KR20230038823A - Learning management system using big data and artificial intelligence capable of personal care through mentoring - Google Patents

Abstract

The present invention relates to a customized learning method and system based on big data and artificial intelligence. A server for the education receives age information of a learner from at least one among the web, an external server, and a terminal; determines a first cycle corresponding to the age information among a pre-stored learning life cycle; and enables the first learning area information related to the first cycle to be received from at least one among the web, the external server, and the terminal. Therefore, the present invention is capable of providing a seamless learning experience.

Description

Learning management system using big data and artificial intelligence capable of personal care through mentoring}

The present invention relates to a customized learning method and system based on big data and artificial intelligence.

Terminals such as personal computers, laptops, and mobile phones may be configured to perform various functions. Examples of such various functions include a data and voice communication function, a function of taking pictures or videos through a camera, a function of recording voice, a function of playing music files through a speaker system, and a function of displaying images or videos. Some terminals include an additional function for playing games, and some terminals are implemented as multimedia devices. Moreover, recent terminals can watch video or television programs by receiving broadcast or multicast signals.

In general, terminals can be divided into mobile/portable terminals and stationary terminals according to whether or not they are movable. Again, the mobile terminal may be divided into a handheld terminal and a vehicle mount terminal depending on whether the user can directly carry the mobile terminal.

As the functions of such a terminal diversify, for example, it is in the form of a multimedia player equipped with complex functions such as taking pictures or videos, playing music or video files, playing games, and receiving broadcasts. is being implemented

In order to support and increase the functions of such a terminal, it may be considered to improve the structural part and/or the software part of the terminal.

Education for users is frequently performed based on contents provided through such terminals.

However, currently applied methods and systems have a problem in that only educational contents are provided to users in one direction, and education reflecting user feedback according to learning events is not performed.

The present invention aims to provide users with a customized learning method and system based on big data and artificial intelligence in order to solve the above conventional problems.

Specifically, the present invention proposes an artificial intelligence (AI)-based adaptive learning platform (ALP1) for equalization of educational opportunities and improvement of learning effects, through which price destruction of high-quality educational services and education with global competitiveness want to provide a service.

In the big data and artificial intelligence-based customized learning method using an educational server in which a network is built with a web, an external server, and a terminal related to an example of the present invention for realizing the above object, the educational server comprises the web, A first step of receiving age information of a learner from at least one of an external server and a terminal; a second step of determining, by the education server, a first cycle corresponding to the age information among pre-stored learning life cycles; a third step of receiving, by the education server, first learning area information related to the first period from at least one of the web, an external server, and a terminal; a fourth step of collecting, by the education server, first learner information related to the learner; a fifth step of providing, by the education server, first educational content determined on the basis of the first learning area information and the first learner information to the learner; a sixth step of accumulating and collecting, by the educational server, second learner information of the learner related to the first educational content; a seventh step of receiving, by the education server, second learning area information related to a second period of the previously stored learning life period when the first period ends; and an eighth step of providing, by the education server, second educational content determined based on the second learning area information and the accumulated second learner information to the learner.

Specifically, the present invention provides an uninterrupted learning experience while an AI tutor serves as a facilitator in a Personalized Learning Environment (PLE) that is customized according to competency/aptitude/interest through a platform can do.

In addition, the present invention enables an international standard-based data collection system and cloud-based big data analysis technology and learning analysis, and can provide users with inference results using AI based on data analysis results on grades and learning habits for each subject.

In addition, the present invention makes it possible to record a learning portfolio for the entire learning period, including semester-level activities, and growth records for aptitude/disposition/interest fields through the construction of an ePortfolio service.

In addition, the present invention can provide users with learning agent technology through which an NUI/NUX-based AI tutor performs voice recognition, chatbot, and learning navigator functions through an AI tutor (tutor) interface.

1 is a view illustrating changes in private education expenses of elementary, middle and high school students in relation to the present invention.
2 is a diagram for explaining the average monthly private education expenses per person for each year and the average monthly private education expenses per person for each school level in relation to the present invention.
3 is a diagram for explaining the necessity of a customized learning platform through sensory content in relation to the present invention.
4 is a diagram for explaining the existing educational status and limitations in relation to the present invention.
5 is a diagram for explaining the direction of education improvement through the present invention and the technology proposed by the present invention.

5 is a diagram for explaining the direction of education improvement through the present invention and the technology proposed by the present invention. Referring to FIG. 5, in the present invention, in line with the needs of the Digital Native generation, who value communication, an immersive service is developed to provide efficient education and inspire learning interest, and provide it to future generations as an omnidirectional channel. .

In other words, through communicative realistic educational services, it is possible to enhance the learning interest and academic achievement of the Digital Native generation and to provide service delivery channels to schools, institutions, students and parents by converging the 4th industrial technology. We want to build a learning platform.

6 is a diagram explaining the AI-based customized learning platform proposed by the present invention. Referring to FIG. 6, the present invention proposes an AI artificial intelligence-based adaptive learning platform (ALP) for equalization of educational opportunities and improvement of learning effects, through which price destruction of high-quality educational services and global competitiveness To provide educational services.

That is, 1) providing equal educational opportunities by providing quality educational services/contents at a minimum price, 2) providing an immersive learning experience by providing a learning environment that encourages learning interest and concentration, and 3 ) To improve learning outcomes by improving academic achievement through continuous learning.

In the present invention, in order to achieve the above object, it is possible to utilize educational big data technology, artificial intelligence technology, realistic content, and student guidance through AI tutors (tutors), through which AI artificial intelligence-based customized learning platform (AdaptiveLearning Platform, ALP) can be built.

Expected effects according to the present invention include low cost and high efficiency, accurate understanding of student data, provision of seamless learning experience, provision of immersive learning experience, provision of competency-based learning experience, provision of international standard and global service, and the like. Meanwhile, FIG. 7 is a diagram illustrating a scenario in which an education service is provided based on an artificial intelligence-based customized learning platform proposed by the present invention.

Referring to FIG. 7, in the present invention, realistic AI tutors (tutors) continuously manage students according to the student's life cycle, and reflect educational big data accumulated through various learning and evaluation in the e-portfolio for each student. Student growth can be continuously monitored.

In other words, education big data accumulated in accordance with the entire learning lifecycle of infants, elementary school (low), elementary school (high), middle school, high school, and adults is reflected in the e-portfolio for each student to continuously monitor student growth.

The learning AI tutor according to the present invention can manage AI tutors according to characteristics/inclinations by age, and as shown in FIG. 7, AI tutor management is applied in an interactive way through the learning area and collected data.

Learning analysis system and service

Next, a learning analysis method and system related to the present invention will be described. Specifically, the present invention establishes an item response model based on the data collection system, applies machine learning to provide personalized learning paths and contents/resources according to achievement level (competency), and provides specific information to learners, teachers, and parents. This study aims to propose a learning analysis method and system that can provide personalized feedback (prescription), diagnose learners' learning performance, and find and solve problems early. The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. 8 shows a block diagram of a learning analysis system related to an embodiment of the present invention.

Referring to FIG. 8 , the learning analysis system 10 may include a terminal 100 and a server 200 . First, the terminal 100 includes a wireless communication unit 110, an audio/video (A/V) input unit 120, a user input unit 130, a sensing unit 140, an output unit 150, a memory 160, and an interface. A unit 170, a control unit 180, and a power supply unit 190 may be included.

However, since the components shown in FIG. 8 are not essential, a learning analysis system having more or fewer components may be implemented.

Hereinafter, the above components are examined in turn.

The wireless communication unit 110 may include one or more modules enabling wireless communication between the learning analysis system and the wireless communication system or between the device and the network in which the device is located.

For example, the wireless communication unit 110 may include a mobile communication module 112, a wireless internet module 113, a short-distance communication module 114, and a location information module 115.

The broadcast reception module 111 receives a broadcast signal and/or broadcast-related information from an external broadcast management server through a broadcast channel.

The broadcast channel may include a satellite channel and a terrestrial channel. The broadcast management server may refer to a server that generates and transmits a broadcast signal and/or broadcast-related information or a server that receives and transmits a previously generated broadcast signal and/or broadcast-related information to a terminal. The broadcast signal includes not only a TV broadcast signal, a radio broadcast signal, and a data broadcast signal, but also a TV broadcast signal or a broadcast signal in which a data broadcast signal is combined with a radio broadcast signal.

The broadcast-related information may refer to information related to a broadcasting channel, a broadcasting program, or a broadcasting service provider. The broadcasting-related information may also be provided through a mobile communication network. In this case, it can be received by the mobile communication module 112.

The broadcast related information may exist in various forms. For example, it may exist in the form of an Electronic Program Guide (EPG) of Digital Multimedia Broadcasting (DMB) or an Electronic Service Guide (ESG) of Digital Video Broadcast-Handheld (DVB-H).

The broadcast receiving module 111 may, for example, use Digital Multimedia Broadcasting-Terrestrial (DMB-T), Digital Multimedia Broadcasting-Satellite (DMBS), Media Forward Link Only (MediaFLO), and Digital Video Broadcasting-Handheld (DVB-H). , DVB-CBMS, OMA-BCAST, ISDB-T (Integrated Services Digital Broadcast-Terrestrial), etc. can be used to receive digital broadcasting signals. Of course, the broadcast receiving module 111 may be configured to be suitable for other broadcasting systems as well as the above-described digital broadcasting system.

Broadcast signals received through the broadcast receiving module 111 and/or broadcast related information may be stored in the memory 160 . The mobile communication module 112 transmits and receives a radio signal with at least one of a base station, an external terminal, and a server on a mobile communication network. The radio signal may include a voice call signal, a video call signal, or various types of data according to text/multimedia message transmission/reception. The wireless Internet module 113 refers to a module for wireless Internet access, and may be built into or external to the terminal 100 .

As the technology of the wireless Internet, WLAN (Wireless LAN) (Wi-Fi), Wibro (Wireless broadband), Wimax (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), and the like can be used. The short-distance communication module 114 refers to a module for short-distance communication. As the short range communication technology, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, and the like may be used.

The location information module 115 is a module for acquiring the location of the terminal 100, and a representative example thereof is a Global Positioning System (GPS) module. According to the current technology, the GPS module 115 calculates distance information and accurate time information from three or more satellites, and then applies trigonometry to the calculated information to obtain a three-dimensional current according to latitude, longitude, and altitude. Location information can be accurately calculated. Currently, a method of calculating location and time information using three satellites and correcting an error in the calculated location and time information using another satellite is widely used. In addition, the GPS module 115 may calculate speed information by continuously calculating the current location in real time.

Referring to FIG. 8 , an audio/video (A/V) input unit 120 is for inputting an audio signal or a video signal, and may include a camera 121 and a microphone 122.

The camera 121 processes an image frame such as a still image or moving image obtained by an image sensor in a photographing mode, and the processed image frame may be displayed on the display unit 151 . The image frames processed by the camera 121 may be stored in the memory 160 or transmitted to the outside through the wireless communication unit 110 .

Two or more cameras 121 may be provided according to the use environment.

The microphone 122 receives an external sound signal through a microphone in a recording mode, a voice recognition mode, and the like, and processes it into electrical voice data.

The processed voice data may be converted into a form transmittable to a mobile communication base station through the mobile communication module 112 and then output.

Various noise removal algorithms for removing noise generated in the process of receiving an external sound signal may be implemented in the microphone 122 .

Next, the user input unit 130 generates input data for the user to control the operation of the learning analysis system. The user input unit 130 may include a key pad, a dome switch, a touch pad (static pressure/capacity), a jog wheel, and a jog switch.

The sensing unit 140 detects the current state of the learning analysis system, such as the opening/closing state of the learning analysis system, the location of the learning analysis system, whether or not there is a user contact, the orientation of the learning analysis system, and the acceleration/deceleration of the learning analysis system. It generates a sensing signal to control the operation of the machine.

The sensing unit 140 may sense whether or not the power supply unit 190 supplies power and whether or not the interface unit 170 is connected to an external device.

On the other hand, the output unit 150 is for generating an output related to sight, hearing or touch, and includes the display unit 151, the sound output module 152, the alarm unit 153, the haptic module 154, and the projector. A module 155, a head-up display (HUD), a head mounted display (HMD), and the like may be included.

The display unit 151 displays (outputs) information processed by the learning analysis system. The display unit 151 may include a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), and a flexible display. ), and at least one of a 3D display.

Some of these displays may be of a transparent type or a light transmission type so that the outside can be seen through them. This may be referred to as a transparent display, and a representative example of the transparent display is TOLED (Transparent OLED) and the like. A rear structure of the display unit 151 may also be configured as a light transmissive structure. With this structure, the user can view objects located behind the body of the learning analysis system through the area occupied by the display unit 151 of the body of the learning analysis system.

Depending on the implementation form of the learning analysis system, two or more display units 151 may exist. For example, in the learning analysis system, a plurality of display units may be spaced apart or integrally disposed on one surface, or may be respectively disposed on different surfaces.

When the display unit 151 and a sensor for detecting a touch operation (hereinafter referred to as a 'touch sensor') form a mutual layer structure (hereinafter referred to as a 'touch screen'), the display unit 151 may be used in addition to an output device. It can also be used as an input device. The touch sensor may have the form of, for example, a touch film, a touch sheet, or a touch pad. The touch sensor is a pressure applied to a specific area of the display unit 151 or a pressure applied to a specific area of the display unit 151. It may be configured to convert a change in capacitance or the like into an electrical input signal. The touch sensor may be configured to detect not only the touched position and area, but also the pressure upon touch.

When there is a touch input to the touch sensor, the corresponding signal(s) are sent to the touch controller. The touch controller processes the signal(s) and then transmits corresponding data to the controller 180. Thus, the controller 180 can know which area of the display unit 151 has been touched.

The proximity sensor 141 may be disposed in an inner area of the learning analysis system surrounded by the touch screen or near the touch screen. The proximity sensor refers to a sensor that detects the presence or absence of an object approaching a predetermined detection surface or an object existing in the vicinity without mechanical contact by using the force of an electromagnetic field or infrared rays. Proximity sensors have a longer lifespan than contact sensors, and their utilization is also high.

Examples of the proximity sensor include a transmissive photoelectric sensor, a direct reflection photoelectric sensor, a mirror reflection photoelectric sensor, a high frequency oscillation type proximity sensor, a capacitive proximity sensor, a magnetic proximity sensor, an infrared proximity sensor, and the like. When the touch screen is capacitive, the proximity of the pointer is detected by a change in electric field according to the proximity of the pointer. In this case, the touch screen (touch sensor) may be classified as a proximity sensor.

Hereinafter, for convenience of explanation, an act of approaching the touch screen without contacting the pointer to recognize that the pointer is located on the touch screen is referred to as a "proximity touch", and the touch An act of actually touching a pointer on the screen is referred to as "contact touch". The position at which the pointer touches on the touch screen means a position at which the pointer vertically corresponds to the touch screen when the pointer is touched.

The proximity sensor detects a proximity touch and a proximity touch pattern (eg, proximity touch distance, proximity touch direction, proximity touch speed, proximity touch time, proximity touch position, proximity touch movement state, etc.). Information corresponding to the detected proximity touch operation and proximity touch pattern may be output on the touch screen.

The audio output module 152 may output audio data received from the wireless communication unit 110 or stored in the memory 160 in a recording mode, a voice recognition mode, a broadcast reception mode, and the like.

The sound output module 152 also outputs sound signals related to functions performed in the learning analysis system. The sound output module 152 may include a receiver, a speaker, a buzzer, and the like.

The alarm unit 153 outputs a signal for notifying the occurrence of an event in the learning analysis system. The alarm unit 153 may output a signal to notify occurrence of an event in a form other than a video signal or an audio signal, for example, vibration.

The video signal or audio signal may be output through the display unit 151 or the audio output module 152, so they 151 and 152 may be classified as part of the alarm unit 153.

A haptic module 154 generates various tactile effects that a user can feel. A representative example of the tactile effect generated by the haptic module 154 is vibration. The strength and pattern of vibration generated by the haptic module 154 can be controlled.

For example, different vibrations may be synthesized and output or sequentially output.

In addition to vibration, the haptic module 154 responds to stimuli such as pin arrays vertically moving with respect to the contact skin surface, air blowing force or suction force through a nozzle or suction port, rubbing against the skin surface, electrode contact, and electrostatic force. It is possible to generate various tactile effects, such as the effect of heat absorption and the effect of reproducing the feeling of coolness and warmth using an element capable of absorbing heat or generating heat. The haptic module 154 not only transmits a tactile effect through direct contact, but also can be implemented so that a user can feel a tactile effect through a muscle sense such as a finger or an arm. Two or more haptic modules 154 may be provided according to the configuration of the learning analysis system.

The projector module 155 is a component for performing an image project function using the learning analysis system, and is the same as or at least the same as the image displayed on the display unit 151 according to the control signal of the controller 180. Some may display other images on external screens or walls.

Specifically, the projector module 155 generates an image to be output to the outside using a light source (not shown) that generates light (eg, laser light) for outputting an image to the outside and light generated by the light source. It may include an image generating unit (not shown) for generating an image, and a lens (not shown) for enlarging and outputting an image to the outside at a certain focal distance. In addition, the projector module 155 may include a lens or a device (not shown) capable of adjusting an image projection direction by mechanically moving the entire module.

The projector module 155 may be divided into a cathode ray tube (CRT) module, a liquid crystal display (LCD) module, and a digital light processing (DLP) module according to the type of element of the display unit. In particular, the DLP module may be advantageous in reducing the size of the projector module 151 by magnifying and projecting an image generated by reflecting light generated from a light source to a digital micromirror device (DMD) chip.

Preferably, the projector module 155 may be provided in the longitudinal direction on the side, front or rear of the learning analysis system. Of course, it is natural that the projector module 155 may be provided at any location in the learning analysis system as needed.

In addition, a head-up display (HUD) 156 refers to a device that projects the current speed of the vehicle, the amount of remaining fuel, navigation information, etc. as a graphic image on a portion of the windshield right in front of the driver.

In addition, a head mounted display (HMD) 157 is a representative device capable of outputting virtual reality information.

Virtual reality is a human-computer interaction that creates a certain environment or situation as 3D content with a three-dimensional effect through a computer, and makes it as if a person using the 3D content is interacting with the real surrounding situation or environment. interfaces, etc.

In general, the three-dimensional effect perceived by humans is the degree of change in the thickness of the lens according to the position of the object being observed, the difference in angle between both eyes and the object, the difference in the position and shape of the object seen by the left and right eyes, and parallax caused by the motion of the object. , and other various psychological and memory effects, etc.

Among them, the most important factor for a person to feel a three-dimensional effect is binocular disparity that appears when the two eyes of a person are separated by about 6.5 cm in a horizontal direction. In other words, due to binocular parallax, you look at an object with a difference in angle, and due to this difference, images entering each eye have different images. When these two images are transmitted to the brain through the retina, the brain converts these two information is precisely fused with each other to feel the original 3D stereoscopic image.

These three-dimensional 3D contents have already been widely used in various media fields and have been well received by consumers. For example, 3D movies, 3D games, and experiential displays are representative.

In this way, along with the generalization of virtual reality technology 3D contents, the development of a technology capable of providing a more immersive virtual reality service is required in various ways.

In general, an image display device is an image that allows a user to see an enlarged virtual image by forming a focus so that a virtual large screen can be configured at a distance using a precise optical device for image light generated at a position very close to the eye. refers to the display device. In addition, the image display device is a see-close type in which the surrounding environment cannot be seen and only the image light emitted from the display element can be seen, and the image light emitted from the display element can be simultaneously viewed while the surrounding environment can be seen through a window. It can be divided into see-through.

A head mounted display (HMD, 157) according to the present invention refers to various digital devices worn on the head like glasses to receive multimedia contents. According to the trend of light weight and small size of digital devices, various wearable computers are being developed, and HMDs are also widely used.

The HMD 157 can provide various conveniences to the user by being combined with augmented reality technology, N-screen technology, etc. beyond a simple display function.

For example, when a microphone and a speaker are mounted on the HMD 157, the user can make a phone call while wearing the HMD 157. In addition, for example, when the far-infrared camera 122 is mounted on the HMD 157, the user wears the HMD 157 __ controllers (micro-controllers), microprocessors, for performing other functions. It may be implemented using at least one of the electrical units. In some cases, the embodiments described herein may be implemented by the controller 180 itself.

According to software implementation, embodiments such as procedures and functions described in this specification may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein. The software code may be implemented as a software application written in any suitable programming language. The software code may be stored in the memory 160 and executed by the controller 180.

In addition, the server 200 may build a database and exchange information with the terminal 100 . At this time, short-distance communication or long-distance communication may be applied between the server 200 and the terminal 100 . As the wireless communication technology that can be used here, WLAN (Wireless LAN) (Wi-Fi), Wibro (Wireless broadband), Wimax (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), etc. may be used. there is.

In addition, as a short range communication technology, Bluetooth, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), ZigBee, and the like may be used.

Figure 9 shows a method for explaining the components and services of the learning analysis system according to the present invention. Referring to FIG. 9 , a service flow performed by the server 200, the data collection/storage unit 181, the data analysis unit 182, and the dashboard unit 183 is illustrated in detail.

First, the server 200 may build a database (S10)

Next, EC2 (Batch Server) of the data collection/storage unit 181 collects items and course information metadata as a daily batch job (S11). In addition, RDS (Aurora) of the data collection/storage unit 181 stores metadata required for Caliper data conversion (S12). In addition, Kinesis of the data collection/storage unit 181 from the API Gateway 210 creates a Home-Learn Lambda Stream, that is, a new stream is created when another service requiring analysis comes in (S14). In addition, Lambda provides the Store_Raw_Data function, which stores raw data and, if necessary, corrects the event occurrence time (S15).

In addition, S3 (Data Lake) collects hl-analyticsraw-data (S16) and delivers it to Lambda (S17). Thereafter, the Lambda communicates with RDS (Aurora) of the data collection/storage unit 181, and transmits the data to SQS when continuous Lambda function calls fail (S18).

In addition, Caliper data, which is a JSON file, is saved through S3 (hl-analyticsraw-data) (S19). Also, S3 (hl-analyticsraw-data) exchanges data with EC2, and EC2 merges files to improve analysis performance (S20). For example, merging of files may be performed in units of 10 minutes. In addition, in conjunction with Lambda, S3 (hl-analyticsraw-data) provides a function to save image/long text data as an image/text file (S21).

In addition, in conjunction with Lambda, S3 (hl-analyticsraw-data) can also store raw data that failed conversion (temporary bucket, S22)). Thereafter, the processes of monitoring (S23), SNS notification service (S24), and Slack (S25) related to execution may be sequentially performed. Meanwhile, in the data analysis unit 182, Glue (1) creates a data catalog from Caliper Data (eg, 3 hour units), (2) inserts data into Redshift after ETL work (eg, 1 day unit) / 6 hour unit) is provided (S26). In addition, Redshift (Data Warehouse) can enable Athena to search for data on the web (S27, S28). In addition, Lambda, which is Step Functions, communicates with Redshift (Data Warehouse) and analyzes daily/weekly/monthly data to perform batch work (S29). Then, RDS (MySQL) analyzes database (DB) Provides a role as (S30). Furthermore, the dashboard unit 183 operates in conjunction with the display unit 151. First, data is transmitted to the dashboard DB, RDS (MySQL) (S31), and EC2 (WAS) communicates with it to receive and feedback, (S32), it communicates data with EC2, which is the web, (S33).

Claims (1)

In the big data and artificial intelligence-based educational server that builds a network with the web, external server, and terminal,
The educational server,
Receiving age information of a learner from at least one of the web, an external server, and a terminal;
determining a first period corresponding to the age information among pre-stored learning life periods;
Receiving first learning area information related to the first period from at least one of the web, an external server, and a terminal;
Collecting first learner information related to the learner;
Providing first educational content determined based on the first learning area information and the first learner information to the learner;
Accumulate and collect second learner information of the learner related to the first educational content;
When the first period ends, receiving second learning area information related to a second period of the previously stored learning life period;
Providing second educational content determined based on the second learning area information and the accumulated second learner information to the learner;
The pre-stored learning life cycle includes an infant cycle, an elementary cycle, a middle school cycle, and a high school cycle;
The elementary cycle is divided into a first elementary cycle for a lower grade based on a pre-specified grade and a second elementary cycle for a grade higher than the criteria,
When the first period is an infant period, the first learning area information is related to the creativity and communication of the learner,
The first learner information is related to creativity index,
The first educational content is related to creativity and learning habits of the learner,
When the first cycle is an elementary cycle,
The first learning area information is related to the learner's creativity, thinking ability, self-management and communication,
The first learner information is related to learning, disposition, intelligence, and aptitude,
When the first period is the first elementary period, the first educational content relates to the learner's thinking ability and learning preference,
When the first cycle is the second elementary school cycle, the first educational content relates to the learner's subject understanding and learning propensity,
When the first cycle is a middle cycle,
The first learning area information is related to the learner's creativity, thinking ability, self-management and communication,
The first learner information is related to learning, disposition, intelligence, aptitude and career,
The first educational content relates to the learner's subject understanding and core competencies,
When the first cycle is a higher cycle,
The first learning area information is related to creativity, thinking ability, self-management, critical thinking ability and communication of the learner,
The first learner information is related to learning, inclination, intelligence, aptitude, career, and advancement,
The first educational content is an educational server, characterized in that about the ability, aptitude and career of the learner.

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