Learning support system and learning support method

Abstract

To provide a technique for enabling learning support by effectively speaking to students.SOLUTION: A learning support system for providing a student with a lecture consisting of video and audio by an avatar lecturer, that is a virtual person imitating a lecturer, stores student attribute information, that is information relating to attributes of each student, specifies specific timing in providing the lecture to the student, as speaking timing, that is timing when the avatar lecturer performs speaking of an action talking to the student by video and audio, specifies a speaking topic, that is a topic of the speaking to be provided at the speaking timing on the basis of the student attribute information and / or the speaking timing, specifies a speaking mode, that is a mode used for the speaking on the basis of the student attribute information, and outputs the speaking according to the speaking topic and the speaking mode at the speaking timing.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a technique for assisting learning using a computer system.

Background Art

[0002] Patent Document 1 discloses a learning system that provides a lecture including video and audio. The learning system makes a determination as to whether a learner attending the lecture is concentrating based on an image acquired for photographing the learner attending the lecture, records concentration determination result information based on the determination result, makes a determination as to whether the learner understands the content of the lecture based on the image and the concentration determination result information, records understanding determination result information based on the determination result, and controls the lecture based on the concentration determination result information and the understanding determination result information. Since the lecture is controlled according to the degree of concentration and the degree of understanding of the learner, it is possible to support effective learning for each learner.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When a real human instructor gives a lecture to learners face-to-face, it is known that making an appropriate call at an appropriate timing increases the motivation and concentration of the learners and improves the learning effect. However, in the technical field of providing a lecture by an avatar instructor using a computer, little consideration has been given to effective calling techniques.

[0005] One object of the present disclosure is to provide a technique for realizing learning support by effective calling to learners.

Means for Solving the Problem

[0006] A learning support system according to an embodiment of the present invention is a learning support system that provides a lecture composed of video and audio to a learner by an avatar lecturer, which is a virtual person imitating a lecturer, and includes a learner attribute information storage unit that stores learner attribute information, which is information regarding the attributes of the learner; a call timing specifying unit that specifies, as a call timing, a specific timing in the provision of the lecture to the learner as a timing at which the avatar lecturer makes a call, which is an action of speaking to the learner by video and audio; a call topic specifying unit that specifies a call topic, which is a topic of the call to be provided at the call timing, based on the learner attribute information and / or the call timing; a call mode specifying unit that specifies a call mode, which is a mode used for the call, based on the learner attribute information; and a call output unit that outputs a call using the call topic and the call mode at the call timing.

Advantages of the Invention

[0007] According to one aspect included in the present disclosure, it enables effective learning support by calling out to the learner.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0010] <First Embodiment> The learning support system of this embodiment is a computer system that provides lecture content composed of video and audio to a learner by a virtual person (hereinafter also referred to as an "avatar instructor") having the appearance of an instructor. Further, the learning support system calls out to the learner from the avatar instructor at a specific timing in the provision of the lecture content, thereby improving the learning effect of the learner.

[0011] FIG. 1 is a block diagram showing the functional configuration of the learning support system according to the first embodiment.

[0012] Referring to FIG. 1, the learning support system 10 has a lecture output unit 19, a calling timing specifying unit 11, a calling topic specifying unit 12, a calling mode specifying unit 13, a calling output unit 14, and a storage unit 15. In the storage unit 15, pre-prepared data necessary for executing the service is recorded. As an example, the lecture output unit 19, the calling timing specifying unit 11, the calling topic specifying unit 12, the calling mode specifying unit 13, and the calling output unit 14 are realized by a processor executing a software program whose processing is defined by the software program.

[0013] FIG. 2 is a block diagram showing the hardware configuration of the learning support system.

[0014] Referring to FIG. 2, the learning support system 10 is composed of a server 20 and an information terminal such as a personal computer or a smartphone that can be connected to the server 20 via a communication network such as the Internet. The learner 90 connects to the server 20 using the browser 33 on the information terminal 30 and uses the services provided by the server 20. The service is a service that provides content related to learning. The service is realized by each part shown in FIG. 1.

[0015] As shown in FIG. 2, the server 20 has, as hardware, a processing device 21, a main memory 22, a storage device 23, a communication device 24, an input device 25, and a display device 26, and they are connected to a bus 27.

[0016] The storage device 23 stores data so that writing and reading are possible. The storage unit 15 shown in FIG. 1 is realized by this storage device 23. The processing device 21 is a processor that reads the data stored in the storage device 23 into the main memory 22 and executes the processing of software programs using the main memory 22. By the processing device 21, the lecture output unit 19, the voice call timing specifying unit 11, the voice call topic specifying unit 12, the voice call mode specifying unit 13, and the voice call output unit 14 shown in FIG. 1 are realized. The communication device 24 transmits the information processed by the processing device 21 via a communication network including wired, wireless, or both, and transmits the information received via the communication network to the processing device 21. The received information is used for software processing by the processing device 21. The input device 25 is a device that receives information input by an operator such as a keyboard or a mouse, and the input information is used for software processing by the processing device 21. The display device 26 is a device that displays image and text information on the display screen in accordance with the software processing by the processing device 21. The input device 25 and the display device 26 are mainly provided for use by an administrator (not shown) rather than the learner 90.

[0017] Returning to FIG. 1, the storage unit 15 stores in advance lecture content, learner attribute information, pre-lecture topic lists, and post-lecture topic lists. As lecture content, content containing lectures for each unit of content to be learned is prepared. As learner attribute information, information regarding the respective attributes of each learner 90 is recorded. The pre-lecture topic list is a list of topics used for greetings provided at a timing before providing the lecture content. The post-lecture topic list is a list of topics used for greetings provided at a timing after providing the lecture content.

[0018] FIG. 3 is a diagram showing a table of learner attribute information.

[0019] In the table of learner attribute information D01, the learner number (No.), name, address, hobbies, date of birth, and preferred dialect of each learner 90 are registered. The name indicates the name of the learner 90. The address indicates the address of the learner 90. The hobbies indicate the hobbies of the learner 90. The date of birth indicates the date of birth of the learner 90. The preferred dialect indicates the dialect that the learner 90 hopes the avatar instructor will use. A dialect is a language system used in a specific region. For example, for a learner 90 with a learner number of 1, the name is "Patent Ichiro", lives in "Nagoya City, Aichi Prefecture...", has hobbies of "cats" and "Chunichi Dragons", was born on October 1, 2001, and hopes that the avatar instructor will use "Nagoya dialect".

[0020] The lecture output unit 19 provides the lecture content stored in the storage unit 15 to the learner 90 through the browser 33 of the information terminal 30 according to the requests of individual learners 90 and the progress of learning.

[0021] The greeting timing determination unit 11, the greeting topic determination unit 12, the greeting mode determination unit 13, and the greeting output unit 14 cooperate to execute a series of processes for greeting the learner 90. Hereinafter, this series of processes is also referred to as a greeting process.

[0022] FIG. 4 is a flowchart of the greeting process.

[0023] In step 101, the calling timing specifying unit 11 specifies, as the calling timing, a specific timing in the provision of the lecture content to the trainee 90, which is the timing of the calling action where the avatar lecturer speaks to the trainee 90 by video and voice. The calling timing specifying unit 11 specifies, for example, the pre-lecture timing, which is the timing before the start of the provision of the lecture content, as the calling timing. The pre-lecture call is an important timing that affects the effect of the subsequent lecture. By making an appropriate call at that timing, the provision of the lecture can be started with the motivation and concentration of the trainee 90 being high. Also, the calling timing specifying unit 11 specifies, for example, the post-lecture timing, which is the timing after the end of the provision of the lecture content, as the calling timing. By not only listening to the lecture but also reviewing the lecture content and practicing problems related to the lecture next, the academic ability can be effectively improved. By making an appropriate call at that timing, the motivation of the trainee 90 for learning after receiving the lecture can be enhanced.

[0024] In step 102, the calling topic specifying unit 12 specifies, based on the trainee attribute information D01 and / or the calling timing, a calling topic, which is the topic of the call to be provided at the calling timing, for the trainee 90. Hereinafter, the process of specifying this calling topic is also referred to as the calling topic specifying process.

[0025] FIG. 5 is a flowchart of the calling topic specifying process.

[0026] In step 201, the calling topic specifying unit 12 determines whether the specified calling timing is the pre-lecture timing or the post-lecture timing.

[0027] If the calling timing is the pre-lecture timing, in step 202, the calling topic specifying unit 12 determines one or more calling topics from the pre-lecture topic list.

[0028] FIG. 6 is a diagram showing a pre-lecture topic list.

[0029] In the pre-lecture topic list D02, "encouragement", "real-time topic", and "hobby topic" are set as suitable greeting topics before starting the lecture content. "Encouragement" is a topic that directly raises the motivation of the attendees. "Real-time topic" is a real-time topic that the attendee is likely to be interested in, for example, including the local weather, news, and sports results of the day of the attendee. Information on actual real-time weather, news, and sports results can be obtained from external sites, for example. "Hobby topic" is a topic related to things that the attendee likes. For example, if the attendee likes cats, a topic about pet cats would be applicable. It is considered that "real-time topic" and "hobby topic" have the effect of directing the attendee's attention towards the lecture.

[0030] The method by which the greeting topic specifying unit 12 determines one or more greeting topics from the pre-lecture topic list D02 is not particularly limited. For example, the greeting topic specifying unit 12 may randomly select one or more greeting topics from the pre-lecture topic list D02. Alternatively, the greeting topic specifying unit 12 may sequentially select greeting topics so as to cycle through the topics in the pre-lecture topic list D02.

[0031] If the greeting timing is after the lecture, in step 203, the greeting topic specifying unit 12 determines one or more greeting topics from the post-lecture topic list.

[0032] FIG. 7 is a diagram showing a post-lecture topic list.

[0033] In the post-lecture topic list D03, "appreciation" and "hobby topics" are set as suitable conversation topics after the lecture content. "Appreciation" is a topic that appreciates the effort of taking the lecture and boosts the motivation for subsequent learning. "Hobby topics" are topics related to things that the said attendee likes, and it is considered to have the effect of relieving the fatigue of the said attendee and boosting the motivation for subsequent learning.

[0034] The method by which the conversation topic specifying unit 12 determines one or more conversation topics from the post-lecture topic list D03 is not particularly limited. For example, the conversation topic specifying unit 12 may randomly select one or more conversation topics from the post-lecture topic list D03. Alternatively, the conversation topic specifying unit 12 may sequentially select conversation topics so as to cycle through the topics in the post-lecture topic list D03.

[0035] Returning to FIG. 4, in step 103, the conversation mode specifying unit 13 specifies a conversation mode, which is a mode used for conversing with the attendee 90, based on the attendee attribute information D01. The conversation mode in the present embodiment is, as an example, a conversation language system, which is a language system used for conversation. For example, if a desired dialect (language system) is registered in the attendee attribute information D01, the conversation mode specifying unit 13 may specify that language system as the conversation language system for conversing with the attendee 90. Further, if a desired dialect (language system) is not registered in the attendee attribute information D01, the conversation mode specifying unit 13 may specify the dialect of the region indicated by the address registered in the attendee attribute information D01 as the conversation language system for conversing with the attendee 90. Alternatively, if a desired dialect (language system) is not registered in the attendee attribute information D01, the conversation mode specifying unit 13 may specify the standard language of Japan as the conversation language system for conversing with the attendee 90.

[0036] In addition, the addressing mode specifying unit 13 may select any one language system as the addressing language system, or may combine a plurality of language systems as the addressing language system. For example, in the attendee attribute information D01 of FIG. 3, the attendee "Patent Ichiro" with the attendee number "1" hopes for "Nagoya dialect" and has a preference for "cats". Therefore, the addressing mode specifying unit 13 may specify an addressing mode that synthesizes "Nagoya dialect" and words (animal words) imitating "cats". As an example, it is possible to make an address such as "Hello. Mr. Patent Ichiro. It's terribly hot in Nagoya today. Are you feeling okay? The Chunichi Dragons that Mr. Patent Ichiro likes also won last night. Meow. Mr. Patent Ichiro should also study hard. Then, let's start the lecture. Meow."

[0037] Subsequently, in step 104, the addressing output unit 14 outputs, at the addressing timing, the video and audio of the address according to the addressing topic and the addressing mode, to the attendee 90 through the browser 33 of the information terminal 30.

[0038] <Second Embodiment> The learning support system according to the second embodiment, in addition to having functions equivalent to those of the first embodiment, acquires the state of the attendee from the image and voice of the attendee taking the lecture, and uses the acquired information on the state of the attendee for subsequent control of the address, thereby improving the effect of the address.

[0039] Since the learning support system according to the second embodiment has the same configuration and operation as that of the first embodiment, the following mainly describes the parts different from the first embodiment in the second embodiment.

[0040] FIG. 8 is a block diagram showing the functional configuration of the learning support system according to the second embodiment. The hardware configuration of the learning support system according to the second embodiment is the same as that of the first embodiment shown in FIG. 2.

[0041] In the second embodiment, the learning support system 10 includes a concentration determination unit 16 in addition to the lecture output unit 19, the calling timing identification unit 11, the calling topic identification unit 12, the calling mode identification unit 13, the calling output unit 14, and the storage unit 15. The concentration determination unit 16 is realized by a processing device 21, which is a processor that executes the processing of a software program, in the same way as the lecture output unit 19, the calling timing identification unit 11, the calling topic identification unit 12, and the calling mode identification unit 13.

[0042] The concentration determination unit 16 makes a determination as to whether the attendee 90 is concentrating on the lecture based on an image acquired for photographing the attendee 90 by the camera 31 of the information terminal 30, and records concentration determination result information based on the result of the determination. The method of concentration determination is not particularly limited. For example, if the conditions that the attendee 90 is in front of the terminal, has a good posture, has open eyes, and is looking at the screen are met, it may be determined that the attendee is concentrating.

[0043] FIG. 9 is a diagram showing an example of the concentration determination result information. Here, the concentration determination result information regarding the calling before the lecture and its calculation method will be described, but the concentration determination result information regarding the calling after the lecture and its calculation method may be the same. In the case of the calling before the lecture, the concentration determination is performed based on the image of the attendee in the lecture after the calling. In the case of the calling after the lecture, the concentration determination may be performed based on the image of the attendee in the review or problem exercise after the lecture.

[0044] In the aggregated determination result information D04, for each topic of the call to start the lecture content, the degree of concentration for each time of the topic and the degree of concentration by topic are recorded. The method for calculating the degree of concentration by topic is not particularly limited. For example, each time the call for the topic is made, the aggregation determination unit 16 calculates and accumulates the degree of concentration for each time, which is the degree of concentration for the subsequent lecture, and calculates the average value by topic of the accumulated degrees of concentration for each time, and may use it as the degree of concentration by topic. Also, the method for calculating the degree of concentration for each time among them is not particularly limited. For example, the aggregation determination unit 16 determines whether the learner is concentrating based on an image at regular intervals, records a concentration flag indicating whether it is concentrated or not concentrated, evaluates it in five levels according to the ratio of the number of concentrated and non-concentrated times during the lecture, and may use the evaluation result as the degree of concentration for each time. Also, the aggregation determination unit 14 may update the degree of concentration by topic each time the call is made, or may update it at regular intervals.

[0045] In the example of FIG. 9, for the topic of "encouragement", the degree of concentration for each time is 4, 5, 5, 5, 4, 4, 5 ···, and the degree of concentration by topic is 4.5. In this example, the degree of concentration for each time is represented in five levels from "1" to "5". "5" indicates the most concentrated state, and "1" indicates the least concentrated state. For the topic of "real-time topic", the degree of concentration for each time is 4, 3, 3, 3, 4, 3, 3 ···, and the degree of concentration by topic is 3.2. For the topic of "hobby topic", the degree of concentration for each time is 3, 4, 4, 4, 3, 4 ···, and the degree of concentration by topic is 3.8.

[0046] Based on the concentration determination result information corresponding to the lecture of the attendee 90 after the greeting, the greeting topic specifying unit 12 selects a pre-lecture timing greeting topic and a post-lecture timing greeting topic. The method for selecting the greeting topic is not particularly limited. For example, the greeting topic specifying unit 12 may select subsequent topics such that the ratio of topics with a high concentration by topic is high and the ratio of topics with a low concentration by topic is low. For example, the greeting topic specifying unit 12 may select topics such that the ratio of each topic matches the ratio of the concentration of each topic to the total value of the concentration by topic. Thereby, it is possible to give many effective greetings to the attendee while preventing the excessive concentration of the same topic and enabling the update of the concentration by topic.

[0047] Regarding the pre-lecture timing, the greeting topic specifying unit 12 tabulates the greeting topic of the greeting provided to the attendee 90 and the concentration determination result information corresponding to the lecture of the attendee 90 after the greeting by that greeting topic, calculates their correlation, and selects the greeting topic to be provided at the pre-lecture timing based on that correlation.

[0048] For example, as a hobby topic, if a greeting about a baseball current event is made before the lecture and the concentration on the lecture is high, then the frequency of the greeting about the baseball current event before the lecture may be increased thereafter. Also, if a greeting about cats is made before the lecture and the concentration on the lecture is high, then the frequency of the greeting about cats before the lecture may be increased thereafter. If a greeting about food is made before the lecture and the concentration on the lecture is high, then the frequency of the greeting about food before the lecture may be increased thereafter.

[0049] Regarding the post-lecture timing, the greeting topic specifying unit 12 selects the greeting topic to be provided at the post-lecture timing of that lecture based on the concentration determination result information corresponding to the lecture of the attendee 90.

[0050] <The Third Embodiment> The learning support system according to the third embodiment sets a personality tailored to the learner for the avatar instructor and makes a call based on that personality. In the real world, it is known that when a learner has a favorable impression or a sense of familiarity with an instructor, the learner's concentration on the lecture and motivation for learning increase. However, since the avatar instructor is different from a real human instructor, it is difficult for the learner to have a favorable impression or a sense of familiarity with the avatar instructor. In this embodiment, by giving the avatar instructor a personality, it is possible to make it easier for the learner to have a favorable impression or a sense of familiarity with the avatar instructor.

[0051] Since the learning support system according to the third embodiment has the same configuration and operation as that of the first embodiment, the following mainly describes the parts different from the first embodiment in the third embodiment.

[0052] The functional configuration and hardware configuration of the learning support system according to the third embodiment are the same as those of the first embodiment shown in FIGS. 1 and 2, respectively.

[0053] In the third embodiment, based on the learner attribute information of the learner 90, the call mode specifying unit 13 sets a personality for the avatar instructor and specifies a call mode based on that personality. As a result, the call provided to the learner 90 from the call output unit 14 is a call by the avatar instructor with the set personality. The personality can be set, for example, according to hobbies, personality, attitude, way of speaking, etc.

[0054] For example, based on the learner attribute information of the learner 90, the call mode specifying unit 13 may set a personality for the avatar instructor with hobbies similar to those of the learner 90 and specify a call mode based on that personality. This makes it easier for the learner to feel a sense of familiarity with the avatar instructor.

[0055] For example, if the learner likes dogs, it is conceivable to set the hobby of liking dogs as the personality of the avatar instructor. Then, for example, in the case of starting a conversation on a hobby topic, the avatar instructor who likes dogs will start a conversation about dogs, and the learner will be more likely to have a good impression and a sense of familiarity with the avatar instructor.

[0056] <Supplementary Note> As described above, the embodiments of the present invention have been described. However, the present invention is not limited to only these embodiments, and within the scope of the technical idea of the present invention, these embodiments may be used in combination, or some configurations may be changed.

[0057] In addition, the above-described embodiments include the following matters. However, the matters included in the above-described embodiments are not limited to only those shown below.

[0058] (Matter 1) A learning support system that provides a lecture composed of video and audio to a learner by an avatar instructor, which is a virtual person imitating an instructor, the learner attribute information storage unit that stores learner attribute information, which is information regarding the attributes of the learner, the timing specifying unit that specifies the timing of the call, which is the timing at which the avatar instructor talks to the learner by video and audio, as the timing of the call, the call topic specifying unit that specifies the call topic, which is the topic of the call to be provided at the call timing, based on the learner attribute information and / or the call timing, the call mode specifying unit that specifies the call mode, which is the mode used for the call, based on the learner attribute information, and the call output unit that outputs the call using the call topic and the call mode at the call timing.

[0059] According to this, along with the provision of the lecture, it is possible to make an effective call by making an appropriate call on an appropriate topic in an appropriate manner at an appropriate timing, so that it is possible to enhance the learning effect of the learner.

[0060] (Item 2) In the learning support system described in Item 1, the calling mode is a calling language system which is the language system used for calling. According to this, since the calling is made in the diction of a specific language system such as a dialect, an effective calling becomes possible.

[0061] (Item 3) In the learning support system described in Item 2, the learner attribute information includes information on the address of the learner and information on the desired language system which is the language system desired by the learner, and the calling mode specifying unit specifies the calling language system based on the address and the desired language system. According to this, since the calling is made in the dialect of the learner's local area or in the diction that the learner likes, an effective calling becomes possible.

[0062] (Item 4) In the learning support system described in Item 2, the learner attribute information includes information on the address of the learner and information on the favorite animal which is the animal that the learner likes, the calling language system is a synthesis of a dialect which is a language system peculiar to each region and an animal language obtained by adding onomatopoeia that imitates the cry of an animal or mimetic words that imitate the behavior of an animal to the end of a word, and the calling mode specifying unit specifies the calling language system based on the dialect determined from the address and the animal language determined from the favorite animal.

[0063] (Item 5) In the learning support system described in Item 1, the calling mode specifying unit sets a personality for the avatar instructor based on the learner attribute information of the learner, and specifies a calling mode based on the personality. According to this, by giving the avatar instructor a personality, it is possible to make it easier for the learner to have a favorable impression and a sense of familiarity with the avatar instructor.

[0064] (Item 6) In the learning support system described in item 5, based on the learner attribute information of the learner, the voice call manner specifying unit sets a personality similar to that of the learner for the avatar lecturer, and specifies a voice call manner based on the personality. According to this, the learner feels less sense of familiarity with the avatar lecturer.

[0065] (Item 7) In the learning support system described in item 1, the voice call timing specifying unit specifies the pre-lecture timing, which is the timing before starting the provision of the lecture, as the voice call timing. According to this, by making an appropriate voice call at the pre-lecture timing, the provision of the lecture can be started in a state where the motivation and concentration of the learner are high.

[0066] (Item 8) In the learning support system described in item 7, the voice call topic specifying unit selects, as the voice call topic to be provided at the pre-lecture timing, a voice call topic that can highly motivate the learner for the lecture to be provided later. According to this, the motivation of the learner can be effectively enhanced before the lecture.

[0067] (Item 9) In the learning support system described in item 8, based on an image acquired to photograph the learner taking the lecture, a determination is made as to whether the learner is concentrating on the lecture, and a concentration determination unit that records concentration determination result information based on the result of the determination is further provided. The voice call topic specifying unit selects the voice call topic to be provided at the pre-lecture timing based on the relationship between the voice call topic and the concentration determination result information corresponding to the lecture after the voice call by the voice call topic. According to this, it becomes possible to select an effective voice call based on the actually measured result.

[0068] (Item 10) In the learning system described in Item 1, the concentration determination unit calculates a topic-specific concentration indicating the concentration of the learner with respect to the lecture after the call for each of the call topics, and the call topic identification unit selects a call topic so that the ratio of the topics with a high topic-specific concentration is lower than the ratio of the topics with a low topic-specific concentration. According to this, it is possible to give many effective calls to the learners while preventing excessive concentration on the same topic and enabling the update of the topic-specific concentration.

[0069] (Item 11) In the learning system described in Item 10, the call topic identification unit selects a call topic so that the ratio of each of the call topics matches the ratio of the concentration of the call topic with respect to the total value of the topic-specific concentrations.

[0070] (Item 12) In the learning support system described in Item 1, the call timing identification unit identifies the post-lecture timing, which is the timing after the end of the provision of the lecture, as the call timing. According to this, by making an appropriate call after the lecture, it is possible to enhance the motivation of the learner for learning after receiving the lecture.

[0071] (Item 13) A learning support method in which a computer having a processing device and a storage device provides a lecture composed of video and audio to a learner by an avatar lecturer, which is a virtual person imitating a lecturer, wherein the storage device stores learner attribute information which is information regarding the attributes of the learner, and the processing device specifies a specific timing in the provision of the lecture to the learner as a calling timing which is a timing at which the avatar lecturer performs a calling action of speaking to the learner by video and audio, specifies a calling topic which is a topic of the calling to be provided at the calling timing based on the learner attribute information and / or the calling timing, specifies a calling mode which is a mode used for the calling based on the learner attribute information, and outputs a calling using the calling topic and the calling mode at the calling timing.

Explanation of Signs

[0072] 10: Learning support system, 11: Timing specifying unit, 12: Topic specifying unit, 13: Mode specifying unit, 14: Output unit, 15: Storage unit, 16: Concentration determination unit, 19: Lecture output unit, 20: Server, 21: Processing device, 22: Main memory, 23: Storage device, 24: Communication device, 25: Input device, 26: Display device, 27: Bus, 30: Information terminal, 31: Camera, 33: Browser, 90: Learner, D01: Learner attribute information, D02: List of topics before lecture, D03: List of topics after lecture

Claims

1. A learning support system that provides a lecture consisting of video and audio to students by an avatar lecturer, which is a virtual person modeled after a lecturer, comprising: a student attribute information storage unit that stores student attribute information that is information related to the attributes of students; a calling-out timing specifying unit that specifies a specific timing in providing the lecture to the students as a calling-out timing, which is a timing for the avatar lecturer to call out to the students by a video and a voice; a call topic specification unit that specifies a call topic to be provided at the call timing based on the student attribute information and / or the call timing; a calling style specification unit that specifies a calling style to be used for the calling based on the student attribute information; a call output unit that outputs a call according to the call topic and the call mode at the call timing; A learning support system having the above structure.

2. The voice-calling style is a voice-calling language system that is a language system used for voice-calling. The learning support system according to claim 1 .

3. The student attribute information includes address information of the student and information on a desired language system, which is a language system desired by the student, the addressing style specification unit specifies the addressing language system based on the address and the desired language system. The learning support system according to claim 2 .

4. The student attribute information includes address information of the student and information on a favorite animal of the student, the favorite animal being an animal of the student; The voice language system is a combination of a dialect, which is a language system peculiar to each region, and an animal language, which adds an onomatopoeia that imitates the sound of an animal or an onomatopoeia that imitates the action of an animal to the end of a word, the calling style specification unit specifies the calling language system based on a dialect determined from the address and an animal word determined from the favorite animal; The learning support system according to claim 2 .

5. the addressing style specification unit sets a personality for the avatar instructor based on the student attribute information of the student, and specifies an addressing style based on the personality. The learning support system according to claim 1 .

6. the addressing style specification unit sets a personality having similar tastes to the student for the avatar instructor based on the student attribute information of the student, and specifies an addressing style based on the personality. The learning support system according to claim 5 .

7. The calling timing specification unit specifies a pre-lecture timing, which is a timing before the start of the provision of the lecture, as the calling timing. The learning support system according to claim 1 .

8. the calling topic specification unit selects, as a calling topic to be provided at the pre-lecture timing, a calling topic that highly motivates the students to attend the lecture to be provided thereafter. The learning support system according to claim 7.

9. a concentration judgment unit that judges whether the students are concentrating on the lecture based on an image acquired to photograph the students attending the lecture, and records concentration judgment result information based on the result of the judgment; The calling topic specification unit selects a calling topic to be provided at the pre-lecture timing based on a relationship between the calling topic and concentration determination result information corresponding to the lecture after the calling by the calling topic. The learning support system according to claim 8.

10. the concentration determination unit calculates a topic-specific concentration degree indicating a concentration degree of the student on the lecture after the calling for each of the called topics, the calling topic identification unit selects the calling topic such that a ratio of topics having a high topic-specific concentration degree is lower than a ratio of topics having a low topic-specific concentration degree. The learning support system according to claim 9.

11. the calling topic identification unit selects a calling topic such that a ratio of each of the calling topics matches a ratio of the concentration degree of the calling topic to a total value of the concentration degrees by topic. The learning support system according to claim 10.

12. The calling timing specification unit specifies a post-lecture timing, which is a timing after the provision of the lecture is ended, as the calling timing. The learning support system according to claim 1 .

13. A learning support method for providing a lecture consisting of video and audio to students by an avatar lecturer, which is a virtual person simulating a lecturer, using a computer having a processing device and a storage device, comprising: The storage device stores attendee attribute information which is information regarding attributes of attendees, The processing device comprises: A specific timing in providing the lecture to the student is identified as a calling timing, which is a timing for the avatar lecturer to call out to the student by speaking to the student through video and audio; Identifying a call topic that is a call topic to be provided at the call timing based on the student attribute information and / or the call timing; Identifying a calling style to be used for the calling based on the student attribute information; outputting a call according to the call topic and the call manner at the call timing; Learning support methods.

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