KR20230150072A - A method for managing user learning progress using artificial intelligence - Google Patents

Abstract

The present invention relates to a method for managing a user learning progress using an artificial intelligence model by a first terminal includes: a step of receiving a user ID and password for login from the user; a step of requesting face pattern data matching the user ID from a second terminal, and receiving the face pattern data to perform a first authentication of the user; a step of receiving a lecture selection from the user and transmitting a lecture request message to the second terminal for the selected lecture; and a step of receiving a lecture response message as a response to the lecture request message. The lecture response message may include 1) learning materials and 2) learning focus period data indicating the period and subjects the user should focus on in the lecture.

Description

How to manage user learning progress using artificial intelligence { A METHOD FOR MANAGING USER LEARNING PROGRESS USING ARTIFICIAL INTELLIGENCE }

This specification relates to a method and device for managing the progress of user learning using artificial intelligence.

With the development of ICT (Information and Communication Technology), the growth potential of edtech, which combines 4th industrial revolution technologies such as VR/AR, AI, and big data, with educational services is expected to be high. Additionally, COVID-19 has further heightened interest in non-face-to-face online education and edtech.

These edutechs are focused on specific educational fields that replace the private education market for children, and the Learning Management System (LMS), which is operated using existing video content, also provides remote video lectures for elementary and middle school students. , It is widely used throughout society by taking advantage of its operational convenience and cost-effectiveness in remote video lectures at universities, remote lectures for office workers, and other remote lectures.

The purpose of this specification is to propose a method for efficiently managing a user's learning progress using an artificial intelligence model.

The technical problems to be achieved by this specification are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clear to those skilled in the art from the detailed description of the specification below. It will be understandable.

One aspect of the present specification is a method for a first terminal to manage a user's learning progress using an artificial intelligence model, comprising: receiving a user ID and password for logging in from the user; Requesting face pattern data matching the user ID from a second terminal and first authenticating the user by receiving the face pattern data; Receiving a lecture selected from the user and transmitting a lecture request message requesting the lecture to the second terminal; and receiving a lecture response message as a response to the lecture request message; It includes, and the lecture response message may include 1) learning materials and 2) learning concentration section data indicating the section and object on which the user should focus in the lecture.

Additionally, the learning intensive section data may be in the form of a text file.

Additionally, based on the lecture response message, proceeding with the lecture and monitoring the user's lecture attendance status; It further includes, and the monitoring may include 1) a general monitoring mode, and 2) a learning intensive section monitoring mode based on the learning intensive section data.

Additionally, based on the results of the monitoring, stopping the progress of the lecture; and secondly authenticating the user and recognizing the user's moving body to determine whether the user is watching the lecture. may further include.

Additionally, generating a history of attendance of the lecture of the user; And transmitting the course attendance history to a second terminal; may further include.

Additionally, the learning intensive section data may be updated through the second terminal based on the course attendance history.

Additionally, the course attendance history may be used to calculate concentration statistics of the user.

Another aspect of the present specification is a first terminal that manages a user's learning progress using an artificial intelligence model, comprising: a memory; communication module; and a processor that functionally controls the memory and the communication module, wherein the processor receives a user ID and password for logging in from the user and requests facial pattern data matching the user ID from the second terminal. Receiving the facial pattern data to first authenticate the user, receiving a lecture selection from the user, transmitting a lecture request message requesting the lecture to the second terminal, and as a response to the lecture request message, A lecture response message is received, and the lecture response message may include 1) learning materials and 2) learning concentration section data indicating the section and object on which the user should focus in the lecture.

According to an embodiment of the present specification, the user's learning progress can be efficiently managed using an artificial intelligence model.

The effects that can be obtained in this specification are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below. .

1 is a block diagram for explaining an electronic device related to this specification.
Figure 2 is a block diagram of an AI device according to an embodiment of the present specification.
Figure 3 is an example of an existing LMS system to which this specification can be applied.
Figure 4 is an example of an improved LMS system to which this specification can be applied.
Figure 5 is an embodiment to which this specification can be applied.
Figure 6 is an example of a learning intensive section to which this specification can be applied.
The accompanying drawings, which are included as part of the detailed description to aid understanding of the present specification, provide embodiments of the present specification and explain technical features of the present specification together with the detailed description.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings. However, identical or similar components will be assigned the same reference numbers regardless of reference numerals, and duplicate descriptions thereof will be omitted. The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. Additionally, in describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions will be omitted. In addition, the attached drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings, and all changes included in the spirit and technical scope of this specification are not limited. , should be understood to include equivalents or substitutes.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

Singular expressions include plural expressions unless the context clearly dictates otherwise.

In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

1 is a block diagram for explaining an electronic device related to this specification.

The electronic device 100 includes a wireless communication unit 110, an input unit 120, a sensing unit 140, an output unit 150, an interface unit 160, a memory 170, a control unit 180, and a power supply unit 190. ), etc. may be included. The components shown in FIG. 1 are not essential for implementing an electronic device, so the electronic device described in this specification may have more or fewer components than the components listed above.

More specifically, among the above components, the wireless communication unit 110 is used between the electronic device 100 and the wireless communication system, between the electronic device 100 and another electronic device 100, or between the electronic device 100 and an external server. It may include one or more modules that enable wireless communication between the devices. Additionally, the wireless communication unit 110 may include one or more modules that connect the electronic device 100 to one or more networks.

This wireless communication unit 110 may include at least one of a broadcast reception module 111, a mobile communication module 112, a wireless Internet module 113, a short-range communication module 114, and a location information module 115. .

The input unit 120 includes a camera 121 or an image input unit for inputting an image signal, a microphone 122 or an audio input unit for inputting an audio signal, and a user input unit 123 for receiving information from a user, for example. , touch keys, push keys (mechanical keys, etc.). Voice data or image data collected by the input unit 120 may be analyzed and processed as a user's control command.

The sensing unit 140 may include one or more sensors for sensing at least one of information within the electronic device, information on the surrounding environment surrounding the electronic device, and user information. For example, the sensing unit 140 includes a proximity sensor (141), an illumination sensor (142), a touch sensor, an acceleration sensor, a magnetic sensor, and a gravity sensor. Sensor (G-sensor), gyroscope sensor, motion sensor, RGB sensor, IR sensor (infrared sensor), fingerprint scan sensor, ultrasonic sensor , optical sensors (e.g., cameras (see 121)), microphones (see 122), battery gauges, environmental sensors (e.g., barometers, hygrometers, thermometers, radiation detection sensors, It may include at least one of a heat detection sensor, a gas detection sensor, etc.) and a chemical sensor (e.g., an electronic nose, a healthcare sensor, a biometric sensor, etc.). Meanwhile, the electronic device disclosed in this specification can utilize information sensed by at least two of these sensors by combining them.

The output unit 150 is for generating output related to vision, hearing, or tactile sense, and includes at least one of a display unit 151, an audio output unit 152, a haptip module 153, and an optical output unit 154. can do. The display unit 151 can implement a touch screen by forming a layered structure or being integrated with the touch sensor. This touch screen functions as a user input unit 123 that provides an input interface between the electronic device 100 and the user, and can simultaneously provide an output interface between the electronic device 100 and the user.

The interface unit 160 serves as a passageway for various types of external devices connected to the electronic device 100. This interface unit 160 connects devices equipped with a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and an identification module. It may include at least one of a port, an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port. In response to the external device being connected to the interface unit 160, the electronic device 100 may perform appropriate control related to the connected external device.

Additionally, the memory 170 stores data supporting various functions of the electronic device 100. The memory 170 may store a plurality of application programs (application programs) running on the electronic device 100, data for operating the electronic device 100, and commands. At least some of these applications may be downloaded from an external server via wireless communication. Additionally, at least some of these applications may be present on the electronic device 100 from the time of shipment for basic functions of the electronic device 100 (e.g., incoming and outgoing calls, receiving and sending functions). Meanwhile, the application program may be stored in the memory 170, installed on the electronic device 100, and driven by the control unit 180 to perform an operation (or function) of the electronic device.

In addition to operations related to the application program, the control unit 180 typically controls the overall operation of the electronic device 100. The control unit 180 can provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the components discussed above, or by running an application program stored in the memory 170.

Additionally, the control unit 180 may control at least some of the components examined with FIG. 1 in order to run an application program stored in the memory 170. Furthermore, the control unit 180 may operate at least two of the components included in the electronic device 100 in combination with each other in order to run the application program.

The power supply unit 190 receives external power and internal power under the control of the control unit 180 and supplies power to each component included in the electronic device 100. This power supply unit 190 includes a battery, and the battery may be a built-in battery or a replaceable battery.

At least some of the components may cooperate with each other to implement operation, control, or a control method of an electronic device according to various embodiments described below. Additionally, the operation, control, or control method of the electronic device may be implemented on the electronic device by running at least one application program stored in the memory 170.

In this specification, the electronic device 100 may be collectively referred to as a terminal (or user terminal).

Figure 2 is a block diagram of an AI device according to an embodiment of the present specification.

The AI device 20 may include an electronic device including an AI module capable of performing AI processing or a server including the AI module. Additionally, the AI device 20 may be included in at least a portion of the electronic device 100 shown in FIG. 1 and may be equipped to perform at least a portion of AI processing.

The AI device 20 may include an AI processor 21, memory 25, and/or a communication unit 27.

The AI device 20 is a computing device capable of learning a neural network, and may be implemented in various electronic devices such as servers, desktop PCs, laptop PCs, tablet PCs, etc.

The AI processor 21 can learn a neural network using a program stored in the memory 25. In particular, the AI processor 21 performs natural language processing, image recognition, authenticates the user's face, recognizes the user's facial motion to manage the user's learning concentration, determines whether the user is drowsy, and determines the user's learning concentration zone. You can create an artificial intelligence model for management.

To this end, the memory 25 may include a database that manages the user's facial image pattern and a database that manages the user's learning concentration section and concentration history.

Meanwhile, the AI processor 21 that performs the functions described above may be a general-purpose processor (e.g., CPU), but may be an AI-specific processor for artificial intelligence learning (e.g., GPU, graphics processing unit). there is.

The memory 25 can store various programs and data necessary for the operation of the AI device 20. The memory 25 can be implemented as non-volatile memory, volatile memory, flash-memory, hard disk drive (HDD), or solid state drive (SDD). The memory 25 is accessed by the AI processor 21, and reading/writing/modifying/deleting/updating data by the AI processor 21 can be performed. Additionally, the memory 25 may store a neural network model (eg, deep learning model) generated through a learning algorithm for data classification/recognition according to an embodiment of the present specification.

Meanwhile, the AI processor 21 may include a data learning unit that learns a neural network for data classification/recognition. For example, the data learning unit may learn a deep learning model by acquiring training data to be used for learning and applying the acquired training data to the deep learning model.

The communication unit 27 can transmit the results of AI processing by the AI processor 21 to an external electronic device.

Here, external electronic devices may include other terminals and servers.

Meanwhile, the AI device 20 shown in FIG. 2 has been described as functionally divided into an AI processor 21, a memory 25, a communication unit 27, etc., but the above-described components are integrated into one module to form an AI module. Alternatively, it may be referred to as an artificial intelligence (AI) model.

Figure 3 is an example of an existing Learning Management System (LMS) system to which this specification can be applied.

Referring to Figure 3, the existing LMS system receives an ID and password from the user to log in (S3010), selects a course from the user (S3020), and proceeds with learning by receiving a lecture start button from the user. (S3040).

Problems that may arise at this time are: 1) It is possible to take the course on your behalf by sharing your ID and password with others during user login, and 2) Even if the user does not study directly, the learning progresses automatically by clicking the start lecture button, and the specific course is taught automatically. Over time, the course will be reflected in the LMS system as completed.

In this specification, the learning progress management method to solve the above-mentioned problem while maintaining the system of existing LMS system video lectures prevents unauthorized course attendance by authenticating whether the student is an actual student through the facial recognition algorithm of the artificial intelligence model, and The class progresses only when the student is looking at the course screen with concentration to determine whether the course has actually been completed. If the student is not concentrating or leaves the class, the class is stopped and notifications/warning phrases and sounds are displayed on the screen to help students concentrate. can be induced.

In addition, by creating and utilizing statistics on the concentration time of each learner for each course, instructors and producers can create better educational materials (lecture videos), and students can use the course pattern to focus more during times when concentration is low. It can be used to guide you to focus.

Figure 4 is an example of an improved LMS system to which this specification can be applied.

Referring to FIG. 4, the terminal may include an improved LMS system 400. The improved LMS system 400 may include an existing LMS system configuration 410, an additional LMS system configuration 420 added herein, and a database 430.

For example, the existing LMS system configuration 410 includes 1) a student support module to support students taking lectures, submitting assignments, taking tests, participating in discussions, etc., 2) lecture management by professors (instructors), student management, 3) an administrator module to support overall course curriculum management, training session management, professor and student management, etc., and 4) learning, education satisfaction, test and student management, etc. It may include a learning statistics module to support statistics such as assignments.

The LMS additional system configuration 420 uses an artificial intelligence model to include a facial recognition module to recognize the user's face, a facial motion recognition module to recognize the motion of the user's face, a drowsiness detection module to determine the user's drowsiness, and It may include a learning concentration section management module to manage the user's learning concentration section.

The database 430 can be linked to the LMS additional system configuration 420 to manage the user's facial image pattern, learning concentration section, and concentration history.

In addition, in order to reduce the communication load in the learning process of the LMS system performed remotely through the terminal, the user downloads some functions for learner face authentication and learning concentration management to the user's learning device (user terminal) and configures the learning device itself. It can also be operated by configuring additional client modules that can operate as . To this end, the user's learning device may include an LMS additional system configuration 420 and temporary data associated therewith.

Figure 5 is an embodiment to which this specification can be applied.

Referring to FIG. 5, the second terminal includes an improved LMS system 400, and the first terminal is a user terminal and includes the user's learning device described above.

The first terminal receives a user ID and password for logging in from the user (S5010). If the user ID has previously been used for login, the first terminal can perform user face authentication through facial pattern temporary data previously used for user face authentication. For this purpose, the first terminal may store temporary facial pattern data as login history.

More specifically, when the user authentication and login procedures are successful, the first terminal (PC, laptop, pad, mobile phone, etc.) can download the corresponding facial pattern data and save it as temporary data. This can be used to re-authenticate the learner and log in the next time while taking the course. This is to reduce the communication burden caused by the generation of separate Internet traffic for face authentication while taking a course and to prevent it from acting as an impediment to the user's smooth learning.

If the first terminal does not have a login history related to the user ID, the first terminal requests facial pattern data for user authentication from the second terminal (S5110).

In response to the request for face pattern data, the second terminal transmits face pattern data matching the user ID to the first terminal (S5120). For example, the second terminal may transmit facial pattern data matching the user ID using a facial recognition database of a pre-registered user. The database for facial recognition may be encrypted and managed.

The first terminal first authenticates the user's face using a camera based on the received facial pattern data (S5020).

When the first authentication is completed, the user selects the course he or she wishes to take (S5030).

The first terminal transmits a lecture request message requesting a lecture that the user wants to take to the second terminal (S5130).

The second terminal transmits a lecture response message to the first terminal as a response to the lecture request message (S5140). For example, the lecture response message may include learning content about the lecture that the user wants to take.

Learning content in this specification may include learning focus section data indicating sections and targets on which users should separately focus, in addition to learning materials for delivering lectures such as videos and images.

Figure 6 is an example of a learning intensive section to which this specification can be applied.

Referring to FIG. 6(a), the section from the 1st time to the 5th time included in the learning content can be set as the learning concentration section.

Table 1 is an example of learning intensive section data.

Category Data Time 00:13 - 00:25 Text Copyright! From theory to experiential classes. Should schools for educational purposes also protect copyright?

Referring to Table 1, the learning concentration section data may include the time the user should focus on and the corresponding subtitles (text). This learning focus section data can be created as a separate text file, similar to producing a subtitle file. The time the user should focus on can be set by setting the video start point to 0:00 (0 minutes 00 seconds). , the corresponding subtitles can be output during this time. The first terminal can monitor the user's course attendance status by considering the time the user must concentrate.

Referring to FIG. 6(b), an example of learning content in which subtitles are output together with the video according to the learning concentration section data in Table 1 is shown.

Table 2 is another example of learning focus section data.

Category Data Time 00:05 - 00:06 Text #Please focus on the blackboard. Time 00:12 - 00:15 Text #Please focus on the table (image).

Referring to Table 2, the learning focus section data may include the object on which the user should focus in the section on which the user should focus. Focus objects can be written as text and classified through identifiers such as #. Through this, the focus target can be easily set by general lecture creators who lack computer knowledge, and the second terminal can easily update it according to the user's lecture history. The first terminal is based on the lecture response message, The lecture is conducted and the user's course attendance status is monitored (S5040). For example, when the user inputs the lecture start button, the first terminal may display the lecture video included in the lecture response message to the user. The first terminal can monitor the user's course attendance status using a camera through an artificial intelligence model.

The first terminal stops the lecture based on the monitoring results (S5050).

This monitoring may include 1) general monitoring mode and 2) learning intensive section monitoring mode.

One) Normal monitoring mode

The first terminal can use a facial motion recognition module to recognize the user's facial movements and whether the user is looking at the screen with his or her head up or lowering his or her head. Additionally, if the user leaves the camera's angle range, it is determined that the user has left the seat, the lecture progress is stopped, and a specific voice (such as "Please resume class") can be output.

Additionally, the first terminal can use the drowsiness detection module to recognize the eyes and eyelids included in the facial pattern data and whether the user's eyes are closed when looking at the screen. If the first terminal recognizes that the user is drowsy, it can stop the lecture and output a specific voice (“Please pay attention to class,” etc.).

2) Monitoring the learning concentration section

If the learning content includes learning focus section data, the first terminal uses the learning focus section management module to determine the focus target (e.g., using learning auxiliary materials such as diagrams) through the time and identifier to focus on the screen. Explanation sections, sections explained by writing on the blackboard, etc.) can be judged. For example, an artificial intelligence model can determine the focus object from learning content corresponding to the concentration time. For this purpose, the artificial intelligence model may be a model that has completed learning for natural language processing and image analysis. If the first terminal is not looking at the screen or closing its eyes during the concentration period, or if it determines that the object of concentration is not being looked at, the first terminal stops the lecture and makes a specific voice (“Look at the table on the screen”) etc.) can be output.

The first terminal performs second authentication of the user's face and recognizes the user's moving body (S5060). For example, the first terminal may stop the lecture if the front of the user's face deviates from the camera by more than a certain range. Thereafter, when the first terminal determines that the front of the user's face has returned to within a certain range based on the camera, it can re-authenticate whether the user is the same user who originally took the lecture. If it is determined that the user is the same, the first terminal determines whether or not the user is attending the lecture through recognition of the user's body. If it is determined that the user's body is oriented toward the learning material, the first terminal resumes the lecture.

The first terminal creates the user's course attendance history (S5070). For example, the course attendance history may be used to determine the user's course concentration and may include logs and monitoring results of the user's overall course attendance progress.

The first terminal transmits the course attendance history to the second terminal (S5150).

The second terminal stores the course attendance history and updates the learning intensive section data (S5160). For example, the second terminal may generate a concentration statistics report for each user using the course attendance history. The second terminal creates and utilizes statistics on concentration time for each subject/user, allowing instructors and lecture producers to create better educational materials (lecture videos), and provides users with a time zone when concentration is low by using lecture patterns. We can provide guidance to help you focus more.

Table 3 is an example of a concentration statistics report generated through course attendance history in the second terminal.

name explanation Report on areas of decreased concentration by student and course - Report on areas in which concentration is lower compared to the average of other students in a course
- Column: Student name, course name, subject (Korean, English, essay writing, mathematics, etc.), overall average concentration time, concentration time of the student, section (time) of low concentration, etc. Report on courses with below average concentration by student - Report on which courses (subjects) among several courses the student has less concentration compared to other students
- Columns: Student name, subject (Korean, English, essay writing, math, etc.), average concentration time, concentration time of the student Instructor pattern report with below average concentration level for each student - Analysis report on student concentration according to instructor pattern
- Columns: Student name, Instructor Type 1 (age group), Instructor Type 2 (gender), average concentration time, concentration time of the student Report on the average concentration time of students in each course - For analysis of courses with below-average concentration among several courses registered by the student
- Columns: Course name, subject, student type, number of students, average concentration time, concentration time for the course, etc. Comparative analysis report on concentration on the same subject - Comparative analysis of courses and concentration levels of various teachers registered for the same subject
- Columns: Subject, teacher name, teacher gender, teacher age group, number of students, concentration time, etc.

Additionally, the second terminal may update the learning concentration section data based on the concentration statistics report. For example, the learning concentration section data can be updated to increase the concentration time for lectures, subjects, or objects of concentration that are lower than the user's average concentration time. In addition, for lectures that have been taken repeatedly a certain number of times, the concentration time can be reduced or deleted, thereby guiding the user to quickly take the lecture. Through this, the present specification can achieve the following effects.

1) Effect of preventing proxy classes

In the existing LMS, the login authentication process is carried out only with an ID and password, and once logged in, video lectures are played without additional authentication. However, the improved LMS presented in this specification provides a face-to-face connection even when logging in for the first time and when resuming learning after interruption. Identity authentication is performed through facial recognition.

2) Manage accurate progress

In the existing LMS, after logging in, you simply press the start button for a specific course, and course progress occurs naturally over time. In other words, class progress continues even if students do not attend class or leave their seats. In this specification, through facial motion recognition, we determine whether the user is lowering their head or leaving their seat, and through eyelid blink recognition, we determine whether the user is closing their eyes while staring at the screen. In addition, when creating learning materials, the screen is identified. By managing the gaze section (concentration time), you can manage the section where the class is held only when you focus on the screen.

Combining these learning progress management functions with the facial recognition function can help users learn properly.

3) Concentration statistical report

In this specification, the second terminal provides a variety of concentration times and sections for each student, thereby providing a report showing which subjects, which instructor types, and which time periods show differences in concentration compared to other students. This can be used as basic data for various counseling materials.

In addition, an analysis report of students' various concentration levels for each instructor's course can be generated. Through this, the instructor can check in which section the concentration differs compared to other instructor-class progress patterns for the same course, and can use it to create better teaching materials.

The above-mentioned specification can be implemented as computer-readable code on a program-recorded medium. Computer-readable media includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. It also includes those implemented in the form of carrier waves (e.g., transmission via the Internet). Accordingly, the above detailed description should not be construed as restrictive in all respects and should be considered illustrative. The scope of this specification should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of this specification are included in the scope of this specification.

In addition, although the description has been made above with a focus on services and embodiments, this is only an example and does not limit the present specification, and those skilled in the art will understand that it does not deviate from the essential characteristics of the services and embodiments. It can be seen that various modifications and applications not exemplified above are possible. For example, each component specifically shown in the embodiments can be modified and implemented. And the differences related to these modifications and applications should be construed as being included in the scope of the present specification as defined in the attached claims.

Claims (8)

In a method for a first terminal to manage a user's learning progress using an artificial intelligence model,
Receiving a user ID and password for logging in from the user;
Requesting face pattern data matching the user ID from a second terminal and first authenticating the user by receiving the face pattern data;
Receiving a lecture selected from the user and transmitting a lecture request message requesting the lecture to the second terminal; and
In response to the lecture request message, receiving a lecture response message;
Includes,
The lecture response message includes 1) learning materials and 2) learning concentration section data indicating the section and object on which the user should focus in the lecture.
According to paragraph 1,
The learning concentration section data is
Management method in the form of a text file.
According to paragraph 2,
Based on the lecture response message, proceeding with the lecture and monitoring the user's lecture attendance status;
It further includes,
The monitoring is
A management method comprising 1) a general monitoring mode, and 2) a learning intensive section monitoring mode based on the learning intensive section data.
According to paragraph 3,
stopping progress of the lecture based on the results of the monitoring; and
secondly authenticating the user and recognizing the user's moving body to determine whether the user is watching the lecture;
A management method further comprising:
According to paragraph 4,
generating a history of attendance of the lecture of the user; and
transmitting the course attendance history to a second terminal;
A management method further comprising:
According to clause 5,
The learning concentration section data is
A management method that is updated through the second terminal based on the course attendance history.
According to clause 5,
The course attendance history is
A management method used to calculate the user's concentration statistics.
In the first terminal that manages the user's learning progress using an artificial intelligence model,
Memory;
communication module; and
a processor that functionally controls the memory and the communication module; Including,
The processor is
Enter the user ID and password for logging in from the user,
Request facial pattern data matching the user ID from a second terminal, receive the facial pattern data, and first authenticate the user,
Receiving a lecture from the user, transmitting a lecture request message requesting the lecture to the second terminal, and receiving a lecture response message as a response to the lecture request message,
The lecture response message includes 1) learning materials and 2) learning concentration section data indicating the section and object on which the user should focus in the lecture, a first terminal.