JP5392906B2 - Distance learning system and distance learning method - Google Patents

Description

  The present invention relates to a distance learning system and a distance learning method for further improving a learner's understanding level in distance education such as e-learning.

  In recent years, e-learning technology using the Internet or the Web has been widely used in the distance learning field. In such a distance learning system, various techniques have been proposed in order to improve the learning effect. For example, Patent Document 1 discloses a technique for disposing a student image window having a high degree of attention of a lecturer above a lecturer screen in a remote learning system that displays videos of the lecturer and students on each other's terminals. According to this conventional technique, the face of the instructor does not face downward and can easily face the front of the video camera, so that the face of the instructor can be suitably photographed.

JP 2003-280506 A

  However, according to the conventional remote learning system, there are cases where only a raw video or sound itself with a low resolution can be transmitted and received. In such a case, the instructor could not grasp the physical and mental situations such as the learner's line of sight, eyes, facial expression, atmosphere, smell, emotion, attitude, sleepiness, and boredom in real time. For this reason, even if the learner misunderstood due to missed or overlooked, the instructor may proceed the lecture without noticing it.

  The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide a remote learning system and a remote learning method in which an instructor can grasp a learner's physical and mental situation in real time. It is.

A first feature of the present invention is that in the remote learning system in which a learner terminal used by a learner and a teacher terminal used by a teacher are connected via a network, the learner and / or the learning A plurality of types of sensors for detecting physical events around the learner, and a correlation between the learner and / or a combination of physical events around the learner and the physical / mental situation of the learner Based on the storage means for storing the rule information, the combination of physical events detected by the plurality of types of sensors, and the rule information stored in the storage means, the physical / mental situation of the learner is estimated. and estimation means for, and a delivery means for delivering the physical and mental circumstances of the learner estimated by the estimating means to the instructor's terminal, the distribution means, estimation accuracy of the situation To improve, inquiry information prompting the reaction of the learner and delivers the information to reaffirm estimation result of circumstances, the estimation means, wherein the reaction of the learner and after the query again the situation The physical / mental situation of the learner is re- estimated based on the information obtained by the confirmation, and the delivery means warns the learner in a manner that stimulates the learner when the learner's willingness to learn decreases. It is to distribute information.

A second feature of the present invention is that, in the distance learning system, when the distribution means decreases in learning motivation of the learner or when the learner misses or overlooks, (A) the learner When the learner has a short short-term memory capacity, a keyword is delivered to the learner's terminal. (B) When the learner is a learner with weak associative learning ability, summary information is sent to the learner's terminal. (C) Distribute keywords and summary information to the learning terminal when the learner is a learner with a short short-term memory capacity and a learner with weak associative learning ability.

The third feature of the present invention is that, in the distance learning system, the estimating means estimates the physical / mental situation of the learner at a plurality of timings.

According to a fourth feature of the present invention, in the distance learning system, the estimating means estimates the learner's physical and mental situation at a plurality of timings, and the same estimation result is obtained at a predetermined number of consecutive timings. It is to adopt the estimation result only when it is obtained.

According to a fifth aspect of the present invention, in the distance learning system, the distribution means changes the color of the face, hair, or clothes of an avatar corresponding to the learner, so that the learner's physical / spirit is changed. Is to distribute the situation to the instructor's terminal.

A sixth feature of the present invention is that, in the distance learning system, the distribution means distributes the physical / mental situation of the learner estimated by the estimation means to the learner's terminal.

According to a seventh feature of the present invention, in the distance learning system, the estimating means includes sleepiness, fatigue, energy, jealousy, enthusiasm, concentration, interest, interest, indifference as the learner's physical / mental situation. Estimating circumstances of boredom, chilliness, consent, approval, neutrality, hesitation, opposition, or repulsion.

  According to the present invention, it is possible to provide a remote learning system and a remote learning method in which an instructor can grasp the physical / mental situation of a learner in real time. As a result, the instructor can proceed with the lecture according to the physical / mental situation of the learner, and thus it is possible to prevent misunderstanding due to missed or overlooked by the learner.

It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure for demonstrating the situation estimation method in embodiment of this invention. It is a figure which shows an example of the rule information in embodiment of this invention. It is a figure which shows the experimental data in embodiment of this invention. It is a figure which shows the experimental data in embodiment of this invention. It is a figure which shows the experimental data in embodiment of this invention. It is a figure which shows the experimental data in embodiment of this invention. It is a figure which shows the experimental data in embodiment of this invention. It is a figure which shows the experimental data in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention. It is a figure which shows the structure of the distance learning system in embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  The present invention relates to a distance learning system for further enhancing a learner's understanding in distance education such as e-learning. Specifically, by estimating the physical and mental conditions (drowsiness, degree of fatigue, interest / indifference of lecture contents, etc.) of the learner in a remote place and feeding this back to the instructor in real time, Help to give more effective lectures. In particular, the present invention is characterized by accurately estimating the physical / mental situation of the learner by synthesizing and analyzing a plurality of types of sensor information instead of using single sensor information.

  As shown in FIG. 1, the remote learning system according to the embodiment of the present invention includes a distribution server 10 that distributes and manages various types of information, and a plurality of user terminals (clients) 20A to 20C used by the user. . These devices are connected to each other via a network such as the Internet. The user terminal 20A is assumed to be a terminal used by a leader, and may be hereinafter referred to as a “leader terminal”. “Instructor” includes the meanings of a speaker, an instructor, a teacher, and the like. On the other hand, the user terminals 20B and 20C are assumed to be terminals used by the learner, and may be hereinafter referred to as “learner terminals”. “Learner” includes the meanings of listeners and students.

  The user terminal 20A includes a situation evaluation (situation information synthesis / analysis) / transmission / reception / display section 21 as shown in FIG. Situation evaluation (situation information synthesis / analysis) / transmission / reception / display unit 21 synthesizes / analyzes sensor information from a plurality of types of sensors 40 to estimate situation information, and between other devices. It has a function of transmitting / receiving various types of information and a function of displaying various types of received information on a display or the like. The sensor 40 includes a sensor mouse 41, a web camera 42, an accelerometer 43, and a microphone 44. In other words, the sensor 40 is a multi-sensor for detecting physical events in and around the user. Details of the multi-sensor will be described later. The detection unit 30 includes a biological information measurement unit 31, a face position measurement unit 32, an acceleration measurement unit 33, and a voice / keyword recognition / extraction unit 34. The biological information measuring unit 31 measures biological information based on sensor information obtained by the sensor mouse 41. The face position measurement unit 32 measures the face position based on the image obtained by the Web camera 42. The acceleration measuring unit 33 measures acceleration based on the sensor information obtained by the accelerometer 43. The voice / keyword recognition / extraction unit 34 recognizes voice or extracts a keyword based on the voice obtained by the microphone 44. Here, the user terminal 20A has been described as an example, but the other user terminals 20B and 20C have the same function.

  Thus, by obtaining a plurality of types of sensor information using a multi-sensor, and processing these sensor information by fusing them, new information that cannot be obtained with a single sensor information can be obtained. That is, as shown in FIG. 3, first, a plurality of types of sensor information such as “electroencephalogram”, “heart rate”, “blood pressure”, “eye movement”, “sweat”, and “face” are obtained from the multi-sensor. Next, signs such as “tension”, “concentration”, “inspiration”, “distraction”, “blurry”, “contrast”, and the like are estimated based on a combination of the plurality of types of sensor information. Furthermore, based on the combination of these signs, “Enthusiasm and enthusiasm”, “Large possibility of leakage due to fatigue”, “Large possibility of leakage due to irritation”, “Large possibility of leakage without enthusiasm”, “Low interest and cold communication ”And so on. Signs may be considered a kind of situation. However, it is important to estimate signs as a pre-process for estimating the situation. In other words, the measurement value may become unstable if an inexpensive sensor is used, but even if some signs appear, the situation can be estimated more accurately by observing the progress of the signs. It becomes possible.

  Next, the multisensor used in the embodiment of the present invention will be described. Here, a mouse sensor, a band sensor such as a wristwatch, an installation sensor attached to a chair or the like, and a wearable sensor attached to clothes or a hat are used. Using these existing sensors, information such as pulse (pulse wave), skin temperature, sweating, acceleration and the like is obtained. “Skin temperature” is a temperature measured at the wrist or back of the hand, and is a value slightly lower than the body temperature. In addition to the pulse rate and skin temperature, it is effective to obtain images and sounds from a Web camera and microphone. By using such an inexpensive and easy sensor, it is possible to estimate the situation without causing discomfort to the user.

  However, if an inexpensive and simple sensor is used, the accuracy of the sensor information may be lowered, and in this case, there is a problem that the accuracy of estimation of the situation is also lowered. Therefore, in the present invention, an inquiry is made to the user according to the estimated situation, and the estimation accuracy is improved by re-estimating the situation based on the reaction. For example, if it is estimated that a certain user is drowsy, a message prompting the user's reaction such as “Is listening” or a sound is output to the user terminal, and the situation based on the subsequent user's reaction Is estimated again. Specifically, when the user answers or raises his face, it is estimated that he was not sleeping, and conversely, when the user did not respond, it is estimated that he was asleep. In this way, it is possible to accurately estimate the user's situation even when an inexpensive and easy sensor is used.

  Messages such as “Are you listening” or “Are you okay?” Can be considered as warning information for the user. Such warning information may be presented in a manner that stimulates the user. For example, when the screen is flashed, or when the user terminal is a mobile terminal, the mobile terminal is vibrated, so that the user can be surprised and sleepiness can be eliminated. Moreover, the warning mode in which the screen is flashed or the terminal is vibrated is suitable for an environment where no sound can be produced, such as in a train.

  The situation information estimated by the learner's terminal is distributed to the instructor terminal via the distribution server 10, and is presented to the instructor by, for example, an avatar. An avatar is a character that appears on the screen as a user's alternate with a communication tool such as chat. When a sign of sleepiness appears, the color of the avatar's hair changes to, for example, white. You may make it change the color of a face and a costume instead of the color of hair. The user can also freely set the color to be changed. As a result, the instructor can grasp the physical / mental situation of the learner in real time and proceed with the lecture according to the physical / mental situation of the learner. Misunderstanding can be prevented.

  The learner's situation information may be distributed not only to the instructor but also to all the learners, or may be aggregated by the distribution server 10 and disclosed only to the related parties. When publishing on the distribution server 10, the learner's situation information may be processed into statistical information such as boredom, sleepiness, approval, interest, etc., or only necessary information may be disclosed adaptively. May be. Further, the characteristic data and situation information of each learner are managed by the distribution server 10, and the keywords appearing in the learning materials and the summary information of the learning contents are adapted to the learner's terminal according to the characteristics of the learner. It is also possible to display it automatically.

  For example, when learning motivation such as sleepiness is reduced, if the learner is a learner with a short short-term memory capacity, the portion of the keyword that has decreased learning motivation is displayed on the learner's terminal. Also good. The short-term memory capacity is measured by using a tool (WebOPsan) that measures the mental calculation ability of an expression composed of a plurality of operations and how many words can be continuously stored including the generation order. The timing for displaying the keyword may be a point in time when it is estimated that the keyword is sleepy, or may be after a certain period of time has elapsed since that point. When displaying a keyword after a certain period of time has elapsed, it is necessary to specify the period of sleepiness. Therefore, in the present invention, the period of sleepiness is recorded, and the keywords displayed on the learner's terminal during that period are extracted. In this way, even if an oversight or oversight occurs, the keyword of that part can be reviewed later, so that the possibility of misunderstanding can be reduced. In addition, not all keywords in the learning material are displayed, but only the keywords that may have been missed or overlooked are displayed, so many keywords are displayed unnecessarily. There is no problem.

  On the other hand, if the learner is a learner with weak associative learning ability due to missing or overlooked by looking away or talking locally, the summary information of the part that has been missed or overlooked is used for the learner. You may make it display on a terminal. The timing for displaying the summary information may be at the time of missing or overlooked, or after a certain period of time has elapsed from that time. Since the method for displaying the summary information after a lapse of a certain period of time is the same as that for keywords, detailed description thereof is omitted here. In this way, even if an oversight or oversight occurs, the summary information of that part can be reviewed later, so that the possibility of misunderstanding can be reduced. Moreover, instead of displaying summary information for the entire study area, summary information for parts that may have been missed or overlooked is displayed, so that summary information with appropriate content is displayed. It is possible.

  Here, keywords are displayed for learners with short short-term memory capacity, and summary information is displayed for learners with weak associative learning ability, but the present invention is not limited to this. Absent. In other words, not only keywords but also summary information may be displayed for learners with short-term memory capacity, and keywords may be displayed not only for summary information but also for learners with weak associative learning ability. . In addition, keywords and summary information may be displayed for learners with short short-term memory capacity and weak associative learning ability. In this way, the learner's understanding is deepened and effective review can be expected.

  By the way, a certain rule is necessary to estimate the physical and mental situation of the learner. This estimation rule is stored in advance as rule information R in a storage means such as a hard disk as shown in FIG. The rule information R includes a combination of physical events in the learner and / or around the learner (for example, a combination of “pulse interval”, “skin temperature”, and “sweat”) and a physical / mental situation of the learner (for example, “sleepiness”). “Stress” “fatigue” “excitement”). By fusing multiple types of sensor information, it is possible to grasp a complicated situation that cannot be obtained with a single sensor information as described in (1) to (6) below. It is possible to estimate. The storage means for storing the rule information R only needs to be in the distance learning system, and the device on which it exists is not particularly limited. Hereinafter, a specific example of the rule information R will be described.

(1) The degree of boredom and sleepiness can be estimated based on an increase in pulse interval, a decrease in skin temperature, or sweating. However, there are cases where high temperature and high humidity and some people do not sweat. Even in this case, yawning (the state where the vertical width ratio of the dark part of the mouth is a certain value or more continues for a certain period of time) or cockle cock (up and down of the face, continued many times, relatively large, but no lateral movement) When the motion is detected by the Web camera, it is possible to improve the estimation accuracy because it is bored or drowsy.

(2) The interest can be estimated based on the rule opposite to (1).

(3) The praise and the like can be estimated based on a nodding motion (up and down motion of the face, relatively small up to 2 to 3 times). That is, when the motion of shaking the neck or face vertically (nodding) is detected by the Web camera, it is estimated that the user is in favor. Also, when a voice such as “Unun” or “Humhum” is input from a microphone, it can be estimated that the voice is converted into a language by a voice recognizer or the like. However, simply detecting that the head has been swung twice vertically cannot distinguish whether it is a sleepy state or a nod “unun”. Therefore, an AND operation is performed for the recognition operation and the recognition of “unun” and “hummum” voices. Furthermore, the detection accuracy is improved by combining an increase in pulse interval, a decrease in skin temperature or sweating, and the like. That is, if there is no increase in pulse interval, decrease in skin temperature, or sweating, it is assumed that the patient is not sleepy and nodded. Alternatively, when it is detected by the Web camera that the mouth is opened vertically or the eyes are closed, it is assumed that the mouth is sleepy by yawning or drowsiness.

(4) Opposition intentions and the like can be estimated based on an action of shaking the neck or face to the left or right or tilting it. That is, when such an operation is detected by the Web camera, it is estimated that there is an opposite intention. In addition, if voices such as “No”, “A”, “Soka”, and “Hoon” are input from a microphone, it is estimated that the intention is opposite if the voice is converted into a language using a voice recognizer or the like. be able to.

(5) As a sign of distraction rather than an intention of opposition, the face may also be swung left and right or turned sideways. Therefore, it may be detected that a person or a document has entered or exited a predetermined area by analyzing an image taken with a Web camera. In this way, it is possible to estimate a sign of distraction, assuming that the person or document has come close and that the face is turned sideways. It may also be estimated that the speech is input from a microphone or a speech recognizer as a sign of distraction. When words such as “no hate” are uttered in a sideways direction, they are said to the person who appeared on the way, and can be regarded as a sign of distraction rather than an opposite intention. Alternatively, if the accelerometer installed on the chair detects that the chair has been moved by pushing the backrest of the chair or turning the chair with the face facing upwards or facing back, It can be presumed that the sign of distraction appeared due to the receipt of.

(6) In addition, under the condition of thick clothing, it is estimated that the patient is sleepy if the pulse is lowered even if the skin temperature is not lowered. Also, in situations where problems are being solved, it is estimated that even if the pulse rises, if the skin temperature or sweating decreases, the patient is sleepy. In addition, under the condition of taking medicine due to a cold, it is estimated that if the pulse decreases even if the skin temperature rises, it is sleepy. Since the pulse decreases before and after yawning, and the pulse also decreases when sleepy, estimating the sleepiness based on both rules can improve the accuracy of the estimation.

  Next, the configuration of the present invention will be described in more detail while showing experimental data. Last spring, we investigated the pulse rate, sweating, skin temperature, and number of correct answers of a learner during normal and sleepy periods. “Number of correct answers” is the number of correct answers of the pre-pelin test. As a result, as shown in FIG. 5, it was found that both the skin temperature and the pulse were lower during sleepiness than during normal time. In particular, the tendency appears after 20 minutes from the start of the experiment when the concentration is reduced. On the other hand, the same tendency is not shown about perspiration. In other words, it may not be known unless a plurality of types of sensors are used, indicating that a multi-sensor may be necessary. Hereinafter, other experimental data and interpretation examples thereof will be described.

(1) For example, excitement or the like is difficult to estimate by using only one type of sensor information, but can be accurately estimated by using a plurality of types of sensor information. In other words, it is known that the subject is nervous when the fluctuation of the pulse wave is small and the blood pressure is high (http://www.seeds.sd.tmu.ac.jp/sys/abstdown .html? d = 2008-PS04.pdf).

  In the situation shown in FIG. 6, FIG. 7, and FIG. 8, since the fluctuation of the pulse wave is small and the correct answer rate is high, it can be estimated that the subject hit the examination with some tension. In this estimation, only one type of sensor information (pulse wave) is used, but it is effective to add more sweat data. For example, in the situation shown in FIG. 8, since the sweating is always high in addition to the small fluctuation of the pulse wave, the possibility of being in an excited state or a tension state is further increased. That is, by using a plurality of types of sensors, it is possible to expect a more accurate situation evaluation.

(2) For example, drowsiness can be estimated by using only one type of sensor information, but it is more effective to use a plurality of types of sensor information such as pulse, sweat, and skin temperature. When multiple types of sensor information are used, the situation can be correctly estimated without causing a measurement error even when the skin temperature is high due to high room temperature or thick clothing.

  As shown in FIG. 8, since the sweating suddenly decreased after 55 minutes from the start of the experiment, the pulse and skin temperature are not low, so it is possible to correctly estimate the situation at least if it is not sleepy (self-reported sleepiness). Not hosted). In such a case, it can be estimated that the hand was initially wet, but the room was dry due to dehumidification, or the person had little sweat. Referring to FIG. 9, the correct answer rate temporarily decreases in a time zone in which the fluctuation of the pulse wave is large. From this, it can be estimated that the subject is not sleeping but is drowsy. Although it is possible to estimate symptoms such as sleepiness by using only one type of sensor information, if we detect sweating or changes in body temperature, as shown in FIG. From “being sleepy, body temperature is rising” to “being sleepy but not sleeping”, as shown in FIG. 6 “being not sleepy but sweating falling” to “sleepy It is possible to evaluate the situation more accurately. Furthermore, if the humidity is detected, it is possible to estimate whether or not the cause is related to the high humidity even if the sweating increases even though it is sleepy.

(3) Depending on the timing, sensor information (symptoms) from each sensor cannot be ANDed and the situation cannot be estimated. Even in such a case, if the AND of the sensor information from each sensor is taken at a plurality of timings, the situation can be estimated at any timing.

  For example, referring to FIG. 10, there is a slight time lag between the decrease in sweating and the decrease in the degree of concentration (number of correct answers). At this time, the temperature was 28 degrees and the room was air-conditioned. This is considered to be the cause of the time lag. In FIG. 7 as well, there is a slight time lag between the decrease in sweating and the decrease in concentration. When a time lag occurs, depending on the timing, sensor information from each sensor cannot be ANDed, and the situation cannot be estimated. In this case, if the AND of the sensor information from each sensor is taken at a plurality of timings, the situation can be estimated at any timing. Of course, the estimation result may be adopted only when the same estimation result is obtained at several successive timings. In this way, the estimation accuracy can be further improved. When an inexpensive sensor is used, the measurement value may become unstable. In this case as well, a technique for estimating the situation at a plurality of timings using a large number of sensors is effective. Note that, by setting the possibility of occurrence of time lag as knowledge data and setting it in advance, it is possible to estimate the situation even if the sensor information and the symptom are not necessarily measured at the same time.

  Hereinafter, a specific configuration example of the distance learning system according to the embodiment of the present invention will be described with reference to FIGS. Here, a case where the program in the client 20 is described in C language or Java (registered trademark) is illustrated, but the programming language is not particularly limited. Although the description will focus on the function of the client 20, the distribution server 10 may be provided with all or part of the same function.

  As shown in FIG. 11, the client 20 includes a distribution request module, a situation information composition / reflection unit, a self-other avatar reflection module, a pulse reduction determination module, a body temperature / sweat reduction determination module, and the like. The distribution request module requests the distribution server 10 for data distribution. The situation information composition / reflection unit estimates the user's situation and instructs the other processing unit to reflect the estimation result. The own / other avatar reflection module reflects the estimation result on the avatar appearing in the user terminal 20 of the user or the user terminal 20 of another person. The pulse reduction determination module determines whether or not the pulse has decreased, and the body temperature / sweat reduction determination module determines whether or not the body temperature and the amount of sweat have decreased. According to such a structure, a user's condition can be estimated based on biological information, such as a pulse, a body temperature, and sweat.

  As shown in FIG. 12, the client 20 includes a distribution request module, a situation information composition / reflection unit, a self-other avatar reflection module, an acceleration determination module, and the like. The acceleration determination module determines acceleration. The other modules are the same as described with reference to FIG. According to such a configuration, the user's situation can be estimated based on the movement of the chair on which the user is sitting.

  As illustrated in FIG. 13, the client 20 includes a distribution request module, a situation information composition / reflection unit, a self-other avatar reflection module, a yawn determination module, a swing up / down determination module, a swing left / right determination module, and the like. The yawn determination module determines whether the user yawns. The swing up / down determination module determines whether or not the user has swung the head up and down. The swing left / right determination module determines whether the user has swung the head left and right. According to such a structure, a user's condition can be estimated based on the image image | photographed around the user.

  As shown in FIG. 14, the client 20 includes a distribution request thread, a voice information monitoring module, a chat screen reflection module, and the like. The voice information monitoring module monitors whether voice information is input from the microphone. The chat screen reflection module reflects the keyword and summary information acquired from the input voice information on the chat screen in consideration of situation information other than voice such as sleepiness and the distribution result thereof. According to such a configuration, the user situation can be estimated based on the voice around the user.

  As shown in FIG. 15, the client 20 includes a situation information monitor / meter creation / display unit, an understanding level meter creation / display module, a drowsiness / boredness meter creation / display module, a concentration meter creation / display module, and the like. . The situation information monitoring / meter creation display unit monitors various input situation information, and if there is a change in the situation information, notifies the meter creation / display module of the contents. The comprehension meter creation / display module creates and displays a meter that shows the user's learning comprehension level, and the sleepiness / boredness meter creation / display module creates and displays a meter that indicates the user's drowsiness / boring degree, and concentrates The degree meter creation / display module creates and displays a meter indicating the degree of user concentration. According to such a configuration, since the user's situation is displayed on the meter, the user's situation can be easily grasped.

  As shown in FIG. 16, the client 20 includes a distribution request module, a situation information monitoring / combining / reflection module, a self-other avatar reflection module, an avatar action determination module, and the like. The avatar motion determination module determines the motion of the avatar, such as whether to swing or shake. According to such a structure, since a user's condition is displayed by an avatar, a user's condition can be grasped | ascertained easily.

  As shown in FIG. 17, the client 20 includes a sleepiness level variable / warning monitoring module, a warning sound module, a warning text display module, and the like. The sleepiness level variable / warning monitoring module monitors the sleepiness level variable, and activates the warning sound module or the warning text display module when the value of the sleepiness level variable reaches a predetermined value. The warning sound module outputs a warning sound, and the warning text display module displays the warning text. According to such a configuration, a warning can be given to the user at an appropriate timing, so that the learning effect can be improved.

  As described above, according to the present invention, it is possible to provide a remote learning system and a remote learning method in which an instructor can grasp the physical / mental situation of a learner in real time. As a result, the instructor can proceed with the lecture according to the physical / mental situation of the learner, and thus it is possible to prevent misunderstanding due to missed or overlooked by the learner. By introducing such a distance learning system, an increase in the needs in distance learning can be expected. In addition, it is not only distance learning but also remote collaborative development, telecommuting that encourages government research, and offshore development overseas. It leads to improvement.

  In the above description, several physical / mental situations have been exemplified, but the physical / mental situations employed in the present invention are not limited to the above-described contents. That is, various situations such as sleepiness, fatigue, spirit, jealousy, enthusiasm, concentration, interest, interest, indifference, boredom, coldness, consent, approval, neutrality, hesitation, opposition, and repulsion can be estimated.

10 Distribution server 20A User terminal (terminal for leader)
20B User terminal (learner terminal)
20C user terminal (learner terminal)

Claims (7)

A distance learning system in which a learner terminal used by a learner and a teacher terminal used by a teacher are connected via a network,
A plurality of types of sensors for detecting physical events in and around the learner; and
Storage means for storing rule information defining a correlation between the learner and / or a combination of physical events around the learner and the physical and mental situation of the learner;
Estimating means for estimating the physical and mental situation of the learner based on a combination of physical events detected by the plurality of types of sensors and the rule information stored in the storage means;
A delivery means for delivering the learner's physical and mental situation estimated by the estimation means to the instructor terminal;
The distribution means distributes inquiry information for prompting the learner's reaction and information for reconfirming the situation estimation result in order to improve the situation estimation accuracy ,
The estimation means re- estimates the learner's physical / mental situation based on the learner's reaction after the inquiry and information obtained by reconfirming the situation,
When the learner's willingness to learn decreases, the delivery means delivers warning information in a manner that stimulates the learner.   When the learner's willingness to learn decreases or the learner misses or overlooks, (A) when the learner is a learner with a short short-term memory capacity, (B) When the learner is a learner with weak associative learning ability, the summary information is delivered to the learner terminal, and (C) the learner has a short-term memory capacity. 2. The distance learning system according to claim 1, wherein when a learner is a learner with weak associative learning ability, a keyword and summary information are distributed to the learning terminal.   The distance learning system according to claim 1, wherein the estimating means estimates a physical / mental situation of the learner at a plurality of timings.   The estimation means estimates the physical / mental situation of the learner at a plurality of timings, and adopts the estimation results only when the same estimation results are obtained at a predetermined number of consecutive timings. The distance learning system according to claim 1.   The distribution means distributes the physical / mental situation of the learner to the instructor terminal by changing the color of the face, hair, or clothes of an avatar corresponding to the learner. The distance learning system according to claim 1.   The remote learning system according to claim 1, wherein the distribution unit distributes the physical / mental situation of the learner estimated by the estimation unit to the learner's terminal.   The estimation means includes the learner's physical / mental situation as sleepiness, fatigue, energy, jealousy, enthusiasm, concentration, interest, interest, indifference, boredom, coldness, consent, approval, neutrality, hesitation, opposition, The distance learning system according to claim 1, wherein the situation of repulsion is estimated.

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