JP2022014889A - Achievement level determination program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To determine an achievement level of a user who audits education content.

SOLUTION: An achievement level determination program for determining an achievement level of a user who audits education content causes a computer to execute: an information acquisition step of acquiring image information by capturing an image of the user who visually recognizes education content being displayed; and a determination step of referring to three or more levels of the degree of association between reference image information obtained by capturing the image of the user who visually recognizes education content having been displayed in the past and the achievement level of the user, so as to determine the achievement level of the user on the basis of the reference image information according to the image information acquired in the information acquisition step.

SELECTED DRAWING: Figure 9

COPYRIGHT: (C)2022,JPO&INPIT

Description

The present invention relates to an achievement determination program that determines the achievement level of a user who listens to educational contents.

In recent years, an education system called adaptive learning has become widespread. Adaptive learning provides learning content customized for each user as a learner according to the degree of understanding and interests. In this adaptive learning, it is possible to analyze the learner's strengths and weaknesses, or the tendency to make mistakes. In addition, as a result of analysis, the most suitable educational content and test questions are selected and displayed to the user. This allows users to listen to educational content that suits their level, solve test questions, improve learning efficiency, and gain confidence by solving test questions that match their level. , It also leads to improvement of learning motivation.

However, it may not be clear whether the users receiving this adaptive learning educational content are motivated or focused. If the user lacks concentration, learning efficiency will be significantly reduced. In addition, the reason why the users are not concentrated may be that the educational content provided to the user does not match the user's level in the first place or is not of interest to the user, and in such a case, it is provided to the user. Educational content needs to be modified and displayed.

However, the current situation is that no method for measuring the concentration of users who listen to educational contents has been proposed.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide an achievement degree determination program for determining the achievement degree of a user who listens to educational contents.

The present invention comprises an information acquisition step of acquiring image information by capturing an image of a user who visually recognizes the displayed educational content in an achievement determination program for determining the achievement level of a user who listens to the educational content. Referencing the reference image information obtained by capturing the image of the user who visually recognizes the displayed educational content and the degree of association between the user's achievement level and the user's achievement level in three or more stages, and for reference according to the image information acquired in the above information acquisition step. It is characterized in that a computer is made to execute a determination step for determining the achievement level of a user based on image information.

Even if there is no special skill, it is possible to determine the achievement level of the user who listens to the educational content with high accuracy.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the achievement degree determination program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a concentration level determination system 1 in which a concentration level determination program is implemented. The concentration level discrimination system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person who utilizes this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of taking an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the discrimination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data on the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of not only humans but also animals. Further, the information acquisition unit 9 may be configured as a device for acquiring information such as drawings by scanning or reading from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects odors and scents.

The database 3 stores various information necessary for determining the degree of concentration. The information necessary for determining the degree of concentration is reference image information obtained by capturing the image of the user who visually recognizes the educational content displayed in the past, and the user's problem regarding the problem to be asked in the educational content displayed in the past. Reference answer content information regarding the answer content, reference answer time information regarding the user's answer time to the question asked in the educational content displayed in the past, reference regarding the attributes of the user who listens to the educational content displayed in the past. A data set of attribute information, reference frequency information regarding the frequency of listening to educational content displayed in the past by the user, and the concentration of the user who actually made a judgment on these is stored.

That is, in the database 3, in addition to such reference image information, any one or more of the reference answer content information, the reference answer time information, the reference attribute information, and the reference frequency information, and the degree of concentration of the user are displayed. It is remembered as being associated with each other.

The discrimination device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted.

The user can obtain a search solution by the discrimination device 2. Further, when the discriminating device 2 is composed of a PC, a tablet terminal, or the like, the educational content to be listened to by the user is displayed on the display screen of the discriminating device 2. This educational content consists of educational programs, lectures, slides and charts provided to users. This educational content may be composed of moving images or a series of still images. This educational content may be one that records a lecture or lecture of a lecturer or a teacher and distributes it as moving image content. In addition, this educational content may be composed of popular characters giving lessons on behalf of teachers for infants and elementary school students. This educational content may be read from the database stored in the database 3 or may be distributed via an internet line from a database (not shown) installed at a distance.

FIG. 2 shows a specific configuration example of the discrimination device 2. The discrimination device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire discrimination device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, a determination unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the discrimination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the discrimination unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The determination unit 27 determines the search solution. The discriminating unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the discriminating operation. The discriminating unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technology.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

The operation in the concentration ratio determination system 1 having the above-described configuration will be described.

In the concentration degree determination system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference image information and the concentration degree are set in advance. The reference image information is an image obtained by capturing the facial expressions, movements, and gestures of the user who takes the educational content with a camera or the like. Since the educational content is actually displayed through the display screen of the discrimination device 2, it suffices to capture the face or body of the user who visually recognizes the display screen of the discrimination device 2.

The degree of concentration referred to here is a degree indicating how concentrated the users who take the educational content flowed from the discrimination device 2 are taking the course. With respect to this concentration, an expert or a trader may analyze facial expressions of various users' faces to categorize or rank the concentration in advance. As a method of categorizing the degree of concentration, for example, the eyes are extremely concentrated, the eyes are lively, sleepy, bored, looking down, incomprehensible, and the eyes are going in various directions (concentration). It may be composed of types including the actual listening attitude. In addition, the degree of concentration may be ranked in a plurality of stages (for example, 100 stages, 10 stages, etc.) from the case where the concentration is extremely concentrated to the case where the concentration is not concentrated at all.

The concentration level may be judged based on the previous experience of the evaluator, or the user may be surveyed or interviewed to determine whether or not the concentration level is actually concentrated. You may.

These reference image information and the degree of concentration may be discriminated based on the feature amount learned in the past. At this time, artificial intelligence is used to learn the user's image data and concentration, and when actually acquiring reference image information, the concentration is compared with these learned image data. May be determined.

In the example of FIG. 3, it is assumed that the input data is, for example, reference image information P01 to P03. The reference image information P01 to P03 as such input data is linked to the degree of concentration as output. In this output, the degree of concentration as an output solution is displayed.

The reference image information is associated with each other through the degree of association of three or more levels with respect to the degree of concentration A to D as the output solution. The reference image information is arranged on the left side through this degree of association, and each concentration degree is arranged on the right side through this degree of association. The degree of association indicates the degree of concentration and the degree of relevance to the reference image information arranged on the left side. In other words, this degree of association is an index showing what concentration each reference image information is likely to be associated with, and is used to select the most probable concentration from the reference image information. It shows the accuracy. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of concentration of each combination as an intermediate node and the degree of relevance to each other. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discrimination device 2 accumulates a past data set and analyzes which of the reference image information and the concentration ratio in that case is adopted and evaluated in discriminating the actual search solution. , The degree of association shown in FIG. 3 is created by analysis.

For example, it is assumed that the degree of concentration A is highly evaluated as the degree of concentration on the reference image information captured in the past. By collecting and analyzing such a data set, the degree of association with the reference image information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01, analysis is performed from various data as a result of evaluating the past concentration degree. In the case of reference image information P01, if there are many cases of concentration A, the degree of association that leads to the evaluation of this concentration is set higher, and if there are many cases of concentration B, this degree of concentration is set. Set a higher degree of association that leads to the evaluation of. For example, in the example of the reference image information P01, the concentration degree A and the concentration degree C are linked, but from the previous case, the degree of association of w13 connected to the concentration degree A is set to 7 points, and the degree of association of w14 connected to the concentration degree C is set to 7. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference image information is input as input data, concentration is output as output data, and at least one hidden layer is provided between the input node and the output node. You may let it learn by machine. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weight of each node, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data through a data set of the image of the user to be evaluated before and the concentration level actually discriminated / evaluated, the above-mentioned description is made in actually determining the concentration level from now on. The degree of concentration will be searched using the trained data. In such a case, the image information of the user to be discriminated is newly acquired. At this time, it is premised that the user is made to take the educational content and the facial expression and body of the user who is taking the course are imaged. This imaging is performed via the information acquisition unit 9 described above.

Based on the image information newly acquired in this way, the degree of concentration is determined. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02, the concentration B is associated with w15 and the concentration C is associated with the association w16 via the degree of association. In such a case, the concentration ratio B having a high degree of association is preferentially selected. That is, the higher the degree of association, the higher the priority of selection.

In this way, it is possible to search for the most suitable concentration level from the newly acquired image information, and notify and display not only the user but also the provider who provides the educational content. By looking at this search result, the provider can know the degree of concentration of the user on each educational content, and it is possible to verify whether the current educational content is really optimal for the user. Become.

As the above-mentioned image, in addition to the image captured by a normal camera, any one or more of a spectral image and an ultrasonic image may be acquired. In such a case, it is necessary to capture one or more of a spectral image, a visible image, and an ultrasonic image according to the image information to be acquired as reference image information.

In the example of FIG. 5, it is premised that a combination of the reference image information and the reference answer content information is formed. Here, the reference answer content information includes all information regarding the user's answer content when a question is asked to the user or a question or test is given in the educational content. If the correct answer is set in advance for the question or question, the user's answer content may be composed of the correct answer rate for the correct answer, any question is the correct answer, and any question is. It may indicate whether the answer is incorrect. Further, the reference answer content information may be, for example, an answer based on the user's gesture or gesture, or an answer based on the user's action itself, when the user is an elementary school student or an infant. In such a case, the reference answer content information may be configured with image data obtained by capturing images of the user's gestures, gestures, and actions. If necessary, such image data may be automatically discriminated based on the feature amount of the analyzed image using a deep learning technique, and the answer contents may be categorized. For example, it may be applied to the type that the infant understands what is said when he raises his hand, and the type that he cannot answer the question when the infant turns down.

Further, the reference answer content information may be obtained by reading the numbers and characters described by the user in the handwriting mode on the tablet terminal and converting them into data, for example, in the case of the answer consisting of the derivation of the formula in mathematics. Further, if the reference answer content information has the property of answering by inputting a number, key input of ○ or ×, clicking with a mouse, or the like, it may be obtained from these input data. Further, when the reference answer content information is answered by the voice of the user, the voice may be obtained by incorporating the voice into text data through the existing voice recognition technology.

The degree of concentration can be determined with higher accuracy by making a judgment by combining the reference answer content information in addition to the reference image information. Therefore, in addition to the reference image information, the reference answer content information is combined to form the above-mentioned degree of association.

In the example of FIG. 5, it is assumed that the input data is, for example, reference image information P01 to P03 and reference answer content information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference image information and reference answer content information as such input data. Each intermediate node is further linked to the output. In this output, the degree of concentration as an output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference answer content information is associated with each other through three or more levels of association with the concentration level as this output solution. The reference image information and the reference answer content information are arranged on the left side through the degree of association, and the concentration degree is arranged on the right side through the degree of association. The degree of association indicates the degree of concentration and the degree of relevance to the reference image information and the reference answer content information arranged on the left side. In other words, this degree of association is an index indicating to what degree of concentration each reference image information and reference answer content information are likely to be associated with, and is a reference image information and reference answer content information. It shows the accuracy in selecting the most probable concentration level from. Therefore, the optimum concentration is searched for by combining the reference image information and the reference answer content information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference image information, the reference answer content information, and the degree of concentration in that case is suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created. At this time, a degree of association is formed between the reference image information showing the contents of the educational content, the question, the facial expression of the user for the test or the question, and the reference answer content information indicating the mode of the answer to these. You may. This makes it possible to determine the degree of concentration from the newly acquired image information and answer content information in relation to the contents of educational content, questions, tests and questions.

For example, it is assumed that the reference image information in the actual case in the past is the image data α. Further, it is assumed that the reference answer content information is the derivation β of the formula answered by the user. In such a case, the concentration degree indicating how much the concentration degree was actually is learned as a data set, and is defined in the form of the above-mentioned association degree.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference answer content information P16, the degree of concentration is analyzed from the past data. When there are many cases of concentration A, the degree of association leading to this concentration A is set higher, and when there are many cases of concentration B and few cases of concentration A, it leads to concentration B. The degree of association is set high, and the degree of association that leads to the concentration A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the concentration degree A and the quality B, but from the previous case, the degree of association of w13 connected to the degree of concentration A is set to 7 points, and the degree of association of w14 connected to the degree of concentration B is set to 7. Is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node of the combination of the reference image information P01 and the reference answer content information P14, and the degree of association C is the degree of association w15 and the degree of concentration E is the association. The degree is w16. The node 61c is a node in which the reference answer content information P15 and P17 are combined with respect to the reference image information P02, and the degree of association of the concentration B is w17 and the degree of association of the concentration D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the degree of concentration from now on, the above-mentioned learned data will be used. In such a case, the image information and the answer content information of the user who actually tries to determine the concentration degree are acquired. The user's image information and answer content information may be acquired in conjunction with the content of the educational content, the question, the test, and the timing of the question. As a result, it is possible to derive the degree of concentration by associating the contents of the educational contents, the questions, the facial expressions of the users with respect to the tests and the questions, and the contents of the answers with each other.

Based on the newly acquired image information and answer content information in this way, the optimum concentration level is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the answer content information is P17, the node 61d is associated with the node 61d via the degree of association. The node 61d is associated with a concentration C of w19 and a concentration D of association w20. In such a case, the concentration C having a higher degree of association is selected as the optimum solution.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

In FIG. 6, in addition to the above-mentioned reference image information, the combination with the reference answer time information instead of the above-mentioned reference answer content information and the degree of concentration for the combination are set to three or more levels of association. Here is an example.

This reference answer time information, which is added as an explanatory variable instead of the reference answer content information, indicates the time until the above-mentioned answer is obtained. The answer time is the time it takes for a question or question to be answered and the answer to be completed. The longer the answer time, the less concentrated the user can be considered.

Therefore, since the answer time included in such reference answer time information also affects the concentration, the discrimination accuracy should be improved by discriminating the concentration through the degree of association in combination with the reference image information. Can be done.

In the example of FIG. 6, it is assumed that the input data is, for example, reference image information P01 to P03 and reference answer time information P18 to 21. The intermediate node shown in FIG. 6 is a combination of reference image information and reference answer time information as such input data. Each intermediate node is further linked to the output. In this output, the degree of concentration as an output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference answer time information is associated with each other through three or more levels of association with the degree of concentration as this output solution. The reference image information and the reference answer time information are arranged on the left side through the degree of association, and the concentration degree is arranged on the right side through the degree of association. The degree of association indicates the degree of concentration and the degree of relevance to the reference image information and the reference answer time information arranged on the left side. In other words, this degree of association is an index showing what degree of concentration each reference image information and reference answer time information are likely to be associated with, and is a reference image information and reference answer time information. It shows the accuracy in selecting the most probable concentration level from.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in discriminating the actual search solution, the discriminating device 2 prefers the reference image information, the reference answer time information obtained when acquiring the reference image information, and the degree of concentration in that case. Or, by accumulating past data and analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference answer time information P20, the degree of concentration is analyzed from the past data. When there are many cases of concentration A, the degree of association that this concentration leads to A is set higher, and when there are many cases of concentration B and few cases of concentration A, the concentration is low. The degree of association that leads to B is set high, and the degree of association that leads to A is set low. For example, in the example of the intermediate node 61a, the output of the concentration A and the concentration B is linked, but from the previous case, the association of w13 connected to the concentration A is set to 7 points, and the association of w14 connected to the concentration B is set to 7 points. The degree is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference answer time information P18 is combined with the reference image information P01, the degree of association C is w15, and the degree of association E is the degree of association E. Is w16. The node 61c is a node in which the reference answer time information P19 and P21 are combined with respect to the reference image information P02, and the degree of association of the concentration B is w17 and the degree of association of the concentration D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of concentration from now on, the above-mentioned learned data will be used. In such a case, the image information of the concentration target to be determined and the answer time information are actually acquired. Here, the answer time information is newly acquired when the degree of concentration is actually estimated, and the acquisition method is the same as the above-mentioned reference answer time information.

Based on the newly acquired image information and the answer time information in this way, the optimum concentration is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the answer time information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with a concentration C of w19 and a concentration D of association w20. In such a case, the concentration C having a higher degree of association is selected as the optimum solution.

In FIG. 7, in addition to the above-mentioned reference image information, the combination with the reference attribute information instead of the above-mentioned reference answer content information and the degree of concentration for the combination are set to three or more levels of association. An example is shown.

This reference attribute information, which is added as an explanatory variable instead of the reference answer content information, is based on the user's age, grade, level, duration of the educational content, gender, current school, place of residence, and working people. If so, show all information about user attributes such as work location, place of employment, years of service, etc. Since such reference attribute information also affects the degree of concentration, the discrimination accuracy can be improved by combining the information with the reference image information and discriminating the degree of concentration through the degree of association.

In the example of FIG. 7, it is assumed that the input data is, for example, reference image information P01 to P03 and reference attribute information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference image information and reference attribute information as such input data. Each intermediate node is further linked to the output. In this output, the degree of concentration as an output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference attribute information is associated with each other through three or more levels of association with the degree of concentration as the output solution. The reference image information and the reference attribute information are arranged on the left side via the degree of association, and the concentration degree is arranged on the right side via the degree of association. The degree of association indicates the degree of concentration and the degree of relevance to the reference image information and the reference attribute information arranged on the left side. In other words, this degree of association is an index showing what degree of concentration each reference image information and reference attribute information are likely to be associated with, and is the most from the reference image information and the reference attribute information. It shows the accuracy in selecting a certain degree of concentration.

The discrimination device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, in discriminating the actual search solution, the discriminating device 2 is more suitable for the reference image information, the reference attribute information obtained when the reference image information is acquired, and the degree of concentration in that case. The degree of association shown in FIG. 7 is created by accumulating past data and analyzing and analyzing them.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference attribute information P20, the degree of concentration is analyzed from the past data. When there are many cases of concentration A, the degree of association that this concentration leads to A is set higher, and when there are many cases of concentration B and few cases of concentration A, the concentration is low. The degree of association that leads to B is set high, and the degree of association that leads to A is set low. For example, in the example of the intermediate node 61a, the output of the concentration A and the concentration B is linked, but from the previous case, the association of w13 connected to the concentration A is set to 7 points, and the association of w14 connected to the concentration B is set to 7 points. The degree is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference attribute information P18 is combined with the reference image information P01, the degree of association C is w15, and the degree of association E is the degree of association E. It is w16. The node 61c is a node in which the reference attribute information P19 and P21 are combined with respect to the reference image information P02, and the degree of association B is w17 and the degree of association D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of concentration from now on, the above-mentioned learned data will be used. In such a case, the image information to be determined for the degree of concentration and the attribute information are actually acquired. Here, the attribute information is newly acquired when the degree of concentration is actually estimated, and the acquisition method is the same as the above-mentioned reference attribute information. The method of acquiring the attribute information and the reference attribute information may be acquired by keyboard input to a device such as a PC or a smartphone.

Based on the newly acquired image information and attribute information in this way, the optimum concentration is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the attribute information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with a concentration C of w19 and a concentration D of association w20. In such a case, the concentration C having a higher degree of association is selected as the optimum solution.

In FIG. 8, in addition to the above-mentioned reference image information, a combination with the reference frequency information instead of the above-mentioned reference answer content information and the degree of concentration for the combination are set to three or more levels of association. An example is shown.

This reference frequency information, which is added as an explanatory variable instead of the reference answer content information, is any information about how often the user listens to the educational content. The reference frequency information may indicate, for example, how many times a month or week the educational content is listened to, or how many intervals there are recently due to a slight skip. good.

In the example of FIG. 8, it is assumed that the input data is, for example, reference image information P01 to P03 and reference frequency information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference frequency information and reference image information as such input data. Each intermediate node is further linked to the output. In this output, the degree of concentration as an output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference frequency information is associated with each other through three or more levels of association with the degree of concentration as the output solution. The reference image information and the reference frequency information are arranged on the left side via the degree of association, and the concentration degree is arranged on the right side via the degree of association. The degree of association indicates the degree of concentration and the degree of relevance to the reference image information and the reference frequency information arranged on the left side. In other words, this degree of association is an index showing what degree of concentration each reference image information and reference frequency information are likely to be associated with, and is the most from the reference image information and the reference frequency information. It shows the accuracy in selecting a certain degree of concentration.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference image information, the reference frequency information, and the degree of concentration in that case is more suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference image information P01 and the reference frequency information P20, the degree of concentration is analyzed from the past data. When there are many cases of concentration A, the degree of association that this concentration leads to A is set higher, and when there are many cases of concentration B and few cases of concentration A, the concentration is low. The degree of association that leads to B is set high, and the degree of association that leads to A is set low. For example, in the example of the intermediate node 61a, the output of the concentration A and the concentration B is linked, but from the previous case, the association of w13 connected to the concentration A is set to 7 points, and the association of w14 connected to the concentration B is set to 7 points. The degree is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference frequency information P18 is combined with the reference image information P01, the degree of association C is w15, and the degree of association E is the degree of association E. It is w16. The node 61c is a node in which the reference frequency information P19 and P21 are combined with respect to the reference image information P02, and the degree of association B is w17 and the degree of association D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of concentration from now on, the above-mentioned learned data will be used. In such a case, the image information of the concentration target to be determined and the frequency information are actually acquired. Here, the frequency information is newly acquired when the degree of concentration is actually estimated, and the acquisition method is the same as the above-mentioned reference frequency information.

Based on the image information newly acquired in this way and the frequency information, the optimum concentration is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the frequency information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with a concentration C of w19 and a concentration D of association w20. In such a case, the concentration C having a higher degree of association is selected as the optimum solution.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily determine and search the concentration level without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the above-mentioned input data and output data are not completely the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

In the above-mentioned degree of association, in addition to the reference image information, there is an example in which the reference answer content information, the reference answer time information, the reference attribute information, and the reference frequency information are combined. I explained to you, but it is not limited to this. That is, the degree of association may be composed of a combination of any two or more of the reference answer content information, the reference answer time information, the reference attribute information, and the reference frequency information, in addition to the reference image information. In addition to the reference image information, the degree of association includes any one or more of the reference answer content information, the reference answer time information, the reference attribute information, and the reference frequency information, and other factors are added to this combination. The degree of association may be formed.

In either case, data is input according to the reference information of the degree of association, and the degree of concentration is obtained by using the degree of association.
In the present invention, the educational content to be displayed next may be selected and displayed based on the desired concentration of the user. At this time, there are a plurality of options after the educational content for determining the degree of concentration of the user, and the educational content is prepared for each option. Then, one of the options may be selected according to the result of determining the concentration degree, and the educational content prepared for the selected option may be played next. For example, the educational content of the options selected when the concentration is low can be composed of the educational contents including the refreshing ones for improving the concentration.
When selecting such educational content, as described below, the educational content may be selected based on the degree of achievement of the educational content.

FIG. 9 shows an example in which the combination of the above-mentioned reference answer content information, the reference answer time information, and the degree of achievement for the combination are set to three or more levels of association. Achievement is an index showing how well the content in educational content has been mastered.

In the example of FIG. 9, it is assumed that the input data is, for example, reference answer content information P01 to P03 and reference answer time information P18 to 21. The intermediate node shown in FIG. 9 is a combination of the reference answer content information and the reference answer time information as such input data. Each intermediate node is further linked to the output. In this output, the degree of achievement as an output solution is displayed.

Each combination (intermediate node) of the reference answer content information and the reference answer time information is associated with each other through three or more levels of association with the achievement level as this output solution. The reference answer content information and the reference answer time information are arranged on the left side through this degree of association, and the achievement degree is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the degree of achievement with respect to the reference answer content information and the reference answer time information arranged on the left side. In other words, this degree of association is an index indicating what degree of achievement each reference answer content information and reference answer time information are likely to be associated with, and is a reference answer content information and reference answer. It shows the accuracy in selecting the most probable achievement level from the time information.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the discrimination device 2 determines the reference answer content information, the reference answer time information obtained when acquiring the reference answer content information, and the degree of achievement in that case. Was suitable, or the past data is accumulated, and by analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference answer content information P01 and the reference answer time information P20, the degree of achievement is analyzed from the past data. If there are many cases of achievement A, the degree of association that this achievement leads to A is set higher, and if there are many cases of achievement B and few cases of achievement A, the achievement is low. The degree of association that leads to B is set high, and the degree of association that leads to A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of achievement degree A and achievement degree B, but from the previous case, the association degree of w13 connected to achievement degree A is set to 7 points, and the association of w14 connected to achievement degree B is linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 9 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 9, the node 61b is a node in which the reference answer time information P18 is combined with the reference answer content information P01, the degree of association C is w15, and the degree of association E is the degree E. The degree is w16. The node 61c is a node of a combination of the reference answer time information P19 and P21 with respect to the reference answer content information P02, and the degree of association of the achievement degree B is w17 and the degree of association of the achievement degree D is w18. ..

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of achievement from now on, the above-mentioned learned data will be used. In such a case, the answer content information to be determined for the degree of achievement and the answer time information are actually acquired. Here, the answer time information is newly acquired when the achievement level is actually estimated, and the acquisition method is the same as the above-mentioned reference answer time information.

Based on the answer content information newly acquired in this way and the answer time information, the optimum achievement level is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired answer content information is the same as or similar to P02 and the answer time information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the achievement degree C by w19 and the achievement degree D by the association degree w20. In such a case, the achievement degree C having a higher degree of association is selected as the optimum solution.

This achievement level may be derived from the answer content information and the attribute information. In such a case, the combination of the reference answer content information and the reference attribute information and the degree of association with the user's achievement level of three or more levels are acquired in advance. Then, the degree of association is referred to, and the degree of achievement of the user is determined based on the reference answer content information according to the newly acquired answer content information and the reference attribute information according to the attribute information.

After obtaining the achievement level in this way, the educational content is selected together with the concentration level described above. In such a case, set in advance what kind of educational content should be selected when the degree of achievement and the degree of concentration are respectively, and select this according to the degree of achievement and the degree of concentration actually obtained. You may try to do it.

Further, as shown in FIG. 10, the present invention determines the degree of concentration and the degree of achievement based on the degree of association between two or more types of information, the reference information U and the reference information V. It is assumed that the reference information Y is the reference image information, and the reference information V is any one of the reference answer content information, the reference answer time information, the reference attribute information, and the reference frequency information.

At this time, as shown in FIG. 10, even if the output obtained for the reference information U is used as input data as it is and is associated with the output (concentration ratio) via the intermediate node 61 in combination with the reference information V. good. For example, for reference information U (reference image information), after outputting an output solution as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. The output (concentration ratio) may be searched.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

By discriminating the most probable concentration level based on the degree of association represented by the numerical values of three or more stages, in a situation where there are multiple possible candidates for the search solution, the degree of association is in descending order. It is also possible to search and display. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. In addition, when each reference information such as reference image information is acquired and the degree of concentration for these, knowledge, information, and data regarding improvement measures are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and training the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Concentration ratio discrimination system 2 Discrimination device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Discrimination unit 28 Storage unit 61 Node

Claims (7)

In the achievement determination program that determines the achievement level of users who listen to educational content
The information acquisition step for acquiring the answer content information regarding the answer content of the above user for the question to be asked in the educational content, and the information acquisition step.
We referred to the reference answer content information regarding the user's answer content to the question in the educational content and the degree of association between the user's achievement level and the user's achievement level in three or more stages, and responded to the answer content information acquired in the above information acquisition step. It has a discrimination step to determine the achievement level of the user based on the reference answer content information.
In the above information acquisition step, the answer content information that categorizes the answer content based on the feature amount of the analysis image obtained by analyzing the image data captured by the user by using the deep learning technology is acquired.
The above discrimination step consists of three stages: answer content information for reference, which categorizes the answer content based on the feature amount of the analysis image obtained by analyzing the image data obtained by capturing the user using deep learning technology, and the achievement level of the user. Achievement level determination program characterized by having a computer execute referring to the above degree of association. In the achievement determination program that determines the achievement level of users who listen to educational content
An information acquisition step of acquiring image information by capturing an image of a user who visually recognizes the displayed educational content and answer content information regarding the answer content of the user to a question to be asked in the educational content.
A combination of reference image information obtained by capturing an image of a user who visually recognizes educational content displayed in the past, and reference answer content information regarding the user's answer content to a question to be asked in the educational content displayed in the past. Based on the reference image information according to the image information acquired in the above information acquisition step and the reference answer content information according to the answer content information, the user refers to the degree of association with the user's achievement level in three or more stages. Has a discrimination step to determine the degree of achievement of
In the above information acquisition step, the answer content information that categorizes the answer content based on the feature amount of the analysis image obtained by analyzing the image data captured by the user by using the deep learning technology is acquired.
The above-mentioned discrimination step is achieved by the above-mentioned combination having the above-mentioned reference answer content information in which the answer contents are categorized based on the feature amount of the analysis image obtained by analyzing the image data obtained by capturing the user by using the deep learning technique. Achievement level determination program characterized by referring to the degree of association with the degree in three or more stages. In the information acquisition step, the answer time information regarding the answer time of the user for the question to be asked in the educational content is acquired.
In the above-mentioned determination step, a combination having the above-mentioned reference answer content information, the reference answer time information regarding the user's answer time for the question to be asked in the educational content displayed in the past, and the user's achievement level 3 The achievement degree according to claim 1, wherein the achievement degree of the user is determined based on the reference answer time information according to the answer time information acquired in the above information acquisition step by referring to the degree of association of one or more stages. Discrimination program. In the above information acquisition step, attribute information regarding the attributes of the user who listens to the above educational content is acquired.
In the above-mentioned determination step, a combination having the above-mentioned reference answer content information and reference attribute information regarding the attributes of the user who listens to the educational content displayed in the past, and the degree of association between the user's achievement level and the degree of association of three or more levels are determined. The achievement degree determination program according to claim 1, wherein the achievement degree of the user is determined based on the reference attribute information according to the attribute information acquired in the above information acquisition step. In the above information acquisition step, frequency information regarding the frequency of listening to the above educational content by the above user is acquired.
In the above-mentioned determination step, the combination having the above-mentioned reference answer content information and the reference frequency information regarding the frequency of listening to the educational content displayed in the past by the user, and the degree of association with the user's achievement level in three or more stages are determined. The achievement degree determination program according to claim 1, wherein the achievement degree of the user is determined based on the reference frequency information according to the frequency information acquired in the above information acquisition step. Achievement according to any one of claims 1 to 5, further comprising a selection step of selecting and displaying educational content to be displayed next based on the achievement level of the user determined in the determination step. Degree determination program. The achievement determination program according to any one of claims 1 to 8, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is formed.

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