WO2024025225A1 - Learning management method and apparatus using neural network - Google Patents

Abstract

According to an embodiment, provided are a learning management method and apparatus using a neural network. According to embodiments of the present invention, a learning management system may use the neural network to calculate learning ability of a learner on the basis of responses of the learner and provide a curriculum according to the learning ability.

Description

Learning management method and device using neural network

Embodiments of the present invention relate to technology for managing student learning, and technology for recommending a learning method appropriate for a student's learning level using a neural network.

As communication technology has recently developed, online education services are gradually increasing in the education field. In the case of existing offline education, learners must travel to the academy in person to take classes, so learners who live far away from the academy cannot take classes, or the number of learners who can take a single lecture is limited. There are various inconveniences such as: However, in the case of online education services, such inconveniences are eliminated and the quality of learning services can actually increase because education service providers can provide services to learners in various ways.

However, existing online education services have the disadvantage of not being able to provide learning content suitable for individual learners because it is difficult for service providers providing learning content to individually check learners' learning abilities.

The background technology described above is possessed or acquired by the inventor in the process of deriving the disclosure of the present application, and cannot necessarily be said to be known technology disclosed to the general public before this application.

According to one embodiment, the learning management system can precisely determine the user's learning status. The learning management system can diagnose the user's learning performance more precisely through study period and relative comparison with other students.

A learning management method according to an embodiment includes the steps of receiving user information including the user's grade, existing progress, number of tests, grades, existing learning period, target university, and target major by a user terminal; An operation of transmitting the user information to a server by the user terminal; Comparing, by the server, the existing progress and a reference progress; When the existing progress is greater than or equal to the reference progress, calculating learning achievement by the server based on the user's grade, the existing progress, the number of tests, and the grade; Calculating, by the server, an achievement potential based on anxiety index, concentration, the learning achievement, and the existing learning period; determining, by the server, a target achievement level corresponding to the target university and the target major; and an operation of providing, by the server, a curriculum for achieving the target achievement level using a curriculum provision model composed of a neural network based on the learning achievement level and the achievement potential.

The method includes: displaying, by the user terminal, a set of questionnaires for measuring an anxiety index; Receiving, by the user terminal, an input signal from the user corresponding to the displayed questionnaire set; and measuring the anxiety index by comparing the received input signal with a set of correct answers corresponding to the questionnaire set, by the user terminal.

The method includes receiving, by the user terminal, a target learning stop time, a mobile phone start time, a target mobile phone stop time, and an actual mobile phone stop time, and transmitting the input to the server; and calculating, by the server, the user's concentration based on the target learning stop time, mobile phone start time, target mobile phone stop time, and actual mobile phone stop time.

In the operation of calculating the achievement potential, the concentration and the learning achievement may form a positive correlation with the achievement potential, and the anxiety index and the existing learning period may form a negative relationship with the achievement potential. .

The operation of calculating the achievement potential includes calculating the achievement potential using Equation 1 based on the concentration, the learning achievement, the anxiety index, and the existing learning period,

[Equation 1]

[Amendment 13.09.2023 according to Rule 26]

[Amendment 13.09.2023 according to Rule 26]
In Equation 1, R(h,s,v,t,n,m) represents the status grade, m is an integer exceeding 2, and represents the number of subdivided grades of learning achievement, and σm is the value of learning achievement. It can represent the corresponding standard deviation and can be obtained based on a plurality of learning achievements collected through a plurality of user terminals, n is an integer exceeding 2, and represents the number of subdivided grades of the anxiety index, and σn may represent the standard deviation corresponding to the anxiety index, and can be obtained based on multiple anxiety indices collected through multiple user terminals, h is concentration, s is learning achievement, v is anxiety index, and t is existing It may refer to a learning period.

The curriculum provision model includes an input layer, one or more hidden layers, and an output layer, and each learning data consisting of learning achievement and achievement potential is input to the input layer of the curriculum provision model to the one or more hidden layers and the output layer. passes through and outputs an output vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer uses a loss function that compares the output vector with the correct answer vector for each training data. A loss value is output, and the parameters of the curriculum provision model can be learned in a direction that reduces the loss value.

A learning management system according to one embodiment includes a user terminal; and a server, wherein the user terminal receives user information including the user's grade, existing progress, number of tests, grades, existing study period, target university, and target major, and transmits the user information to the server. The server calculates learning achievement based on the user's grade, existing progress, number of tests, and grade, and calculates achievement potential based on anxiety index, concentration, learning achievement, and existing learning period, The target achievement level corresponding to the target university and the target major can be determined, and a curriculum for achieving the target achievement level can be provided using a curriculum provision model composed of a neural network based on the learning achievement and the achievement potential. .

The computer program according to one embodiment may be combined with hardware and stored in a computer-readable recording medium to execute the method of claim 1.

The computer-readable recording medium according to one embodiment may store a computer program that is combined with hardware to execute the method of claim 1.

According to embodiments, the learning management system may calculate the learner's learning ability using a neural network based on the learner's response and provide a curriculum according to the learning ability.

The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.

1 is a diagram showing the configuration of an electronic device according to an embodiment.

Figure 2 is a diagram showing the configuration of a program according to one embodiment.

Figure 3 is a diagram showing the overall configuration of a learning management system according to an embodiment.

Figure 4 is a flowchart showing the operation of a learning management method according to an embodiment.

Figure 5 is a diagram showing the configuration of a server according to one embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

1 is a diagram showing the configuration of an electronic device according to an embodiment.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or operations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide services to the connected electronic device 101. In addition, the server 108 may perform a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. Additionally, the server 108 may share execution data of service applications running on each of the plurality of electronic devices 101 in real time so that services can be shared between users. This server 108 may have the same hardware configuration as a typical web server or WAP server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Visual Basic, and Visual C, and may include program modules that perform various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 108 includes a series of application programs running on the server 108 and, in some cases, various databases (DBs) built internally or externally, hereinafter " It should be understood as a broad concept including “DB”). Accordingly, the server 108 classifies membership registration information and various information and data about games, stores them in a DB, and manages this DB, which may be implemented inside or outside the server 108. In addition, the server 108 uses a variety of server programs provided depending on the operating system such as DOS, Windows, Linux, UNIX, and Macintosh on general server hardware. Representative examples include Website, IIS (Internet Information Server) used in a Windows environment, and CERN, NCSA, and APPACH used in a Unix environment. Additionally, the server 108 may be linked with an authentication system and payment system for user authentication of the service or payment for purchases related to the service.

The first network 198 and the second network 199 are a connection structure that allows information exchange between each node, such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. It means (Network). The first network 198 and the second network 199 are the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and 3G. , 4G, LTE, 5G, Wi-Fi, etc., but are not limited to these. The first network 198 and the second network 199 may be closed, such as a LAN or WAN, but are preferably open, such as the Internet. The Internet uses the TCP/IP protocol and several services existing at its upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), and SNMP ( It refers to a worldwide open computer first network (198) and second network (199) structure that provides Simple Network Management Protocol (NFS), Network File Service (NFS), and Network Information Service (NIS).

A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage format that allows for free search (extraction), deletion, editing, addition, etc. of data. Databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and an XML native database such as Excelon, Tamino, and Sekaiju, and has its own functions. To achieve this, you can have appropriate fields or elements.

Figure 2 is a diagram showing the configuration of a program according to one embodiment.

Figure 2 is a block diagram 200 illustrating program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. It can be included. Operating system 142 may include, for example, AndroidTM, iOSTM, WindowsTM, SymbianTM, TizenTM, or BadaTM. At least some of the programs 140 are preloaded into the electronic device 101, for example, at the time of manufacture, or are stored in an external electronic device (e.g., the electronic device 102 or 104, or a server) when used by a user. It can be downloaded or updated from 108)). All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively operate on other hardware devices of electronic device 101, such as input module 150, audio output module 155, display module 160, and audio module 170. , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 can be used by the application 146. The middleware 144 includes, for example, an application manager 201, a window manager 203, a multimedia manager 205, a resource manager 207, a power manager 209, a database manager 211, and a package manager 213. ), connectivity manager (215), notification manager (217), location manager (219), graphics manager (221), security manager (223), call manager (225), or voice recognition manager (227). You can.

The application manager 201 may, for example, manage the life cycle of the application 146. The window manager 203 may, for example, manage one or more GUI resources used on the screen. For example, the multimedia manager 205 identifies one or more formats required for playing media files, and encodes or decodes the corresponding media file using a codec suitable for the selected format. It can be done. The resource manager 207 may, for example, manage the source code of the application 146 or the memory space of the memory 130. The power manager 209 manages, for example, the capacity, temperature, or power of the battery 189, and may use this information to determine or provide related information necessary for the operation of the electronic device 101. . According to one embodiment, the power manager 209 may interface with a basic input/output system (BIOS) (not shown) of the electronic device 101.

Database manager 211 may create, search, or change a database to be used by application 146, for example. The package manager 213 may, for example, manage the installation or update of applications distributed in the form of package files. The connectivity manager 215 may manage, for example, a wireless connection or direct connection between the electronic device 101 and an external electronic device. For example, the notification manager 217 may provide a function for notifying the user of the occurrence of a designated event (eg, an incoming call, message, or alarm). The location manager 219 may, for example, manage location information of the electronic device 101. The graphics manager 221 may, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101. For example, the voice recognition manager 227 transmits the user's voice data to the server 108 and provides a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, Alternatively, text data converted based at least in part on the voice data may be received from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least a portion of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142.

The application 146 includes, for example, home 251, dialer 253, SMS/MMS (255), instant message (IM) 257, browser 259, camera 261, and alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Album (275), Watch (277), Health (279) (such as exercise amount or blood sugar) It may include applications that measure biometric information) or environmental information 281 (e.g., measure atmospheric pressure, humidity, or temperature information). According to one embodiment, the application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to deliver designated information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage the external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a specified event (e.g., mail reception) generated in another application (e.g., email application 269) of the electronic device 101 to an external electronic device. You can. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide it to the user of the electronic device 101.

The device management application, for example, controls the power (e.g., turn-on or turn-off) of an external electronic device or some component thereof (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device 101. ) or functions (such as brightness, resolution, or focus). A device management application may additionally or alternatively support installation, deletion, or update of applications running on external electronic devices.

Throughout this specification, neural network, neural network, and network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may generally be referred to as “nodes.” These “nodes” may also be referred to as “neurons.” A neural network is composed of at least two or more nodes. Nodes (or neurons) that make up neural networks may be interconnected by one or more “links.”

Within a neural network, two or more nodes connected through a link can relatively form a relationship as an input node and an output node. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, nodes connecting the input node and the output node may have weights. Weights may be variable and may be varied by a user or algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if there are two neural networks with the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

Figure 3 is a diagram showing the overall configuration of a learning management system according to an embodiment.

According to one embodiment, the learning management system can precisely determine the user's learning status. The learning management system can diagnose the user's learning performance more precisely through study period and relative comparison with other students.

According to one embodiment, the learning management system can provide more accurate diagnosis results by evaluating not only the user's current status but also the user's achievement potential. A learning management system can determine a more holistic achievement potential by considering not only the potential for the learning ability itself but also psychological factors.

According to one embodiment, the learning management system may suggest a curriculum for the user to achieve the target grade based on the user's learning status and achievement potential. A learning management system can provide a curriculum suitable for users by using a neural network model pre-trained through large amounts of learning data.

Hereinafter, the terms are defined as follows.

Existing progress refers to the curriculum studied by the user included in a specific subject that is the subject of judgment of the user's learning achievement.

The number of tests refers to the number of times a user has performed a test consisting of a series of problems whose scope is limited to the existing progress.

The grade refers to the score calculated for each test, which consists of a series of questions whose scope is limited to the existing progress.

The existing learning period refers to the time taken to learn the curriculum learned by the user included in the specific subject subject to judgment of learning achievement.

Learning achievement is an indicator of the user's learning status relative to existing progress, and may mean a score calculated based on the user's grade, number of tests, and grades. For example, learning achievement is calculated by adding up grades and subtracting them by applying a certain weight as the user's grade increases, and the number of tests can be used to calculate the reliability of learning achievement.

The anxiety index is an indicator of the user's psychological state other than his or her learning ability, and may refer to an indicator scored based on responses to a predetermined set of questionnaires. For example, the anxiety index may be measured by comparing the user's input signal corresponding to the questionnaire set with the set of correct answers corresponding to the questionnaire set.

Concentration is an indicator of the psychological state related to the user's learning ability, and can refer to an indicator measured indirectly by measuring factors that interrupt learning during learning time. For example, concentration may be calculated based on the target learning stop time, mobile phone start time, target mobile phone stop time, and actual mobile phone stop time.

Achievement potential may refer to an indicator indicating future learning achievement for a curriculum that the user has not learned. Achievement potential may indicate the user's learning achievement potential for future progress based on the user's learning ability and psychological factors evaluated from the user's existing progress.

Goal achievement may refer to the learning achievement aimed by the user.

Referring to FIG. 3, the learning management system 300 may include a server 310, a network 340, a user terminal 321, and a database 311.

According to one embodiment, the user terminal 321 may receive user information including the user's grade, existing progress, number of tests, grades, existing study period, target university, and target major.

The user terminal 321 can transmit user information to the server.

The server 310 can compare the existing progress and the reference progress. The learning management system 300 may determine whether to determine the number of tests based on the existing progress of the user's learning process. The learning management system 300 may provide a recommended learning time based on the difficulty level of each course when the existing progress is less than the reference progress, which is a threshold value. The learning management system 300 may determine whether the number of tests satisfies the standard when the existing progress exceeds the threshold.

The learning management system 300 may compare the number of tests to a threshold. If the number of tests is less than the threshold, the learning management system 300 can set a test cycle based on the difficulty level of each course and provide a future test schedule. The learning management system 300 may calculate achievement potential when the number of tests is greater than or equal to a threshold. In this way, the learning management system 300 can increase the reliability of achievement potential by determining achievement potential only when the number of tests is more than a certain number.

If the existing progress is greater than the standard progress and the number of tests is greater than the threshold, the server 310 may calculate learning achievement based on the user's grade, existing progress, number of tests, and grades.

The learning management system 300 can receive information necessary for learning achievement through API communication using the user terminal 321. The learning management system 300 may provide its own solution for evaluating learning achievement. For example, the learning management system 300 may provide periodic tests and measure learning achievement based on the results.

The learning management system 300 may collect data for evaluating learning achievement through user input of information such as school report cards. The learning management system 300 may evaluate the user's learning achievement through the percentage of correct answers for commercially available textbooks or textbooks or input of answers for each question.

The learning management system 300 may consider the user's progress and evaluate the level of learning achievement for that progress. For example, the learning management system 300 may evaluate the learner's learning ability in a range of -3 to +3 for each progress or for the entire course.

The learning management system 300 can determine the user's learning level by precisely evaluating the user's learning achievement. The learning management system 300 can obtain a user's strategic index for learning through learning achievement.

The learning management system 300 may evaluate the user's psychological state, which may affect learning achievement, in addition to learning ability. The learning management system 300 can analyze the user's psychological state that affects learning through a psychological test consisting of predetermined psychological test items.

The user terminal 321 can measure the anxiety index, which is one of the independent variables for calculating achievement potential. The user terminal 321 may display a questionnaire set to measure the anxiety index. The user terminal 321 may receive a user's input signal corresponding to the displayed questionnaire set. The user terminal 321 may measure the anxiety index by comparing the received input signal with the set of correct answers corresponding to the survey set.

The user terminal 321 can measure concentration, which is one of the independent variables for calculating achievement potential. The user terminal 321 may receive input of the target learning stop time, mobile phone start time, target mobile phone stop time, and actual mobile phone stop time and transmit them to the server. The server 310 may calculate the user's concentration based on the target learning stop time, mobile phone start time, target mobile phone stop time, and actual mobile phone stop time.

The server 310 may calculate achievement potential based on anxiety index, concentration, learning achievement, and existing learning period. Here, concentration and learning achievement can form a positive correlation with achievement potential, and anxiety index and existing learning period can form a negative relationship with achievement potential.

For example, the server 310 may calculate achievement potential using Equation 1 based on concentration, learning achievement, anxiety index, and existing learning period.

[Equation 1]

[Amendment 13.09.2023 according to Rule 26]

[Amendment 13.09.2023 according to Rule 26]
In Equation 1, R(h,s,v,t,n,m) represents the status grade, m is an integer exceeding 2, and represents the number of subdivided grades of learning achievement, and σm is the value of learning achievement. It can represent the corresponding standard deviation and can be obtained based on a plurality of learning achievements collected through a plurality of user terminals, n is an integer exceeding 2, and represents the number of subdivided grades of the anxiety index, and σn may represent the standard deviation corresponding to the anxiety index, and can be obtained based on multiple anxiety indices collected through multiple user terminals, h is concentration, s is learning achievement, v is anxiety index, and t is existing It may refer to a learning period.

The server 310 may determine the target achievement level corresponding to the target university and target major.

The learning management system 300 can use a curriculum provision model composed of a neural network to provide a curriculum suitable for the user based on the user's learning status, target college, major, or grade, and the number of remaining study days. For example, if the current user's grade is the first year of middle school and the current time is 2022.02.08, a curriculum suitable for the user may be provided by the learning management system 300 for a period of approximately four and a half years until the CSAT.

The server 310 may provide a curriculum to achieve the goal achievement using a curriculum provision model composed of a neural network based on learning achievement and achievement potential.

Here, the curriculum provision model may include an input layer, one or more hidden layers, and an output layer.

Each learning data consisting of learning achievement and achievement potential is input to the input layer of the curriculum provision model, passes through one or more hidden layers and an output layer to output an output vector, and the output vector is input to a loss function layer connected to the output layer. , The loss function layer outputs the loss value using a loss function that compares the output vector and the correct answer vector for each learning data, and the parameters of the curriculum provision model can be learned in the direction of decreasing the loss value.

For example, the loss function used for learning the curriculum provision model may follow Equation 2.

[Equation 2]

In Equation 2, n is the number of learning data for each class, y and j are identifiers representing the classes, C is a constant value, M is the number of classes, x_y is the probability that the learning data belongs to class y, and x_j is the learning data The probability value of belonging to class j, L, may mean the loss value.

Since Equation 2 reflects the number of learning data for each class, a class with a small number of learning data may have a small impact on learning, and a class with a large number of learning data may have a large impact on learning.

The learning management system 300 can recommend and monitor a curriculum related to the user's learning. The learning management system 300 can coach users regarding their learning and provide feedback. The learning management system 300 can predict a user's slump and provide a solution to overcome it.

Figure 4 is a flowchart showing the operation of a learning management method according to an embodiment.

According to one embodiment, in operation 401, learning management system 300 may input user information including the user's grade, previous progress, number of tests, grades, previous study period, target college, and target major. .

According to one embodiment, in operation 403, learning management system 300 may transmit user information to a server.

According to one embodiment, in operation 405, learning management system 300 may compare existing progress to a baseline progress.

According to one embodiment, in operation 407, the learning management system 300 calculates the learning achievement based on the user's grade, existing progress, number of tests, and grades by the server if the existing progress is greater than or equal to the reference progress. You can.

According to one embodiment, in operation 409, learning management system 300 may calculate achievement potential based on anxiety level, concentration, learning achievement, and existing learning period.

According to one embodiment, in operation 411, learning management system 300 may determine a target achievement level corresponding to the target college and target major.

According to one embodiment, in operation 413, the learning management system 300 may provide a curriculum to achieve the goal achievement using a curriculum provision model composed of a neural network based on learning achievement and achievement potential. .

Figure 5 is a diagram showing the configuration of a server according to one embodiment.

According to one embodiment, the learning management system 300 may include a user terminal 321 and a server 310.

The user terminal 321 can receive user information including the user's grade, existing progress, number of tests, grades, existing study period, target university, and target major. The user terminal 321 can transmit user information to the server.

The server 310 may include a memory 530, a processor 510, and a communication unit 520.

The processor 510 of the server 310 may calculate learning achievement based on the user's grade, existing progress, number of tests, and grades.

The processor 510 of the server 310 may calculate achievement potential based on anxiety index, concentration, learning achievement, and existing learning period.

The processor 510 of the server 310 may determine the target achievement level corresponding to the target university and target major.

The processor 510 of the server 310 may provide a curriculum to achieve the goal achievement using a curriculum provision model composed of a neural network based on learning achievement and achievement potential.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (8)

1. An operation of inputting user information including the user's grade, existing progress, number of tests, grades, existing study period, target university, and target major by the user terminal;

   An operation of transmitting the user information to a server by the user terminal;

   Comparing, by the server, the existing progress and a reference progress;

   When the existing progress is greater than or equal to the reference progress, calculating learning achievement by the server based on the user's grade, the existing progress, the number of tests, and the grade;

   Calculating, by the server, an achievement potential based on anxiety index, concentration, the learning achievement, and the existing learning period;

   determining, by the server, a target achievement level corresponding to the target university and the target major; and

   An operation of providing, by the server, a curriculum for achieving the goal achievement using a curriculum provision model composed of a neural network based on the learning achievement and the achievement potential.

   Learning management method, including.
2. According to paragraph 1,

   An operation of displaying, by the user terminal, a set of questionnaires for measuring an anxiety index;

   Receiving, by the user terminal, an input signal from the user corresponding to the displayed questionnaire set; and

   An operation of measuring the anxiety index by comparing the received input signal with a set of correct answers corresponding to the set of questionnaires, by the user terminal.

   A learning management method further comprising:
3. According to paragraph 1,

   receiving a target learning stop time, a mobile phone start time, a target mobile phone stop time, and an actual mobile phone stop time by the user terminal and transmitting the input to the server; and

   An operation of calculating, by the server, the concentration of the user based on the target learning stop time, mobile phone start time, target mobile phone stop time, and actual mobile phone stop time.

   A learning management method further comprising:
4. According to paragraph 1,

   The operation of calculating the achievement potential is,

   The concentration and the learning achievement form a positive correlation with the achievement potential, and the anxiety index and the existing learning period form a negative relationship with the achievement potential,

   How to manage learning.
5. According to paragraph 1,

   The curriculum provision model includes an input layer, one or more hidden layers, and an output layer,

   Each learning data consisting of learning achievement and achievement potential is input to the input layer of the curriculum provision model and passes through the one or more hidden layers and the output layer to output an output vector, and the output vector is a loss connected to the output layer. It is input to the function layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the curriculum provision model are such that the loss value becomes smaller. learned in a direction,

   How to manage learning.
6. user terminal; and

   Contains servers,

   The user terminal is,

   Receive user information including the user's grade, existing progress, number of tests, grades, existing study period, target university, and target major;

   Sending the user information to the server,

   The server is,

   Calculate learning achievement based on the user's grade, the existing progress, the number of tests, and the grade,

   Calculate achievement potential based on anxiety index, concentration, learning achievement, and the existing learning period,

   Determine the target achievement level corresponding to the target university and the target major,

   Providing a curriculum to achieve the goal achievement using a curriculum provision model composed of a neural network based on the learning achievement and the achievement potential,

   Learning Management System.
7. A computer program combined with hardware and stored on a computer-readable recording medium to execute the method of claim 1.
8. A computer-readable recording medium storing a computer program combined with hardware to execute the method of claim 1.

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