JP2023003517A - Achievement degree determination program - Google Patents

Abstract

To determine an achievement degree of a user who takes a lecture about an educational content.SOLUTION: A concentration degree determination program comprises: an information acquisition step of acquiring answer content information related to an answer content of a user to a question presented in an educational content; and a determination step of determining an achievement degree of the user, in reference to a relation between reference answer content information related to the answer content of the user to the question presented in the educational content and the achievement degree of the user and on the basis of the reference answer content information in accordance with the answer content information acquired in the information acquisition step. The information acquisition step acquires answer content information in which answer contents are categorized on the basis of, feature quantities of analysis images obtained by analyzing image data imaged by an information display device mounted on the head of the user, with the use of a deep learning technique. The determination step refers to the relation between the reference answer content information in which the answer contents are categorized on the basis of, the feature quantities of the analysis images and the achievement degree of the user.SELECTED DRAWING: Figure 1

Description

The present invention relates to an achievement level determination program for determining the level of achievement of a user who has taken educational content.

In recent years, an educational system called adaptive learning has spread. Adaptive learning provides learning content customized for each user as a learner according to their level of understanding and interests. This adaptive learning can analyze a learner's strengths and weaknesses, or tendency to make mistakes. Also, as a result of the analysis, the most suitable educational contents and test questions are selected and displayed to the user. As a result, users can take educational content that matches their level, solve test questions, and improve their learning efficiency. , leading to increased motivation to learn.

However, there are cases where it is not known whether the user who is receiving the educational content of this adaptive learning is motivated or concentrated. If the user lacks concentration, the efficiency of learning will be greatly reduced. In addition, it is conceivable that the user is not concentrating because 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. Educational content needs to be modified and displayed.

However, the current situation is that no method for measuring the concentration of a user who takes educational content has been proposed.

Accordingly, the present invention has been devised in view of the above-described problems, and its object is to provide an achievement level determination program for determining the level of achievement of a user who attends educational content.

The present invention provides an achievement level determination program for determining the level of achievement of a user who attends an educational content, and an achievement level determination program for determining the level of achievement of a user who takes an educational content. With reference to the information acquisition step of acquiring answer content information related to the user's answer content, the reference answer content information related to the user's answer content to the question set in the educational content, and the association with the user's achievement level, the above a determination step of determining the degree of achievement of the user based on reference answer content information corresponding to the answer content information acquired in the information acquisition step, wherein the information acquisition step comprises an information display worn on the user's head; Obtaining 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 device using deep learning technology, and the determining step is based on the feature amount of the analysis image. The method is characterized in that the computer is caused to refer to the relationship between the reference answer content information, which is a typed answer content, and the user's achievement level.

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

1 is a block diagram showing the overall configuration of a system to which the present invention is applied; FIG. It is a figure which shows the specific structural example of a search apparatus. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention.

Hereinafter, an 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 the overall configuration of a concentration determination system 1 in which a concentration determination program is installed. The concentration degree discrimination system 1 includes an information acquisition section 9 , a discrimination device 2 connected to the information acquisition section 9 , and a database 3 connected to the discrimination device 2 .

The information acquisition unit 9 is a device for a person using this system to input various commands and information. 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 imaging device capable of capturing an image, such as a camera. The information acquisition unit 9 acquires video information (video image or still image) may be configured with a camera or the like. An example of a user terminal that is integrally or partially worn on the user's head or eyeglasses may include a display unit that displays information generated based on captured image information in a transparent state. The user terminal may be, for example, Hololens (registered trademark), which is one type of HMD (head mounted display). The user can check the display information of the user terminal through the work area and the device to be evaluated through a transparent display unit such as a head-mounted display or HoloLens. This makes it possible to realize VR, AR, and the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper document. Further, the information acquisition unit 9 may be integrated with the discriminating device 2 to be 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 position information by scanning map information. Further, the information acquisition unit 9 may be configured with an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. The information acquisition unit 9 may also be configured with a communication interface that acquires weather data from the Meteorological Agency or a private weather forecast company. The information acquisition unit 9 may be composed of a body sensor worn on 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 configured with a sensor for Also, the body sensor may acquire biological data not only of humans but also of animals. The information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading from a database. The information acquisition unit 9 may be configured by an odor sensor that detects odors and scents in addition to these.

The database 3 accumulates various information necessary for determining the degree of concentration. The information necessary for determining the degree of concentration includes reference image information that captures the image of the user viewing the educational content displayed in the past, and the user's response to the questions asked in the educational content displayed in the past. Reference answer content information related to answer content, reference answer time information related to the user's answer time for questions presented in educational content displayed in the past, reference related to attributes of users who take educational content displayed in the past A data set of attribute information, reference frequency information relating to the frequency of taking educational content displayed in the past by the user, and the degree of concentration of the user actually judged on these is stored.

In other words, in addition to such reference image information, the database 3 stores one or more of reference answer content information, reference answer time information, reference attribute information, and reference frequency information, and the user's degree of concentration. They are linked to each other and stored.

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

A user can obtain a search solution by this discriminating device 2 . Further, when the discriminating device 2 is composed of a PC, a tablet terminal, or the like, the educational content that the user takes 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 may be composed of a series of still images. This educational content may be a lecturer or teacher's class or lecture that is recorded and distributed as moving image content. Also, this educational content may be composed of popular characters teaching classes for children and elementary school students in place of the teacher. This educational content may be read out from among those stored in the database 3, or may be distributed via the Internet line from a database (not shown) installed far away.

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

The control section 24 is a so-called central control unit for controlling each component mounted in the discriminating device 2 by transmitting control signals via the internal bus 21 . In addition, the control unit 24 transmits commands for various controls through the internal bus 21 according to operations through the operation unit 25 .

The operation unit 25 is embodied by a keyboard or a touch panel, and a user inputs an execution command for executing a program. The operation unit 25 notifies the control unit 24 when the execution command is input by the user. Upon receiving this notification, the control unit 24 cooperates with each component including the determination unit 27 to execute desired processing operations. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

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

The display unit 23 is composed of a graphic controller that creates a display image based on control by the control unit 24 . The display unit 23 is implemented by, for example, a liquid crystal display (LCD).

When the storage unit 28 is configured with a hard disk, predetermined information is written to each address based on the control by the control unit 24, and this information is read as necessary. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24 .

The operation of the concentration degree determination system 1 configured as described above will be described.

In the concentration degree determination system 1, as shown in FIG. 3, for example, it is assumed that three or more degrees of association between the reference image information and the degree of concentration are set in advance. The reference image information is an image captured by a camera or the like of the facial expressions, actions, and gestures of the user who is taking the educational content. Since the educational content is actually displayed through the display screen of the discriminating device 2 , it is sufficient to image the face and body of the user viewing the display screen of the discriminating device 2 .

The degree of concentration referred to here is a degree indicating how much the user who attends the educational content streamed from the discriminating device 2 is concentrating on the course. Concerning the degree of concentration, an expert or a trader may analyze the expressions of various user's facial images to categorize or rank the degree of concentration in advance. As a method of classifying the degree of concentration, for example, the subject is extremely concentrated, the eyes are vivid, the subject is sleepy, the subject looks bored, the subject looks down, the person does not seem to understand, and the subject looks in various directions (not concentrated). It may be configured in a pattern including the actual attendance attitude, such as "No"). Also, the degree of concentration may be ranked in a plurality of stages (eg, 100 stages, 10 stages, etc.) from extremely concentrated to completely unconcentrated.

The degree of concentration may be determined based on previous experience by the evaluator, or may be determined by conducting a questionnaire survey or interviewing the user as to whether or not the user is actually concentrating. may

These reference image information and the degree of concentration may be determined based on previously learned feature amounts. At this time, artificial intelligence is used to learn the user's image data and the degree of concentration. may be determined.

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

The reference image information is associated with each other through three or more levels of association with the concentrations A to D as output solutions. The image information for reference is arranged on the left side through the degree of association, and each degree of concentration is arranged on the right side through the degree of association. The degree of relevance indicates the degree of concentration and degree of high relevance to the reference image information arranged on the left side. In other words, the degree of association is an index indicating to what degree of concentration each piece of reference image information is likely to be linked, and is an index for selecting the most probable degree of concentration from the reference image information. It shows accuracy. In the example of FIG. 3, w13 to w19 are shown as association degrees. 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 and mutual relevance of each combination as an intermediate node as an output. , conversely, the closer to one point, the lower the degree of correlation between each combination as an intermediate node and the price as an output.

The discrimination device 2 acquires in advance the degrees of association w13 to w19 of three or more stages shown in FIG. In other words, the discriminating device 2 accumulates past data sets to determine which of the reference image information and the degree of concentration in that case was adopted and evaluated in discriminating the actual search solution, and analyzes these. , to create the degree of association shown in FIG.

For example, it is assumed that the degree of concentration A is highly evaluated as the degree of concentration for reference image information captured in the past. By collecting and analyzing such data sets, the degree of association with the reference image information is strengthened.

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

Further, the degree of association shown in FIG. 3 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In such a case, as shown in FIG. 4, reference image information is input as input data, a degree of concentration is output as output data, at least one or more hidden layers are provided between the input node and the output node, Machine learning may be used. Either or both of the input nodes and the hidden layer nodes are set with the above-mentioned degree of relevance, which serves as a weighting for each node, based on which the output is selected. Then, when the degree of association exceeds a certain threshold, the output may be selected.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data through a data set of the image of the user to be evaluated in the past and the actually discriminated/evaluated concentration level, in actually newly discriminating the concentration level, the above-mentioned The degree of concentration is searched for using learned data. In such a case, the image information of the user to be actually determined is newly obtained. At this time, it is premised that the user takes educational content and images the user's facial expression and body while taking the course. This imaging is performed via the information acquisition unit 9 described above.

The degree of concentration is determined based on the image information newly acquired in this way. In such a case, reference is made to the degree of association shown in FIG. 3 (Table 1), which has been acquired in advance. For example, when the newly acquired image information is the same as or similar to P02, the degree of concentration B is associated with w15 and the degree of concentration C is associated with the degree of association w16. In such a case, priority is given to the degree of concentration B, which has a high degree of association. 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 degree of concentration from the newly acquired image information, and notify and display it not only to the user but also to the educational content providers. By looking at this search result, the provider can know the degree of concentration of the user on each educational content, and can verify whether the current educational content is really optimal for the user. Become.

Note that the above-described image may be one or more of a spectral image and an ultrasonic image other than an image captured by a normal camera. In such a case, one or more of a spectral image, a visible image, and an ultrasonic image corresponding to the image information to be obtained must be captured as reference image information.

It should be noted that it is not essential to use three or more levels of relevance, and a relevance made up of two levels of relevance may be referred to. The two-stage association indicates whether or not there is a relationship, that is, by a binary value (two stages) of 0 or 1. In the example of FIG. It is related to B, but not to concentration A. The reference image information P02 is related to the degree of concentration C, but not related to the degree of concentration B, and the like. These may be configured as a general template or table and referred to.

In the example of FIG. 5, it is assumed that a combination of reference image information and reference answer content information is formed. Here, the reference answer content information includes all information related to the user's answer content when a question is asked to the user or a question or test is given in the educational content.

The questions here are not just textual questions in the form of text or graphics, such as general tests, but only images showing the actual situation, such as questions for welding work or machining training. may be displayed in For example, as a problem of welding work, it is assumed that the quality of welding is displayed on the screen as a still image or moving image. In such a case, when the question of what kind of welding operation should be performed is required as an answer, a still image or a moving image showing the progress of such welding can be a problem.

The content of the user's answer here may be composed of the rate of correct answers to the correct answers when the correct answers are set in advance for the questions or questions, or any question is the correct answer, and any question is correct. It may indicate whether the answer is incorrect. For example, if the user is an elementary school student or an infant, the answer content information for reference may be an answer based on the user's gestures or gestures, or an answer based on the user's behavior itself. In such a case, the reference answer content information may be composed of 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 analysis image using deep learning technology, and the content of the answer may be categorized. For example, if an infant raises his/her hand, it may be applied to the pattern of understanding what was said, and if the infant looks down, it may be applied to the pattern of being unable to answer the question.

For example, in the case of an answer consisting of derivation of a mathematical formula, the reference answer content information may be obtained by reading numbers and characters written by the user on a tablet terminal in handwriting mode and converting them into data. Also, if the reference answer content information has the property of answering by inputting a number, keying in o or x, or clicking with a mouse, etc., it may be obtained from these input data. Furthermore, if the answer is given by the user's voice, the answer content information for reference may be obtained by converting the voice into text data through existing voice recognition technology.

By combining the reference image information and the reference answer content information for determination, the degree of concentration can be determined with higher accuracy. Therefore, in addition to reference image information, reference answer content information is combined to form the above-described degree of association.

In the example of FIG. 5, it is assumed that the input data are reference image information P01 to P03 and reference answer content information P14 to P17. An intermediate node shown in FIG. 5 is a combination of reference image information as input data and reference answer content information. Each intermediate node is also connected to an output. In this output, the degree of concentration is displayed as the output solution.

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 degree of concentration as the output solution. The image information for reference and the answer content information for reference are arranged on the left side through the degree of association, and the degree of concentration is arranged on the right side through the degree of association. The degree of relevance indicates the degree of concentration and degree of relevance to the reference image information and the reference answer content information arranged on the left side. In other words, the degree of association is an index indicating the degree of concentration with which each of the reference image information and the reference answer content information is likely to be linked. This indicates the accuracy in selecting the most probable degree of concentration from the Therefore, the optimum degree of 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 association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The discrimination device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, 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 was suitable for discriminating the actual search solution. , and by analyzing these, the degree of association shown in FIG. 5 is created. At this time, the degree of association is formed between the content of the educational content, the question, the reference image information showing the user's facial expression for the test or problem, and the reference answer content information indicating the mode of answering to these. may As a result, it becomes possible to determine the degree of concentration from the newly acquired image information and answer content information in association with the contents of educational content, questions, tests, and problems.

For example, it is assumed that reference image information in an actual case in the past is image data α. It is also assumed that the reference answer content information is the derivation β of the formula answered by the user. In such a case, the degree of concentration indicating the actual degree of concentration is learned as a data set and defined in the form of the degree of association described above.

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

Further, the degree of association shown in FIG. 5 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 5, a node 61b is a node of a combination of reference image information P01 and reference answer content information P14. degree is w16. The node 61c is a node that is a combination of the reference answer content information P15 and P17 with respect to the reference image information P02.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually determining the degree of concentration. In such a case, image information and answer content information of the user who actually tries to determine the degree of concentration are acquired. The user's image information and answer content information may be acquired in conjunction with the content of educational content, the timing of questions, tests, and questions. As a result, it is possible to derive the degree of concentration by associating the content of the educational content, the question, the user's facial expression and the content of the answer to the test or problem with each other.

Based on the newly acquired image information and answer content information in this way, the optimum degree of concentration is searched for. In such a case, reference is made to the degrees of association shown in FIG. 5 (Table 1) that have been acquired in advance. 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 via the degree of association, and this The node 61d is associated with the degree of concentration C with w19 and with the degree of association D with w20. In such a case, the concentration C with a higher degree of association is selected as the optimum solution.

Table 2 below shows examples of degrees of association w1 to w12 extending from the input.

Intermediate nodes 61 may be selected based on degrees of association w1 to w12 extending from this input. In other words, the greater the degree of association w1 to w12, the heavier the weight in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weighting in selecting the intermediate nodes 61 may all be the same.

The answer content information and the reference answer content information are obtained from images captured by a camera provided in a user terminal (information display device) that is integrally or partially worn on the user's head or eyeglasses. good too. In some cases, the content of the answer is formed depending on what object the user is visually recognizing. For example, when conducting training for various operations such as welding and machining, it is possible to determine whether the event is being checked correctly by checking what object the user is visually recognizing, and this is one answer content. .

By analyzing the content of the image captured by such a camera and classifying it, the answer content information is obtained. For example, in the case of training for machining work, an image taken from a camera is analyzed to determine whether an object to be visually recognized is a handle, a button, an object to be processed, etc., and these are categorized. In the process of this categorization, if necessary, deep learning technology may be used for the image data to automatically discriminate based on the feature amount of the analysis image, and to categorize the content of the answer. For example, if it is an image of a steering wheel, it may be applied to the correct answer or the type of the steering wheel, and if it faces the feet, it may be applied to the incorrect answer or the type of the foot.

Similarly, the answer content information for reference may be configured by categorizing the answer content based on the feature amount of the analysis image.

When such answer content information and reference answer content information are provided via a user terminal (information display device) that is integrally or partially attached to the user's head or eyeglasses, the screen of the user terminal , a question consisting of a character string or an image may be displayed.

At this time, the answer content that constitutes the answer content information and the reference answer content information may be categorized according to the movement of the information display device. A motion sensor, an acceleration sensor, and the like are mounted on the information display device, and the motion of the information display device can be detected through these sensors according to the movement of the head of the user wearing the information display device. Conversely, the movement of the user's head can be detected by detecting the movement of the information display device. Since the movement of the user's head itself may be the content of the answer, it is possible to create answer content information and reference answer content information in which the content of the answer is categorized based on this. In such a case, it can be realized by determining in advance what type of answer content is to be assigned to what kind of movement of the information display device. For example, if the user's face is turned sideways, the type is "unknown", if the user's face is turned upwards, it is "different", and if the user's face is turned downwards, it is "correct". It becomes possible to detect and apply to the type through the information display device.

In FIG. 6, in addition to the reference image information described above, three or more levels of association are set for combinations of reference answer time information in place of the reference answer content information described above, and the degree of concentration for the combination. 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-described answer is given. The answer time is the time from when a question or question is asked until the answer is completed. It can be considered that the longer the answering time is, the less the user is concentrating.

For this reason, since the answer time included in such reference answer time information also affects concentration, it is possible to improve the discrimination accuracy by combining with reference image information and discriminating the degree of concentration through the degree of association. can be done.

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

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 the output solution. The image information for reference and the answer time information for reference are arranged on the left side through the degree of association, and the degree of concentration is arranged on the right side through the degree of association. The degree of relevance indicates the degree of concentration and degree of relevance to the reference image information and the reference answer time information arranged on the left side. In other words, the degree of association is an index indicating the degree of concentration with which each reference image information and reference answer time information is likely to be linked. This indicates the accuracy in selecting the most probable degree of concentration from the

The discrimination device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, in determining the actual search solution, the determination device 2 determines which of the reference image information, the reference answer time information obtained when acquiring the reference image information, and the degree of concentration in that case is preferable. By accumulating past data and analyzing the past data, 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 reference image information P01 and reference answer time information P20, the degree of concentration is analyzed from past data. If there are many cases with a concentration degree A, the degree of association that leads to this concentration degree A is set higher, and if there are many cases with a concentration degree B and few cases with a concentration degree A The degree of association leading to B is set high, and the degree of association leading to concentration A is set low. For example, in the example of the intermediate node 61a, the outputs of concentration A and concentration B are linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of a combination of the reference image information P01 and the reference answer time information P18. 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 image information P02.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the degree of concentration. In such a case, the image information for which the degree of concentration is to be determined and the answer time information are actually obtained. Here, the answering time information is newly acquired when actually estimating the degree of concentration, and the acquisition method is the same as that for the reference answering time information described above.

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

In FIG. 7, in addition to the reference image information described above, three or more levels of association are set for combinations of reference attribute information in place of the reference answer content information described above and concentration levels for the combinations. shows an example.

This reference attribute information, which is added as an explanatory variable instead of the reference answer content information, includes the user's age, grade, level, duration of education content, gender, current school, place of residence, and working age. Shows all information related to the user's attributes, such as place of work, place of work, years of service, etc., if any. Since such reference attribute information also affects the degree of concentration, it is possible to improve the determination accuracy by combining it with the reference image information and determining the degree of concentration through the degree of association.

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

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 concentration as the output solution. The image information for reference and the attribute information for reference are arranged on the left side through the degree of association, and the degree of concentration is arranged on the right side through the degree of association. The degree of relevance indicates the degree of concentration and degree of relevance to the reference image information and the reference attribute information arranged on the left side. In other words, the degree of association is an index indicating the degree of concentration with which each piece of reference image information and reference attribute information is highly likely to be linked. This indicates accuracy in selecting a probable degree of concentration.

The discrimination device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, the determination device 2 determines which of the reference image information, the reference attribute information obtained when acquiring the reference image information, and the degree of concentration in that case is preferable in determining the actual search solution. Whether or not there was, the past data is accumulated, and the degree of association shown in FIG. 7 is created by analyzing the data.

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

Also, the degree of association shown in FIG. 7 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 7, the node 61b is a node of the combination of the attribute information for reference P18 with the image information for reference P01. It is w16. The node 61c is a node that is a combination of the reference attribute information P19 and P21 with respect to the reference image information P02.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the degree of concentration. In such a case, the image information for which the degree of concentration is to be determined and the attribute information are actually obtained. Here, the attribute information is newly acquired when actually estimating the degree of concentration, and the acquisition method is the same as that for the reference attribute information described above. Attribute information and reference attribute information may be obtained by keyboard input to a device such as a PC or a smartphone.

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

In FIG. 8, in addition to the reference image information described above, three or more levels of association are set for combinations of frequency information for reference instead of the answer content information for reference described above, and the degree of concentration with respect to the combination. shows an example.

This reference frequency information, which is added as an explanatory variable instead of the reference answer content information, is all information relating to the frequency with which the user attends educational content. The reference frequency information may indicate, for example, how many times a month or a week and how many hours the educational content is taken, or it may indicate how many intervals there are due to recent slacking. good.

In the example of FIG. 8, the input data are, for example, reference image information P01 to P03 and reference frequency information P18 to P21. An intermediate node shown in FIG. 8 is a combination of reference image information as such input data and reference frequency information. Each intermediate node is also connected to an output. In this output, the degree of concentration is displayed as the output solution.

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 concentration as the output solution. The image information for reference and the frequency information for reference are arranged on the left side through the degree of association, and the degree of concentration is arranged on the right side through the degree of association. The degree of relevance indicates the degree of concentration and degree of relevance to the image information for reference and the frequency information for reference arranged on the left side. In other words, the degree of association is an index indicating the degree of concentration with which each piece of reference image information and reference frequency information is highly likely to be linked. This indicates accuracy in selecting a probable degree of concentration.

The discrimination device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, the discrimination device 2 accumulates past data indicating which of the reference image information, the reference frequency information, and the degree of concentration in that case was preferable when discriminating the actual search solution, By 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 reference image information P01 and reference frequency information P20, the degree of concentration is analyzed from past data. If there are many cases with a concentration degree A, the degree of association that leads to this concentration degree A is set higher, and if there are many cases with a concentration degree B and few cases with a concentration degree A The degree of association leading to B is set high, and the degree of association leading to concentration A is set low. For example, in the example of the intermediate node 61a, the outputs of concentration A and concentration B are linked. The degree is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 8, the node 61b is a node of a combination of the frequency information for reference P18 and the image information for reference P01. It is w16. The node 61c is a node that is a combination of the reference frequency information P19 and P21 with respect to the reference image information P02.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the degree of concentration. In such a case, the image information for which the degree of concentration is to be determined and the frequency information are actually acquired. Here, the frequency information is newly acquired when actually estimating the degree of concentration, and the acquisition method is the same as that for the reference frequency information described above.

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

In the above-mentioned degree of relevance, the degree of relevance is expressed by a 10-level evaluation, but it is not limited to this, and it may be expressed by a degree of relevance of 3 or more stages. 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 associated with each other, either 1 or 0.

According to the present invention configured as described above, anyone can easily determine and search for the degree of concentration without special skill or experience. Further, according to the present invention, it is possible to judge the search solution with a higher degree of accuracy than a human being does. Furthermore, by configuring the degree of association described above with artificial intelligence (neural network, etc.) and making it learn, it is possible to further improve the discrimination accuracy.

Since the above-described input data and output data may not be exactly the same in many cases during the learning process, information obtained by classifying these input data and output data according to type may be used. That is, the information P01, P02, . . . P15, 16, . A data set may be created between the data and the output data for learning.

The present invention may determine the degree of concentration and the degree of achievement of the user based on the answer mode information regarding the user's answer mode for the questions given in the educational content.

Text data is acquired from a character string that a user answers by voice or an input character string to a question presented in an educational content. In such a case, the voice of the conversational sentence is received via a microphone and converted into text using a known voice recognition technology. A character string drawn on a screen or a paper medium is converted into text using a known character recognition technology.

Next, this text data is subjected to a known natural language analysis. In this natural language analysis, morphological analysis and, if necessary, syntactic analysis are performed to extract verbs and at least words (nouns or noun phrases, adjectives, adverbs) depending on the verbs. For example, if the text data is "Snow is gradually melting", natural language analysis is performed to extract "snow" and "dandan" as words (nouns or noun phrases), and as verbs, " Extract "dissolve". Such words and verbs serve as answer mode information about the answer mode. This answer mode information may be composed not only of verbs and words, but also of words only or verbs only.

Next, the answer modes are categorized based on words, or verbs and related words. This type of answer mode may actually indicate the degree of conformity to the model answer. Depending on the number of characters or words, the percentage may be increased or decreased to categorize. Further, in addition to the suitability for the model answer, the word, verb, or verb and its dependent word may be indicated by a predetermined pattern, such as good, normal, bad, etc., or an answer mode. This itself may be a typed guideline for the next learning.

In such a case, three or more levels of association between the reference answer mode and the user's concentration level are similarly formed. The answer mode for reference referred to here is a categorized answer mode based on a word, a verb, or a verb and a dependent word. Three or more levels of correlation are formed between this reference answer mode information and the user's concentration level, and the user's concentration level is determined based on the reference answer mode corresponding to the acquired answer mode information. Since the method of determining the actual degree of concentration is the same as described above, the description thereof will be omitted.

At this time, it is also possible to acquire answer mode information in which answer modes are categorized based on feature amounts of words, verbs, or verbs and words that depend on them. The feature quantity referred to here is based on the frequency of each type of answer mode for words, verbs, verbs, and words that depend on them by learning previous students and students' answers. good. In addition to categorizing the answer modes in this manner, the answer mode information for reference is similarly categorized based on the feature amounts of words, verbs, or verbs and related words. Thereby, similarly, the degree of concentration of the user can be determined based on the reference answer style information corresponding to the acquired answer style information.

In addition to the image information for reference and the answer mode information for reference, the degree of association described above is composed of a combination of any of the reference answer content information, reference answer time information, reference attribute information, and reference frequency information. This includes, but is not limited to, cases where That is, the degree of association may be composed of a combination of two or more of the reference image information, 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, the degree of association is obtained by adding one or more of reference answer content information, reference answer time information, reference attribute information, reference frequency information, and other factors to this combination. A degree of association may be formed. The degree of association is composed of a combination of two or more of reference image information, reference answer mode information, reference answer content information, reference answer time information, reference attribute information, and reference frequency information. may be

In either case, data is input in accordance with the reference information of the degree of association, and the degree of concentration is obtained using the degree of association.

In the present invention, the next educational content to be displayed may be selected and displayed based on the user's degree of concentration obtained. At this time, after the educational content for which the user's degree of concentration is determined, there are a plurality of options, and an educational content is prepared for each option. Then, one of the options may be selected according to the result of determining the degree of concentration, and the educational content prepared for the selected option may be played next. For example, the educational content selected as an option when concentration is low can be configured to include content that is refreshing for improving concentration.

In addition, when selecting such educational content, as described below, the selection may be made based on the degree of achievement with respect to the educational content.

FIG. 9 shows an example in which three or more levels of association are set between the combination of the reference answer content information and the reference answer time information and the achievement level for the combination. The degree of achievement is an index indicating how much the content in the educational content has been mastered. In addition to this, this achievement level is a graph showing various analysis results based on the achievement content, effort goals that show what kind of effort is required in the future based on the achievement content, and the current achievement level of each subject. It may be a graph showing the degree of achievement for each unit in the subject.

In the example of FIG. 9, the input data are, for example, reference answer content information P01-P03 and reference answer time information P18-21. An intermediate node shown in FIG. 9 is a combination of the answer content information for reference and the answer time information for reference as such input data. Each intermediate node is also connected to an output. In this output, the degree of achievement is displayed as the output solution.

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 the output solution. The answer content information for reference and the answer time information for reference are arranged on the left side through the degree of association, and the achievement degree is arranged on the right side through the degree of association. The degree of relevance indicates the degree of high degree of relevance to the reference answer content information and the reference answer time information arranged on the left side. In other words, the degree of association is an index indicating the degree of achievement that each reference answer content information and reference answer time information is likely to be associated with. It indicates the accuracy in selecting the most probable degree of achievement from the time information.

The discrimination device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. That is, in determining an actual search solution, the determination device 2 determines which of 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, the past data is accumulated, and the degree of association shown in FIG. 6 is created by analyzing the past data.

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

Further, the degree of association shown in FIG. 9 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence. In addition, the configuration related to artificial intelligence is the same as described in FIG.

In the example of the degree of association shown in FIG. 9, the node 61b is a node of a combination of the answer content information for reference P01 and the answer time information for reference P18. degree is w16. The node 61c is a node that is a combination of the reference answer time information P19 and P21 with respect to the reference answer content information P02. .

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually searching for the degree of achievement. In such a case, the answer content information for which the degree of achievement is to be determined and the answer time information are actually acquired. Here, the answering time information is newly acquired when actually estimating the degree of achievement, and the acquisition method is the same as that for the reference answering time information described above.

Based on the newly acquired answer content information and answer time information in this manner, the optimal achievement level is searched for. In such a case, reference is made to the degrees of association shown in FIG. 9 (Table 1) that have been acquired in advance. 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 or similar to P21, the node 61d is associated via the degree of association. This node 61d is associated with the degree of achievement C with w19 and the degree of achievement D with w20. In such a case, the degree of achievement C with the higher degree of association is selected as the optimum solution.

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

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

Further, according to the present invention, as shown in FIG. 10, the degree of concentration and the degree of achievement are determined based on the degree of relevance of a combination of two or more types of information such as reference information U and reference information V. FIG. This reference information Y is reference image information, and reference information V is any of reference answer content information, reference answer time information, reference attribute information, and 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 degree) through the intermediate node 61 of the combination with the reference information V good. For example, after providing an output solution for reference information U (reference image information) as shown in FIG. You may make it search an output (concentration degree).

Moreover, according to the present invention, it is characterized in 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 a numerical value of, for example, 0 to 100% in addition to the 10 stages described above, but is not limited to this, and can be described by a numerical value of 3 or more stages. may be configured.

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

In addition to this, according to the present invention, it is possible to make a judgment without overlooking even a very low output discrimination result such as a correlation degree of 1%. Remind the user that even discrimination results with an extremely low degree of association are connected as slight signs, and that once in dozens or hundreds of times, the discrimination results may be useful. be able to.

Furthermore, according to the present invention, there is an advantage that a search policy can be determined by setting thresholds by performing a search based on three or more degrees of association. If the threshold value is lowered, even if the degree of association is 1%, it can be picked up without omission. be. On the other hand, if the threshold is set high, there is a high possibility that the optimal search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to place emphasis on based on the way of thinking of the user side and the system side, and it is possible to increase the degree of freedom in selecting such points to place emphasis on.

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

In other words, this update corresponds to learning in terms of artificial intelligence. Since new data is acquired and reflected in the learned data, it can be said that it is a learning act.

In addition to updating the degree of association based on information that can be obtained from the public communication network, the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts It may be updated manually or automatically. Artificial intelligence may be used in these update processes.

Also, the process of initially building a trained model and the above-described updating 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 loading and learning datasets of input and output data, learning is performed by loading information corresponding to the input data, and from there self-forming the degree of association related to the output data. You can let it run.

Also, the present invention is not limited to the above-described embodiments, and for example, as shown in FIG. may In such a case, the solution search is performed based on three or more levels of association with the degree of concentration corresponding to the newly acquired information. The basic reference information is reference answer content information, etc., but is not limited to this. Reference information (reference image information, reference answer time information, reference attribute information, reference frequency information, answer style information for reference, etc.) are also applicable.

In these cases, similarly, when information corresponding to reference information used as learning data is input, solution search is performed based on the above-described method.

The search solution obtained through the degree of association may be further modified or weighted based on other reference information.

The other reference information referred to here corresponds to any reference information other than the basic reference information when any of the reference information described above is used as the basic reference information.

For example, it is assumed that in the reference answer time information P14, which is one of the other reference information, B was often determined as the degree of concentration in the past. When answering time information corresponding to such reference answering time information P14 is newly acquired, the search solution B as the degree of concentration is weighted. It is set in advance to perform processing to connect to.

For example, the other reference information G is an analysis result that suggests a search solution C with a higher degree of concentration, and the reference information F is an analysis result that suggests a search solution D with a higher degree of concentration. Assume that there is After the setting with the reference information in this way, if the actually obtained information is the same as or similar to the reference information G, processing is performed to increase the weight of the search solution C as the degree of concentration. On the other hand, when the actually acquired information is the same as or similar to the reference information F, processing is performed to increase the weight of the search solution D as the degree of concentration. That is, the degree of association itself leading to the search solution of the degree of concentration may be controlled based on the reference information FH. Alternatively, after the degree of concentration is determined only by the degree of association described above, the obtained search solution may be modified based on the reference information FH. In the latter case, how and with what weight the degree of concentration as a search solution is modified based on the reference information F to H is to reflect what is designed on the system side each time.

The other reference information is not limited to any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the discrimination pattern as a search solution obtained via the degree of association may be corrected to be higher for a case that leads to the degree of concentration suggested by the reference information.

In the example of FIG. 11, the case of searching for the degree of concentration has been described as an example, but the present invention is not limited to this, and of course the same can be applied to the case of searching for the degree of achievement.

1 Concentration degree determination 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 (9)

In an achievement level determination program for determining the level of achievement of a user who attends educational content,
an information acquisition step of acquiring answer content information regarding the content of the user's answer to a question set in the educational content;
Referencing the relationship between the reference answer content information about the user's answer content to the question set in the educational content and the user's achievement level, and referring to the reference answer content according to the answer content information acquired in the information acquisition step a determination step of determining the user's degree of achievement based on the information;
In the information acquisition step, the answer content information is obtained by categorizing the answer content based on the feature amount of the analysis image obtained by analyzing the image data captured by the information display device worn on the user's head using the deep learning technology. Acquired,
wherein the determination step causes a computer to refer to a relationship between the reference answer content information, which is the answer content categorized based on the feature amount of the analysis image, and the user's achievement level. degree determination program. The information obtaining step further obtains the answer content information obtained by classifying the answer content according to the movement of the information display device,
2. The determination step refers to three or more levels of association between the reference answer content information in which the answer content is categorized according to the movement of the information display device and the user's achievement level. Achievement determination program described. In an achievement level determination program for determining the level of achievement of a user who attends educational content,
an information acquisition step of acquiring answer content information regarding the content of the user's answer to a question set in the educational content;
Referencing the relationship between the reference answer content information about the user's answer content to the question set in the educational content and the user's achievement level, and referring to the reference answer content according to the answer content information acquired in the information acquisition step a determination step of determining the user's degree of achievement based on the information;
The information acquisition step acquires the answer content information, which is the answer content categorized according to the movement of the information display device worn on the user's head,
The determining step causes the computer to refer to three or more degrees of association between the reference answer content information in which the answer content is categorized according to the movement of the information display device and the user's achievement level. Characteristic achievement level determination program. The information acquisition step acquires answer mode information about the user's answer mode for a question set in the educational content,
In the determining step, a combination of the reference answer content information and the reference answer form information relating to the user's answer form to the question set in the educational content and the degree of association of the user's achievement level in three or more stages Determine the degree of achievement of the user based on the reference answer content information corresponding to the answer content information and the reference answer manner information corresponding to the answer manner information obtained in the information obtaining step, with reference to the association consisting of
The information acquisition step acquires text data from a character string or conversational sentence answered by the user, and performs natural language analysis on the acquired text data, based on words, verbs, or verbs and related words. Acquiring the above-mentioned answer mode information that typifies the answer mode,
2. The determining step refers to the relationship between a word, a verb, or a verb and the reference answer pattern information in which answer patterns are categorized based on a word dependent on the verb. 3. Achievement level determination program according to 3. 4. The method according to any one of claims 1 to 3, wherein said determination step refers to a relationship consisting of three or more levels of relationship between said answer content information for reference and the user's achievement level. achievement degree determination program. The information acquisition step acquires answer time information regarding the user's answer time for a question set in the educational content,
In the determination step, a combination of the reference answer content information and reference answer time information regarding the user's answer time for questions presented in the educational content displayed in the past and the user's achievement level. 2. The degree of achievement according to claim 1, wherein the user's degree of achievement is determined by referring to the degrees of association of stages or higher and further based on the reference answering time information corresponding to the answering time information obtained in the information obtaining step. discrimination program. In the information acquisition step, attribute information relating to the attributes of the user who attends the educational content is acquired;
In the determining step, a combination of the reference answer content information and the reference attribute information relating to the attribute of the user who takes the educational content displayed in the past and the degree of association of the user with the achievement level of the user are determined in three or more stages. 2. The achievement level determination program according to claim 1, wherein the user's achievement level is determined based on reference attribute information corresponding to the attribute information acquired in the information acquisition step. The achievement according to any one of claims 1 to 7, further comprising a selection step of selecting and displaying an educational content to be displayed next based on the user's achievement level determined in the determining step. degree determination program. The achievement level determination program according to any one of claims 1 to 8, wherein the determination step forms the degree of association corresponding to a weighting factor of each output of a neural network node in artificial intelligence.

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