KR20240067224A - Method for providing user-based language learning content - Google Patents

Abstract

This disclosure relates to a method for providing user-based language learning content, performed by at least one processor. A method of providing user-based language learning content includes receiving a first test result of a first user for a test sentence containing a first pattern, a second test result of the first user for a test sentence containing a second pattern, receiving, based on the first test results, determining a proficiency score of the first pattern of the first user, and determining a proficiency score of the second pattern of the first user based on the second test results. and determining at least one recommended sentence among a plurality of learning sentences based on the proficiency score of the first pattern and the proficiency score of the second pattern.

Description

{METHOD FOR PROVIDING USER-BASED LANGUAGE LEARNING CONTENT}

This disclosure relates to a method of providing user-based language learning content, and specifically, to a method of calculating a pattern proficiency score for each user and providing recommended sentences for language learning based on this.

Due to the spread of mobile devices such as smartphones and the development of the Internet, the number of users learning languages using mobile devices is increasing. According to these needs, various applications or platforms for providing language learning content have recently been distributed and are being used by many users.

According to applications that provide conventional language learning content, a method of providing a set of problems for each level prepared in advance according to the user's language learning level is used. In this method, problems classified by the language learning content provider according to criteria such as vocabulary difficulty, grammar difficulty, and sentence length are collectively provided to users of the same level. However, in the case of the conventional method, there is a problem that it is difficult to provide customized language learning content to complement different weak areas for each user because each user has different weak areas independently of the difficulty of grammar or vocabulary.

The present disclosure provides a method of providing language learning content to solve the above problems.

The present disclosure may be implemented in various ways, including as a method, device (system), or computer program stored in a readable storage medium.

According to one embodiment of the present disclosure, a method of providing user-based language learning content, performed by at least one processor, includes receiving a first test result of a first user for a test sentence including a first pattern; Receiving a second test result of a first user for a test sentence including a second pattern, determining a proficiency score of the first pattern of the first user based on the first test result, the second test result Based on this, determining a proficiency score of the second pattern of the first user and determining at least one recommended sentence among a plurality of sentences for learning based on the proficiency score of the first pattern and the proficiency score of the second pattern. Includes.

According to an embodiment of the present disclosure, the first test result of the first user is determined based on whether the first user answers the test sentence including the first pattern correctly.

According to an embodiment of the present disclosure, a method for providing user-based language learning content includes receiving a third test result of a second user for a test sentence including a first pattern, based on the third test result, 2 determining a proficiency score of the first pattern associated with the user and, based on the proficiency score of the first pattern associated with the first user and the proficiency score of the first pattern associated with the second user, determining a proficiency score of the first pattern associated with the third user. It further includes the step of estimating the proficiency score.

According to an embodiment of the present disclosure, the step of determining at least one recommended sentence among a plurality of sentences for learning is based on the proficiency score of the first user including the proficiency score of the first pattern and the proficiency score of the second pattern. , determining a sentence level score for each of the plurality of sentences for learning, and determining one or more recommended sentences among the plurality of sentences for learning based on the sentence level score.

According to an embodiment of the present disclosure, the plurality of sentences for learning includes a first sentence for learning, the first sentence for learning includes a first pattern and a second pattern, and determines a sentence level score for each of the plurality of sentences for learning. The step of determining the sum of the proficiency score of the first pattern and the proficiency score of the second pattern of the first user divided by the number of patterns included in the first learning sentence is the sentence level score of the first learning sentence. Includes.

According to an embodiment of the present disclosure, the step of determining one or more recommended sentences among a plurality of learning sentences includes determining a learning sentence having a sentence level score within a threshold range among the plurality of learning sentences as a recommendation candidate sentence. do.

According to an embodiment of the present disclosure, the step of determining a recommendation candidate sentence includes extracting a recommendation sentence based on the recommendation candidate sentence using a sentence level score and pattern usage frequency.

According to an embodiment of the present disclosure, the step of extracting recommended sentences includes extracting sentences included in a predetermined range in descending order of sentence level scores from recommendation candidate sentences, and extracting sentences with a predetermined frequency of pattern use among the extracted sentences. It includes the step of determining a sentence that is greater than or equal to a threshold value as a recommended sentence.

In order to execute the method according to an embodiment of the present disclosure on a computer, a computer program stored in a computer-readable recording medium is provided.

According to one embodiment of the present disclosure, the information processing system includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, and at least one The program is configured to: receive a first test result of a first user for a test sentence containing a first pattern, receive a second test result of a first user for a test sentence containing a second pattern, and perform a first test result. Based on the results, a proficiency score of the first pattern of the first user is determined, and based on the second test result, a proficiency score of the first user of the second pattern is determined, and a proficiency score of the first pattern and a second It includes commands for determining at least one recommended sentence among a plurality of sentences for learning, based on the proficiency score of the pattern.

According to some embodiments of the present disclosure, recommended sentences are provided based on the user's proficiency score for each pattern using user test results for test sentences including patterns, so that the user can enjoy systematic language learning content classified by pattern. learning efficiency can be improved.

According to some embodiments of the present disclosure, a sentence level score may be calculated for a sentence complexly containing one or more patterns associated with the user's proficiency score for each pattern, and a recommended sentence may be provided based on this, so that the user can learn By using various sentences including this necessary pattern, specific patterns can be learned in various ways.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are clear to a person skilled in the art (referred to as “a person skilled in the art”) in the technical field to which this disclosure pertains from the description of the claims. It will be understandable.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, in which like reference numerals indicate like elements, but are not limited thereto.
Figure 1 shows an example of providing recommended sentences for each user based on test results for each user's pattern by an information processing system according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram showing a configuration in which an information processing system according to an embodiment of the present disclosure is connected to enable communication with a plurality of user terminals.
Figure 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to an embodiment of the present disclosure.
Figure 4 is a block diagram showing the internal configuration of a processor according to an embodiment of the present disclosure.
Figure 5 is a diagram illustrating an example of classifying a plurality of pattern sentence sets from a plurality of sentences according to an embodiment of the present disclosure.
FIG. 6 is a diagram illustrating an example of a method of estimating another user's pattern score based on the pattern scores of a plurality of users according to an embodiment of the present disclosure.
Figure 7 is a diagram illustrating an example of a method of determining a sentence level score for each user's learning sentence based on the user's score for each pattern according to an embodiment of the present disclosure.
Figure 8 is a diagram illustrating an example of a method for determining recommended sentences for language learning for a user based on the user's frequency of use for each pattern according to an embodiment of the present disclosure.
Figure 9 is a flowchart illustrating an example of a method for providing user-based language learning content according to an embodiment of the present disclosure.

Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, detailed descriptions of well-known functions or configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

In the accompanying drawings, identical or corresponding components are given the same reference numerals. Additionally, in the description of the following embodiments, overlapping descriptions of identical or corresponding components may be omitted. However, even if descriptions of components are omitted, it is not intended that such components are not included in any embodiment.

Advantages and features of the disclosed embodiments and methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the present disclosure is complete and that the present disclosure does not convey the scope of the invention to those skilled in the art. It is provided only for complete information.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification are general terms that are currently widely used as much as possible while considering the functions in the present disclosure, but this may vary depending on the intention or precedent of a technician working in the related field, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Accordingly, the terms used in this disclosure should be defined based on the meaning of the term and the overall content of the present disclosure, rather than simply the name of the term.

In this specification, singular expressions include plural expressions, unless the context clearly specifies the singular. Additionally, plural expressions include singular expressions, unless the context clearly specifies plural expressions. When it is said that a certain part includes a certain element throughout the specification, this does not mean excluding other elements, but may further include other elements, unless specifically stated to the contrary.

Additionally, the term 'module' or 'unit' used in the specification refers to a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not limited to software or hardware. A 'module' or 'unit' may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a 'module' or 'part' refers to components such as software components, object-oriented software components, class components and task components, processes, functions and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. Components and 'modules' or 'parts' may be combined into smaller components and 'modules' or 'parts' or further components and 'modules' or 'parts'. Could be further separated.

According to an embodiment of the present disclosure, a 'module' or 'unit' may be implemented with a processor and memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc. In some contexts, 'processor' may refer to an application-specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), etc. 'Processor' refers to a combination of processing devices, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such combination of configurations. You may. Additionally, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' refers to random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), May also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. A memory is said to be in electronic communication with a processor if the processor can read information from and/or write information to the memory. The memory integrated into the processor is in electronic communication with the processor.

In the present disclosure, 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated with a server device and a cloud device configured together.

In this disclosure, 'machine learning model' may include any model used to infer an answer to a given input. According to one embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include multiple nodes.

In this disclosure, 'display' may refer to any display device associated with a computing device, e.g., any display device capable of displaying any information/data controlled by or provided by the computing device. can refer to.

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A, or may refer to each of some components included in a plurality of A. .

In this disclosure, 'pattern' may include expressions and usage within sentences that can be the subject of language learning, such as words, idioms, idioms, and grammar. For example, the sentence 'Why don't you have Farrah take you to some parties?' contains the patterns 'Why', 'don't', 'you', 'have', 'Farrah', 'take', 'to ', 'some', 'parties(party)', 'Why don't you', 'have + object + verb infinitive', etc. According to one embodiment, even the same word may be treated as a different pattern depending on the meaning, usage, part of speech, etc. used in the sentence. For example, 'what' used as an interrogative word in a sentence and 'what' used as a relative pronoun can be treated as different patterns.

In the present disclosure, 'user' may refer to a user terminal that the user can access or use. Additionally, a user terminal may refer to a user. In the present disclosure, the terms user and user terminal may be used interchangeably.

Figure 1 shows an example of providing recommended sentences 122 and 124 for each user based on test results 112 and 114 for each user's pattern by the information processing system 100 according to an embodiment of the present disclosure. As shown, the information processing system 100 may provide recommended sentences 122 and 124 for each user for language learning based on test results 112 and 114 for each user pattern. For example, the information processing system 100 may provide a recommended sentence 122 for the first user based on the test result 112 for each pattern of the first user. Similarly, the information processing system 100 may provide a recommended sentence 124 for the second user based on the test result 114 for each pattern of the second user.

In one embodiment, the information processing system 100 may transmit a test sentence to a user (eg, a user terminal accessible to the user, etc.) in order to obtain test results 112 and 114 for each user's pattern. For example, the information processing system 100 transmits a test sentence containing a first pattern and a test sentence containing a second pattern to a first user, and receives the first user's test results for each test sentence. can do. Here, the test result of the first user may be determined based on whether the first user answers the test sentence including the first pattern and/or the second pattern.

In one embodiment, the information processing system 100 may determine the user's proficiency score for each pattern based on the user's test results 112 and 114 for each pattern. For example, the information processing system 100 may determine a proficiency score for the first pattern of the first user based on a test sentence including the first pattern. Similarly, the information processing system 100 may determine a proficiency score for the second pattern of the first user based on a test sentence including the second pattern.

According to one embodiment, the information processing system 100 may determine a recommended sentence for the user based on the user's proficiency score for each pattern. For example, the information processing system 100 may determine a sentence level score for a learning sentence including one or more patterns based on the user's proficiency score for each pattern. Then, the information processing system 100 may determine a learning sentence with a sentence level score within a critical range among the plurality of learning sentences as a recommendation candidate sentence. Here, the plurality of learning sentences may represent sentences stored in any storage medium (eg, learning content database, etc.) accessible by the information processing system 100, but is not limited thereto.

According to one embodiment, the information processing system 100 may extract a recommended sentence from among recommendation candidate sentences using the sentence level score and/or the frequency of use of the pattern. For example, the processor may extract sentences included in a predetermined range from recommendation candidate sentences in descending order of sentence level score. Then, the processor may determine a sentence whose pattern usage frequency is greater than or equal to a predetermined threshold among the extracted sentences as a recommended sentence. For example, in order to extract sentences of appropriate difficulty to improve the user's concentration on language learning content, the information processing system prioritizes sentences with sentence level scores in the top 80% to 90% range among a plurality of learning sentences. It can be extracted and determined as a recommended candidate sentence. Then, the information processing system may determine the top 10 sentences for the most frequently used pattern with low sentence level scores among the recommendation candidate sentences as recommended sentences. Here, the threshold range of the sentence level score and the threshold value of the frequency of use are not limited to the examples described above.

Through this configuration, users can receive learning content appropriate to their level using test results for test sentences containing patterns and recommended sentences for learning based on the user's proficiency score for each pattern. Learning efficiency can be improved.

In the present disclosure, language learning content may include learning content in any language, such as English, Korean, Japanese, Chinese, French, German, or a computer programming language.

Figure 2 is a schematic diagram showing a configuration in which the information processing system 230 according to an embodiment of the present disclosure is connected to communicate with a plurality of user terminals 210_1, 210_2, and 210_3. As shown, a plurality of user terminals 210_1, 210_2, and 210_3 may be connected to the information processing system 230 that can provide a language learning content provision service through the network 220. Here, the plurality of user terminals 210_1, 210_2, and 210_3 may include terminals of users, teachers, and/or administrators who will be provided with language learning content provision services. In one embodiment, information processing system 230 may be one or more server devices and/or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to language learning content provision services, etc. , or may include one or more distributed computing devices and/or distributed databases based on cloud computing services. Additionally, the information processing system 230 may be the same or similar to the information processing system 100 of FIG. 1 .

The language learning content provision service provided by the information processing system 230 is provided to users through a language learning content provision application, mobile browser application, web browser, etc. installed on each of the plurality of user terminals 210_1, 210_2, and 210_3. It can be. For example, the information processing system 230 may provide information corresponding to a learning content request received from the user terminals 210_1, 210_2, and 210_3 through a language learning content providing application or perform corresponding processing.

A plurality of user terminals 210_1, 210_2, and 210_3 may communicate with the information processing system 230 through the network 220. The network 220 may be configured to enable communication between a plurality of user terminals 210_1, 210_2, and 210_3 and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of wireless networks such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include communication methods utilizing communication networks that the network 220 may include (e.g., mobile communication networks, wired Internet, wireless Internet, broadcasting networks, satellite networks, etc.) as well as user terminals (210_1, 210_2, 210_3). ) may also include short-range wireless communication between

In Figure 2, the mobile phone terminal (210_1), tablet terminal (210_2), and PC terminal (210_3) are shown as examples of user terminals, but they are not limited thereto, and the user terminals (210_1, 210_2, 210_3) use wired and/or wireless communication. This may be any computing device capable of installing and executing a language learning content providing application, a mobile browser application, a web browser, etc. For example, user terminals include AI speakers, smartphones, mobile phones, navigation, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, It may include wearable devices, IoT (internet of things) devices, VR (virtual reality) devices, AR (augmented reality) devices, set-top boxes, etc. In addition, in Figure 2, three user terminals (210_1, 210_2, 210_3) are shown as communicating with the information processing system 230 through the network 220, but this is not limited to this, and a different number of user terminals are connected to the network ( It may be configured to communicate with the information processing system 230 through 220).

Figure 3 is a block diagram showing the internal configuration of the user terminal 210 and the information processing system 230 according to an embodiment of the present disclosure. The user terminal 210 may refer to any computing device capable of executing a language learning content providing application, a mobile browser application, or a web browser and capable of wired/wireless communication. For example, the mobile phone terminal 210_1 of FIG. 2 ), a tablet terminal (210_2), a PC terminal (210_3), etc. As shown, the user terminal 210 may include a memory 312, a processor 314, a communication module 316, and an input/output interface 318. Similarly, information processing system 230 may include memory 332, processor 334, communication module 336, and input/output interface 338. As shown in FIG. 3, the user terminal 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. It can be. Additionally, the input/output device 320 may be configured to input information and/or data to the user terminal 210 through the input/output interface 318 or to output information and/or data generated from the user terminal 210.

Memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 are non-permanent mass storage devices such as read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. It can be included. As another example, non-perishable mass storage devices such as ROM, SSD, flash memory, disk drive, etc. may be included in the user terminal 210 or the information processing system 230 as a separate persistent storage device that is distinct from memory. Additionally, the memories 312 and 332 may store an operating system and at least one program code (for example, code for a language learning content providing application installed and running on the user terminal 210).

These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332. This separate computer-readable recording medium may include a recording medium directly connectable to the user terminal 210 and the information processing system 230, for example, a floppy drive, disk, tape, DVD/CD- It may include computer-readable recording media such as ROM drives and memory cards. As another example, software components may be loaded into the memories 312 and 332 through a communication module rather than a computer-readable recording medium. For example, at least one program is loaded into memory 312, 332 based on a computer program installed by files provided over the network 220 by developers or a file distribution system that distributes installation files for applications. It can be.

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processors 314 and 334 by memories 312 and 332 or communication modules 316 and 336. For example, processors 314 and 334 may be configured to execute received instructions according to program codes stored in recording devices such as memories 312 and 332.

The communication modules 316 and 336 may provide a configuration or function for the user terminal 210 and the information processing system 230 to communicate with each other through the network 220, and may provide a configuration or function for the user terminal 210 and/or information processing. The system 230 may provide a configuration or function for communicating with other user terminals or other systems (for example, a separate cloud system, etc.). For example, a request or data (e.g., learning content request, etc.) generated by the processor 314 of the user terminal 210 according to a program code stored in a recording device such as the memory 312 is transmitted to the communication module 316. Depending on the control, it may be transmitted to the information processing system 230 through the network 220. Conversely, a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 316 of the user terminal 210 through the communication module 336 and the network 220. It may be received by the user terminal 210. For example, the user terminal 210 may receive data associated with learning content, a list of questions associated with at least one pattern included in the learning content, etc. from the information processing system 230 through the communication module 316.

The input/output interface 318 may be a means for interfacing with the input/output device 320. As an example, input devices may include devices such as cameras, keyboards, microphones, mice, etc., including audio sensors and/or image sensors, and output devices may include devices such as displays, speakers, haptic feedback devices, etc. You can. As another example, the input/output interface 318 may be a means for interfacing with a device that has components or functions for performing input and output, such as a touch screen, integrated into one. For example, the processor 314 of the user terminal 210 uses information and/or data provided by the information processing system 230 or another user terminal when processing instructions of a computer program loaded in the memory 312. A service screen, etc. constructed by doing so may be displayed on the display through the input/output interface 318. In FIG. 3 , the input/output device 320 is shown not to be included in the user terminal 210, but the present invention is not limited to this and may be configured as a single device with the user terminal 210. Additionally, the input/output interface 338 of the information processing system 230 may be connected to the information processing system 230 or means for interfacing with a device (not shown) for input or output that the information processing system 230 may include. It can be. In FIG. 3, the input/output interfaces 318 and 338 are shown as elements configured separately from the processors 314 and 334, but the present invention is not limited thereto, and the input/output interfaces 318 and 338 may be configured to be included in the processors 314 and 334. there is.

The user terminal 210 and information processing system 230 may include more components than those in FIG. 3 . However, there is no need to clearly show most prior art components. According to one embodiment, the user terminal 210 may be implemented to include at least some of the input/output devices 320 described above. Additionally, the user terminal 210 may further include other components such as a transceiver, a global positioning system (GPS) module, a camera, various sensors, and a database. For example, if the user terminal 210 is a smartphone, it may include components generally included in a smartphone, such as an acceleration sensor, a gyro sensor, a camera module, various physical buttons, and a touch screen. Various components such as buttons using a panel, input/output ports, and a vibrator for vibration may be implemented to be further included in the user terminal 210. According to one embodiment, the processor 314 of the user terminal 210 may be configured to operate an application that provides a language learning content provision service. At this time, code associated with the corresponding application and/or program may be loaded into the memory 312 of the user terminal 210.

While a program for an application providing a language learning content provision service is operated, the processor 314 inputs input such as a touch screen, keyboard, camera including an audio sensor and/or an image sensor, and a microphone connected to the input/output interface 318. Text, images, videos, voices and/or actions input or selected through the device can be received, and the received text, images, videos, voices and/or actions can be stored in the memory 312 or stored in the communication module 316. ) and can be provided to the information processing system 230 through the network 220. For example, the processor 314 may receive input representing a user's response to a language test sentence, etc., and provide the input to the information processing system 230 through the communication module 316 and the network 220. As another example, the processor 314 may receive a user input requesting learning content and provide it to the information processing system 230 through the communication module 316 and the network 220.

The processor 314 of the user terminal 210 manages, processes, and/or stores information and/or data received from the input device 320, other user terminals, the information processing system 230, and/or a plurality of external systems. It can be configured to do so. Information and/or data processed by processor 314 may be provided to information processing system 230 via communication module 316 and network 220. The processor 314 of the user terminal 210 may transmit information and/or data to the input/output device 320 through the input/output interface 318 and output the information. For example, the processor 314 may output the received information and/or data so that the received information and/or data is displayed on the screen of the user terminal.

The processor 334 of the information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of user terminals 210 and/or a plurality of external systems. Information and/or data processed by the processor 334 may be provided to the user terminal 210 through the communication module 336 and the network 220. In one embodiment, the processor 334 of the information processing system 230 determines the user's proficiency score for each pattern based on the user's test results for test sentences including the pattern, and based on this, among the plurality of learning sentences. You can decide on the recommended sentence.

The processor 334 of the information processing system 230 uses the output device 320, such as a display output capable device (e.g., touch screen, display, etc.), a sound output capable device (e.g., speaker), of the user terminal 210. It may be configured to output processed information and/or data. For example, the processor 334 of the information processing system 230 provides data associated with learning content to the user terminal 210 through the communication module 336 and the network 220, and sends the learning content to the user terminal 210. ) can be configured to output through a display output capable device, etc. As another example, the processor 334 of the information processing system 230 provides a list of questions associated with at least one pattern included in the learning content to the user terminal 210 through the communication module 336 and the network 220. and may be configured to output through a display output capable device of the user terminal 210, etc.

Figure 4 is a block diagram showing the internal configuration of the processor 334 according to an embodiment of the present disclosure. As shown, the processor 334 may include a pattern classification unit 410, a pattern proficiency determination unit 420, a sentence level determination unit 430, and a recommended sentence determination unit 440. The internal configuration of the processor 334 shown in FIG. 4 is only an example and may be implemented differently in some embodiments. For example, the processor 334 may further include other configurations than those shown, and at least some of the illustrated configurations may be omitted. Additionally, although the processor 334 in FIG. 4 is shown as a single processor, it is not limited thereto and may be comprised of a plurality of processors. Additionally, in Figure 4, each part is shown divided from a functional perspective, but this does not necessarily mean that they are physically separated.

According to one embodiment, the pattern classification unit 410 may classify and store a plurality of sentences included in the language learning content database into one or more patterns. For example, the pattern classification unit 410 may classify and store a plurality of sentences by pattern based on one or more predetermined patterns. Here, the plurality of sentences may be composed of text and/or voice corresponding to the text. Under this configuration, the pattern classification unit 410 can classify and store test sentences for determining a pattern proficiency score for the user and learning sentences for providing language learning content to the user. Here, the test sentence may be a sentence containing a specific pattern to determine the pattern proficiency score for the specific pattern. Additionally, a learning sentence may include at least one user's pattern. For example, a learning sentence may contain multiple patterns in complex form. Additionally, sentences for learning may be classified and provided to the user according to the length of the sentence and the speed of the voice corresponding to the sentence. The pattern classification unit 410 may use a plurality of sentences as input, and may include or use a machine learning model learned to classify patterns included in the plurality of sentences, but is not limited thereto, and may classify the plurality of sentences through a rule-based algorithm. The patterns included can be classified.

According to one embodiment, the pattern proficiency determination unit 420 may determine the user's proficiency with a specific pattern based on the user's test results for test sentences including the specific pattern. Specifically, the pattern proficiency determination unit 420 may determine a proficiency score for the user's specific pattern based on the user's percentage of correct answers for each of a plurality of test sentences including the specific pattern. For example, if the correct answer rate for the user's first pattern is 40%, the pattern proficiency determination unit 420 may calculate the proficiency score for the user's first pattern as 4 points or 40 points, and for the second pattern If the correct answer rate is 80%, the proficiency score of the user's second pattern can be calculated as 8 points or 80 points, thereby determining the user's proficiency score. The method of calculating the proficiency score is not limited to the above-described example, and various methods of calculating the proficiency score that characterize or indicate the correct answer rate for the user's pattern can be applied.

According to one embodiment, the pattern proficiency determination unit 420 may estimate the pattern proficiency of other users based on the pattern proficiency of each of a plurality of users for a specific pattern. Specifically, the proficiency score of the third user's first pattern may be estimated based on the proficiency score of the first pattern of the first user and the proficiency score of the first pattern of the second user. For example, the proficiency score of the third user's first pattern may be calculated by the arithmetic mean of the proficiency score of the first user's first pattern and the proficiency score of the second user's first pattern. In another example, the proficiency score of the third user's first pattern is based on the proficiency score of the first pattern of the first user and the proficiency score of the second user's first pattern, and the proficiency score of the first pattern of the other user is It can be estimated by an artificial neural network model trained to estimate. The method of estimating another user's pattern proficiency is not limited to the above-described examples, and various methods that can characterize or represent the proficiency score of the first user's first pattern and the proficiency score of the second user's first pattern may be applied. You can.

According to one embodiment, the sentence level determination unit 430 may determine a sentence level score for each of a plurality of learning sentences based on the user's proficiency score for each pattern. For example, the sentence level determination unit 430 divides the sum of the user's first pattern proficiency score and the second pattern proficiency score by the number of patterns included in a specific learning sentence as the sentence level score of the specific learning sentence. You can decide. The method of determining the sentence level score of a learning sentence is not limited to the above-described examples and can be determined in various ways. For example, the sentence level determination unit 430 applies a weight calculated based on sentence attribute information such as the length of the sentence, the difficulty of the vocabulary included in the sentence, and the speed of the voice corresponding to the sentence, to determine the sentence level of the learning sentence. The score can decide.

According to one embodiment, the recommended sentence determination unit 440 may select a recommended sentence from a plurality of learning sentences based on the sentence level score of the learning sentence. For example, the recommended sentence decision unit 440 may determine a learning sentence with a sentence level score within a critical range among a plurality of learning sentences as a recommendation candidate sentence. Additionally, the recommended sentence determination unit 440 may extract a recommended sentence using the mid-sentence level score and pattern usage frequency based on the recommendation candidate sentence. For example, the recommended sentence determination unit 440 may extract sentences included in a predetermined range from among recommendation candidate sentences in descending order of sentence level score. Then, the recommended sentence determination unit 440 may determine a sentence with a pattern usage frequency greater than or equal to a predetermined threshold value among the extracted sentences as a recommended sentence. For example, the recommended sentence determination unit 440 determines a sentence with a sentence level score associated with the user between 80 and 90 points (or 8 to 9 points) as a recommendation candidate sentence, and selects a sentence level among the recommendation candidate sentences. Learning sentences with low scores and pattern usage frequency within the top 10% can be determined as recommended sentences.

FIG. 5 is a diagram illustrating an example of classifying a plurality of pattern sentence sets 532, 534, and 536 from a plurality of sentences 510 according to an embodiment of the present disclosure. As shown, the pattern classification unit 520 may classify the plurality of sentences 510 into a plurality of pattern sentence sets 532, 534, and 536. Here, the pattern classification unit 520 may be the same or similar to the pattern classification unit 410 of FIG. 4 .

The plurality of sentences 510 may be composed of text and/or voice corresponding to the text. For example, the pattern classification unit 520 may classify and store a plurality of sentences 510 by pattern based on one or more predetermined patterns. That is, as shown, the pattern classification unit 520 divides a plurality of sentences 510 into a first pattern sentence set 532, a second pattern sentence set 534, and a third pattern based on one or more predetermined patterns. It can be classified and stored into sentence sets (536), etc. Here, the first pattern sentence set 532 may be a sentence set including the first pattern among a plurality of patterns. Similarly, the second pattern sentence set 534 and the third pattern sentence set 536 may be sentence sets including a second pattern and a third pattern, respectively.

In one embodiment, the first pattern sentence set 532, the second pattern sentence set 534, and the third pattern sentence set 536 classified by pattern are used as test sentences to determine the pattern proficiency score for the user. It can be. Here, the test sentence may include a sentence including a pattern associated with it. For example, the first pattern sentence set 532 may include only the first pattern. In another embodiment, the first pattern sentence set 532, the second pattern sentence set 534, and the third pattern sentence set 536 may be used as learning sentences to provide language learning content to users. When the plurality of pattern sentence sets 532, 534, and 536 are used as learning sentences, each of the plurality of pattern sentence sets 532, 534, and 536 may further include a plurality of other patterns. For example, the learning sentences included in the first pattern sentence set 532 may include a second pattern and/or a third pattern. Similarly, the learning sentences included in the second pattern sentence set 534 may include the first pattern and/or the third pattern.

FIG. 6 is a diagram illustrating an example of a method of estimating another user's pattern score based on the pattern scores of a plurality of users according to an embodiment of the present disclosure. According to one embodiment, the information processing system may estimate the pattern proficiency of other users based on the pattern proficiency of each of a plurality of users for a specific pattern. Specifically, the proficiency score of the third user's first pattern may be estimated based on the proficiency score of the first pattern of the first user and the proficiency score of the first pattern of the second user. For example, as shown, if the third user's first pattern score (e.g., A) is unknown, the information processing system may determine the first user's first pattern score (e.g., 5 points), the second user The proficiency score of the third user's first pattern can be estimated based on the first pattern score (e.g., 7 points). For example, the information processing system may perform the arithmetic mean of the proficiency score of the first pattern of the first user (e.g., 5 points) and the proficiency score of the first pattern of the second user (e.g., 7 points) to obtain the third user's proficiency score of the first pattern (e.g., 7 points). The proficiency level of 1 pattern can be calculated as 6 points.

In another example, the proficiency score of the third user's first pattern is based on the proficiency score of the first pattern of the first user and the proficiency score of the second user's first pattern, and the proficiency score of the first pattern of the other user is It can be estimated by an artificial neural network model trained to estimate. The method of estimating another user's pattern proficiency is not limited to the above-described examples and can be estimated in various ways.

FIG. 7 is a diagram illustrating an example of a method of determining a sentence level score 720 for each user's learning sentence based on the user's score 710 for each pattern according to an embodiment of the present disclosure. For example, a score for each pattern associated with the first user may be determined according to the method described with reference to FIGS. 4 and 6 . That is, for the first user, the first pattern score may be determined as 10 points, the second pattern score may be determined as 2 points, and each of the third pattern score and fourth pattern score may be determined as 8 points. Then, the information processing system may determine a sentence level score for each of the plurality of learning sentences based on the user's proficiency score for each pattern. For example, the information processing system may determine the sum of the user's proficiency score of the first pattern and the proficiency score of the second pattern divided by the number of patterns included in the specific learning sentence as the sentence level score of the specific learning sentence. For example, as shown, the first learning sentence may include a first pattern and a second pattern. Since the first pattern score of the first user is 10 points and the second pattern score is 2 points, the sentence level score for the first learning sentence of the first user is 6 points by the arithmetic mean (e.g. (10+2)/ It can be calculated as 2 = 6)). Similarly, in the case of the second learning sentence including the first pattern and the third pattern, the first user's first pattern score is 10 points and the third pattern score is 8 points, so the The sentence level score can be calculated as 9 points (e.g. (10+8)/2 = 9). In addition, in the case of the third learning sentence including the first pattern and the fourth pattern, the first user's first pattern score is 10 points and the fourth pattern score is 8 points, so the sentence for the first user's third learning sentence The level score can be calculated as 9 points (e.g. (10+8)/2 = 9). Additionally, the sentence level score may be determined by further applying weights based on sentence attribute information, such as the length of the sentence, the difficulty of vocabulary included in the sentence, and the speed of the voice corresponding to the sentence.

According to one embodiment, the information processing system may determine a learning sentence having a sentence level score within a critical range among a plurality of learning sentences as a recommendation candidate sentence. For example, the information processing system may determine learning sentences whose sentence level scores fall within the critical range of 8 to 9 points as recommendation candidate sentences. for example. As shown, the second learning sentence and the third learning sentence each have a sentence level score of 9 points, so they fall within the critical range of 8 to 9 points and can be determined as recommendation candidate sentences. The sentence level score and recommended candidate sentences calculated and determined in this way can be used to select recommended sentences for the user's language learning content.

FIG. 8 is a diagram illustrating an example of a method for determining a recommended sentence 830 for language learning for a user based on the user's frequency of use for each pattern 810 according to an embodiment of the present disclosure. According to one embodiment, the information processing system may determine a learning sentence with a sentence level score within a critical range among a plurality of learning sentences as a recommendation candidate sentence 820. Then, the information processing system can extract a recommended sentence using the mid-sentence level score and pattern usage frequency based on the recommendation candidate sentence 820. For example, among the recommended candidate sentences 820 (e.g., the second learning sentence and the third learning sentence) determined according to the method described with reference to FIG. 7, the recommended sentence 830 is selected using the user's sentence level score and pattern usage frequency. ) can be extracted.

In one embodiment, the processor may extract sentences included in a predetermined range from the recommendation candidate sentences 820 in descending order of sentence level score. Then, among the extracted sentences, a sentence with a pattern usage frequency greater than or equal to a predetermined threshold may be determined as the recommended sentence 830. For example, the processor may extract two sentences from recommendation candidate sentences in descending order of sentence level score, and determine a sentence with a pattern usage frequency of more than a predetermined threshold among these sentences as a recommended sentence. In another example, when the sentence level scores of the recommendation candidate sentences are the same, the processor may determine a sentence containing a pattern with a high frequency of use among recommendation candidate sentences with the corresponding sentence level score as the recommended sentence 830. As shown, among the first to fourth patterns, the pattern with the highest frequency of use for each pattern may be the fourth pattern (eg, 10 points). In this case, the information processing system may determine the recommendation candidate sentence 820 (e.g., the second learning sentence and the third learning sentence) as the recommendation sentence 830, which includes the fourth pattern with the highest frequency of use for each pattern. there is. That is, the information processing system may determine the third learning sentence including the fourth pattern with the highest pattern usage frequency as the recommendation sentence 830 among the second and third learning sentences that are the recommendation candidate sentences 820. The recommended sentences determined in this way may be included in the user's language learning content and provided to the user.

Figure 8 shows that one recommended sentence is determined, but this is for convenience of explanation, and the recommended sentence may include one or more sentences.

FIG. 9 is a flowchart illustrating an example of a method 900 for providing user-based language learning content according to an embodiment of the present disclosure. The method 900 may begin with at least one processor of the information processing system receiving a first test result of a first user for a test sentence including a first pattern (S910). Additionally, the processor may receive the first user's second test result for the test sentence including the second pattern (S920). Here, the first test result of the first user may be determined based on whether the first user answers the test sentence containing the first pattern, and the second test result of the first user may include the first pattern. It may be determined based on whether the first user answers the test sentence correctly.

Then, the processor may determine the proficiency score of the first pattern of the first user based on the first test result (S930). Additionally, the processor may determine the proficiency score of the first user's second pattern based on the second test result (S940).

Additionally, the processor may estimate the user's proficiency score for each pattern based on the proficiency score for each pattern of another user. For example, the processor may receive a third test result of a second user for a test sentence that includes the first pattern. And, the processor may determine the proficiency score of the first pattern of the second user based on the third test result. The processor may then estimate a proficiency score of the first pattern associated with a third user based on the proficiency score of the first pattern associated with the first user and the proficiency score of the first pattern associated with the second user. .

Then, the processor may determine at least one recommended sentence among the plurality of learning sentences based on the proficiency score of the first pattern and the proficiency score of the second pattern (S950). According to one embodiment, the processor may determine a sentence level score for each of a plurality of sentences for learning based on the first user's proficiency score including the proficiency score of the first pattern and the proficiency score of the second pattern. Thereafter, the processor may determine one or more recommended sentences among the plurality of sentences for learning based on the sentence level score. Here, the plurality of sentences for learning includes a first sentence for learning, the first sentence for learning includes a first pattern and a second pattern, and the step of determining a sentence level score for each of the plurality of sentences for learning includes the first user It may include determining the sum of the proficiency score of the first pattern and the proficiency score of the second pattern divided by the number of patterns included in the first learning sentence as the sentence level score of the first learning sentence.

In one embodiment, the processor may determine a learning sentence with a sentence level score within a threshold range among a plurality of learning sentences as a recommendation candidate sentence. Specifically, the processor may extract a recommended sentence using the mid-sentence level score and pattern usage frequency based on the recommendation candidate sentence. For example, the processor may extract sentences included in a predetermined range from recommendation candidate sentences in descending order of sentence level score. Then, the processor may determine a sentence whose pattern usage frequency is greater than or equal to a predetermined threshold among the extracted sentences as a recommended sentence.

The flowchart of FIG. 9 and the above description are only examples, and the scope of the present disclosure is not limited thereto. For example, at least one step may be added/changed/deleted, or the order of each step may be changed.

The above-described method may be provided as a computer program stored in a computer-readable recording medium for execution on a computer. Media may be used to continuously store executable programs on a computer, or may be temporarily stored for execution or download. Additionally, the medium may be a variety of recording or storage means in the form of a single or several pieces of hardware combined. It is not limited to a medium directly connected to a computer system and may be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and There may be something configured to store program instructions, including ROM, RAM, flash memory, etc. Additionally, examples of other media include recording or storage media managed by app stores that distribute applications, sites or servers that supply or distribute various other software, etc.

The methods, operations, or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or It may be implemented or performed as any combination of those designed to perform the functions described in. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

For firmware and/or software implementations, techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), and PROM ( on computer-readable media such as programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. It may also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

When implemented in software, the techniques may be stored on or transmitted through a computer-readable medium as one or more instructions or code. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a computer. By way of non-limiting example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or the desired program code in the form of instructions or data structures. It can be used to transfer or store data and can include any other media that can be accessed by a computer. Any connection is also properly termed a computer-readable medium.

For example, if the Software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, , fiber optic cable, twisted pair, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. As used herein, disk and disk include CD, laser disk, optical disk, digital versatile disc (DVD), floppy disk, and Blu-ray disk, where disks are usually magnetic. It reproduces data optically, while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known. An exemplary storage medium may be coupled to the processor such that the processor may read information from or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and storage medium may reside within an ASIC. ASIC may exist within the user terminal. Alternatively, the processor and storage medium may exist as separate components in the user terminal.

Although the above-described embodiments have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the disclosure is not limited thereto and may also be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Furthermore, aspects of the subject matter of this disclosure may be implemented in multiple processing chips or devices, and storage may be similarly effected across the multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure as can be understood by those skilled in the art to which the invention pertains. Additionally, such modifications and changes should be considered to fall within the scope of the claims appended hereto.

100: Information processing system
112: Test results for each pattern of the first user
114: Test results for each pattern of the second user
122: Recommendation sentence for the first user
124: Recommendation sentence for the second user

Claims (6)

A method for providing user-based language learning content, performed by at least one processor, comprising:
Receiving a plurality of test results including test results corresponding to each of the plurality of patterns;
determining a plurality of proficiency scores including proficiency scores corresponding to each of the plurality of patterns based on the plurality of test results;
Based on the plurality of proficiency scores, determining a plurality of sentence level scores including a sentence level score corresponding to each of the plurality of learning sentences;
determining a plurality of recommendation candidate sentences among the plurality of learning sentences based on the determined plurality of sentence level scores; and
Extracting recommended sentences from the plurality of recommendation candidate sentences based on the pattern usage frequency associated with each of the plurality of recommendation candidate sentences.
Including,
The step of determining the plurality of sentence level scores is:
determining a first user's sentence level score for a first one of the plurality of training sentences based on a first set of proficiency scores associated with the first user; and
determining a second user's sentence level score for the first sentence based on a second set of proficiency scores associated with the second user.
Including,
The step of outputting the plurality of recommendation candidate sentences is,
extracting sentences within a predetermined range from among the plurality of sentences for learning in descending order of sentence level score; and
Using the pattern usage frequency associated with each of the extracted sentences, determining a sentence with a pattern usage frequency greater than or equal to a predetermined threshold as a recommended sentence.
A method of providing user-based language learning content, including.
According to paragraph 1,
Receiving a third test result of a third user for a specific pattern among the plurality of patterns, wherein the third user is different from the first user and the second user;
determining a proficiency score of the third user for the specific pattern based on the third test result;
estimating a proficiency score of the specific pattern associated with a fourth user based on the proficiency score of the specific pattern associated with the first user and the proficiency score of the specific pattern associated with the third user, wherein the fourth user Different from the first user, the second user and the third user -
A method of providing user-based language learning content, further comprising:
According to paragraph 1,
The step of determining the sentence level score is,
For each of the plurality of learning sentences, determining the sum of the proficiency scores of patterns included in each learning sentence divided by the number of patterns included in each learning sentence as the sentence level score of each learning sentence.
containing. A user-based method of providing language learning content.
According to paragraph 1,
The step of determining the plurality of recommended candidate sentences is,
A step of determining a learning sentence having a sentence level score within a critical range among the plurality of learning sentences as a recommendation candidate sentence.
A method of providing user-based language learning content, including.
A computer program stored in a computer-readable recording medium for executing the method according to any one of claims 1 to 4 on a computer.
As an information processing system,
communication module;
Memory; and
At least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory
Including,
The at least one program is,
Receiving a plurality of test results including test results corresponding to each of the plurality of patterns,
Based on the plurality of test results, determining a plurality of proficiency scores including a proficiency score corresponding to each of the plurality of patterns,
Based on the plurality of proficiency scores, determine a plurality of sentence level scores including a sentence level score corresponding to each of the plurality of learning sentences,
Based on the determined plurality of sentence level scores, determine a plurality of recommendation candidate sentences among the plurality of learning sentences,
Includes instructions for extracting recommended sentences from the plurality of recommendation candidate sentences based on a pattern usage frequency associated with each of the plurality of recommendation candidate sentences,
Determining the plurality of sentence level scores includes:
determine a sentence level score of the first user for a first sentence of the plurality of training sentences based on a first set of proficiency scores associated with the first user;
determining a second user's sentence level score for the first sentence based on a second set of proficiency scores associated with the second user;
Outputting the plurality of recommended candidate sentences includes:
Extract sentences within a predetermined range from the plurality of learning sentences in descending order of sentence level score,
An information processing system comprising: using a pattern usage frequency associated with each of the extracted sentences, to determine a sentence whose pattern usage frequency is greater than or equal to a predetermined threshold as a recommended sentence.

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