JP2019086602A - Learning support system and learning support method - Google Patents

Abstract

To present an optimal teacher who suits a student.SOLUTION: A learning supporting system for supporting learning by means of a computer includes: storage means (student DB 611) for storing attribute information of students; determination means (teaching processing unit 605) which determines progress of learning of each student; search means (teaching processing unit 605) which searches, when a new student is registered, the storage means for a student having attribute information similar to the new student; specification means (teaching processing unit 605) which specifies a teacher evaluated to be relatively high, from among teachers in charge of the student having the attribute information similar to the new student acquired by the search means; and presentation means (object processing unit 608) which presents the teacher specified by the specification means.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a learning support system and a learning support method.

  Patent Document 1 includes a student terminal having question transmitting means for issuing a question to a teacher's terminal and sending an e-mail via LAN, and an answer receiving means for receiving an answer from the teacher's terminal; A technique is disclosed that includes a teacher terminal having a question receiving unit that receives a question from a terminal, and an answer transmitting unit that sends an answer to a student terminal via a mail via a LAN.

  According to such a technology, it is possible to realize a learning support system in which a dialogue can be made between a teacher and a student in CAI on a LAN.

JP-A-09-185598

  However, in the technique disclosed in Patent Document 1, there is a problem that learning does not progress as expected when the teacher and the student do not have a good compatibility.

  An object of the present invention is to provide a learning support system and a learning support method capable of presenting the most suitable teacher to a student.

In order to solve the above problems, according to the present invention, in a learning support system that supports learning by a computer, storage means for storing student attribute information, and determination means for determining the progress of learning of each student, In the case of registering a new student, the search means for searching the storage means for the student having the attribute information similar to the new student, and the new student similar to the new student acquired by the search means Among the teachers in charge of the students having the attribute information, a specification means for specifying the teacher whose evaluation is determined to be relatively high, and a presentation means for presenting the teacher specified by the specification means. It is characterized by having.
According to such a configuration, it is possible to present the most suitable teacher for the student.

Further, according to the present invention, the attribute information of the student includes surface attribute information indicating a surface attribute of the student, and deep layer attribute information indicating a deep attribute, and the search means includes the surface attribute The student having the attribute information similar to the new student is searched in comparison with at least one of information and the deep layer attribute information.
According to such a configuration, it is possible to present a more optimal teacher based on the surface layer attribute information and the deep layer attribute information of the student.

Further, in the present invention, the storage means includes the surface layer attribute information indicating a surface layer attribute of the teacher and the deep layer attribute information indicating a deep layer attribute, and the teacher is drawn by the computer. The learning contents are taught to the students by the avatar and the voice synthesized by the computer, and the computer renders the avatar based on the surface layer attribute information and synthesizes the voice and is based on the deep layer attribute information It is characterized by teaching by a method.
According to such a configuration, the teacher can also modify the teacher according to the student by having the surface layer attribute information and the deep layer attribute information.

Further, the present invention is characterized in that the teacher has a sorting means for teaching the learning content to the student based on a predetermined method, and selecting the method according to the level of the learning efficiency.
According to such a configuration, it is possible to further enhance the learning efficiency by selecting methods.

Also, the present invention is characterized by having modifying means for modifying the contents of the method, and the sorting means sorting out the method with high learning effect from the methods modified by the modifying means. .
According to such a configuration, it is possible to further improve the learning efficiency by the method evolution by modifying the method.

Further, according to the present invention, in a learning support method for supporting learning by a computer, a storage step of storing attribute information of a student in a storage means, a determination step of determining a progress state of each learning of the student, new When registering a student, a search step of searching the storage means for the student having the attribute information similar to the new student, and the attribute information similar to the new student acquired in the search step And identifying the teacher whose evaluation is determined to be relatively high among the teachers in charge of the students having a student, and presenting the teacher identified in the specifying step. It is characterized by
According to such a method, it is possible to present the most suitable teacher for the students.

  According to the present invention, it is possible to provide a learning support system and a learning support method capable of presenting an optimal teacher suited to a student.

It is a figure showing an example of composition of a learning support system concerning an embodiment of the present invention. It is a figure which shows the example of a detailed structure of the client apparatus shown in FIG. It is a figure which shows the detailed structural example of the server apparatus shown in FIG. It is a functional block implemented by the hardware and software of the server apparatus shown in FIG. 3 cooperating with each other. It is a figure which shows an example of the attribute information of a student. It is a figure which shows the detail of the attribute information of the student shown in FIG. It is a figure which shows an example of a teacher's attribute information. It is a figure which shows the detail of the attribute information of the teacher shown in FIG. It is a flowchart which shows an example of the process performed when registering a new student. It is a figure which shows an example of a teacher's avatar. It is a flowchart which shows an example of the process performed when teaching from a teacher to a student. It is a flowchart which shows the detail of the modification process of the teacher of FIG. 11, and a method. It is a flow chart which shows an example of processing performed when coaching from a teacher to a student.

  Next, an embodiment of the present invention will be described.

(A) Description of Configuration of an Embodiment of the Present Invention FIG. 1 is a diagram showing an example of a configuration of a learning support system according to an embodiment of the present invention. As shown in FIG. 1, the learning support system 1 includes a position information input device 10, a head mount display (HMD) 20, a voice input / output device 30, a client device 40, and a server device 60. The server device 60 is connected by the network 50 in a communicable state.

  The position information input device 10 is configured, for example, as a wristband type device worn by a student on a wrist or the like. The position information input device 10 detects, for example, the movement of the wrist by an acceleration sensor, acquires information drawn on a desk or a space by a student, and supplies the information to the client device 40. As the position information input device 10, for example, a joystick, a pointing stick, a touch pad, a data glove, a trackball, a pen tablet, a mouse, a light pen, and a joypad can also be used.

  The HMD 20 is a display that the student wears on his / her head and displays an image supplied from the client device 40 as different images for both eyes. The HMD 20 also has an acceleration sensor, detects the direction of the head, and supplies an image according to the detection result from the client device 40, thereby functioning as a so-called virtual reality (VR) display. In addition, even if HMD20 is "non-transmission type" which can not completely cover the eyes and can not see the appearance of the outside, or the display is made a half mirror so that the outside can be seen or a small video that shoots the outside It may be "transmissive" in which the image of the camera can be viewed. Further, an imaging element may be provided in the client device 40, and the direction of the HMD 20 may be detected by this imaging element.

  The audio input / output device 30 is a device for inputting and outputting audio, and is configured by, for example, a microphone and a speaker or headphones. The voice input / output device 30 converts the voice uttered by the student into an electrical signal and supplies the electrical signal to the client device 40. Also, the voice input / output device 30 converts the electrical signal supplied from the client device 40 into voice and outputs it. In addition, according to the movement of the head detected by HMD20, you may make it implement | achieve three-dimensional sound by changing the volume and the phase of the audio | voice output from headphones with a right ear and a left ear.

  The network 50 is configured by, for example, the Internet or a LAN (Local Area Network), and can transmit and receive information by exchanging packet format data between the client device 40 and the server device 60.

  The server device 60 is constituted by, for example, a computer such as a work station, supplies images, sounds, text data, and processes information supplied from the client device 40 in response to a request from the client device 40.

  FIG. 2 is a view showing a detailed configuration example of the client device 40 shown in FIG. As shown in FIG. 2, the client device 40 includes a central processing unit (CPU) 41, a read only memory (ROM) 42, a read access memory (RAM) 43, a hard disk drive (HDD) 44, and a graphics processing unit (GPU). 45, an I / F (Interface) 46, and a bus 47.

  Here, the CPU 41 executes arithmetic processing or controls each part of the apparatus based on programs stored in the ROM 42, the RAM 43, and the HDD 44.

  The ROM 42 is a semiconductor storage device that stores basic programs and data executed by the CPU 41. The RAM 43 is a semiconductor storage device that temporarily stores a program to be executed by the CPU 41 and also temporarily stores data generated when the program is executed.

  The HDD 44 is a magnetic storage device that stores basic programs and data executed by the CPU 41.

  The GPU 45 executes drawing processing based on drawing data supplied from the CPU 41, and supplies the obtained image to the HMD 20 for display.

  The I / F 46 inputs data supplied from the position information input device 10, the HMD 20, and the voice input / output device 30, and supplies voice data to the voice input / output device 30. The I / F 46 also transmits and receives data to and from the server device 60 via the network 50.

  The bus 47 is a signal line group for electrically connecting the CPU 41, the ROM 42, the RAM 43, the HDD 44, the GPU 45, and the I / F 46 to each other and enabling data exchange between them.

  FIG. 3 is a diagram showing a detailed configuration example of the server device 60 shown in FIG. As shown in FIG. 3, the server device 60 includes a CPU 61, a ROM 62, a RAM 63, an HDD 64, an I / F 65, and a bus 66.

  Here, the CPU 61 executes arithmetic processing or controls each part of the apparatus based on programs stored in the ROM 62, the RAM 63, and the HDD 64.

  The ROM 62 is a semiconductor storage device that stores basic programs and data executed by the CPU 61. The RAM 63 is a semiconductor storage device that temporarily stores a program to be executed by the CPU 61 and also temporarily stores data generated when the program is executed.

  The HDD 64 is a magnetic storage device that stores basic programs and data executed by the CPU 61.

  The I / F 65 transmits and receives data to and from the client device 40 via the network 50.

  The bus 66 is a signal line group for electrically connecting the CPU 61, the ROM 62, the RAM 63, the HDD 64, and the I / F 65 to each other and enabling data exchange between them.

  FIG. 4 is a functional block realized by the CPU 61 executing the programs stored in the ROM 62 and the HDD 64 in the server device 60 shown in FIG. An example shown in FIG. 4 is a position information processing unit 601, a speech recognition processing unit 602, a speech synthesis processing unit 603, an image generation processing unit 604, a teaching processing unit 605, a coaching processing unit 606, a text processing unit 607, an object processing unit 608. , A teaching DB (Data Base) 609, a coaching DB 610, a student DB 611, and a teacher DB 612.

  Here, the position information processing unit 601 receives position information input from the position information input device 10 of the client device 40 and transmitted via the client device 40 and the network 50, and detects the movement of the student's wrist. Run.

  The voice recognition processing unit 602 receives voice data input from the voice input / output device 30 of the client device 40 and transmitted via the client device 40 and the network 50 and executes processing of converting the voice data into text data by voice recognition processing. Do.

  The voice synthesis processing unit 603 converts the data into voice data (for example, WAV data) based on the text data supplied from the text processing unit 607 and voice attribute information, and supplies the voice data to the client device 40 via the network 50. The client device 40 supplies voice data to the voice input / output device 30 to output it as voice. Note that the attribute information supplied from the text processing unit 607 is also supplied with information related to the fundamental frequency of voice, gender, intonation, utterance speed, arrangement of formant components, and size of voice, and based on these pieces of information. By synthesizing the voice, it is possible to reproduce the voice including the features and emotions of the speaker.

  The image generation processing unit 604 executes processing for generating polygon data based on data (for example, data related to an object) supplied from the object processing unit 608, and the obtained polygon data is transmitted to the client device via the network 50. Send to 40 In the client device 40, polygon data is received, and an image is generated by the GPU 45 by drawing processing, layout processing, rendering processing and the like, and supplied to the HMD 20 for display.

  The teaching processing unit 605 inputs the position of the student's wrist supplied from the position information processing unit 601 and the information on the student's utterance supplied from the text processing unit 607, and based on the teaching data stored in the teaching DB 609 Information for drawing an object (for example, a teacher's avatar) is supplied to the object processing unit 608, and text data for causing the object to utter is supplied to the text processing unit 607. In this way, it is possible to teach a student the desired content, obtain a reaction from the student, and execute an operation of presenting information corresponding to the reaction to the student.

  The coaching processing unit 606 inputs the position of the student's wrist supplied from the position information processing unit 601 and the information on the student's utterance supplied from the text processing unit 607, and the student's utterance supplied from the text processing unit 607 Information related to the object is input to the object processing unit 608 based on the coaching data stored in the coaching DB 6410, and information for drawing an object (for example, a teacher avatar) is supplied to the text processing unit 607. Provide text data to make the avatar speak. In this way, it is possible to hear from students the opinions and impressions at a deeper psychological level, and to spread the opinions and impressions heard.

  The text processing unit 607 executes morphological analysis processing as syntactic language analysis processing, syntactic analysis processing, semantic analysis processing, and context analysis processing on text data supplied from the speech recognition processing unit 602 and is obtained. The processing result is supplied to the teaching processing unit 605 and the coaching processing unit 606. In addition, the text processing unit 607 performs morphological analysis processing, syntactic analysis processing, semantic analysis processing, and context analysis processing on the text data supplied from the teaching processing unit 605 and the coaching processing unit 606 as necessary. , And performs conversion processing of text data and the like, and supplies it to the speech synthesis processing unit 603.

  The object processing unit 608 generates the avatar of the teacher based on the information supplied from the supply to the teaching processing unit 605 and the coaching processing unit 606, and supplies the avatar to the image generation processing unit 604. In addition, the object processing unit 608 also generates an object (for example, a problem sentence, an image, a moving image, and a stereoscopic image) indicating the content to be taught based on the information supplied from the supply to the teaching processing unit 605 and the coaching processing unit 606. It generates and supplies it to the image generation processing unit 604.

  The teaching DB 609 is a database that stores learning content to be taught to students. The coaching DB 610 is a database that stores information on coaching to be performed on students.

  The student DB 611 is a database storing attribute information on students. The teacher DB 612 is a database storing attribute information on teachers.

  FIG. 5 is a view showing an example of student attribute information stored in the student DB 611 shown in FIG. As shown in FIG. 5, the student attribute information includes personal attribute information and collective attribute information. Here, the personal attribute information is attribute information of each student, and has surface attribute information and deep attribute information. The surface layer attribute information is information on surface attributes which can be observed from the appearance, and the deep layer attribute information is information on deep attributes which can not be observed from the appearance. Also, collective deep layer attribute information is included as an attribute including a plurality of deep layer attribute information. The collective deep layer attribute information is attribute information that includes a plurality of deep layer attribute information and whose contents change with the passage of time.

  FIG. 6 is a view showing an example of the student attribute information shown in FIG. As shown on the left side of FIG. 6, surface layer attributes constituting student attribute information include, for example, age, gender, height, weight, deviation value, and IQ (Intelligence Quotient). Of course, these are only examples and may have attribute information other than these.

  Further, as shown in the right side of FIG. 6, the deep layer attribute information includes interest, affiliation, problems, mission, skepticism, fear and the like. The term “interest” indicates the subject of interest, “affiliation” indicates the area or group that the student thinks belongs to, “problem” indicates the sense of problems that the student has, and “mission” indicates the student's own mission drawn by the student. The skepticism indicates the object of skepticism held by the student, and the awe indicates the object of awe that the student imagines. Of course, these are only examples and may have attribute information other than these.

  FIG. 7 is a diagram showing an example of attribute information stored in the teacher DB 612 shown in FIG. As shown in FIG. 7, the teacher attribute information includes personal attribute information and collective deep attribute information as in the case of the student. Here, the personal attribute information is attribute information of each teacher, and has surface attribute information and deep attribute information. The surface layer attribute information is information indicating a superficial attribute that can be observed from the appearance, and the deep layer attribute information is information indicating a deep layer attribute that can not be observed from the appearance. Also, collective deep layer attribute information is included as an attribute including a plurality of deep layer attribute information. The collective deep layer attribute information is attribute information that includes a plurality of deep layer attribute information and whose contents change with the passage of time.

  FIG. 8 is a view showing an example of the attribute information of the teacher shown in FIG. As shown in FIG. 8, surface layer attribute information constituting teacher attribute information includes, for example, age, gender, height, weight, fundamental frequency of voice, speaking speed, and avatar. Of course, these are only examples and may have attribute information other than these.

  The deep layer attribute information includes first to third deep layer attribute information. Here, the first deep attribute information has interest, affiliation, problems, mission, skepticism, fear and the like. The term “interest” indicates the subject of the teacher's interest, “affiliation” indicates the area or group that the teacher considers to belong to, “problem” indicates the sense of problems that the teacher has, and “mission” is his or her own mission that the teacher envisions The skepticism indicates the target of skepticism held by the teacher, and the awe indicates the target of the awe that the teacher envisions. Of course, these are only examples and may have attribute information other than these.

  The second depth attribute information indicates the priority in teaching, etc., and has emphasis on understanding, emphasis on basics, emphasis on applications, emphasis on progress, emphasis on memories, and emphasis on tests. Here, emphasis on understanding emphasizes students' understanding, emphasis on basics emphasizes students' basic power, emphasis on applied emphasis on students' applied power, emphasis on progress emphasizes learning progress, and emphasis on memories is student's Emphasis is placed on retention of memory, and emphasis on exams indicates emphasis on student exam results. Note that these may be shown as numerical values between 1 and 10, for example, with 10 as the maximum. Of course, these are only examples and may have attribute information other than these.

  The third depth attribute information includes first to sixth methods. Note that these methods show how to teach. As a first method, for example, in the case of a language, a method of teaching a phonetic symbol of a word first is shown, and as a second method, for example, a method of teaching a word and a phonetic symbol is taught later. The method may be a very detailed method of teaching, or may be a slightly rough method. Of course, these are only examples and may have attribute information other than these.

(B) Description of Operation of the Embodiment of the Present Invention Next, the operation of the embodiment of the present invention will be described. FIG. 9 is a flowchart showing an example of processing executed by the server device 60 when registering a new student. For example, when a new student registers in the learning support system 1 shown in FIG. 1 shown in FIG. 1, the process shown in FIG. 9 is started. When the process shown in FIG. 9 is started, the following steps are performed.

  In step S10, surface attribute information such as age, sex, height and the like of a newly registered student is input. More specifically, the teaching processing unit 605 shown in FIG. 4 reads out text data for registration of a new student (for example, text data prompting to input each item of surface layer attribute information) from the teaching DB 609, and performs text processing The voice synthesis processor 603 is supplied via the unit 607. As a result, text data is converted into voice data by the voice synthesis processing unit 603, and is supplied to the client device 40 via the network 50. The client device 40 supplies the received voice data to the voice input / output device 30. The voice input / output device 30 generates and outputs voice corresponding to the new student's registration text data. At this time, the object processing unit 608 generates an avatar for registration (for example, an avatar of a guidance combination), supplies it to the client device 40 via the image generation processing unit 604 and the network 50, and sends VR (Virtual Device) to the HMD 20. Reality) Images may be displayed in synchronization with audio. The student operates, for example, the age, sex, height, weight, and the like as position information by operating the position information input device 10 according to the guidance of the voice by the avatar image. The position information indicating the surface layer attribute information input in this manner is supplied from the client device 40 to the server device 60 via the network 50.

  In step S 11, the position information processing unit 601 of the server device 60 processes the position information supplied from the client device 40 to acquire attribute information, and supplies the attribute information to the teaching processing unit 605. The teaching processing unit 605 registers the supplied surface layer attribute information in the student DB 611 as attribute information of a new student.

  In step S12, the object processing unit 608 generates the avatar of the student with reference to the surface layer attribute information registered in the student DB 611 in step S11, and registers the avatar in the student DB 611. The student avatar generated in this manner is supplied from the object processing unit 608 to the image generation processing unit 604, displayed on the HMD 20 via the network 50 and the client device 40, and subjected to correction or the like as necessary. You may do so. For example, the color of the hair, the facial expression, the height, the weight, and the like may be corrected based on the information input from the position information input device 10.

In step S13, the teaching processing unit 605 reads out data (image data, text data, etc.) related to the achievement test registered in the teaching DB 609, and supplies the data to the text processing unit 607 and the object processing unit 608. The object processing unit 608 generates object (for example, an object relating to a problem) data for performing a performance test and an avatar for explaining the data, and supplies the data to the image generation processing unit 604. The image generation processing unit 604 generates a polygon based on the object data supplied from the object processing unit 608 and executes rendering processing and the like, and supplies this to the client device 40 via the network 50. The client device 40 causes the HMD 20 to display the supplied information. As a result, the problem of the academic ability test and the avatar of the teacher are displayed on the HMD 20. Also, the text processing unit 607 supplies text data for explaining the problem to the speech synthesis processing unit 603. The speech synthesis processing unit 603 executes speech synthesis processing based on the text data supplied from the text processing unit 607, and supplies the obtained speech data to the client device 40 via the network 50. The client device 40 outputs the supplied voice information via the voice input / output device 30. As a result, the question of the achievement test and the voice of the teacher avatar are output from the voice input / output device 30. For more details, for example, the teacher's avatar utters "Please write a graph of y = 1/4 × x ² " and "Please write a graph of y = 1/4 × x ² ". Is displayed as a letter. In addition, a virtual graph paper is displayed on the HMD 20 in front of the student. The student can move the hand and draw a curve that has been determined on a virtual graph paper. In addition, in the case of science, the avatar of the teacher utters "Please perform an experiment to generate oxygen", and "Please perform an experiment to generate oxygen" is displayed as characters. In addition, on the HMD 20, laboratory equipment such as test tubes and flasks, and chemicals such as hydrogen peroxide water, hydrochloric acid, sulfuric acid, ammonia water, manganese dioxide, platinum, and boric acid are displayed in front of the student. Students can move their hands to place the labware and select and inject medicines to perform oxygen generation. Such an academic ability test can be conducted for each department.

  In step S14, the teaching processing unit 605 acquires and scores the result of the achievement test performed in step S13. Then, the deviation value of the newly registered student is calculated based on the graded result and the result of the achievement test for all the students stored in the teaching DB 609. For example, in the example of the mathematical test described above, if the drawn curve is completely correct, it is 10 points, if only the shape (convex curve downward) is 5 points, if nothing can be drawn Is scored as 0. Also, in the science experiment example, if the procedure of all experiments is correct, it is 10 points, and if the selection of drugs is correct even if some of the procedures are wrong, it is 5 points. If the selection is also wrong, it can be scored as zero. The deviation value can be determined by summing the results of scoring as described above.

  In step S15, the teaching processing unit 605 registers the deviation value calculated in step S14 in the student DB 611 as the "deviation value" of the surface layer attribute information. For example, “Mathematics = 65”, “English = 55”, “Japanese language = 67”, “Social = 71”, and “Science = 72” are registered as deviation values of newly registered students. Not only the achievement test but also the IQ test may be performed in the same manner, and the result of the IQ test may be registered in the student DB 611 as surface layer attribute information.

  In step S16, the teaching processing unit 605 reads out information related to the psychological test registered in the teaching DB 609, and supplies the information to the text processing unit 607 and the object processing unit 608. The object processing unit 608 generates an object for performing a psychological test and an avatar for explaining the object, and supplies the object to the image generation processing unit 604. The image generation processing unit 604 generates a polygon based on the object information supplied from the object processing unit 608 and executes rendering processing and the like, and supplies this to the client device 40 via the network 50. The client device 40 displays the supplied information on the HMD 20. As a result, the psychological test problem and the avatar of the teacher are displayed on the HMD 20. Also, the text processing unit 607 supplies text data for explaining the problem and voice attribute information to the voice synthesis processing unit 603. The speech synthesis processing unit 603 executes speech synthesis processing based on the text data supplied from the text processing unit 607 and the attribute information, and supplies the obtained speech data to the client device 40 via the network 50. . The client device 40 outputs the supplied voice information via the voice input / output device 30. As a result, the sound of the psychological test question and the avatar of the teacher is output from the sound input / output device 30. More specifically, for example, a plurality of ruined castles are displayed in front of HMD 20 students, and each of the plurality of castles is, for example, a scientist, a mathematician, a national literary, a sociologist, etc. , Today there is a narration with the absence of the lord. There is a narration that the students are allowed to enter by selecting one of these castles. As a result, it is judged that the old castle selected by the student shows the "interest" of the deep attribute. For example, in the case of selecting an old castle where "scientist" lives, it is determined that the subject of interest is "science". In addition, the above is an example and can perform various psychological tests besides this.

  In step S17, the teaching processing unit 605 registers the result of the psychological test performed in step S16 in the student DB 611 as deep layer attribute information. For example, "Science" is registered as "interest" of the newly registered student, "Science of School" is registered as "Affiliation", "We are not good at memorized subjects" as "Problem", and "Mission" As "the medicine of incurable disease in the future is made" is registered, "is the current way of study correct" is registered as "skeptical", and "the grandfather of the former university professor" is registered as "fear". The above is only an example, and other than these may be registered. Also, instead of registering as "words", for example, numerical values may be registered. For example, in the case of registering "outwardness" and "inwardity" as the deep layer attribute, a large value may be registered when the outward tendency is strong, and a small value may be registered when the inward tendency is strong. Of course, in addition to the extroversion or the introversion, indices such as sociability, nervousness, adhesive, manifestation, schizophrenia, and circulatory quality may be registered as numerical values. The deep layer attribute information is not fixed. For example, new deep layer attribute information may be introduced according to the change of society. As an example, if the belongingness to a group is a social condition to be evaluated, the belongingness is added as deep attribute information, and if it is a social condition not to be evaluated to the contrary, an independent consciousness is added as deep attribute information You may That is, since the group deep attribute is a deep psychological attribute common to the society, it is considered to be an attribute which changes with the change of the society or the fashion.

  In step S18, the teaching processing unit 605 performs an attribute similar to the surface layer attribute information registered in step S11, the result of the academic ability test registered in step S15, and the deep layer attribute information which is the result of the psychological test registered in step S17. The existing student who has it is specified from student DB611. For example, a weight value is set for each of the various types of attribute information described above, and a value obtained by adding the product of the weight value and each attribute information is calculated as the similarity. Then, the existing students are sorted in descending order of the degree of similarity, and the sorted upper students are identified as similar students. For example, when the degree of similarity is set to a value of 0 to 100, students can be sorted in descending order of the degree of similarity, and the top 100 people can be selected. The weighting value may be changed by, for example, learning processing based on regression analysis or the like using the evaluation of a teacher in processing described later as an index, and the determination accuracy of the degree of similarity may be improved.

  In step S19, the teaching processing unit 605 identifies a teacher who is determined to be compatible with the student determined to be similar in step S18. For example, in the above-described example, the top 100 students determined to have a high degree of similarity are sorted in the order in which the deviation values are rising. Then, the student positioned in the upper rank can be judged as a student having good affinity with the teacher. Then, a teacher in charge of a student with a high degree of similarity can be identified as a compatible teacher. For example, identify four teachers who are estimated to be compatible. It should be noted that the quality of the compatibility is judged not by judging the compatibility from the rising tendency of the deviation value, but by other index values, for example, an index that the student can relax (relief), is easy to understand, talks easily, etc. You may do it.

  In step S20, the teaching processing unit 605 acquires data on the teacher specified in step S19 from the teacher DB 612, and supplies the data to the text processing unit 607 and the object processing unit 608. For example, data on the four teachers specified in step S 19 is selected from the teacher DB 612 and supplied to the text processing unit 607 and the object processing unit 608. As a result, on the HMD 20, avatar images and attribute information of four teachers as shown in FIG. 10 are displayed. By operating the position information input device 10, a student can select a desired teacher and listen to the teacher's story. Of course, you may be able to talk. By displaying such an avatar image and listening to the desired teacher's speech etc., the student can specify the teacher as his / her ideal. For example, the student selects "XX teacher" as an ideal teacher in FIG.

  In step S21, the teacher selected in step S20 is set as a teacher in charge. More specifically, the teaching processing unit 605 registers the teacher selected in step S20 in the student DB 611 as a teacher in charge of the student.

  As described above, the surface layer attribute information and the deep layer attribute information of the student are registered and the existing student having the surface layer attribute information and the deep layer attribute information having high similarity is specified by the process of the flowchart shown in FIG. It is possible to identify a teacher who is estimated to be compatible with the identified student.

  Next, the teaching process will be described with reference to FIG. When the process of the flowchart shown in FIG. 11 is started, the following steps are performed. The following flowchart is executed, for example, when the student wears the HMD 20 to receive a lesson (teaching) and wears the position information input device 10 and the voice input / output device 30.

  In step S30, the teaching processing unit 605 determines whether it is the first teaching for the student. If it is determined that it is the first teaching (step S30: Y), the process proceeds to step S31, otherwise (step S30) In step S30: N), the process proceeds to step S32. For example, when a student takes a lesson for the first time, it is determined as Y and the process proceeds to step S31.

  In step S31, the teaching processing unit 605 selects the subject selected by the student (for example, Japanese language, society, science, mathematics, English, etc.), the deviation value of the selected subject at that time, and the deep layer attribute information of the student. Create a curriculum based on the teacher's deep attribute information. For example, the subject selected by the student is "English", the deviation value at that time is "50", the interest of the student's deep layer attribute information is "science", and the teacher emphasizes the second deep layer attribute information Of the second method (eg, emphasis on grammar) is high. In that case, the student's English grades are normal, the students have high logical thinking ability, and the teacher emphasizes the basics and emphasizes the grammar, so create a curriculum that teaches the grammar logically. Do. In addition, the above is an example and you may make it produce a curriculum by methods other than this.

  In step S32, the teaching processing unit 605 specifies, for example, a portion which has been ended last time from the curriculum of the student stored in the teaching DB 609. For example, if the student is in English grammar class, it will start from the applied problem if it has progressed to the second sentence basic problem of the 5 sentence patterns in the previous class. In this way, as the student wears the HMD 20, etc., the class from the previous time is automatically resumed, so there is no need to memorize the contents of the previous class, and the class will be started promptly. be able to.

  In step S33, the avatar of the teacher is displayed on the HMD 20. More specifically, the teaching processing unit 605 searches the student DB 611 based on information (for example, ID information) for specifying a student to specify a student, and information on a teacher selected by the specified student is Acquired from the teacher DB 612. For example, in FIG. 10, when the xx teacher is selected, information corresponding to the xx teacher is acquired from the teacher DB 612. At this time, avatar and voice attribute information (basic frequency of voice, speaking speed, intonation, etc.) are also acquired.

  In step S34, the teaching processing unit 605 executes a process of displaying the learning content. More specifically, the teaching processing unit 605 acquires information on the learning content of the student from the teaching DB 609, and supplies the information to the text processing unit 607 and the object processing unit 608. As a result, the object processing unit 608 generates information for displaying (operating) the avatar of the teacher and, for example, information related to the learning content (for example, an example sentence in the case of English). Supply. The image generation processing unit 604 executes polygon processing, rendering processing, and the like based on the information supplied from the object processing unit 608, and supplies the obtained image information to the client device 40 via the network 50. The client device 40 executes drawing processing based on the received polygon information, and supplies the HMD 20 for display. Further, the text processing unit 607 generates text data for causing the avatar of the teacher to utter and attribute information of the teacher's voice, and supplies the generated text data to the voice synthesis processing unit 603. The speech synthesis processing unit 603 synthesizes speech data based on the text data supplied from the text processing unit 607, and supplies the speech data to the client device 40 via the network 50. The client device 40 outputs the received voice data from the voice input / output device 30. Thus, for example, the avatar of the XX teacher shown in FIG. 10 is synthesized as a VR image and displayed on the HMD 20, and the XX blackboard example sentence (eg, "She is painting the wall blue") on the virtual blackboard. Can be displayed. In addition, in order to promote the retention in the student's memory, an avatar (female avatar painting a wall in blue), an example sentence (She is painting the wall blue.), And a sentence pattern (fifth sentence pattern) (SOVC)) may be displayed.

  In step S35, the teaching processing unit 605 acquires a student response. For example, the teaching processing unit 605 causes the voice recognition processing unit 602 to acquire a reaction (emotion or the like) of the student based on a change in frequency characteristics of the voice uttered by the student. For example, when the frequency is lower than normal, it can be determined that the user is bored. In addition, the teaching processing unit 605 causes the position information processing unit 601 to detect the movement of the student, thereby acquiring the reaction of the student. The student's expression may be captured by a camera or the like, and the student response may be acquired from the student's expression captured in the captured image. In addition, based on these pieces of information acquired at this time, the reaction of the student can be classified into three of “active”, “normal”, and “dull”. Alternatively, it may be evaluated by a 10-step evaluation with 10 being the most active state.

  In step S36, the teaching processing unit 605 performs an understanding level test. For example, in the present example, the comprehension test on the fifth sentence pattern in English is performed. More specifically, the teaching processing unit 605 acquires information on the understanding level test from the teaching DB 609, and supplies the information to the text processing unit 607 and the object processing unit 608. As a result, the HMD 20 displays the comprehension test question and the teacher's avatar, and the voice input / output device 30 outputs voice guidance. The student can answer the comprehension test by operating the position information input device 10 or speaking to the voice input / output device 30.

  In step S37, the teaching processing unit 605 receives an input of evaluation of a teacher. More specifically, the teaching processing unit 605 acquires information on the evaluation of the teacher from the teaching DB 609, and supplies the information to the text processing unit 607 and the object processing unit 608. As a result, the HMD 20 displays information for evaluating a teacher (for example, an input box for performing 5-step evaluation), and a voice guidance is output from the voice input / output device 30. The student can evaluate the teacher by operating the position information input device 10 or speaking to the voice input / output device 30.

  In step S38, the teaching processing unit 605 registers the student reaction, the result of the understanding level test, and the evaluation of the teacher in the student DB 611. For example, "slow" as a student's reaction, "deviation 50" as a result of comprehension test, and "3 in 5 grades" as a teacher's evaluation are registered as the student's current teacher's evaluation . The comprehension test, the teacher's evaluation, and the student's response do not have to be acquired each time, and may be acquired and registered as often as necessary.

  In step S39, the teaching processing unit 605 executes a process of modifying the teacher and the method based on the student's response registered in step S38, the result of the understanding level test, and the evaluation of the teacher. The details of this process will be described later with reference to FIG.

  According to the above process, the teaching process from the teacher to the student can be executed.

  Next, with reference to FIG. 12, the details of the teacher and method modifying process shown in step S39 of FIG. 11 will be described. When the process of FIG. 12 is started, the following steps are performed.

  In step S50, the teaching processing unit 605 creates an A / B model in which the surface layer attribute information and the deep layer attribute information are changed. For example, as one example, it is possible to change the fundamental frequency of the voice of the surface attribute information, change the speaking speed, and change the avatar. At this time, instead of changing all at once, one set of teachers who have changed a part is created (A / B model is created). In addition, the first deep attribute information that affects the subject of parables and talks during class may be changed according to the A / B test as in the above-mentioned case.

  In step S51, the teaching processing unit 605 sets so as to alternately present the A / B model created in step S50. As a result, for example, a teacher A whose fundamental frequency of speech is several hertz higher and a teacher B whose fundamental frequency is lower several hertz are alternately presented. Also, if you create a set of teachers who changed part of the first deep attribute information that speaks in class, that affects parables and talks, and set these as A / B models. Good.

  In step S52, the teaching processing unit 605 executes a process of selecting the previously set A / B model based on the student's reaction stored in step S38 of FIG. 11, the result of the understanding level test, and the teacher's evaluation. Do. For example, for each of the A / B models, the value obtained by calculating the value obtained by adding the weight function to each of the student's response, the comprehension test result, and the teacher's evaluation is added and calculated. You can select a model. Needless to say, the evaluation may be performed using three or more models.

  In step S53, the teaching processing unit 605 specifies a method with high effect in the third deep layer attribute shown in FIG. More specifically, a method with relatively high effect (relatively high comprehension test result) when applied to the same student is identified. As an example, in the case of English, the first method is the first method when comparing the first method that teaches the actual pronunciation after teaching the phonetic symbols and the second method that teaches the pronunciation without teaching the phonetic symbols. If the result of the comprehension test is better than the 2 methods, select the 1st method.

  In step S54, the teaching processing unit 605 executes a process of modifying a method. For example, in the previous English example, the first method of teaching phonetic symbols and then teaching the actual pronunciation is modified, for example, as a method of teaching similar consonants or vowels as an alternative structure of difference. As a method of modification, there are a method of changing the order of teaching, a method of changing the viewpoint of teaching, a method of changing the priority of teaching, and the like. Of course, other change methods may be used. The point is that the purpose is to obtain a more effective method by changing the content of teaching instead of fixing it, so it may be a small change.

  In step S55, the teaching processing unit 605 copies the modified method. More specifically, the method modified in step S54 is copied as another method. For example, in the above-mentioned example, the first method of teaching the phonetic symbols and then teaching the actual pronunciation is modified, for example, as a method of teaching similar consonants or vowels as an alternative structure of differences, for example, 7 Copy as a method (new method).

  In step S56, the teaching processing unit 605 identifies a method with low effectiveness from among the methods before and after modification. For example, in the above-mentioned example, the first method before modification and the seventh method after modification are compared, and the first method is specified as a method with relatively low effect. In addition, when there is a method whose effect is obviously low among the methods before and after modification, the method is selected, and when the effects are substantially the same, for example, based on a random number or the like, either at random May be selected.

  In step S57, the teaching processing unit 605 deletes the method with low effect specified in step S56.

  In step S58, the teaching processing unit 605 identifies a method with high effect from among the methods before and after modification. For example, in the above-mentioned example, the first method before modification and the second method after modification are compared, and the seventh method is specified as a method having a relatively high effect. If the effect is substantially the same in the methods before and after the modification, the specification is not performed in the process of step S58. Since the process of step S58 is a process of identifying a method whose effect is obviously high, it is not specified when the effect is not clear.

  In step S59, the teaching processing unit 605 executes processing to diffuse the highly effective method identified in step S58. More specifically, as shown in FIG. 8, the method belongs to the third deep layer attribute information, and as shown in FIG. 7, the deep layer attribute information belongs to the personal attribute information of each teacher. Therefore, if it is found that a method belonging to a certain teacher (generated by modification) is effective, it is effective by spreading (copying) the method to other teachers' deep layer attribute information as well. Methods can be shared as collective deep attribute information. When the process of step S59 ends, the process returns to the original process (return).

  According to the above processing, it is possible to evolve the teacher's surface attribute information and deep layer attribute information to an optimum one for each student by the A / B test. Also, the method can be evolved into a more effective method by modifying, copying (copying) and selecting (淘汰).

  Next, with reference to FIG. 13, a process performed when the teacher coaches a student will be described. In addition, coaching is a method to lead students' self-actualization and achievement of goals through dialogue. The teacher can encourage spontaneous behavior by listening to the students' stories, telling and approving what they felt, and asking questions.

  In step S70, the coaching processing unit 606 determines the theme of coaching. In addition, as a method of determining the theme of coaching, for example, a list of the subjects can be presented via the HMD 20 or the voice input / output device 30, and can be selected from those presented. Alternatively, when a predetermined keyword appears in the conversation between the student and the teacher, the theme of coaching can be determined by using it as a trigger. For example, if a student makes a statement such as "I am aiming for a career about OO in the future," "future course" can be the theme of coaching.

  In step S71, the coaching processing unit 606 acquires a question corresponding to the theme specified in step S70 from the coaching DB 610. For example, in the case of the "future course" mentioned above, "What kind of occupation do you have a longing for?", "What is the reason for having a longing for that occupation?" Are there any people in charge? “Interesting”, “(future) affiliation”, “problems (consciousness)”, etc. of deep attribute information such as “What is the purpose or reason for working for you?” Identify questions that lead to "mission", "skeptical", and "fear" by searching.

  In step S72, the speech recognition processing unit 602 acquires the remarks of the student. More specifically, voice data uttered by a student, converted into voice data by the voice input / output device 30, and transmitted through the client device 40 and the network 50 is acquired. As a result, "I want to get a job using English" acquired by the student is acquired.

  In step S73, the voice recognition processing unit 602 performs voice recognition processing on voice data acquired via the network 50 in step S72, and the text processing unit 607 executes textification. In the above-described example, the spoken voice data "I want to get a job using English in the future" is converted into text.

  In step S74, the coaching processing unit 606 acquires the remarks of the student converted into texts in step S73, and acquires a question corresponding to the remark from the coaching DB 610. For example, if the above-mentioned "I would like to get an occupation using English" is the student's statement, "Why English?" Is selected. The content of such a conversation can be realized by assuming the content of the conversation in advance and storing an appropriate conversation based on the assumed conversation and based on the coaching technique. Coaching is a conversation to draw out deep opinions and emotions of the students, and compared with free conversation (everyday conversation etc.), the teachers are passive, so the number of questions to be prepared is small. It's over.

  In step S75, the coaching processing unit 606 supplies the question acquired in step S74 to the text processing unit 607, and the speech synthesis processing unit 603 converts the question into speech and outputs it. As a result, the question acquired in step S74 is output from the voice input / output device 30 of the client device 40.

  In step S76, the coaching processing unit 606 determines whether the conclusion has been reached by coaching. If it is determined that the conclusion has been reached (step S76: Y), the process proceeds to step S77, otherwise (step S76: In N), the process returns to step S72, and the same process as described above is repeated. For example, when a student utters a specific keyword (for example, "Sorry.", "Surely.", "I see.", Etc.), it is estimated from speech that the student's emotions are high (eg, The conclusion has been reached, for example, when the fundamental frequency of the voice is higher than normal) or when the student indicates the intention to finish (for example, when a predetermined command is input from the position information input device 10) It can be determined and the process can proceed to step S77.

  In step S77, the coaching processing unit 606 stores the conclusion reached in step S76. For example, "I would like to get a job that uses English in the future, but I want to be a doctor or a nurse because I have a fundamental desire to help people." .

  In step S78, the coaching processing unit 606 searches the student DB 611 for a student having coached on the same theme in the past while having an attribute similar to that of the student who has performed coaching, and acquires the conclusion. More specifically, for example, while having deep attribute information similar to the student, in the above example, identify a student who has been coached on the theme of "future course" and obtain the coaching conclusion . The similarity of the deep layer attribute information can be determined, for example, by comparing the values obtained by the product-sum operation of each of the deep layer attribute information shown in FIG. 6 and the weight value. Of course, other methods may be used.

  In step S79, the coaching processing unit 606 feeds back the validity of the conclusion. More specifically, first, the present conclusion is compared with the plurality of conclusions acquired in step S78. As a result, it feeds back that it is valid if the conclusion is the same as or similar to the conclusion included in multiple conclusions. Note that a model that estimates a conclusion similar to a certain conclusion may be constructed using a technique such as Skip-gram, and the validity of the result may be determined based on such an estimation result. Further, in the case of a theme such as a future course, real information of a student (senior) who has advanced to the course may be registered and compared with the registration result. For example, when the theme is the future course, deep attribute information (for example, satisfaction with work, ease of work, good work, high salary, work life balance, etc.) of students who have actually become nurses and doctors (Attribute indicating “f”) may be registered, and comparison with such deep layer attribute information may be performed. With such comparison, for example, with regard to the course, the student's ability and aptitude can be accurately determined by comparing deep attribute information between a student who is successful in the field and the student.

  According to the above-described flowchart, by coaching students, for example, it is possible to increase awareness of learning by further clarifying the course of the future and enhancing motivation. Further, by registering the result obtained by the coaching as deep layer attribute information, it is possible to more accurately determine the student's attribute. In addition, by using avatars synthesized by the server device 60 as coaching, a more probable conclusion is obtained by making an unexpected thought and judgment as compared to when confronted with a living human being who has an interest. be able to.

  Further, the question used by the teacher during the coaching may be optimized by optimizing it by the same processing as the method modification processing related to teaching described based on FIG.

(C) Description of Modified Embodiments It goes without saying that each of the above-described embodiments is an example, and the present invention is not limited only to the case described above. For example, in the above example, the position information processing unit 601, the voice recognition processing unit 602, the voice synthesis processing unit 603, the image generation processing unit 604, the teaching processing unit 605, the coaching processing unit 606, the text processing unit 607, the object processing unit 608 , The teaching DB 609, the coaching DB 610, the student DB, and the teacher DB 612 are all disposed in the server device 60, but these are disposed in the client device 40, or are distributed to the client device 40 and the server device 60. May be arranged.

  Also, in the above embodiments, the teacher uses avatars and synthetic speech generated by computer processing. For example, a teacher who is a living human may be used for a student who does not like computer teaching. It may be allocated. Also, at the time of initial registration and at the time of coaching, a teacher by a live human may be assigned. That is, the attribute of the teacher shown in FIG. 7 and FIG. 8 may include attribute data of a teacher of life.

  Further, in the above embodiments, the devices disposed on the student side are the position information input device 10, the HMD 20, the voice input / output device 30, and the client device 40, but instead of these or one of them A mobile terminal (for example, a smartphone) may be used as the unit. For example, by attaching a smartphone to goggles for head mounting, the HMD 20, the voice input / output device 30, and the client device 40 can be substituted. Of course, a two-dimensional image or video may be used instead of a VR image.

  Further, in the above embodiment, in the teaching, learning is taught, but other contents may be taught. For example, as the employee education, the contents of work, the correspondence to the customer, etc. may be taught. In this embodiment, since teaching is performed using a VR image, for example, in employee education, training with higher reality can be performed by using a VR image of an actual customer.

  Further, in the above embodiment, the teacher's evaluation is made based on the progress of learning, etc., but it may be made by other methods. For example, it may be determined not by the student itself but by the guardian of the student based on a change in the approach to learning of the student.

  In addition, students may be allowed to freely change the teacher's attributes (surface attribute information and deep attribute information), and thus the teacher who has been altered in this way may be exchangeable with other students for a fee / free of charge. . This allows the students to create teachers who are better at teaching, and allows a plurality of students to share the teacher thus created.

  Also, modification of the method may allow students to be involved. For example, the student may be able to find his own method. In addition, the method obtained by such modification may be exchangeable with other students for a fee / free of charge.

  Further, in the above embodiment, the case where both the teacher and the student are one is described as an example, but a plurality of teachers and / or students may be provided. For example, there may be one teacher and a plurality of students who are compatible with the teacher may be together to advance the class. Also, there may be a plurality of teachers, and a plurality of students who are compatible with the teachers may gather to advance the class. That is, the ratio of the number of teachers and students may be single or plural.

  Also, as an academic ability test, it may be possible to take an examination by VR at home. Specifically, it may be possible to take an examination at a place convenient to the student (for example, a home, a school, a library, etc.) without visiting the venue. Further, immediately after the examination, by making the answer by the teacher in charge, the error may be corrected early and the memory may be fixed.

  Moreover, in the above embodiment, although the reference book etc. are not described in detail, for example, multiple types of reference books, dictionaries, teaching materials etc. are registered, and from among the registered reference books etc. It may be possible to select reference books and the like that match the level or match the textbook used in the school.

  The flowcharts shown in FIGS. 9 and 11 to 19 are merely examples, and the present invention is not limited to these flowcharts.

1: Learning support system 10: Position information input device 20: HMD
30: voice input / output device 40: client device 41: CPU
42: ROM
43: RAM
44: HDD
46: I / F
47: bus 50: network 60: server device 61: CPU
62: ROM
63: RAM
64: HDD
65: I / F
66: bus 601: position information processing unit 602: speech recognition processing unit 603: speech synthesis processing unit 604: image generation processing unit 605: teaching processing unit 606: coaching processing unit 607: text processing unit 608: object processing unit 609: teaching DB
610: Coaching DB
611: Student DB
612: Teacher DB

In order to solve the above problems, the present invention is a learning support system that supports learning by a computer, and in the learning support system that supports learning by a computer, surface attribute information indicating surface attributes that can be measured by students ; Presenting a virtual teacher having first storage means for storing deep layer attribute information indicating deep layer attributes other than the surface layer attribute information, avatar drawn by the computer, and voice synthesized by the computer Teaching means for teaching the learning contents to the students through the virtual teacher, the type of the avatar of the virtual teacher, the feature of the synthesized speech, the teaching contents and the teaching a second storage means for storing attribute information indicating the method, a determination unit configured to determine progress status of each of the learning of the student, When registering the student Do has, search the a new surface layer attribute information of the student, with reference to said deep attribute information, searching for the students with the attribute information similar to the first storing means It is determined that the evaluation is relatively high among the virtual teachers presented to the student having the attribute information similar to the new student obtained by the search means and the means. the specifying means for specifying the attribute information about the virtual teacher from said second storage means, based on said attribute information specified by the specifying means, the said virtual teacher for the new the student a control means for performing control to be presented to the teaching means, and has a changing means for modifying the teaching contents or the teaching method according to the teaching means, said teaching means, said changing means Thus among the modified the teaching contents or the teaching methods, for those learning effect is high, also be used for other of the student, and wherein the.
According to such a configuration, it is possible to present the most suitable teacher for the student.

Further, according to the present invention, the deep layer attribute information includes collective deep layer attribute information as a common attribute including the deep layer attribute information of a plurality of the students, and the collective deep layer attribute information includes the passage of time. It is characterized in that the content changes along with it .

Further, according to the present invention, the teaching means executes so-called coaching for drawing out opinions and feelings buried deep in the student by sequentially presenting a plurality of questions on a specific subject to the student. The learning support system according to claim 1 or 2, characterized by

Further, the present invention is characterized in that the teaching means judges validity by comparing the result obtained as a result of the coaching with the result of the other students .

Further, according to the present invention, in a learning support method for supporting learning by a computer, surface attribute information indicating surface attributes that can be measured by a student, and deep attribute information indicating deep attributes other than the surface attribute information. Presenting a virtual teacher having a first storing step stored in the storing means, an avatar drawn by the computer, and a voice synthesized by the computer, and learning through the virtual teacher presented A second step of storing in the storage means a teaching step of teaching contents to the students, a type of the avatar of the virtual teacher, a feature of the synthesized speech, and attribute information indicating teaching contents and teaching methods and storing step, and each of the determining step the progress of learning of the student, when registering a new the student, the new piece of the Said surface layer attribute information adversaries, with reference to the deep attribute information, a search step of searching the student with the attribute information similar from said storing means, said new said student acquired in the search step Identifying, from the storage means, the attribute information related to the virtual teacher whose evaluation is judged to be relatively high among the virtual teachers presented to the student having the attribute information similar to And a control step of performing control to cause the new student to present the virtual teacher to the teaching step based on the attribute information specified in the specifying step ; There is a modification step for modifying the teaching content or the teaching method according to the teaching step, the teaching step being modified in the modification step Among the teaching contents or the teaching methods it was, for those learning effect is high, also be used for other of the student, and wherein the.
According to such a method, it is possible to present the most suitable teacher for the students.

In order to solve the above problems, the present invention provides a learning support system for supporting learning by a computer, comprising surface attribute information indicating measurable surface attributes of a student and deep attributes other than the surface attribute information. Presenting a virtual teacher having a first storage means for storing deep attribute information to indicate, an avatar drawn by the computer, and a voice synthesized by the computer; Second teaching means for teaching learning contents to the students via the second learning means, type of the avatar of the virtual teacher, characteristics of the synthesized speech, and attribute information indicating a teaching method ; A determination unit that determines a progress state of each learning of the student, and, when registering a new student, the surface attribute information of the new student, and Searching means for searching the first storage means for the student having similar attribute information with reference to the layer attribute information, and the new attribute information similar to the new student acquired by the searching means Specifying means for specifying, from the second storage means, the attribute information regarding the virtual teacher whose evaluation is determined to be relatively high among the virtual teachers presented to the students having the virtual teacher; based on said attribute information specified by the specifying means, and a control means for performing control to be presented to the teaching means the virtual teacher for the new the student, the teaching according to the teaching means has changing means for modifying to identify the effect is high the teaching method of the method, the teaching means, among the teaching methods that have been modified by the modification means are learning effect is higher For, also used for other of the student, and wherein the.
According to such a configuration, it is possible to present the most suitable teacher for the student.

Further, according to the present invention, in a learning support method for supporting learning by a computer, surface attribute information indicating surface attributes that can be measured by a student, and deep attribute information indicating deep attributes other than the surface attribute information. Presenting a virtual teacher having a first storing step stored in the storing means, an avatar drawn by the computer, and a voice synthesized by the computer, and learning through the virtual teacher presented A second storing step of storing, in the storage means, a teaching step of teaching contents to the student, a type of the avatar of the virtual teacher, a feature of the synthesized speech, and attribute information indicating a teaching method A determination step of determining a progress state of each learning of the student, and when registering a new student, the surface layer of the new student A search step of searching the storage unit for the student having similar attribute information with reference to the sex information and the deep layer attribute information, and the new search similar to the new student acquired in the search step And identifying, from the storage means, the attribute information related to the virtual teacher whose evaluation is determined to be relatively high among the virtual teachers presented to the student having the attribute information. , on the basis of the said attribute information specified in the specifying step, and a control step of performing control to present the virtual teacher to the teaching steps for the new the student, the by the teaching step to identify the effect is high the teaching method of teaching methods have altered step of modifying the teaching methods, the teaching step, the modified scan Of the modified the teaching methods in-up, for those learning effect is high, also be used for other of the student, and wherein the.
According to such a method, it is possible to present the most suitable teacher for the students.

Claims (6)

In a learning support system that supports learning by computer,
Storage means for storing student attribute information;
Determining means for determining the progress of each learning of the students;
Retrieval means for retrieving from the storage means the student having the attribute information similar to the new student when registering the new student;
From the teacher in charge of the student having the attribute information similar to the new student acquired by the search means, an identification means for specifying the teacher judged to have a relatively high evaluation;
Presentation means for presenting the teacher identified by the identification means;
A learning support system characterized by having. The attribute information of the student includes surface attribute information indicating a surface attribute of the student and deep attribute information indicating a deep attribute.
The search means searches for the student having the attribute information similar to the new student by comparing with at least one of the surface layer attribute information and the deep layer attribute information.
The learning support system according to claim 1, characterized in that: The storage means includes the surface layer attribute information indicating a superficial attribute of the teacher and the deep layer attribute information indicating a deep layer attribute.
The teacher teaches learning contents to the students by an avatar drawn by the computer and a voice synthesized by the computer.
The computer renders an avatar based on the surface layer attribute information and synthesizes the voice and performs teaching by a method based on the deep layer attribute information.
The learning support system according to claim 1 or 2, characterized in that: The teacher teaches learning contents to the students based on a predetermined method.
Having a sorting means for sorting the method according to the level of learning efficiency,
The learning support system according to any one of claims 1 to 3, characterized in that: Having modifying means for modifying the contents of the method;
The selection means selects the method having a high learning effect from the methods modified by the modification means.
The learning support system according to claim 4, characterized in that: In a learning support method for supporting learning by a computer,
A storage step of storing student attribute information in storage means;
A determination step of determining the progress of each learning of the students;
A search step of searching the storage means for the student having the attribute information similar to the new student when registering the new student;
Identifying the teacher whose evaluation is determined to be relatively high among the teachers in charge of the student having the attribute information similar to the new student acquired in the search step;
Presenting the teacher identified in the identifying step;
A learning support method characterized by having:

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