KR20240006986A - Method and apparatus for adjusting problem difficulty using a neural network - Google Patents

Abstract

Embodiments provide a method and device for adjusting problem difficulty using a neural network. The problem difficulty adjustment method using a neural network according to an embodiment includes selecting, by a learning server, a first learning check problem classified into a specific chapter of a specific subject in a database, and transmitting the first learning check problem to a learner terminal. step; Receiving the learner's first learning check response to the first learning check problem from the learner terminal; calculating a unique difficulty level for the first learning check problem; Measuring the learner's learning achievement for learning content corresponding to the first learning check problem; calculating the learner's perceived difficulty level for the first learning check problem based on the unique difficulty level and the learning achievement level; Selecting a second learning check problem having a unique difficulty level different from that of the first learning check problem from the database based on the perceived difficulty level, and transmitting the second learning check problem to the learner terminal. can do.

Description

Method and device for adjusting problem difficulty using a neural network {METHOD AND APPARATUS FOR ADJUSTING PROBLEM DIFFICULTY USING A NEURAL NETWORK}

Embodiments of the present invention relate to a method and device for adjusting problem difficulty using a neural network. More specifically, using a neural network to calculate the perceived difficulty level felt by the learner based on the learner's response to the problem, It relates to a device and method for adjusting the difficulty level of a problem accordingly.

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. 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. In addition, the objective difficulty of the problem provided to the learner generally varies depending on the learner's amount of learning. Accordingly, there is a need for technology that accurately measures the level of difficulty perceived by learners using artificial intelligence and provides appropriate learning content according to the learner's current learning status.

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.

Embodiments provide a method and device for adjusting problem difficulty using a neural network.

The technical challenges to be achieved in the embodiments are not limited to the matters mentioned above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. You can.

In the problem difficulty adjustment method using a neural network according to an embodiment of the present invention, a learning server selects a first learning check problem classified into a specific chapter of a specific subject in a database, and sends the first learning check to a learner terminal. Submitting a problem; Receiving the learner's first learning check response to the first learning check problem from the learner terminal; calculating a unique difficulty level for the first learning check problem; Measuring the learner's learning achievement for learning content corresponding to the first learning check problem; calculating the learner's perceived difficulty level for the first learning check problem based on the unique difficulty level and the learning achievement level; Selecting a second learning check problem having a unique difficulty level different from that of the first learning check problem from the database based on the perceived difficulty level, and transmitting the second learning check problem to the learner terminal. can do.

The learning achievement may include a first learning achievement indicating learning achievement for the specific subject and a second learning achievement indicating learning achievement for the specific chapter.

The unique difficulty level may be calculated based on the learner's solving time of the first learning check problem and the correct answer rate of the first learning check problem.

[Equation]

The learning server calculates the perceived difficulty using the above equation. In the above equation, D_f is the perceived difficulty, D_i is the inherent difficulty, A is the learner's learning achievement according to the learning content classification standard, and k is the learning content classification. It may refer to a natural number for identifying a standard.

The learning server corrects the perceived difficulty using a difficulty correction model, and the difficulty correction model includes an input layer, one or more hidden layers, and an output layer, and the inherent difficulty, the learning achievement, and the perceived difficulty A plurality of learning data is input to the input layer of the difficulty correction model, passes through the one or more hidden layers and the output layer, and is output as an output vector, and the output vector is input to a loss function layer connected to the output layer. The loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each of the learning data, and the parameters of the difficulty correction model are learned in the direction of decreasing the loss value. It can be.

According to embodiments, the problem difficulty adjustment method using a neural network can provide a learning check problem with a difficulty level suitable for the learner based on the inherent difficulty of the learning check problem and the learner's learning achievement with respect to the learning content. In other words, the learner will be able to obtain appropriate feedback according to the amount of learning by solving appropriate learning check problems according to the learning achievement measured according to the learner's learning progress, learning time, etc.

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 of the present invention.
Figure 2 is a diagram showing the structure of a program according to an embodiment of the present invention.
Figure 3 is a diagram schematically showing a network environment including a learning server according to an embodiment of the present invention.
FIG. 4 is a block diagram for explaining the learning server 300 shown in FIG. 3.
Figure 5 is a flowchart illustrating a method of adjusting the difficulty level of a learning check problem according to an embodiment of the present invention.

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 of the present invention.

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 calculations 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 structure of a program according to an embodiment of the present invention.

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.

Figure 3 is a diagram schematically showing a network environment including a learning server according to an embodiment of the present invention.

Referring to FIG. 3, in a network environment, the learner terminal 400 can communicate with the learning server 300 through the network 500. The learner terminal 400 may receive the learning application installation file provided from the learning server 300 through the network 500. The learning server 300 may provide learning feedback to the learner terminal 400. Alternatively, the learning server 300 may evaluate the metacognitive ability of a learner (corresponding to the learner terminal 400) using the learner terminal 400. Accordingly, the learning server 300 may be referred to as a learning feedback providing device or a metacognitive ability evaluation device.

In one embodiment, the learner terminal 400 may include a processor, memory, an input module, a display module, a camera module, and a communication module. In another embodiment, the learner terminal 400 may omit at least one of these components, or one or more other components may be added. Unless otherwise specified, each component of the learner terminal 400 may be the same or similar to the electronic device 101 described with reference to FIG. 1 .

The processor of the learner terminal 400 may, for example, execute software to control at least one other component of the learner terminal 400 connected to the processor and perform various data processing or calculations. In one embodiment, as at least part of data processing or computation, the processor of the learner terminal 400 stores commands or data received from other components in a volatile memory, processes the commands or data stored in the volatile memory, and produces a result. Data can be stored in non-volatile memory.

The memory of the learner terminal 400 may store a learning application installation file. The processor of the learner terminal 400 may install the learning application on the learner terminal 400 by executing the learning application installation file stored in the memory of the learner terminal 400. In one embodiment, the content of the learning application may include learning content related to mathematics education, for example. The content of the learning application may include one or more mathematical concepts and definitions, and problem data. The content of the learning application may include one or more videos explaining mathematical concepts. Learning application content may correspond to learning content that will be described later.

The input module may include, for example, a microphone, mouse, keyboard, keys (e.g. buttons), or digital pen. The input module can receive the learner's response to the problem. The learner terminal 400 may generate a response to the problem based on the content of the learner's response.

The display module can visually provide information to the outside of the learner terminal 400. The display module can visually provide content of the learning application based on control of the processor of the learner terminal 400. For example, the display module may visually provide an image explaining the mathematical concept based on control of the processor of the learner terminal 400. The display module 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 camera module can shoot still images and videos. A camera module may include one or more lenses, image sensors, image signal processors, or flashes. If the learner looks at the provided problem and writes a response using paper or a writing instrument, the camera module can capture the response. The processor of the learner terminal 400 may generate a response using optical character recognition technology based on the captured content.

The communication module may support establishment of a communication channel between the learner terminal 400 and the learning server 300 connected through the network 500, and communication through the established communication channel.

The learning server 300 is connected to the learner terminal 400 and can provide a personalized learning service to the connected learner terminal 400. In addition, the learning server 300 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 learning server 300 may have the same hardware configuration as a typical web server or WAP server. Additionally, the learning server 300 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 learning server 300 may transmit a plurality of learning contents to the learner terminal 400. Multiple learning contents may be classified into multiple subjects and multiple chapters. The plurality of chapters may include the first chapter, the second chapter, and the third chapter. Here, the first, second, and third are only elements for specifying a plurality of chapters and do not limit the order of the plurality of chapters. Learning content classified as Chapter 1 is learning content that must be preceded in order for the learner to learn learning content classified as Chapter 2 (i.e., for the learner to recognize and understand the learning content classified as Chapter 2). may include.

The learning server 300 may receive learner information from the learner terminal 400. The learner information may include data that the learner inputs through the learner terminal 400, or may be data generated by the learner terminal 400 based on data input by the learner through the learner terminal 400. Learner information may be data indicating the learner's learning level. For example, learner information may include information corresponding to the learner's age, study period, test score, etc. When a learner subscribes to a personalized learning service according to an embodiment of the present invention, the learning server 300 may receive the learner information from the learner terminal 400 at the subscription stage.

The learning server 300 and the learner terminal 400 shown in FIG. 3 can transmit or receive data through the network 500 connected wirelessly or wired. The network 500 may include networks based on wired Internet technology, wireless Internet technology, and short-range communication technology. Wired Internet technology may include, for example, at least one of a local area network (LAN) and a wide area network (WAN).

Wireless Internet technologies include, for example, Wireless LAN (WLAN), DLNA (Digital Living Network Alliance), Wibro (Wireless Broadband), Wimax (World Interoperability for Microwave Access: Wimax), and HSDPA (High Speed Downlink Packet). Access), HSUPA (High Speed Uplink Packet Access), IEEE 802.16, Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced), Wireless Mobile Broadband Service (WMBS) and 5G NR (New Radio) technology. However, this embodiment is not limited to this.

When the learning server 300 and the learner terminal 400 transmit/receive data by wireless communication, the learning server 300 and the learner terminal 400 can comply with technical standards and standard communication methods for mobile communication. there is. For example, standard communication methods include GSM (Global System for Mobilommunication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000), EV-DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), At least one of Wideband CDMA (WCDMA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTEA), and 5G New Radio (NR) It can be included. However, this embodiment is not limited to this.

FIG. 4 is a block diagram for explaining the learning server 300 shown in FIG. 3.

Referring to FIG. 4, the learning server 300 may include a database, a processor, and a learner management module.

The database can store multiple learning contents. Learning content may include, for example, learning content related to mathematics education. Learning content may include one or more mathematical concepts, definitions, and problem data. Learning content may include one or more videos explaining mathematical concepts. The learning server 300 may select one of one or more learning contents stored in the database and transmit it to the learner terminal 400. The display module of the learner terminal 400 may visually output the selected learning content based on the control of the processor of the learner terminal 400. When a plurality of learning contents are classified into a plurality of chapters, the database may have a storage structure for the learning contents classified into each chapter.

The database may store learning check problems corresponding to each of one or more learning contents. A learning check problem may be a problem provided to determine whether the learner has understood the learning content. For example, if the learning content is content explaining the definition of the trigonometric ratio, the learning check problem corresponding to the learning content may be a question asking about the definition of the trigonometric ratio. Additionally, the database may store learning check problem answers and learning check problem explanations corresponding to the learning check problems. In this case, the database can store contents as a pair of learning check problems, learning check problem answers, and learning check problem explanations. For example, the first learning check problem and the first learning check problem answer corresponding to the first learning check problem may include the same first meta information as part of the data packet. Based on the first meta information, the processor may determine the learning check problem answer corresponding to the first learning check problem among the plurality of learning check problem answers as the first learning check problem answer.

The database may have a general data structure implemented in the storage space of the learning server 300 using a database management program (DBMS). The database may use a database management program to select one of one or more learning contents stored in the database in response to a control signal from the processor.

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 form 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.

The processor may select one of one or more learning contents stored in the database and transmit it to the learner terminal 400. The processor may also transmit a learning check problem corresponding to the selected learning content to the learner terminal 400. The processor may receive a learning check response corresponding to the learning check problem from the learner terminal 400. The processor may determine whether the received learning check response is correct or incorrect. Additionally, the processor may determine the learner ability corresponding to the learner terminal 400 based on the received learning check response.

The learner management module can receive information about learners who have subscribed to the personalized learning service provided by the learning server 300 and manage the information. The learner management module may store learner information based on unique information of the learner terminal 400 connected to the learning server 300. Here, the learner's information may include information corresponding to the learner's age, the learner's evaluation score on the pre-test, the learner's accumulated learning time, and whether the learner understands specific learning content.

Each component included in the learning server 300 is connected to a communication path connecting software modules or hardware modules within the device and can operate organically with each other. These components may communicate using one or more communication buses or signal lines. Each component of the learning server 300 refers to a unit that processes at least one function or operation, and may be implemented as a software module, a hardware module, or a combination of software and hardware.

The configuration of the learning server 300 shown in FIG. 4 is not necessarily essential, and some configurations may be omitted or other configurations not shown in FIG. 4 may be added as needed. That is, the configuration shown in FIG. 4 is for convenience of explanation, and the embodiments are not limited thereto. A person skilled in the art will be able to modify and practice the embodiments of the present invention without departing from the scope of the present invention.

Figure 5 is a flowchart illustrating a method of adjusting the difficulty level of a learning check problem according to an embodiment of the present invention.

Referring to Figure 5, first, when the learner terminal 400 is connected to the learning server 300, the processor selects one or more of the plurality of learning contents stored in the database and transmits it to the learner terminal 400 ( S510).

The processor may select one of a plurality of learning contents stored in the database based on the learner's learner information and transmit it to the learner terminal 400.

In one embodiment, a plurality of learning contents may be classified according to a plurality of classification criteria. Here, the plurality of classification criteria may include subjects, chapters, etc. That is, a plurality of learning contents can be classified into a plurality of subjects. For example, a plurality of learning contents may be classified into math learning content, English learning content, and Korean learning content. Additionally, multiple learning contents of a specific subject may be classified into multiple chapters. The plurality of chapters may include the first chapter, the second chapter, and the third chapter. Here, the first, second, and third are only elements for specifying a plurality of chapters and do not limit the order of the plurality of chapters. Learning content classified as Chapter 1 is learning content that must be preceded in order for the learner to learn learning content classified as Chapter 2 (i.e., for the learner to recognize and understand the learning content classified as Chapter 2). may include. Accordingly, the processor selects learning contents classified into the first chapter from among the plurality of learning contents stored in the database and transmits them to the learner terminal 400, and subsequently selects learning contents classified into the second chapter to the learner terminal ( 400). However, the embodiment of the present invention is not limited to this, and the plurality of classification criteria may include various classification criteria in addition to subjects and chapters.

When the learner terminal 400 receives the selected learning content, the display module of the learner terminal 400 may output the learning content. The learner can recognize the learning content through the display module of the learner terminal 400 and learn the explanation content included in the learning content.

Next, the first learning check problem corresponding to the transmitted learning content is selected from among the plurality of learning check problems stored in the database and transmitted to the learner terminal 400 (S520).

The processor may select a learning check problem corresponding to the learning content selected in S510 from among a plurality of learning check problems stored in the database and transmit it to the learner terminal 400. For example, if the learning content selected by the processor in S510 is data explaining the content of the definition of trigonometric ratio, the processor sends a learning check problem including a question asking for the definition of a trigonometric ratio among a plurality of learning check problems stored in the database to the learner terminal ( 400). In this case, learning check problems can be classified according to the classification of learning content. For example, if learning content is classified into a 'mathematics' subject and a 'trigonometric ratio' chapter, learning check problems may also be classified into a 'mathematics' subject and a 'trigonometric ratio' chapter.

When the learner terminal 400 receives a learning check problem, the display module of the learner terminal 400 may output the learning check problem. That is, the learning check problem included in the learning check problem may be displayed on the display module of the learner terminal 400. The learner may recognize the learning check problem output on the display module of the learner terminal 400 and input a response to the learning check problem into the learner terminal 400. There may be various ways for a learner to input a response to a learning check question into the learner terminal 400. For example, a learner may input a solution process for a learning check problem through a touch display included in the learner terminal 400. In this case, the learner terminal 400 may generate a learning check response based on the solution process for the input learning check problem using an optical character recognition (OCR) method. The embodiment of the present invention is not limited to this, and the method by which the learner terminal 400 generates a learning check response based on the solution process for the learning check problem entered by the learner may vary, and is not limited to a specific method. no. Here, the learning check response may include the learner's problem solving content (response content) corresponding to the learning check problem. For example, if the learning check problem includes a question asking for the definition of a trigonometric ratio, the learning check response may include the learner's text explaining the definition of the trigonometric ratio.

The processor of the learning server 300 receives a first learning check response corresponding to the first learning check problem from the learner terminal (S530).

In one embodiment, when the selected learning check problem includes a plurality of learning check problems, the processor of the learning server 300 receives a plurality of learning check responses corresponding to each of the plurality of learning check problems from the learner terminal. You can.

The processor of the learning server 300 may determine whether the received learning check response is correct or incorrect. The processor of the learning server 300 may compare the learning check response to the learning check problem with the learning check problem answer corresponding to the learning check problem. The processor of the learning server 300 may determine whether the learning check response is correct or incorrect by comparing the learning check response with the correct answer to the learning check problem.

In one embodiment, when the processor of the learning server 300 receives a plurality of learning check responses corresponding to each of a plurality of learning check problems from the learner terminal, the processor of the learning server 300 receives each of the plurality of learning check responses. You can determine whether the answer is correct or incorrect.

Next, the processor of the learning server 300 calculates the unique difficulty level for the first learning check problem (S540).

In one embodiment, the inherent difficulty level may be calculated based on the solving time of the learner's first learning check problem. That is, the processor of the learning server 300 may calculate a unique difficulty level for the first learning check problem based on the learner's solving time for the first learning check problem. To this end, the processor of the learning server 300 can measure the solving time for the learner's first learning check problem. The processor of the learning server 300 may calculate a unique difficulty level based on the learner's actual solving time for the first learning check problem.

In another embodiment, the inherent difficulty level may be calculated based on the correct answer rate of the first learning check problem. That is, the processor of the learning server 300 may calculate the difficulty level of the first learning check problem based on the percentage of correct answers to the first learning check problem. The processor of the learning server 300 transmits a first learning check problem to each of a plurality of learner terminals connected to the learning server 300, and a plurality of first learning check responses corresponding to the first learning check problem. can be received from each learner terminal. The processor of the learning server 300 may determine whether each of the plurality of first learning check responses is correct. The processor of the learning server 300 may calculate the difficulty level for the first learning check problem based on the ratio of the first learning check response that is determined to be correct among the plurality of first learning check responses, that is, the percentage of correct answers.

In another embodiment, the inherent difficulty level may be calculated based on the solving time of the first learning check problem and the correct answer rate of the first learning check problem. That is, the processor of the learning server 300 may calculate a unique difficulty level for the first learning check problem based on the learner's solving time of the first learning check problem and the percentage of correct answers to the first learning check problem.

In one embodiment, the intrinsic difficulty can have a value from 0 to 100. In other words, the inherent difficulty can be expressed as a number in the range of 0 to 100. However, embodiments of the present invention are not limited thereto, and the range of inherent difficulty may vary.

In one embodiment, the processor of the learning server 300 may calculate the inherent difficulty of the first learning check problem using [Equation 1]. In [Equation 1], D_i is the inherent difficulty level, t is the problem solving time for the learner's first learning check problem, P_c is the correct answer rate of the first learning check problem, and m and n are unique constants.

The processor of the learning server 300 measures the learner's learning achievement level for the learning content corresponding to the first learning check problem (S550).

In one embodiment, learning achievement may be measured according to each of a plurality of classification criteria of learning content. For example, the learning achievement may include a first learning achievement indicating learning achievement for a specific subject, and a second learning achievement indicating learning achievement for a specific chapter. That is, when learning content is classified according to a plurality of classification criteria, the processor of the learning server 300 can measure the learner's learning achievement for the learning content according to each classification criterion.

The processor of the learning server 300 may refer to the learner's learning time and learning progress for the learning content in order to measure the learner's learning achievement for the learning content. In addition, the processor of the learning server 300 transmits a plurality of learning check problems classified into a specific chapter of a specific subject to the learner terminal 400 corresponding to the learner, and each of the plurality of learning check problems is sent from the learner terminal 400. A plurality of learning check responses corresponding to may be received. In this case, the processor of the learning server 300 may measure the learner's learning achievement based on the number of learning check responses that are determined to be correct among a plurality of learning check responses.

For example, if the learning content is data explaining the definition of trigonometric ratios, the learning content may be primarily classified as a 'mathematics' subject and secondarily as a 'trigonometric ratio' chapter. In this case, the processor of the learning server 300 measures the learner's learning achievement for the 'mathematics' subject as the first learning achievement, and the learner's learning achievement for the 'trigonometric ratio' chapter of the 'mathematics' subject as the second learning achievement. It can be measured. Learning achievement can have values from 0 to 100. In other words, learning achievement can be expressed as a number ranging from 0 to 100. However, embodiments of the present invention are not limited thereto, and the range of learning achievement may vary.

The processor of the learning server 300 calculates the learner's perceived difficulty level for the first learning check problem based on the inherent difficulty level and learning achievement level (S560).

In one embodiment, the processor of the learning server 300 may calculate the perceived difficulty level using [Equation 2]. In [Equation 2], D_f is the perceived difficulty, D_i is the inherent difficulty, A is the learner's learning achievement according to the learning content classification standard, and k is a natural number for identifying the learning content classification standard.

In one embodiment, the processor of the learning server 300 may correct the perceived difficulty level using a difficulty correction model. To this end, the difficulty correction model may include an input layer, one or more hidden layers, and an output layer. The difficulty correction model can be learned in advance so that the perceived difficulty can be accurately corrected through the learning process. To this end, learning data regarding learning achievement, intrinsic difficulty, and perceived difficulty may be input to the input layer of the difficulty correction model and pass through one or more hidden layers and output layers to output an output vector. The output vector can be input to the loss function layer connected to the output layer. The loss function layer can output loss values using a loss function that compares the output vector with the correct answer vector for each training data. The parameters of the difficulty correction model can be learned in a direction that reduces the loss value.

For example, the loss function can calculate the loss value according to Equation 3. In <Equation 3>, N is the number of a plurality of learning data, n is a natural number identifying the learning data, k is a natural number identifying the value of the nth learning data, and nk is the kth value of the nth learning data. , t may mean the correct answer data, y may mean the output vector, and E may mean the loss value.

Alternatively, the loss function can calculate the loss value according to Equation 4. In <Equation 4>, n is the number of training 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 training data belongs to class y, and x_j is the learning data. The probability value that the data belongs to class j, L may mean the loss value.

To this end, the processor of the learning server 300 may include an artificial intelligence machine learning model. When the learning server 300 is implemented as hardware, the processor of the learning server 300 may include a hardware structure specialized for processing machine learning models. Machine learning models can be created through artificial intelligence machine learning.

Machine learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but examples of the foregoing It is not limited to 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 processor of the learning server 300 selects a second learning check problem with a unique difficulty level different from the first learning check problem from the database based on the perceived difficulty level, and transmits the second learning check problem to the learner terminal (S570) .

The processor of the learning server 300 may compare the inherent difficulty of the first learning check problem with the learner's perceived difficulty of the first learning problem. If the intrinsic difficulty of the first learning check problem is higher than the learner's perceived difficulty of the first learning problem, the processor of the learning server 300 selects a learning check problem in the database that has a higher intrinsic difficulty than the first learning check problem as a second learner. It can be selected as a check problem and transmitted to the learner terminal 400.

If the intrinsic difficulty of the first learning check problem is lower than the learner's perceived difficulty of the first learning problem, the processor of the learning server 300 selects a learning check problem in the database that has a lower intrinsic difficulty than the first learning check problem for second learning. It can be selected as a check problem and transmitted to the learner terminal 400.

As described above, the problem difficulty adjustment method according to an embodiment of the present invention can provide a learning check problem with a difficulty level suitable for the learner based on the unique difficulty of the learning check problem and the learner's learning achievement with respect to the learning content. In other words, the learner will be able to obtain appropriate feedback according to the amount of learning by solving appropriate learning check problems according to the learning achievement measured according to the learner's learning progress, learning time, etc.

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 (5)

selecting, by a learning server, a first learning check problem classified into a specific chapter of a specific subject in a database, and transmitting the first learning check problem to a learner terminal;
Receiving the learner's first learning check response to the first learning check problem from the learner terminal;
calculating a unique difficulty level for the first learning check problem;
Measuring the learner's learning achievement for learning content corresponding to the first learning check problem;
calculating the learner's perceived difficulty level for the first learning check problem based on the unique difficulty level and the learning achievement level;
Selecting a second learning check problem having a unique difficulty level different from that of the first learning check problem from the database based on the perceived difficulty level, and transmitting the second learning check problem to the learner terminal. doing,
How to adjust problem difficulty using a neural network. According to paragraph 1,
The learning achievement includes a first learning achievement indicating learning achievement for the specific subject and a second learning achievement indicating learning achievement for the specific chapter,
How to adjust problem difficulty using a neural network. According to paragraph 1,
The unique difficulty level is calculated based on the learner's solving time of the first learning check problem and the correct answer rate of the first learning check problem,
How to adjust problem difficulty using a neural network. According to paragraph 1,
[Equation]

The learning server calculates the perceived difficulty level using the above equation,
In the above equation, D_f is the perceived difficulty, D_i is the inherent difficulty, A is the learner's learning achievement according to the learning content classification standard, and k is a natural number for identifying the learning content classification standard.
How to adjust problem difficulty using a neural network. According to paragraph 1,
The learning server corrects the perceived difficulty level using a difficulty correction model,
The difficulty correction model includes an input layer, one or more hidden layers, and an output layer,
A plurality of learning data regarding the unique difficulty, the learning achievement, and the perceived difficulty are input to the input layer of the difficulty correction model, pass through the one or more hidden layers and the output layer, and are output as an output vector,
The output vector is input to a loss function layer connected to the output 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 of the training data,
The parameters of the difficulty correction model are learned in the direction of decreasing the loss value,
How to adjust problem difficulty using a neural network.

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