JP2022014474A - Education content selective display program - Google Patents

Abstract

To select the best education content according to the level or the motivation of a user who is getting an education content, and display the best education content.SOLUTION: An education content selective display program of selecting and displaying an education content to a user causes a computer to execute an information acquisition step of acquiring answer content information on the content of answers of the user to questions provided in the education content; and a selection step of selecting an education content to display to the user on the basis of reference answer content information according to answer content information acquired in the information acquisition step with reference to the relevance of at least three different levels between reference answer content information on the contents of answers of the user to the questions provided in the education content displayed in the past and the type of the education content.SELECTED DRAWING: Figure 4

Description

The present invention relates to an educational content selection display program that selects and displays educational content to a user.

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.

In order to improve the effect of this adaptive learning, it is necessary to select and display the most suitable educational content according to the level and motivation of the user receiving the educational content, but the technology to realize this with high accuracy is available. The reality was that it had not been proposed yet.
Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to improve the effect in an educational content selection display program for selecting and displaying educational content to a user. The purpose is to provide an educational content selection display program capable of selecting and displaying the optimum educational content according to the level and motivation of the user receiving the educational content.

The present invention comprises an information acquisition step of acquiring answer content information regarding the answer content of the user to a question to be asked in the educational content in an educational content selection display program that selects and displays the educational content to the user. The answer obtained in the above information acquisition step by referring to the three or more levels of association between the reference answer content information regarding the user's answer content to the question asked in the educational content displayed in the past and the type of educational content. Based on the reference answer content information according to the content information, the user is made to execute a selection step of selecting the educational content to be displayed next.

In order to improve the effect of the educational content selection display program that selects and displays educational content to users, the optimum educational content is selected and displayed according to the level and motivation of the user receiving the educational content. It becomes possible to do.

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.

Hereinafter, the educational content discrimination program to which the present invention is applied will be described in detail with reference to the drawings.

The first embodiment FIG. 1 is a block diagram showing an overall configuration of an educational content discriminating system 1 in which an educational content discriminating program as a first embodiment is implemented. The educational content 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 capturing 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 discriminating educational contents. The information necessary for determining the educational content includes 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. Attribute information, reference frequency information regarding the frequency of listening to educational content displayed in the past by the user, and a data set of educational content are 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 are linked to each other. It is attached and remembered.

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 educational content discrimination system 1 having the above-described configuration will be described.

In the educational content discrimination system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference answer content information and the educational content are set in advance. The reference answer content information includes all information about 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 educational content as an output solution is the same as described above, but here, the actual educational content itself does not have to be associated with the input data, but may be associated with the type of educational content. The type of educational content is, for example, displayed through symbols, numbers, etc. that can identify the educational content P to R when there are three types of educational content P, Q, and R in which the lecture content is recorded. May be good.

These reference answer content information and educational content may be created by analyzing the learning records and achievements of the user in the past, or by the experience and judgment of experts and vendors. You may do it. What kind of educational content was good for the answer shown in any reference answer content information is, for example, the degree of increase or decrease in the student's interview and subsequent grades, and the facial expression of the user who is taking the educational content. May be imaged as an image and applied to or ranked in advance typified facial expressions. As a method of categorizing facial expressions, for example, the eyes are extremely concentrated, the eyes are lively, sleepy, bored, looking down, incomprehensible, and the eyes are going in various directions (concentrated). It may be composed of types including the actual listening attitude. Then, in the relationship between the reference answer content information of the user who gave a certain answer and the educational content provided after that, the higher the concentration rank or the higher the score, or the interview or questionnaire to the user. The higher the evaluation of, or the higher the degree of increase in grades, the higher the degree of association.

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

The reference answer content information is associated with each other through the degree of association of three or more levels with respect to the educational contents A to D as the output solution. The reference answer content information is arranged on the left side through this degree of association, and each educational content is arranged on the right side through this degree of association. The degree of association indicates which educational content is highly relevant to the reference answer content information arranged on the left side. In other words, this degree of association is an index showing what kind of educational content each reference answer content information is likely to be associated with, and is used to select the most probable educational content from the reference answer content information. It shows the accuracy in. 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 relevance of each combination as an intermediate node to the educational content as output. 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 discriminating device 2 accumulates past data sets and analyzes which of the reference answer content information and the educational content in that case was highly 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 educational content A is highly evaluated as the educational content for the reference answer content information captured in the past. By collecting and analyzing such a data set, the degree of association with the reference answer content information becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference answer content information P01, analysis is performed from various data as a result of evaluating past educational contents. In the case of reference answer content information P01, if there are many cases of educational content A, the degree of association that leads to the evaluation of this educational content is set higher, and if there are many cases of educational content B, this education Set a higher degree of association that leads to content evaluation. For example, in the example of the answer content information P01 for reference, the educational content A and the educational content C are linked, but the degree of association of w13 connected to the educational content A from the previous case is 7 points, and the w14 connected to the educational content C. 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 answer content information is input as input data, educational content is output as output data, and at least one hidden layer is provided between the input node and the output node. , Machine learning may be done. 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 learned data through a dataset of the answer content information for reference by the user to be evaluated before and the educational content actually discriminated and evaluated, it is necessary to actually select a new educational content from now on. Then, the educational content will be searched using the above-mentioned learned data. In such a case, the answer content information of the user to be discriminated is newly acquired. At this time, the user is made to take the educational content, and the answer content information is acquired from the user who is taking the course.

Based on the answer content information newly acquired in this way, the educational content to be selected and displayed next is searched. 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 answer content information is the same as or similar to P02, the educational content B is associated with w15 and the educational content C is associated with the association degree w16 via the degree of association. In such a case, the educational content 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 educational content from the newly acquired answer content 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 user's educational content for each educational content, and it is possible to verify whether the current educational content is really optimal for the user. Become.

In particular, in the present invention, there are a plurality of options after the first educational content, and the educational content is prepared for each option. Then, it is particularly useful in the case where one of the options is selected according to the result of determining the educational content and the educational content prepared for the selected option is sent to the next. For example, the educational content of the options selected when the answer content is not good can be composed of easy-to-understand content from the basics.

In the example of FIG. 5, it is premised that a combination of the reference answer content information and the reference answer time information is formed. Here, the reference answer time 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.

Educational content can be discriminated with higher accuracy by making a judgment by combining the reference answer content information and the reference answer time information. Therefore, in addition to the reference answer content information, the reference answer time 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 answer content information P01 to P03 and reference answer time information P14 to 17. The intermediate node shown in FIG. 5 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, educational content 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 educational content 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 educational content is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the educational content 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 kind of educational content the reference answer content information and the reference answer time information are likely to be associated with, and the reference answer content information and the reference answer. It shows the accuracy in selecting the most probable educational content from the time information. Therefore, the optimum educational content is searched for by combining the reference answer content information and the reference answer time 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 answer content information, the reference answer time information, and the educational content 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 answer content information indicating the content of the educational content, the question, the test, and the content of the user's answer to the question, and the reference answer time information indicating the answer time. You may. This makes it possible to discriminate the educational content from the newly acquired answer content information and answer time information in relation to the content of the educational content, the question, the test, and the question.

For example, it is assumed that the reference answer content information in an actual case in the past is the answer α. Further, it is assumed that the answer time information for reference is time β. In such a case, the educational content that was actually highly evaluated is learned as a data set and defined in the form of the above-mentioned degree of association.

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 P16, the educational content is analyzed from the past data. If there are many cases of educational content A, the degree of association that leads to this educational content A is set higher, and if there are many cases of educational content B and there are few cases of educational content A, it leads to educational content B. The degree of association is high, and the degree of association that leads to educational content A is set low. For example, in the example of the intermediate node 61a, the output of the educational content A and the quality B is linked, but from the previous case, the degree of association of w13 connected to the educational content A is 7 points, and the degree of association of w14 connected to the educational content 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 answer content information P01 and the reference answer time information P14, and the degree of association of the educational content C is w15 and the educational content E. The degree of association is w16. The node 61c is a node of a combination of the reference answer time information P15 and P17 with respect to the reference answer content information P02, and the degree of association of the educational content B is w17 and the degree of association of the educational content 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 educational content from now on, the above-mentioned learned data will be used. In such a case, the answer content information and the answer time information of the user who actually tries to determine the educational content are acquired. The user's answer content information and answer time 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 educational content by associating the contents of the educational content, the question, the facial expression of the user for the test or the question, and the answer content with each other.

Based on the newly acquired answer content information and answer time information in this way, the optimum educational content 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 answer content information is the same as or similar to P02 and the answer time information is P17, the node 61d is associated via the degree of association. The node 61d is associated with the educational content C by w19 and the educational content D by the degree of association w20. In such a case, the educational content 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 answer content information, a combination with the reference attribute information instead of the above-mentioned reference answer time information and three or more levels of association with the educational content for the combination are set. Here is an example.

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 the user's attributes, such as work location, place of employment, years of service, etc. Since such reference attribute information also affects the educational content, the discrimination accuracy can be improved by combining the reference answer content information and discriminating the educational content through the degree of association.

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

Each combination (intermediate node) of the reference answer content information and the reference attribute information is associated with each other through three or more levels of association with the educational content as this output solution. The reference answer content information and the reference attribute information are arranged on the left side through this degree of association, and the educational content is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the educational content with respect to the reference answer content information and the reference attribute information arranged on the left side. In other words, this degree of association is an index indicating what kind of educational content is likely to be associated with each reference answer content information and reference attribute information, and is a reference answer content information and reference attribute information. It shows the accuracy in selecting the most probable educational content 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, which of the reference answer content information, the reference attribute information obtained when acquiring the reference answer content information, and the educational content in that case is used by the discrimination device 2 to discriminate the actual search solution. Whether it was suitable or not, 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 attribute information P20, the educational content is analyzed from the past data. If there are many cases of educational content A, the degree of association that this educational content leads to A is set higher, and if there are many cases of educational content B and few cases of educational content A, the educational content is Set the degree of association that connects to B high and the degree of association that educational content connects to A low. For example, in the example of the intermediate node 61a, the output of the educational content A and the educational content B is linked. 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 attribute information P18 is combined with the reference answer content information P01, the degree of association of the educational content C is w15, and the degree of association of the educational content E is. Is w16. The node 61c is a node in which the reference attribute information P19 and P21 are combined with respect to the reference answer content information P02, and the degree of association of the educational content B is w17 and the degree of association of the educational content 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 educational contents from now on, the above-mentioned learned data will be used. In such a case, the answer content information to be discriminated from the educational content and the attribute information are actually acquired. Here, the attribute information is newly acquired when actually estimating the educational content, 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 answer content information newly acquired in this way and the attribute information, the optimum educational content 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 answer content 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 the educational content C by w19 and the educational content D by the degree of association w20. In such a case, the educational content C having a higher degree of association is selected as the optimum solution.

In FIG. 7, in addition to the above-mentioned reference answer content information, a combination with the reference frequency information instead of the above-mentioned reference answer time information and three or more levels of association with the educational content for the combination are set. Here is an example.

This reference frequency information, which is added as an explanatory variable instead of the reference answer time 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. 7, it is assumed that the input data is, for example, reference answer content 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 answer content information as such input data. Each intermediate node is further linked to the output. In this output, educational content as an output solution is displayed.

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

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, the discriminating device 2 accumulates past data as to which of the reference answer content information, the reference frequency information, and the educational content in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 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 frequency information P20, the educational content is analyzed from the past data. If there are many cases of educational content A, the degree of association that this educational content leads to A is set higher, and if there are many cases of educational content B and few cases of educational content A, the educational content is Set the degree of association that connects to B high and the degree of association that educational content connects to A low. For example, in the example of the intermediate node 61a, the output of the educational content A and the educational content B is linked. 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 frequency information P18 is combined with the reference answer content information P01, the degree of association of the educational content C is w15, and the degree of association of the educational content E. Is w16. The node 61c is a node in which the reference frequency information P19 and P21 are combined with respect to the reference answer content information P02, and the degree of association of the educational content B is w17 and the degree of association of the educational content 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 educational contents from now on, the above-mentioned learned data will be used. In such a case, the answer content information to be discriminated on the educational content and the frequency information are actually acquired. Here, the frequency information is newly acquired when actually estimating the educational content, and the acquisition method is the same as the above-mentioned reference frequency information.

Based on the answer content information newly acquired in this way and the frequency information, the optimum educational content 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 answer content 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 the educational content C by w19 and the educational content D by the degree of association w20. In such a case, the educational content C having a higher degree of association is selected as the optimum solution.

In FIG. 8, in addition to the above-mentioned reference answer content information, a combination with the reference image information instead of the above-mentioned reference answer time information and three or more levels of association with the educational content for the combination are set. Here is an example.

This reference image information, which is added as an explanatory variable instead of the reference answer time 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. From the facial expressions and attitudes of the users, it is possible to determine the actual interest in the educational content, the tests and questions provided by the educational content, and the performance of the problem. It is something to make use of. In addition, it is possible to select content that considers not only the user's education level but also motivation.

In the example of FIG. 8, it is assumed that the input data is, for example, reference answer content information P01 to P03 and reference image information P18 to 21. The intermediate node shown in FIG. 8 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, educational content as an output solution is displayed.

Each combination (intermediate node) of the reference answer content information and the reference image information is associated with each other through three or more levels of association with the educational content as this output solution. The reference answer content information and the reference image information are arranged on the left side through this degree of association, and the educational content is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the educational content with respect to the reference answer content information and the reference image information arranged on the left side. In other words, this degree of association is an index indicating what kind of educational content the reference answer content information and the reference image information are likely to be associated with, and the reference answer content information and the reference image information. It shows the accuracy in selecting the most probable educational content 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, the discriminating device 2 accumulates past data as to which of the reference answer content information, the reference image information, and the educational content in that case was 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 answer content information P01 and the reference image information P20, the educational content is analyzed from the past data. If there are many cases of educational content A, the degree of association that this educational content leads to A is set higher, and if there are many cases of educational content B and few cases of educational content A, the educational content is Set the degree of association that connects to B high and the degree of association that educational content connects to A low. For example, in the example of the intermediate node 61a, the output of the educational content A and the educational content B is linked. 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 image information P18 is combined with the reference answer content information P01, the degree of association of the educational content C is w15, and the degree of association of the educational content E. Is w16. The node 61c is a node in which the reference image information P19 and P21 are combined with respect to the reference answer content information P02, and the degree of association of the educational content B is w17 and the degree of association of the educational content 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 educational contents from now on, the above-mentioned learned data will be used. In such a case, the answer content information to be discriminated on the educational content and the image information are actually acquired. Here, the image information is newly acquired when actually estimating the educational content, and the acquisition method is the same as the above-mentioned reference image information.

Based on the answer content information newly acquired in this way and the image information, the optimum educational content 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 answer content information is the same as or similar to P02 and the image 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 educational content C by w19 and the educational content D by the degree of association w20. In such a case, the educational content C having a higher degree of association is selected as the optimum solution.

After acquiring this image information, it may be temporarily replaced with a parameter called concentration. 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.

When deriving this concentration once, the reference image information shown in FIG. 8 is replaced with the reference concentration, and the degree of association with the educational content is formed in advance. Then, the degree of concentration is actually derived from the image information, and the educational content is searched for through the degree of concentration for reference that is the same as or similar to this degree of concentration.

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 discriminate and search educational contents 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 answer content information, the case where the reference image information, the reference answer time information, the reference attribute information, and the reference frequency information are combined is taken as an example. I explained, 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 image information, the reference answer time information, the reference attribute information, and the reference frequency information, in addition to the reference answer content information. In addition to the reference answer content information, the degree of association includes any one or more of reference image information, reference answer time information, reference attribute information, and 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 educational content is requested using the degree of association.

Further, as shown in FIG. 9, the present invention determines the educational content 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. 9, even if the output obtained for the reference information U is used as input data as it is and is associated with the output (educational content) 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 (educational content) 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 educational content based on the degree of association expressed by the numerical values of three or more stages, in a situation where there are multiple possible candidates for the search solution, the order of the degree of association is high. 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 educational contents, 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.

Further, in the present invention, a plurality of educational contents prepared in advance may be grouped into two stages, selectable contents and preliminary contents. Selectable content is actually listed as a type of educational content as an output solution. Preliminary content is accumulated as stock, which is not actually mentioned as a type of educational content as this output solution. However, this preliminary content can be promoted to selectable content, and in such a case, it is actually listed as a type of educational content as an output solution. On the other hand, selectable content may be demoted to preliminary content.

In fact, some types of selectable content are demoted to preliminary content. Then, some of the preliminary contents are promoted to selectable contents. That is, a process of exchanging a part of the selectable content type and a part of the preliminary content type is executed. When performing the replacement process, the number of demotions from the action details to the action details candidates and the number of promotions from the action details candidates to the action details may be the same number or different numbers from each other.

When executing this replacement process, the above-mentioned degree of association is used in selecting the type of selectable content to be demoted. In terms of the degree of association, the type of selectable content as an output with a smaller weighting w has a higher degree of demotion to preliminary content, and the type of preliminary content as an output with a larger weighting w has a lower degree of demotion to selectable content. do. For example, three types may be demoted to preliminary content in order from the one with the smallest weighting w, and the rest may be left as they are in the selectable content.

When promoting from the preliminary content to the selectable content, it may be promoted randomly from the types of the preliminary content. Other than that, based on the previous promotion record from the preliminary content to the selectable content or the demotion record from the selectable content to the preliminary content, the demoted number should be promoted from the preliminary content to the selectable content. You may do it. Some of the preliminary content was previously selectable content but has been demoted, or some have made one or more round trips between the preliminary content and the selectable content. Similarly, some selectable content was previously preliminary content but has been promoted, or some have made one or more round trips between the preliminary content and the selectable content. Based on such previous promotion and demotion results, various rules are decided on the system side or the operation side, such as giving priority to those with a large number of promotions and lowering the priority for those with a large number of demotions. You may choose the preliminary content to be promoted to selectable content based on.

This makes it possible to update the types of educational content presented to the user to more interesting and fresh content.

1 Educational content 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 educational content selection display program that selects and displays educational content to the user
The information acquisition step for acquiring the answer content information regarding the answer content of the user for the question to be asked in the educational content, and the information acquisition step.
The answer obtained in the above information acquisition step by referring to the three or more levels of association between the reference answer content information regarding the user's answer content to the question asked in the educational content displayed in the past and the type of educational content. An educational content selection display program characterized by having a computer perform a selection step of selecting the educational content to be displayed next to the user based on the reference answer content information according to the content information. 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 selection step, the combination of the 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 type of the educational content are 3; A request characterized by selecting the educational content to be displayed next to the user 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 the stage or higher. The educational content selection display program described in Item 1. 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 selection 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 with the type of educational content at three or more levels are determined. The educational content selection display according to claim 1, wherein the educational content to be displayed next is selected for the user based on the reference attribute information according to the attribute information acquired in the above information acquisition step. program. 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 selection step, a combination having the above-mentioned reference answer content information and reference frequency information regarding the frequency of listening to the educational content displayed in the past by the user, and the degree of association of three or more levels of the educational content type are determined. The educational content selection display according to claim 1, wherein the educational content to be displayed next is selected for the user based on the reference frequency information according to the frequency information acquired in the above information acquisition step. program. In the above information acquisition step, image information is acquired by capturing an image of a user who visually recognizes the displayed educational content.
In the selection step, there are three or more stages of association between the combination of the reference answer content information, the reference image information obtained by capturing the image of the user who visually recognizes the educational content displayed in the past, and the type of the educational content. The educational content according to claim 1, wherein the educational content to be displayed next is selected for the user based on the reference image information according to the image information acquired in the above information acquisition step with reference to the degree. Selective display program. The educational content is composed of selectable content listed as a type of educational content and preliminary content as a candidate to be promoted to the selectable content.
Selectable content with a smaller degree of association weighting is demoted to preliminary content, and at random from the preliminary content, or from previous preliminary content to selectable content, or from selectable content to preliminary content. The educational content selection display program according to any one of claims 1 to 5, further comprising a promotion processing step for promoting from preliminary content to selectable content based on the above. The educational content selection display program according to any one of claims 1 to 6, wherein in the selection 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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