KR102465768B1 - A system that provides personalized and customized learning analysis - Google Patents

Abstract

A personalized learning analysis system of the present invention comprises: a sensor unit for detecting in real time first event information about a biological change of a learner; a control unit for obtaining second event information about the learning activity of the learner and personal information of the learner from the first terminal while communicating by establishing a network with the first and second terminals, the web, and an external server, creating arbitrary multimodal analysis information by mutually matching data of the first and second event information and the personal information with pre-specified learning competency measurement criterion data, and outputting a preset first notification signal according to the generated multimodal analysis information; and a notification unit provided in the first and second terminals and visually displaying the notification signal on an arbitrary screen according to the notification signal output from the control unit. In generating the multimodal analysis information, the control unit encrypts only the name data among the data of the personal information so that it is displayed as a pseudonym on the screen of the notification unit. Based on at least one of the first event information and the personal information, explanatory information capable of customized commentary on the multimodal analysis information in consideration of the age and nationality of the learner is extracted. When a commentary request signal requesting commentary on the multimodal analysis information is input from the first terminal, a second notification signal corresponding to the extracted explanatory information is output. Thus, it is possible to achieve learner privacy protection and easy learning analysis monitoring environment.

Description

Personalized customized learning analysis provision system {A SYSTEM THAT PROVIDES PERSONALIZED AND CUSTOMIZED LEARNING ANALYSIS}

In the process of deriving the statistical results of the learning analysis for each learner, the present invention prevents indiscriminate leakage and theft of the learner's personal information through the configuration of pseudonymizing the learner's name, while monitoring the result of the individual learning analysis. The present invention relates to a personalized learning analysis providing system that can achieve a more convenient learning analysis monitoring environment through a configuration in which customized commentary information according to the learner's age and nationality is supplementarily provided.

In general, artificial intelligence (AI) assistants understand and execute the intentions of commands uttered by users, and the convenience of voice interfaces that control peripheral devices is the main feature, whereas AI tutoring systems provide learners with the intervention of teachers and instructors. The main feature is the context-oriented active response that identifies the necessary situation and provides diagnosis and prescription appropriate to the situation.

Currently, the domestic education technology market, which includes technology for artificial intelligence tutoring, is expanding, but its growth rate is low compared to the global market, and the proportion of small businesses is high.

For example, sales are concentrated in a small number of large operators, while sales of small operators, which account for half of the total number of operators, account for only 1.6%, resulting in polarization within the industry. It takes over a business, launches services using artificial intelligence technology and robot technology, and seeks new opportunities by directly discovering and supporting promising edutech startups.

Accordingly, edutech startups are growing in various fields such as customized learning services, game-based learning, foreign language education, and coding education based on their technological prowess. The use of AI (artificial intelligence) provided is most prominent.

In particular, as the opening of school has recently been postponed due to COVID-19 and interest in non-face-to-face education has increased, the industry is responding quickly to the spread of edtech, and domestic education services and edutech startups are helping students affected by COVID-19. It is trying to attract customers by giving free use of its service to customers.

Going forward, the global edutech market size is expected to reach $342 billion by 2025, more than doubling from $153 billion in 2018, and the number of listed companies with a corporate value of more than $1 billion is expected to increase significantly to more than 100.

Among these, the customized learning technologies that have been commercialized in the past depended on linkage between learning contents or simply counted the number of wrong problems to diagnose weak concepts, so there was a problem in that the diagnosis of learners' learning capabilities was not sophisticated.

In other words, even in the case of providing personalized customized learning, it is based on an inaccurate diagnosis of learning competency, so there is a limitation that the efficiency and reliability of the customized learning system cannot be secured.

In addition, there was a disadvantage that the collection system of learning-related data remained at a rather low level because the specific reference basis for collecting learning data was somewhat insufficient.

Therefore, in order to solve the problems of the prior art as described above, a more advanced data collection system is applied to more accurately and efficiently analyze and diagnose learners' learning capabilities, as well as guide learners to more reliable customized learning prescriptions. There is an increasing need for a multimodal learning data collection system based on a learning analysis standard that can be used.

Furthermore, as much as personalized learning analysis is provided, personal information security of individual learners is essential, and learning analysis results can be easily monitored in response to learners of different ages and, in particular, of various nationalities globally. environment is demanding.

Republic of Korea Patent Registration No. 10-2056822 (Title of Invention: Method and Device for Providing Learning Services)

Therefore, the present invention has been made to solve the above problems, and in the process of deriving statistical results of learning analysis for each learner, through a configuration of pseudonymizing the learner's name, indiscriminate leakage and In the process of monitoring the results of individual learning analysis while preventing theft, personalized customized learning that can achieve a more convenient learning analysis monitoring environment through a configuration in which customized commentary information according to the age and nationality of the learner is provided as a supplement. The purpose is to provide an analysis providing system.

However, the technical problem to be achieved in the present invention is not limited to the above-mentioned technical problems, and other technical problems not mentioned will become clear to those skilled in the art from the description below. You will be able to understand.

In a system for providing a personalized learning analysis service in a non-face-to-face environment by remotely connecting a learner's first terminal and a teacher's second terminal according to an embodiment of the present invention, which is a technical means for achieving the above object, , a sensor unit for detecting in real time the first event information on the biometric change of the learner; While communicating by establishing a network with the first and second terminals, the web, and an external server, second event information about the learner's learning activity and personal information of the learner are obtained from the first terminal, and the first and second events A control unit that generates arbitrary multimodal analysis information by matching information and data of the personal information with predetermined learning competency measurement criterion data, and outputs a preset first notification signal according to the generated multimodal analysis information; and a notification unit provided in the first and second terminals and visually displaying the notification signal on an arbitrary screen according to a notification signal output from the controller, wherein the controller performs the above in generating the multimodal analysis information. Among the data of personal information, only the data for the name may be encrypted and displayed on the screen of the notification unit as a pseudonym, and the age of the learner based on at least one data of the first event information and the personal information Extracting explanatory information capable of customized commentary on the multimodal analysis information considering nationality and responding to the extracted explanatory information when a commentary request signal requesting commentary on the multimodal analysis information is input from the first terminal It is possible to output a second notification signal to.

In addition, the personal information may be personal data for each learner including name, age, nationality, and region.

In addition, the control unit may collect data of the first and second event information based on two learning analysis standard models provided from the web or an external server, and the two learning analysis standard models determine learning activities. It may be the IMS Caliper Analytics standard of the IMS Global Learning Consortium defined as a behavior model, and the xAPI (Experience Application Programming Interface) standard that defines syntax rules for events managed by the IEEE.

Also, the IMS Caliper Analytics standard may be applied to the second event information, and the xAPI standard may be applied to the first event information.

In addition, the control unit may include: a data generation unit for converting the event information and the personal information into data; The data of the event information generated by the data generator is collected and recorded based on the learning analysis standard model, the data of the personal information is stored for each learner, and the learning competency designated in advance by the second terminal is measured. a database unit for storing and managing reference data; a data processing unit which encrypts only name data among data of the personal information so that it can be displayed as a pseudonym on the screen of the notification unit; The data of the event information recorded in the database unit is compared and analyzed over time to calculate a change value, and the calculated change value and the data of the personal information processed through the data processing unit are used as the learning capability measurement standard. A data analysis unit for generating arbitrary multimodal analysis information by matching with data; Explanatory information enabling customized commentary on the multimodal analysis information considering the age and nationality of the learner based on at least one of the event information and the personal information using the preloaded keyword adjustment SDK for each age group and multilingual support SDK a data extraction unit for extracting; and when a commentary request signal for requesting a commentary on the multimodal analysis information is input from the first terminal while controlling a first notification signal corresponding to the multimodal analysis information generated by the data analysis unit to be output, the data and a notification signal output unit for controlling output of a second notification signal corresponding to the explanatory information extracted from the extraction unit, wherein at least one data of the event information and the personal information is accumulated in the database unit in real time. and can be updated.

In addition, the sensor unit may be an image sensor that captures the learner, and the first event information may be at least one image of the learner's face, eyes, pupils, and eyelids. When event information is detected, the data generating unit may have a pixel value of the first event information as data through a pre-stored image processing program.

The sensor unit may further include a voice sensor for detecting the learner's voice, and the first event information may further include at least one sound of the learner's intonation, pronunciation, and voice pitch and volume. can

In addition, the notification unit may further include a sound output unit provided in the first terminal and outputting a preset sound according to a notification signal output from the control unit, and the voice sensor and the sound output unit include a voice recognition SDK ( interface) can be installed.

In addition, the second event information may be at least one of consumption amount for each content, learning time, solution time for each item, and whether or not an answer is correct by the learner.

In addition, the multimodal analysis information includes the learner's understanding (achievement) level, vulnerability, learning tendency (pattern), preference for each content, and psychology that can be analyzed through mutual matching of the second event information data and the learning competency measurement criterion data. Single analysis data for at least one learning result among test results; At least one of learner's learning concentration, concentration by content, learning posture (attitude), and emotional state during learning that can be analyzed through mutual matching of the first event information data, the second event information data, and the learning competency measurement criterion data. mixed analysis data on learning responses; Characteristics of learners that can be analyzed through mutual matching of the single analysis data, the mixed analysis data, and the learning competency measurement standard data, the risk of academic abandonment, recommended comparative study, recommended experiential learning program, recommended career path, recommended aptitude, recommended content, In-depth analysis data on at least one customized learning diagnosis/treatment and guide among recommended learning schedule (learning schedule), recommended learning amount, recommended test items, and recommended counseling items; And learning results and learning by age/nationality/region of the cluster to which the learner belongs, which can be analyzed through mutual matching of the single analysis data, the mixed analysis data, the in-depth analysis data, and the personal information data and the learning competency measurement standard data. It may include group analysis data on the statistical results of at least one of response, learning diagnosis/treatment, and guide.

In the process of deriving statistical results of learning analysis for each learner, the present invention pseudonyms the learner's name, and in the process of monitoring the result of individual learning analysis, customized commentary information according to the learner's age and nationality is supplementarily Through the configuration of the control unit to be provided, there is an effect of achieving a more convenient learning analysis monitoring environment while preventing indiscriminate leakage and theft of learners' personal information.

In addition, according to the present invention, there is an advantage in that a more advanced data collection system system in relation to learning analysis can be expected through a configuration in which a new data collection system is modeled based on two learning analysis standard models.

In addition, according to the present invention, by using the learning competency measurement criterion data, single analysis data that analyzes only one event information, mixed analysis data that is analyzed by matching two event information data together, and single analysis data and mixed analysis data More accurate and efficient learning analysis and learning diagnosis through the configuration of the control unit that acquires in-depth analysis data that is analyzed by matching together, single analysis data and mixed analysis data, and group analysis data that is analyzed by matching and analyzing in-depth analysis data and personal information data together As this becomes possible, even customized learning prescriptions can be achieved.

Furthermore, according to the present invention, an abnormal behavior detection technology using a learner's behavior model and biometric information can be provided as a SaaS (Software as a Service) type service to a learning management platform (LMS) that complies with learning event data standards. .

However, the effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

1 is a diagram showing an external configuration and usage example of a system for providing personalized learning analysis according to an embodiment of the present invention.
Figure 2 is a block diagram schematically showing the electronic configuration of the personalized learning analysis providing system.
3 is a block diagram showing the configuration of a control unit in the personalized learning analysis providing system.
4 is a table showing an example in which data of first and second event information is separately collected based on two learning analysis standard models.
5 is a table showing mapping rules of Caliper and xAPI conversion guide international standard marks and Caliper and xAPI conversion standards by way of example.
Figure 6 is a table showing the principles for personal information protection of international organizations.
7 is a flow chart illustrating a workflow reflecting international standard personal information protection measures by way of example.
8 is a diagram conceptually illustrating a connection environment of a multimodal data network formed by mutual matching of data of the first and second event information;
9 is an exemplary use diagram illustrating an example in which multimodal analysis information is displayed on a notification unit in relation to the present invention.
10 is a conceptual diagram showing an example of an interface in which a teacher automatically adjusts an individual learner's learning schedule by reflecting an AI recommendation model.
11 is a view showing a recommendation service screen for each learner level and propensity of a home run by way of example.
12 is a diagram showing an example of a custom learning analysis dashboard in Seocho-gu.
13 is a diagram showing an example of expressing a learning analysis dashboard and generated messages in multiple languages in an AI life record.
14 is a conceptual diagram of the E-portfolio service that provides diagnosis and prescription messages for each learner's life cycle.

Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily carry out the present invention. However, since the description of the present invention is only an embodiment for structural or functional description, the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, since the embodiment can be changed in various ways and can have various forms, it should be understood that the scope of the present invention includes equivalents capable of realizing the technical idea. In addition, since the object or effect presented in the present invention does not mean that a specific embodiment should include all of them or only such effects, the scope of the present invention should not be construed as being limited thereto.

The meaning of terms described in the present invention should be understood as follows.

Terms such as "first" and "second" are used to distinguish one component from another, and the scope of rights should not be limited by these terms. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element. It should be understood that when an element is referred to as “connected” to another element, it may be directly connected to the other element, but other elements may exist in the middle. On the other hand, when an element is referred to as being “directly connected” to another element, it should be understood that no intervening elements exist. Meanwhile, other expressions describing the relationship between components, such as “between” and “immediately between” or “adjacent to” and “directly adjacent to” should be interpreted similarly.

Singular expressions should be understood to include plural expressions unless the context clearly dictates otherwise, and terms such as “comprise” or “having” refer to a described feature, number, step, operation, component, part, or It should be understood that it is intended to indicate that a combination exists, and does not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs, unless defined otherwise. Terms defined in commonly used dictionaries should be interpreted as consistent with meanings in the context of related art, and cannot be interpreted as having ideal or excessively formal meanings unless explicitly defined in the present invention.

1 is a diagram showing the external configuration and usage examples of a system for providing personalized learning analysis according to an embodiment of the present invention, and FIG. 2 is a block diagram schematically showing the electronic configuration of the system for providing personalized learning analysis. 3 is a block diagram showing the configuration of the control unit in the personalized learning analysis providing system, and FIG. 4 is a table showing an example in which data of first and second event information is collected separately based on two learning analysis standard models. 5 is a table showing the international standard mark of the Caliper and xAPI conversion guide and the mapping rules of the Caliper and xAPI conversion standards by way of example, FIG. 6 is a table showing the principles for personal information protection of international organizations, and FIG. It is a flow chart illustrating a workflow reflecting personal information protection measures by way of example, and FIG. 8 is a diagram conceptually showing a connection environment of a multimodal data network formed by mutual matching of data of the first and second event information, and FIG. 9 is a usage example diagram showing an example in which multimodal analysis information is displayed on a notification unit in relation to the present invention, and FIG. 10 is an exemplary interface for a teacher to automatically adjust an individual learner's learning schedule by reflecting an AI recommendation model. Fig. 11 is a diagram showing an example of a recommendation service screen for each learner level and tendency of home run, Fig. 12 is a diagram showing an example of a custom learning analysis dashboard in Seocho-gu, and Fig. 13 is an AI life 14 is a conceptual diagram of the E-portfolio service that provides diagnosis and prescription messages by field for each learner's life cycle.

As shown in FIGS. 1 and 2, the system 100 for providing personalized learning analysis according to the present invention includes a first terminal 110, a second terminal 120, a sensor unit 130, a web 140, It can be configured to include an external server 150, a control unit 160, and a notification unit 170.

However, the components shown in FIGS. 1 and 2 are only the minimum configuration necessary for the present invention, and a learning analysis providing system having more additional components may be implemented.

The first terminal 110 is configured to include various known input devices (not shown) such as a keyboard, a pad, and a mouse. According to a preferred embodiment of the present invention, the first terminal 110 is located on the learner L to Provides an input environment in which second event information (E2), personal information (I1), and a commentary request signal, which will be described later, are input to the web 140, the external server 150, and the control unit 160.

In addition, the second terminal 120 is configured to remotely communicate with the above-described first terminal 110, and may have the same components as the first terminal 110, and according to the present invention, It is located in the teacher (T) and provides an input environment in which the teacher (T) can input the learning competency measurement criterion data (D3) to the web 140, external server 150, and control unit 160. .

Here, the first and second terminals 110 and 120 may be configured in plurality, and preferably, a tablet may be applied, but it is not limited thereto, and a PC, a smart phone, and the like may be applied.

Since the configurations of the first and second terminals 110 and 120 correspond to general terminal devices, detailed configurations are not shown in the drawings, and detailed descriptions thereof will be omitted.

The sensor unit 130 performs a function of detecting the first event information E1 of the learner L in real time, and the first terminal ( 110) is preferred.

That is, in the present invention, the first event information (E1) may mean information about changes in the body of the learner (L), and the sensor unit 130 is an image sensor that captures the appearance and state of the learner (L). Preferably, the first event information E1 may be an image of at least one of the face, eyes, pupils, and eyelids of the learner L.

At this time, the sensor unit 130 is preferably a CMOS camera or a CCD camera, but is not limited thereto, and a speed dome camera, a pinhole camera, a pan-tilt-zoom camera, and the like may be used, which is within the technical scope of the present invention. It may be variously modified and designed by those skilled in the art having ordinary knowledge in the related field.

Furthermore, the sensor unit 130 may be additionally provided with an infrared camera so that photography can be performed even at night.

On the other hand, the sensor unit 130 uses a voice sensor 131 for detecting the learner's (L's) voice so as to detect even biological change information related to sound, such as the learner's (L's) intonation, pronunciation, and voice pitch and volume. It may be configured to include more.

At this time, the voice sensor 131 is preferably a microphone module configured to be equipped with a voice recognition SDK (interface) that converts voice into an electrical signal, and its types are carbon type, ribbon (velocity) type, and coin (Dynamic) type, piezoelectric (crystal) type, capacitor type, etc. can be freely handled and used by those skilled in the art within the technical scope of the present invention capable of detecting voice.

The web 140 is an Internet service configured to support various multimedia such as text, graphics, images, sound, and video. According to the present invention, the web 140 is connected to the first and second terminals 110 and 120, It can be configured so that various known educational contents are provided to learners (L) and teachers (T) who respectively use the 2 terminals (110, 120).

Preferably, the external server 150 is configured to correspond to the aforementioned web 140 and forms a network with the first and second terminals 110 and 120, the web 140, and the control unit 160. 140 to perform a function of servicing a preset program in the control unit 160, and more preferably, according to the present invention, configured to interwork with the control unit 160 in a SaaS (Software as a Service) method It can be.

Here, the program may be a platform for learning management (LMS) consisting of various data related to education, which is a well-known technology and can be freely modified and designed by those skilled in the art, of course.

The control unit 160 actively reacts according to the first and second event information E1 and E2 while forming an educational program corresponding to the web 140 and the external server 150, thereby providing the first terminal 110 with For the learner (L) who uses the self-diagnosis and learning progress guide environment, and for the teacher (T) using the second terminal 120, an in-depth analysis of the learning competency of the learner (L) and a learning prescription guide As a control server for learning capability analysis to provide an environment, preferably, it is configured to establish a network with the first and second terminals 110 and 120, the web 140 and the external server 150 to communicate with each other. desirable.

According to the present invention, the controller 160 controls the second event information (E2) input from the first terminal 110 and is serviced through the web 140 and the external server 150 and the learner (L). Acquiring the personal information (I1) of, and converting the data (D1) of the first and second event information (E1, E2) and the data (D2) of the personal information (I1) into the pre-designated learning competency measurement criterion data (D3) It generates arbitrary multimodal analysis information (I2) by matching with each other, and performs a function of outputting a first notification signal of a preset notification signal according to the generated multimodal analysis information (I2).

In addition, in generating the multimodal analysis information I2 described above, the control unit 160 encrypts only the data for the name among the personal information data D2 to display the information on the screen of the notification unit 170 to be described later. It is preferable to configure it to be displayed with a pseudonym.

Furthermore, the control unit 160 determines the multimodal analysis information I2 considering the age and nationality of the learner L based on at least one of the first event information E1 and the personal information I1. When a commentary request signal for requesting a commentary on the multimodal analysis information (I2) is input from the first terminal 110 by extracting explanation information (I3) capable of customized commentary, the extracted explanation information (I3) It may also perform a function of outputting a second notification signal corresponding to, and more specifically, the data generator 161, the database unit 162, the data processor 163, the data analyzer 164, the data It may be configured to include an extraction unit 165 and a notification signal output unit 166.

Here, the second event information (E2) is information that can be generated by the learning activity of the learner (L), and is at least one of the amount of consumption for each content of the learner (L), learning time, solving time for each question, and whether or not an answer is correct. Preferably, the personal information is personal data for each learner including name, age, nationality, and region.

On the other hand, according to a preferred embodiment of the present invention, the control unit 160, with reference to FIGS. 4 and 5, based on two learning analysis standard models provided from the web 140 or the external server 150 It may be configured to collect data D1 of the first and second event information E1 and E2.

At this time, it is preferable that the two learning analysis standard models are the IMS Caliper Analytics standard and the xAPI (Experience Application Programming Interface) standard.

More specifically, the IMS Caliper Analytics standard is a behavioral model for learning activities by the IMS Global Learning Consortium (IMS GLC), a non-profit international member organization that defines international standards for e-learning systems. It is a standard that enables educational institutions to collect learning data from digital materials in order to better understand and visualize learning activities.

According to the present invention, the IMS Caliper Analytics standard is preferably applied to the second event information E2.

In addition, the xAPI standard is a successor to the SCORM standard established by ADL (Advanced Distributed Learning), an American e-learning standard research and development agency, and defines syntax rules for events managed by IEEE. More specifically, smart It refers to the application program interface (API) standard for exchanging data between different learning systems by defining learner experience data in an e-learning (e-learning) environment that utilizes various media such as phones, augmented reality, and games.

Unlike the SCOM standard, which was compatible only with systems and contents to which the SCOM standard was applied, the xAPI standard can exchange data with learning management systems (LMS) and learning tools to which the xAPI standard is not applied, enabling various learning activities There is a feature that can track and manage data (learning time, progress rate, etc.), and according to the present invention, it is preferable to apply to the first event information (E1).

Hereinafter, with reference to FIG. 3, a detailed configuration of the controller 160 described above will be described.

The data generator 161 performs a function of converting the above-described event information E1 and E2 and personal information I1 so that the database unit 162 to be described later can recognize them, and the event information E1, E2) and personal information (I1) can be converted into data to generate arbitrary event information data (D1) and personal information data (D2).

For example, when first event information E1' is detected from the sensor unit 130 and the first event information E1' is an image of the learner L, the data generator 161 preliminarily It may be configured to have a pixel value of the first event information E1' as data through a stored image processing program.

That is, at this time, the event information data D1' is a pixel value.

Here, the image processing program analyzes the pixels to recognize the color, shade, position, size, etc. of objects and body parts of pixels defined as vectors, or to approximate edges and contours of objects or body parts such as boundaries and contours. Line segments, curves, etc. may be obtained and recognized, or recognition may be performed by extracting invariant feature points including corner points existing in the image.

Such an image processing program is a generally known technology, and various modifications and designs are possible by those skilled in the art.

Meanwhile, when first event information E1" is detected from the sensor unit 130 and the first event information E1" is the voice of the learner L, the data generator 161 preliminarily It may be configured to have the sound wave value of the first event information (E1") as data through a stored sound processing program.

That is, the event information data D1" at this time becomes a sound wave value.

Here, since the sound processing program is also a commonly published technology, it is not limited thereto, and various known sound processing programs such as a voiceprint recognition program and a frequency analysis program may be applied, and the voice sensor according to the present invention ( The voice recognition SDK (interface) of 131) may be applied.

The database unit 162 collects and records the data D1 of the event information E1 and E2 generated by the data generator 161 based on the learning analysis standard model, and records the personal information I1. ) of data D2 is stored for each learner, and the learning competency measurement criterion data D3 designated in advance by the second terminal 120 is stored and managed.

For example, the learning competency measurement criterion data (D3) is configured to correspond to the event information data (D1) that changes on a real-time basis to measure and diagnose the learning competency of the learner (L) in various learning categories, and the measurement / As data that serves as an indicator for future learning management (guide) such as learning prescription and treatment required according to the diagnosis result, through an input device (not shown) of the second terminal 120 used by the teacher (T) It can be designated in advance while being input, and can be designated in advance by being input to the web 140 and the external server 150 as well.

In addition, the learning competency measurement criterion data D3 may be stored in the database unit 162 so as to be modified, accumulated, and updated in real time by a preset statistical algorithm in relation to the above-described event information data D1.

According to the present invention, at least one of the event information E1 and E2 and the personal information I1 can be accumulated and updated in the database unit 162 in real time.

That is, the learning capability measurement criterion data D3 can be processed based on deep learning technology, and the deep learning technology transforms and updates continuously accumulated data through a statistical algorithm that clusters or classifies data. As an artificial intelligence data processing technology configured to perform reinforcement learning, since this is a commonly published and known technology, a more detailed description will be omitted.

At this time, the learning competency measurement standard data (D3) is data quantifying learning behavior and patterns occurring in the learning process, subject understanding level, total amount and pattern of learning amount by period, statistical distribution and trend and cause of grades, and prescriptions for it, etc. , 3 types of learning (learning strategy, learning motivation, learning maladjustment), 1 type of career/aptitude (interest, aptitude), and 4 types of personality/personality (interpersonal environment, academic stress, self-esteem, emotional behavior) It is desirable to include data recorded on the type of diagnostic test.

According to the present invention, the learning competency measurement criterion data D3 can be quantified based on the IMS Caliper Analytics standard, and the AI analysis service program "Home Run" currently commercialized on the web 140 or external server 150 It can also be extracted through the sharing of "AI life record", and of course, it is possible to design various changes by those skilled in the art within the technical scope of the present invention.

The home run AI life record is a learning analysis service provided by Ice Cream Edu Co., Ltd., and has been certified for the IMS Caliper Analytics 1.1 standard by the IMS Global Learning Consortium, and the learner (L)'s learning status, learning pattern, study habits, etc. It is a technology that analyzes and summarizes monthly and provides visual data in the form of an ePortfolio to learners (L) and teachers (T).

The data processing unit 163 encrypts only the name data among the personal information data D2 so that it can be displayed on the screen of the notification unit 170 as a pseudonym.

The data processing unit 163 may be configured to perform an encryption operation on data by applying a known code conversion SDK, which according to the present invention, with reference to FIG. By comparing and analyzing the different frameworks, it is possible to comply with the service use license that reflects commonly required responsibilities, minimum data collection, and definition of specific data utilization purposes.

That is, in the present invention, it is intended to establish personal information de-identification measures and SLA (Service License Agreement, Consumer Terms of Use) at the level of domestic law and GDPR (EU Personal Information Protection Regulation) through the data processing unit 163. To this end, A process as shown in FIG. 7 may be referred to.

The data analysis unit 164 compares and analyzes the event information data D1 recorded in the database unit 162 over time to calculate a change value, and the calculated change value and the data processing unit 163 ) It may be configured to generate arbitrary multimodal analysis information (I2) by matching the personal information data (D2) processed through the learning competency measurement criterion data (D3).

For example, referring to FIG. 8 , each embodiment of the data analyzer 164 according to the present invention will be separately described as follows.

In the first embodiment, in the data analysis unit 164, the change value of the pixel position value of the pupil of the learner L is continuously calculated in response to the change value of the amount of content consumption of any one of the event information data D1. In this case, the data analysis unit 164 may determine that the learner L is "concentrating on the corresponding content", and match the learning competency measurement criterion data D3 corresponding thereto to the multimodal analysis information. (I2) can be generated.

In the second embodiment, the change value of the content consumption amount of any one of the event information data D1 is continuously calculated in the data analyzer 164, but the pixel for at least one of the body parts of the learner L If the change value of the position value is not calculated (change value = 0), the data analysis unit 164 may determine that the learner L is "displaced", and the learning competency measurement standard data corresponding to this ( D3) may be matched to generate the multimodal analysis information I2.

In the third embodiment, the change value of the content consumption amount of any one of the event information data D1 is continuously calculated in the data analysis unit 164, but the change value of the pixel position value of the pupil of the learner L In the case where it is not calculated (change value = 0) and the change value of the content consumption amount of any one of the event information data D1 is continuously calculated, the change in the pixel position value for the pupil or face of the learner L If the value is calculated to exceed the area of the preset object location pixel value (object image location pixel value on the screen), the data analysis unit 164 may determine that the learner L's "concentration level has declined", and thus The multimodal analysis information I2 may be generated by matching the corresponding learning capability measurement criterion data D3.

In the fourth embodiment, the data analysis unit 164 continuously calculates the change value of the position value of the face pixel of the learner L in response to the change value of the amount of content consumption of any one of the event information data D1. However, if the change value of the pixel size value of the pupil L of the learner L is irregularly calculated based on a certain point in time, or the change value of the pixel position value of the pupil L of the learner L is not calculated for a certain period of time, the data analysis unit (164) can determine that the learner (L) is in a "drowsy state", and the multimodal analysis information (I2) can be generated by matching the learning competency measurement criterion data (D3) corresponding thereto.

In the fifth embodiment, the change value of any one of the event information data D1 in the data analysis unit 164 is continuously calculated, but the pixel position relative to the contours of the eyes and mouth of the learner L. At least one of the change value of the value, the change value of the shading value or color value for the face pixel of the learner (L), and the change value of the sound wave value for the voice of the learner (L) is the average change value for a certain period of time. When calculated out of the area, the data analysis unit 164 may determine "joyful state, interesting state" or "depressed state, anger state" of the learner L for the corresponding content, and corresponding to this The multimodal analysis information I2 may be generated by matching the learning competency measurement criterion data D3.

That is, the data analysis unit 164 may be configured to have a time series analysis function, and as shown in FIG. 8, time series analysis of the first and second event information E1 and E2, and the first, Measurement of learning performance, measurement of learning concentration, measurement of stress, measurement of emotional state, tone/expression Data that more in-depth and richer measurement and diagnosis of learners’ learning, such as manner diagnosis, basic emotion measurement, social emotion change measurement, basic emotion and social emotion impact measurement, influence between emotions, and relational quantification criteria measurement, That is, the multimodal analysis information I2 is generated.

Here, according to a more preferred embodiment, the multimodal analysis information I2 may include single analysis data, mixed analysis data, in-depth analysis data, and group analysis data.

The single analysis data is the level of understanding (achievement) of the learner (L), vulnerability, learning propensity (pattern), It may include data on at least one learning result among preference for each content and psychological test result.

In addition, the mixed analysis data is the data of the learner (L) that can be analyzed through mutual matching of the data of the first event information (E1), the data of the second event information (E2), and the learning competency measurement criterion data (D3). Data on at least one learning reaction among learning concentration, concentration by content, learning posture (attitude), and emotional state during learning may be included.

In addition, the in-depth analysis data is the personality of the learner (L) that can be analyzed through mutual matching of the above-mentioned single analysis data, mixed analysis data, and the learning competency measurement criterion data (D3), risk of dropping out of school, recommendation comparison and learning, Data on at least one customized learning diagnosis/treatment and guide among recommended experiential learning programs, recommended career paths, recommended aptitude, recommended content, recommended learning schedule (learning schedule), recommended learning amount, recommended test questions, and recommended counseling items may be included. have.

Furthermore, the group analysis data is the learner (L) that can be analyzed through mutual matching of the above-mentioned single analysis data, mixed analysis data and in-depth analysis data, and the personal information data (D2) and the learning competency measurement criterion data (D3). It may include data on at least one statistical result among learning results by age group/nationality/region, learning response, learning diagnosis/treatment, and guide of the cluster to which A belongs.

The multimodal analysis information I2 configured as described above may be displayed on the notification unit 170 to be described later as an example as shown in FIGS. 9 to 12, which is only one embodiment, but is not limited thereto, It can be configured to be displayed in various forms within the technical scope of the present invention.

As shown in FIGS. 13 and 14 , the data extraction unit 165 uses a preloaded keyword adjustment SDK for each age group and multilingual support SDK to at least one of the event information E1 and E2 and the personal information I1. Based on one data, it performs a function of extracting explanatory information (I3) capable of customized explanation of the multimodal analysis information (I2) considering the age and nationality of the learner (L).

For example, if the data of the personal information I1 is used, the age, nationality, region, etc. of the learner L can be directly grasped by the data analysis unit 164 .

On the other hand, if the data of the event information (E1, E2) is used, it is possible to guess the age group, nationality, etc. of the learner (L) through at least one of face recognition and voice recognition of the data analysis unit 164. .

According to the present invention, since the learner (L) may be the learner himself or a parent (of a multicultural family) who wants to check the learning level of his/her child learner, a commentary capable of actively responding according to the age or nationality is essential. to be.

To this end, it is preferable to configure the keyword adjustment SDK by age group to be implemented through deep learning technology by storing frequently used words and sentences by age group in the database unit 162, and multilingual support is possible in the multilingual support SDK. Various well-known global translation SDKs/APIs such as Google's or Amazon's translation SDKs and Naver's translation APIs can be utilized.

The notification signal output unit 166 is configured to control a notification signal corresponding to the multimodal analysis information I2 generated by the above-described data analysis unit 164 to be output, preferably, the notification unit 170 ) outputs a notification signal that can be displayed in the form of text or image.

In addition, according to the present invention, the notification signal output unit 166 controls the first notification signal corresponding to the multimodal analysis information I2 generated by the data analysis unit 164 to be output, while controlling the output of the first terminal. When a commentary request signal for requesting commentary on the multimodal analysis information I2 is input from 110, a second notification signal corresponding to the explanation information I3 extracted from the data extractor 165 is output. By controlling, the notification unit 170 can be visually displayed on an arbitrary screen according to the output notification signal.

The notification unit 170 visually displays preset text and images on an arbitrary screen according to a notification signal output from the notification signal output unit 166 of the control unit 160. Preferably, the learner It is preferable to be configured to be provided in the first terminal 110 of (L) and the second terminal 120 of the teacher (T), respectively.

The notification unit 170 may include a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), and a flexible display. It may be configured to include at least one of (flexible display) and 3D display.

In addition, the notification unit 170 may further include a sound output unit 171 that outputs a preset sound according to a notification signal output from the notification signal output unit 166 of the control unit 160 .

The sound output unit 171 is preferably provided in the first terminal 110, and according to the present invention, a voice recognition SDK (interface) may be mounted separately from the voice sensor 131.

That is, the voice sensor 131 and the sound output unit 171 can take a known AI speaker configuration, and furthermore, an AI tutor module can be configured by the connection relationship between the configurations with the present invention.

In addition, the voice sensor 131 and the sound output unit 171 can have a multi-turn process through the voice recognition SDK (interface) installed in each, and the learner (transcribed through the voice recognition SDK (interface)) Unstructured data (text) for the voice of L) may be accumulated in the database unit 162.

In addition, the voice sensor 131 and the sound output unit 171 may be applied with various known global voice recognition SDKs/APIs such as Google or Amazon's voice recognition SDK and Naver Clover API to enable multilingual support.

Furthermore, in the drawing according to a preferred embodiment of the present invention, the voice sensor 131 and the sound output unit 171 are shown to be located and provided in the first terminal 110, but are not limited thereto, and the The second terminal 120 may also be positioned and equipped so that a part of the function is provided to the teacher T, which is omitted (not shown) in the drawings in the present invention.

Detailed descriptions of the preferred embodiments of the present invention disclosed as described above are provided to enable those skilled in the art to implement and practice the present invention. Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will understand that the present invention can be variously modified and changed without departing from the scope of the present invention. For example, those skilled in the art can use each configuration described in the above-described embodiments in a manner of combining with each other. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The present invention may be embodied in other specific forms without departing from the spirit and essential characteristics of the present invention. Accordingly, the above detailed description should not be construed as limiting in all respects and should be considered as illustrative. The scope of the present invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention. The invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. In addition, claims that do not have an explicit citation relationship in the claims may be combined to form an embodiment or may be included as new claims by amendment after filing.

100: learning analysis service providing system 110: first terminal
120: second terminal 130: sensor unit
131: voice sensor 140: web
150: external server 160: control unit
161: data generation unit 162: database unit
163: data processing unit 164: data analysis unit
165: data extraction unit 166: notification signal output unit
170: notification unit 171: sound output unit
E1: First event information E2: Second event information
D1: Event information data D2: Personal information data
D3: Learning competency measurement criteria data I1: Personal information
I2: Multimodal analysis information I3: Explanatory information
L: learner T: teacher

Claims (10)

In a system that provides a personalized learning analysis service in a non-face-to-face environment by remotely connecting a learner's first terminal and a teacher's second terminal,
a sensor unit detecting in real time first event information about the biometric change of the learner;
While communicating by establishing a network with the first and second terminals, the web, and an external server, second event information about the learner's learning activity and personal information of the learner are obtained from the first terminal, and the first and second events A control unit that generates arbitrary multimodal analysis information by matching information and data of the personal information with predetermined learning competency measurement criterion data, and outputs a preset first notification signal according to the generated multimodal analysis information; and
A notification unit provided in the first and second terminals to visually display on an arbitrary screen according to a notification signal output from the control unit;
The control unit,
Characterized in that, in generating the multimodal analysis information, only data for a name among data of the personal information is encrypted and displayed as a pseudonym on the screen of the notification unit, and the first event information and the personal information Commentary request requesting commentary on the multimodal analysis information from the first terminal by extracting explanation information capable of customized commentary on the multimodal analysis information considering the age and nationality of the learner based on at least one data of When a signal is input, it is characterized in that a second notification signal corresponding to the extracted explanatory information is output,
The above personal information,
Characterized in that it is personal data for each learner, including name, age, nationality, and region,
The control unit,
Characterized in that the data of the first and second event information is collected based on two learning analysis standard models provided from the web or external server,
The two learning analysis standard models,
It is characterized by the IMS Caliper Analytics standard of the IMS Global Learning Consortium, which defines learning activities as behavioral models, and the xAPI (Experience Application Programming Interface) standard, which defines the syntax rules of events managed by IEEE,
The IMS Caliper Analytics standard,
Characterized in that it is applied to the second event information,
The xAPI standard,
Characterized in that it is applied to the first event information,
The control unit,
a data generating unit that transforms the event information and personal information into data;
The data of the event information generated by the data generator is collected and recorded based on the learning analysis standard model, the data of the personal information is stored for each learner, and the learning competency designated in advance by the second terminal is measured. a database unit for storing and managing reference data;
a data processing unit which encrypts only name data among data of the personal information so that it can be displayed as a pseudonym on the screen of the notification unit;
The data of the event information recorded in the database unit is compared and analyzed over time to calculate a change value, and the calculated change value and the data of the personal information processed through the data processing unit are used as the learning capability measurement standard. A data analysis unit for generating arbitrary multimodal analysis information by matching with data;
Explanatory information enabling customized commentary on the multimodal analysis information considering the age and nationality of the learner based on at least one of the event information and the personal information using the preloaded keyword adjustment SDK for each age group and multilingual support SDK a data extraction unit for extracting; and
When a commentary request signal for requesting commentary on the multimodal analysis information is input from the first terminal while controlling the output of the first notification signal corresponding to the multimodal analysis information generated by the data analysis unit, the data extraction A notification signal output unit for controlling output of a second notification signal corresponding to the explanatory information extracted from the unit;
In the database section,
Characterized in that at least one data of the event information and the personal information is accumulated and updated in real time,
The sensor unit,
It is characterized by an image sensor for photographing the learner,
The first event information,
Characterized in that it is an image of at least one of the learner's face, eyes, pupils, and eyelids,
When the first event information detected by the sensor unit is an image of the learner, the data generating unit has a pixel value of the first event information as data through a pre-stored image processing program,
The sensor unit,
Characterized in that it further comprises a voice sensor for detecting the learner's voice,
The first event information,
Characterized in that it further includes at least one sound of the learner's intonation, pronunciation, voice pitch and volume,
When the first event information detected by the sensor unit is the learner's voice, the data generating unit has a sound wave value of the first event information as data through a pre-stored sound processing program,
The learning competency measurement criterion data,
Data quantifying learning behaviors and patterns occurring in the learning process, subject understanding level, total amount and pattern of learning amount by period, statistical distribution and trend of grades and causes, and prescriptions for them; and
Diagnosis of a total of 8 types of emotions: 3 types of learning (learning strategy, learning motivation, maladjustment to learning), 1 type of career/aptitude (interest, aptitude), and 4 types of personality/personality (interpersonal environment, academic stress, self-esteem, emotional behavior) Characterized in that it includes data recording the inspection type,
The data analysis unit,
When the change value of the pixel position value of the learner's pupil is continuously calculated in response to the change value of the content consumption amount of any one of the data of the event information, it is determined that the learner is concentrating on the corresponding content, and corresponding learning is performed. Create multimodal analysis information by matching competency measurement data,
If the change value of the content consumption of any one of the data of the event information is continuously calculated, but the change value of the pixel position value for at least one of the learner's body parts is not calculated, it is determined that the learner is out of seat , Create multimodal analysis information by matching learning competency measurement criterion data corresponding thereto,
The change in content consumption amount of any one of the event information data is continuously calculated, but the change value of the pupil pixel position value of the learner is not calculated, and the change in content consumption amount of any one of the event information data The value is continuously calculated, but if the change value of the pixel position value for the learner's pupil or face is calculated in excess of the area of the preset object position pixel value, it is determined that the learner's concentration level has decreased, and the corresponding learning Create multimodal analysis information by matching competency measurement data,
The change value of the pixel position value of the learner's face is continuously calculated in response to the change value of the content consumption amount of any one of the data of the event information, but the change value of the pixel size value of the learner's eyeball is irregular at a certain point in time. calculated, or if the change value of the pupil pixel position value of the learner is not calculated for a certain period of time, determining that the learner is in a drowsy state and matching the learning competency measurement criterion data corresponding thereto to generate multimodal analysis information,
The change value of any one content consumption amount among the data of the event information is continuously calculated, but the change value of the pixel position value for the learner's eye and mouth contour and the shade value or color value for the learner's face pixel If at least one of the change value and the change value of the sound wave value for the learner's voice is calculated out of the average change value range for a certain period of time, it is determined that the learner is in a joyful/interesting state or depressed/angry state for the corresponding content. while generating multimodal analysis information by matching learning competency measurement criterion data corresponding thereto,
The notification unit,
It is provided in the first terminal and characterized in that it further comprises a sound output unit for outputting a preset sound according to a notification signal output from the control unit,
The voice sensor and sound output unit,
Characterized in that it is equipped with a voice recognition SDK (interface),
The second event information,
It is characterized in that at least one of the learner's consumption amount for each content, learning time, solution time for each question, and correct answer,
The multimodal analysis information,
At least one of the learner's understanding (achievement) level, vulnerability, learning tendency (pattern), preference for each content, and psychological test result that can be analyzed through mutual matching of the second event information data and the learning competency measurement criterion data single analysis data for;
At least one of learner's learning concentration, concentration by content, learning posture (attitude), and emotional state during learning that can be analyzed through mutual matching of the first event information data, the second event information data, and the learning competency measurement criterion data. Mixed analysis data on learning responses of children;
Characteristics of learners that can be analyzed through mutual matching of the single analysis data, the mixed analysis data, and the learning competency measurement standard data, the risk of academic abandonment, recommended comparative study, recommended experiential learning program, recommended career path, recommended aptitude, recommended content, In-depth analysis data on at least one customized learning diagnosis/treatment and guide among recommended learning schedule (learning schedule), recommended learning amount, recommended test items, and recommended counseling items; and
The single analysis data, the mixed analysis data, the in-depth analysis data, and the learning results and learning responses by age/nationality/region of the cluster to which the learner belongs that can be analyzed through mutual matching of the personal information data and the learning competency measurement standard data. , A personalized learning analysis service providing system, characterized in that it includes group analysis data on the statistical results of at least one of learning diagnosis / treatment and guide. delete delete delete delete delete delete delete delete delete

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