO), the control part 110 returns a process to step S104, and continues learning instruction | indication.

  On the other hand, when it is determined that a learning-related event or a biological-related event has occurred (step S106; YES), the control unit 110 executes the expression content corresponding to the generated event (step S107). The control unit 110 (the expression determination unit 115) refers to the teacher learning related event table and the teacher biometric event table, and performs an output and an operation according to the expression content corresponding to the generated event. The unit 140 is controlled.

  Subsequently, the control unit 110 (pseudo emotion determination unit 113) determines whether or not the cumulative value of the emotion points of the pseudo emotion has exceeded a threshold value (step S108). If it is determined that the cumulative value of the emotion points of any pseudo emotion does not exceed the threshold value (step S108; NO), the control unit 110 returns the process to step S104 and continues the learning instruction.

  On the other hand, when it is determined that the cumulative value of the emotion points of any of the pseudo emotions has exceeded the threshold (step S108; YES), the control unit 110 (pseudo emotion determination unit 113) performs a simulation that exceeds the threshold from the current pseudo emotion. Transition to emotion (step S109).

  Moreover, the control part 110 (learning instruction | indication content determination part 114) changes learning instruction | indication content with having changed to the pseudo emotion (step S110). The control unit 110 (learning instruction content determination unit 114) refers to the learning instruction content change table stored in the storage unit 130 and determines to change to learning instruction contents corresponding to the pseudo emotion before and after the transition.

  After performing the process of step S110, the control part 110 returns a process to step S104, changes the order and progress degree of the subject contained in a learning curriculum, and continues learning instruction | indication.

  Next, the learning support process executed by the student robot 200 will be described with reference to the flowchart shown in FIG. The learning support process is a process of appropriately determining the content of learning support to be performed on the user U based on the learning result and biological information of the user U. Here, the learning support process is executed in the same manner as the learning instruction process shown in FIG. The control unit 210 starts the learning support process in response to receiving an operation signal instructing to start learning via the communication terminal 300 by the operation of the user U.

  When starting the learning support process, the control unit 210 sets the pseudo personality of the student robot 200 (step S201). The control unit 210 determines the pseudo-character of the student robot 200 according to the selection by the operation of the user U or the setting content at the previous execution.

  Next, the control unit 210 (pseudo emotion determination unit 213) determines the pseudo emotion of the student robot 200 (step S202). For example, the control unit 210 refers to the learning history table stored in the storage unit 230 and determines the pseudo emotion of the student robot 200 according to the learning frequency and learning result of the user U.

  Subsequently, the control unit 210 acquires a learning curriculum to be executed this time (step S203). The control unit 210 acquires the learning curriculum to be executed this time determined by the learning instruction content determination unit 114 of the teacher robot 100 via the communication unit 250.

  After executing the process of step S203, the control unit 210 starts learning support according to the acquired learning curriculum (step S204). The control unit 210 controls the expression unit 240 to execute voice output and operation according to the learning content for each subject. When the learning instruction content determination unit 114 of the teacher robot 100 changes the order and progress of the subjects included in the learning curriculum, the control unit 210 receives the changed contents from the teacher robot 100. Then, the expression unit 240 is controlled so as to execute a sound output or operation according to the changed content.

  Subsequently, the control unit 210 determines whether or not to end the learning support (step S205). The control unit 210 ends the learning support depending on whether an operation signal instructing the end of learning is received by the operation of the user U or whether all subjects of the current learning curriculum have been implemented. It is determined whether or not. When it determines with complete | finishing learning assistance (step S205; YES), the control part 210 complete | finishes a learning assistance process.

  When it determines with continuing learning support (step S205; NO), the control part 210 determines whether the learning related event or the biological body related event generate | occur | produced (step S206). Based on the analysis result of the biometric information analysis unit 211 and the determination result of the learning status determination unit 212, the control unit 210 determines any of the student-related event table for student and the biometric event table for student stored in the storage unit 230. It is determined whether or not such an event has occurred. When it determines with the learning related event or the biological body related event not having generate | occur | produced (step S206; NO), the control part 210 returns a process to step S204, and continues learning support.

  On the other hand, when it is determined that a learning related event or a living body related event has occurred (step S206; YES), the control unit 210 executes the expression content corresponding to the generated event (step S207). The control unit 210 (expression determination unit 215) refers to the student learning-related event table and the student bio-related event table, and performs expression so as to execute sound output and operation corresponding to the expression content corresponding to the event that has occurred. The unit 240 is controlled.

  Subsequently, the control unit 210 (pseudo emotion determination unit 213) determines whether or not the cumulative value of the emotion points of the pseudo emotion has exceeded a threshold value (step S208). If it is determined that the cumulative value of the emotion points of any pseudo emotion does not exceed the threshold (step S208; NO), the control unit 210 returns the process to step S204 and continues the learning instruction.

  On the other hand, when it is determined that the cumulative value of the emotion points of any of the pseudo emotions exceeds the threshold value (step S208; YES), the control unit 210 (pseudo emotion determination unit 213) performs the pseudo test that exceeds the threshold value from the current pseudo emotion. It changes to emotion (step S209), returns a process to step S204, and continues learning support.

  In this way, in response to the instruction to start learning by the user U, the teacher robot 100 executes a learning instruction process and the student robot 200 executes a learning support process. The robot 200 cooperates to implement learning support.

  As described above, according to the present embodiment, the teacher robot 100 and the student robot 200 have pseudo-characters that are set with pseudo-characters and transition in different manners according to the set pseudo-characters. . In addition, each robot performs learning guidance while expressing emotions by voice or action to the user U according to the current pseudo emotion. As described above, the learning support system 1 including the teacher robot 100 and the student robot 200 can contact the user U as if it were a real person with a personality, and can support diverse learning. In addition to improving the academic ability of the user U, the learning support system 1 can improve the ability to build an interpersonal relationship.

  In addition, this invention is not limited to said embodiment, A various deformation | transformation and application are possible. The above embodiment may be modified as follows.

  For example, in the learning support system 1, a learning support device that includes all or part of the functions of the teacher robot 100 may be used instead of the teacher robot 100. The learning support device may be any electronic device including at least a computer, and is arbitrary. The same applies to the student robot 200.

  For example, the classification of pseudo personality and pseudo emotion is not limited to the above embodiment, but is arbitrary. The pseudo personality and the pseudo emotion may be further classified and learning instruction contents corresponding to these states may be set. Moreover, the method of changing the pseudo emotion is not limited to the above embodiment, but is arbitrary. Note that either the pseudo personality or the pseudo emotion may be omitted, and the expression pattern of the teacher robot 100 may be simplified.

  For example, the biological information acquisition unit 120 may be configured to acquire biological information from a sensor installed outside. The biometric information acquisition unit 120 is a user based on measurement values of various sensors such as a face (expression) detection sensor, a GPS (Global Positioning System) sensor, an acceleration sensor, a blood pressure sensor, a pulse sensor, an electroencephalogram sensor, and a sweat sensor, for example. The state of U may be recognized, and the expression content and learning guidance content for the user U may be determined. Furthermore, the expression content and learning guidance content for the user U may be determined in consideration of the surrounding environment of the user U (for example, lighting state and noise level).

  In the above embodiment, the teacher robot 100 and the student robot 200 have been described as having substantially the same functions, but the functions provided in the student robot 200 may be simplified. For example, the teacher robot 100 provides the student robot 200 with the result analyzed by the biological information analysis unit 111 via the communication unit 150, so that the student robot 200 includes the biological information acquisition unit 220 and the biological information analysis unit 211. You may comprise so that it may not. In addition, the teacher robot 100 determines the pseudo emotion of the student robot 200 in the pseudo emotion determination unit 113, and determines the expression of the student robot 200 for the user U in the expression determination unit 115. The student robot 200 may be configured not to include the pseudo emotion determination unit 213, the expression determination unit 215, or the like.

  In the above embodiment, the learning guidance content change table defines the learning guidance content to be changed in association with the combination of pseudo emotions before and after the transition. However, depending on the event that occurred immediately before the transition (immediately before the cumulative value of emotion points exceeds the predetermined threshold) or the event that occurred most frequently before the cumulative value of emotion points exceeded the predetermined threshold, May be defined.

  Various embodiments and modifications can be made to the present invention without departing from the broad spirit and scope of the present invention. The above-described embodiments are for explaining the present invention and do not limit the scope of the present invention. In other words, the scope of the present invention is shown not by the embodiments but by the claims. Various modifications within the scope of the claims and within the scope of the equivalent invention are considered to be within the scope of the present invention.

  Hereinafter, the invention described in the scope of claims of the present application will be appended.

(Appendix 1)
Biometric information acquisition means for acquiring biometric information that changes depending on the state of the learner;
Learning instruction content determination means for determining the content of learning support to be performed for the learner based on the biological information acquired by the biological information acquisition means,
Learning support device.

(Appendix 2)
Expression determining means for determining an expression for the learner based on the biological information acquired by the biological information acquiring means;
The learning support apparatus according to attachment 1.

(Appendix 3)
Learning status determination means for determining the learning status of the learner,
The expression determining means determines an expression for the learner based on the biological information and a determination result by the learning status determining means,
The learning instruction content determination means determines the content of learning support to be performed for the learner based on the biological information and the determination result by the learning status determination means.
The learning support apparatus according to attachment 2.

(Appendix 4)
Pseudo emotion determination means for determining one pseudo emotion from a plurality of pseudo emotions based on the pseudo personality selected by the learner from a plurality of pseudo characters;
The expression determining means determines an expression for the learner based on the biological information, the determination result, and the pseudo emotion determined by the pseudo emotion determining means,
The learning instruction content determination unit determines the content of learning support to be performed for the learner based on the biological information, the determination result, and the pseudo emotion determined by the pseudo emotion determination unit.
The learning support device according to attachment 3.

(Appendix 5)
Each of the plurality of pseudo emotions has a different weight corresponding to each of the plurality of pseudo personalities.
The learning support apparatus according to appendix 4.

(Appendix 6)
A teacher role robot equipped with the learning support device according to any one of appendices 1 to 5.

(Appendix 7)
A robot as a teacher as described in appendix 6,
A student role robot that receives support from the teacher role robot together with the learner,
Learning support system.

(Appendix 8)
The student role robot is
Second biological information acquisition means for acquiring biological information that changes according to the state of the learner;
Second expression determination means for determining an expression for the learner based on the biological information acquired by the second biological information acquisition means,
The learning support system according to appendix 7.

(Appendix 9)
The student role robot receives biometric information that changes depending on the state of the learner from the teacher role robot.
The learning support system according to attachment 8.

(Appendix 10)
The learning support system according to appendix 7, wherein the student role robot receives expression determination information indicating an expression for the learner from the teacher role robot.

(Appendix 11)
A biometric information acquisition step of acquiring biometric information that changes according to the state of the learner;
Based on the biometric information acquired in the biometric information acquisition step, a learning instruction content determination step for determining the content of learning support to be performed for the learner,
Learning support method.

(Appendix 12)
On the computer,
Biometric information acquisition processing for acquiring biometric information that changes depending on the state of the learner;
Based on the biometric information acquired in the biometric information acquisition process, a learning instruction content determination process for determining the content of learning support to be performed for the learner is executed.
,program.

DESCRIPTION OF SYMBOLS 1 ... Learning support system, 100 ... Teacher robot, 110 ... Control part, 111 ... Biometric information analysis part, 112 ... Learning situation determination part, 113 ... Pseudo emotion determination part, 114 ... Learning guidance content determination part, 115 ... Expression determination part DESCRIPTION OF SYMBOLS 120 ... Biometric information acquisition part 130 ... Memory | storage part 140 ... Expression part 141 ... Audio | voice output part 142 ... Movable part 150 ... Communication part 200 ... Student robot 210 ... Control part 211 ... Biometric information analysis part 212, learning state determination unit, 213 ... pseudo emotion determination unit, 215 ... expression determination unit, 220 ... biological information acquisition unit, 230 ... storage unit, 240 ... expression unit, 241 ... voice output unit, 242 ... movable unit, 250 ... communication unit, 300 ... communication terminal, U ... user (learner)

Claims (12)

Biometric information acquisition means for acquiring biometric information that changes depending on the state of the learner;
Learning instruction content determination means for determining the content of learning support to be performed for the learner based on the biological information acquired by the biological information acquisition means,
Learning support device. Expression determining means for determining an expression for the learner based on the biological information acquired by the biological information acquiring means;
The learning support apparatus according to claim 1. Learning status determination means for determining the learning status of the learner,
The expression determining means determines an expression for the learner based on the biological information and a determination result by the learning status determining means,
The learning instruction content determination means determines the content of learning support to be performed for the learner based on the biological information and the determination result by the learning status determination means.
The learning support apparatus according to claim 2. Pseudo emotion determination means for determining one pseudo emotion from a plurality of pseudo emotions based on the pseudo personality selected by the learner from a plurality of pseudo characters,
The expression determining means determines an expression for the learner based on the biological information, the determination result, and the pseudo emotion determined by the pseudo emotion determining means,
The learning instruction content determination unit determines the content of learning support to be performed for the learner based on the biological information, the determination result, and the pseudo emotion determined by the pseudo emotion determination unit.
The learning support device according to claim 3. Each of the plurality of pseudo emotions has a different weight corresponding to each of the plurality of pseudo personalities.
The learning support device according to claim 4.   A robot serving as a teacher equipped with the learning support device according to any one of claims 1 to 5. A robot as a teacher according to claim 6;
A student role robot that receives support from the teacher role robot together with the learner,
Learning support system. The student role robot is
Second biological information acquisition means for acquiring biological information that changes according to the state of the learner;
Second expression determination means for determining an expression for the learner based on the biological information acquired by the second biological information acquisition means,
The learning support system according to claim 7. The student role robot receives biometric information that changes depending on the state of the learner from the teacher role robot.
The learning support system according to claim 8.   The learning support system according to claim 7, wherein the student role robot receives expression determination information indicating an expression for the learner from the teacher role robot. A biometric information acquisition step of acquiring biometric information that changes according to the state of the learner;
Based on the biometric information acquired in the biometric information acquisition step, a learning instruction content determination step for determining the content of learning support to be performed for the learner,
Learning support method. On the computer,
Biometric information acquisition processing for acquiring biometric information that changes depending on the state of the learner;
Based on the biometric information acquired in the biometric information acquisition process, a learning instruction content determination process for determining the content of learning support to be performed for the learner is executed.
,program.

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