System for determining learning tendency information for a genetic testing information infrastructure and method for determining learning tendency information for a genetic testing information infrastructure

Abstract

A learning tendency information determination system for a gene testing information infrastructure according to one embodiment of the present invention includes: a basic information generation unit that receives information of a person to be tested and generates basic information; a gene testing information generation unit that analyzes a gene sample of the person to be tested and generates gene information; and a learning tendency information determination unit that generates cognitive category learning tendency information based on the gene information, generates non-cognitive category learning tendency information based on the gene information and the basic information, and generates learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information.

Description

【Technical Field】 【0001】 The present invention relates to a learning aptitude information judgment system for a genetic test information base and a method for judging learning aptitude information of a genetic test information base. More specifically, it relates to a learning aptitude information judgment system that judges learning aptitude based on the genetic test information of a subject, presents consultations on academic directions, and improves the attainment level of academic performance, and a method for judging learning aptitude information of a genetic test information base using the said system. 【Background Art】 【0002】 There are various personality suitability test methods for grasping personality and aptitude in the education of children and adolescents or the re-education of adults. Most of the existing personality suitability test methods are implemented by conducting a questionnaire survey for a certain period of time using a pre-made questionnaire. 【0003】 Such a personality suitability test method has a high possibility that the result will change due to the psychological environment where problems given for a certain period of time must be solved, and the result can be changed by the will, knowledge level, time environment and space environment, and emotions of the subject. Since the test results are analyzed by quantifying or classifying them, there is a limit to accurately grasping the particularity that only an individual has. 【0004】 Also, there is a limit to grasping characteristics such as behaviors, emotions, cognitions, and judgments that are performed in an unconscious dimension. 【0005】 In order to overcome such limitations, a solution-providing system using DNA suitability testing has been developed, but it is a system that classifies genetic test results and matches pre-set solutions for each type, and it is difficult to determine the accurate learning aptitude of the subject, and thus there is also a limit to recommending an appropriate educational program. 【Prior Art Documents】 【Patent Documents】 【0006】 [Patent Document 1] Korean Registered Patent Publication No. 10-2073195 [Overview of the project] [Problems that the invention aims to solve] 【0007】 Therefore, the technical problem of the present invention is focused on these points, and the object of the present invention is to provide a learning tendency information determination system for a gene testing information infrastructure. 【0008】 Another object of the present invention is to provide a method for determining learning tendency information in a genetic testing information infrastructure. [Means for solving the problem] 【0009】 A learning tendency information determination system for a gene testing information infrastructure according to one embodiment for achieving the above-described objectives of the present invention includes: a basic information generation unit that receives information of a person to be tested and generates basic information; a gene testing information generation unit that analyzes a gene sample of the person to be tested and generates gene information; and a learning tendency information determination unit that generates cognitive category learning tendency information based on the gene information, generates non-cognitive category learning tendency information based on the gene information and the basic information, and generates learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information. 【0010】 In one embodiment of the present invention, the learning tendency information determination system of the gene testing information infrastructure may further include a consulting unit. The learning tendency information determination system of the gene testing information infrastructure further includes an educational program provision unit that provides educational program information, wherein the consulting unit uses the learning tendency information to match an educational program from the educational program information that is suitable for the person being tested, or a customized educational program provision unit further includes a consulting unit that uses the learning tendency information to generate a customized educational program that is suitable for the person being tested. 【0011】 In one embodiment of the present invention, the consulting department may provide the person being tested or the provider of the educational program with a report containing the learning preference information. The report may further include the basic information. 【0012】 In one embodiment of the present invention, the learning tendency information determination system of the gene testing information infrastructure may further include a learning tendency information update unit that, after being provided with the learning tendency information, updates the learning tendency information to reflect the learning results of the person being tested. 【0013】 In one embodiment of the present invention, when the learning tendency information determination unit generates cognitive category learning tendency information based on the gene information, it generates cognitive categories by classifying detailed categories for cognitive tendencies that affect learning, collects papers on genes that affect each of the cognitive categories, selects genes and assigns weights based on the reliability and impact factor of each paper, determines the relationship between genes and learning tendency information for each cognitive category, and generates learning tendency information for each cognitive category based on this. 【0014】 In one embodiment of the present invention, when the learning tendency information determination unit generates non-cognitive category learning tendency information based on the gene information and the basic information, it generates non-cognitive categories by classifying detailed categories for non-cognitive tendencies that affect learning, collects papers on genes that affect each of the non-cognitive categories, selects genes and assigns weights based on the reliability and impact factor of each paper, determines the relationship between genes and the learning tendency information for each non-cognitive category, and uses the gene information to generate learning tendency information for each non-cognitive category based on genetic factors. It can also generate learning tendency information for each non-cognitive category based on environmental factors using the basic information, and integrates the learning tendency information for each non-cognitive category based on genetic factors and the learning tendency information for each non-cognitive category based on environmental factors to generate the non-cognitive category learning tendency information. 【0015】 In one embodiment of the present invention, when the learning tendency information determination unit generates learning tendency information by integrating the cognitive category learning tendency information and the non-cognitive category learning tendency information, it uses an artificial intelligence-based determination algorithm for generating the learning tendency information. The determination algorithm uses genetic information and basic information relating to multiple subjects to classify types of combinations of the cognitive category learning tendency information and the non-cognitive category learning tendency information. It uses learning outcome information included in the basic information to analyze the differences in the types for different subjects for the same outcome, and can analyze the differences in outcomes for different subjects for similar types of cognitive category learning tendency information. It can analyze the differences in outcomes for different subjects for similar types of non-cognitive category learning tendency information, and uses learning environment information included in the basic information to analyze the reinforcement conditions for the non-cognitive category learning tendency information, and utilize these correlations. 【0016】 A method for determining learning tendency information for a gene testing information infrastructure according to one embodiment for achieving the above-described objectives of the present invention includes: a gene sample collection step of collecting a gene sample of a person to be tested; a questionnaire information input step of inputting information about the person to be tested; a step of generating gene information by analyzing the gene sample; a step of generating basic information using the questionnaire information; a step of generating cognitive category learning tendency information based on the gene information; a step of generating non-cognitive category learning tendency information based on the gene information and the basic information; and a step of generating learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information. 【0017】 In one embodiment of the present invention, the method for determining learning tendency information of the gene testing information infrastructure may include an educational program information input step of inputting educational program information of an educational program provider, and an educational program matching step of matching an educational program suitable for the person being tested with the educational program information using the learning tendency information. It may further include a customized program generation step of generating a customized educational program suitable for the person being tested using the learning tendency information. 【0018】 In one embodiment of the present invention, the method for determining learning tendency information of the gene testing information infrastructure may further include the step of providing the person being tested or the provider of the educational program with a report containing the learning tendency information. The report may further include the basic information and the questionnaire information. 【0019】 In one embodiment of the present invention, the method for determining the learning tendency information of the gene testing information infrastructure may further include a learning tendency information update step, in which, after being provided with the learning tendency information, the learning tendency information is updated to reflect the learning results of the person being tested. 【0020】 In one embodiment of the present invention, the step of generating cognitive category learning tendency information based on the genetic information involves classifying detailed categories for cognitive tendencies that affect learning to generate cognitive categories, collecting papers on genes that affect each of the cognitive categories, selecting genes and assigning weights based on the reliability and impact factor of each paper, determining the relationship between the genes and the learning tendency information for each cognitive category, and generating the learning tendency information for each cognitive category based on this. 【0021】 In one embodiment of the present invention, in the step of generating non-cognitive category learning tendency information based on the gene information and the basic information, detailed categories are classified for non-cognitive tendencies that affect learning to generate non-cognitive categories, papers on genes that affect each of the non-cognitive categories are collected, genes are selected and weighted based on the reliability and impact factor of each paper, the relationship between genes and the learning tendency information for each non-cognitive category is determined, and based on this, the gene information can be used to generate learning tendency information for each non-cognitive category due to genetic factors. The basic information can be used to generate learning tendency information for each non-cognitive category due to environmental factors. The learning tendency information for each non-cognitive category due to genetic factors and the learning tendency information for each non-cognitive category due to environmental factors can be combined to generate the non-cognitive category learning tendency information. 【0022】 In one embodiment of the present invention, in the step of generating learning tendency information by integrating the cognitive category learning tendency information and the non-cognitive category learning tendency information, an artificial intelligence-based judgment algorithm can be used to generate the learning tendency information. The judgment algorithm uses genetic information and basic information relating to multiple subjects to classify types of combinations of the cognitive category learning tendency information and the non-cognitive category learning tendency information, uses learning outcome information included in the basic information to analyze differences in the types for the same outcome for each subject, analyzes differences in outcomes for similar types of cognitive category learning tendency information for each subject, analyzes differences in outcomes for similar types of non-cognitive category learning tendency information for each subject, and uses learning environment information included in the basic information to analyze reinforcement conditions for the non-cognitive category learning tendency information, and can utilize these correlations. 【0023】 In one embodiment of the present invention, the method for determining the learning tendency information of the gene testing information infrastructure may further include a step of updating the learning tendency information by updating the learning tendency information. [Effects of the Invention] 【0024】 According to an embodiment of the present invention, cognitive category learning aptitude information and non-cognitive category learning aptitude information are generated based on the genetic information and basic information of the subject, and by synthesizing these, the innate biological temperament (genetic factors) and the environment are grasped, and the learning aptitude information is obtained for each of the cognitive / non-cognitive categories, so that the learning aptitude information can be judged more accurately. Through this, a more effective educational program can be provided. 【0025】 However, the effects of the present invention are not limited to the above effects, and can be variously extended without departing from the spirit and scope of the present invention. 【Brief Description of the Drawings】 【0026】 [Figure 1] It is a schematic diagram of a learning aptitude information judgment system of a genetic test information infrastructure according to an embodiment of the present invention. [Figure 2] It is a flowchart of a learning aptitude information judgment method of a genetic test information infrastructure according to an embodiment of the present invention. [Figure 3] It is a flowchart showing in detail the learning aptitude information generation stage of FIG. 2. [Figure 4] It is a flowchart showing the consulting stage of a learning aptitude information judgment method of a genetic test information infrastructure according to an embodiment of the present invention. 【Modes for Carrying Out the Invention】 【0027】 Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings. 【0028】 The present invention can be subjected to various changes and can have various forms. Specific embodiments are illustrated in the drawings and will be described in detail in the text. However, this is not intended to limit the present invention to a specific disclosed form, and it should be understood to include all changes, equivalents, and alternatives included in the spirit and technical scope of the present invention. 【0029】 Figure 1 is a schematic diagram of a learning tendency information determination system for a gene testing information infrastructure according to one embodiment of the present invention. 【0030】 Referring to Figure 1, the learning tendency information determination system of the gene testing information infrastructure may include a basic information generation unit 100, a gene testing information generation unit 200, a learning tendency information determination unit 300, a learning tendency information update unit 350, a consulting unit 400, an educational program provision unit 500, and a customized educational program provision unit 550. 【0031】 The basic information generation unit 100 can receive information about the person being inspected and generate basic information. 【0032】 The gene testing information generation unit 200 can analyze the genetic sample of the person being tested and generate genetic information. 【0033】 The learning tendency information determination unit 300 can generate learning tendency information. It can generate cognitive category learning tendency information based on the genetic information or based on the genetic information and the basic information, generate non-cognitive category learning tendency information based on the genetic information and the basic information, and generate the learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information. 【0034】 Specifically, the learning tendency information determination unit 300 can generate cognitive category learning tendency information based on the gene information. It can generate cognitive categories by classifying detailed categories for cognitive tendencies that affect learning, collect papers on genes that affect each of the cognitive categories, select genes and assign weights based on the reliability and impact factor of each paper, determine the relationship between genes and the learning tendency information for each cognitive category, and based on this, generate the learning tendency information for each cognitive category for the gene information. 【0035】 Furthermore, the learning tendency information determination unit 300 can generate non-cognitive category learning tendency information based on the gene information and the basic information. It can generate non-cognitive categories by classifying detailed categories for non-cognitive tendencies that affect learning, collect papers on genes that affect each of the non-cognitive categories, select genes and assign weights based on the reliability and impact factor of each paper, determine the relationship between genes and the learning tendency information for each non-cognitive category, and based on this, generate learning tendency information for each non-cognitive category based on genetic factors using the gene information. 【0036】 When the learning tendency information determination unit 300 generates learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information, it can use the determination algorithm of the artificial intelligence platform for generating the learning tendency information. 【0037】 The judgment algorithm uses genetic information and basic information of multiple test subjects to classify types of combinations of cognitive category learning tendency information and non-cognitive category learning tendency information, uses learning outcome information included in the basic information to analyze differences in the types of the above for the same outcome for each test subject, analyzes differences in outcomes for each test subject for similar types of cognitive category learning tendency information, analyzes differences in outcomes for each test subject for similar types of non-cognitive category learning tendency information, and uses learning environment information included in the basic information to analyze reinforcement conditions for the non-cognitive category learning tendency information, and uses these correlations to generate the learning tendency information. 【0038】 For example, the judgment algorithm may be an AI-based judgment algorithm that grasps the correlation between each item of cognitive category learning tendency information and the degree of learning achievement, and the correlation between each item of non-cognitive category learning tendency information and the degree of learning achievement, and based on this, assigns weighted values ​​to each item that comprehensively affects the degree of learning achievement, thereby establishing a criterion for determining the learning tendency. 【0039】 The aforementioned judgment algorithm can further select and recommend educational programs (lectures) that can strengthen the strengths of each item and compensate for their weaknesses based on this. While taking the recommended educational program, data (learning outcomes, learning attitudes, learning environment, etc.) can be tracked and collected, compared with the data used to determine the learning aptitude, and the differences can be analyzed and verified. Using this process, the weighted values ​​based on the degree of each item that affects the degree of learning achievement can be modified and supplemented to continuously improve the judgment algorithm on the AI ​​platform. The learning aptitude information update unit 350 can update the learning aptitude information after being provided with the learning aptitude information, reflecting the learning outcomes of the person being tested. Such updates can reflect information on the achievement level generated by the institution providing the educational program described later, reflecting the learning outcomes of the person being tested. 【0040】 The consulting unit 400 can receive the learning tendency information and provide the subject or educational program provider with a report containing the learning tendency information. The report analyzes the presence or absence of gene mutations related to learning categories (cognitive, non-cognitive, multiple intelligences, neuroscience, etc.) in the genetic test result data to provide a report that grasps the subject's genetic capabilities, and can provide a customized report by selecting only the desired categories. In other words, the report can provide the subject or educational program provider with information that combines basic information, questionnaire information, and genetic learning tendency information. 【0041】 The education program provision unit 500 provides the consulting unit 400 with information on available education programs, and the consulting unit 400 uses the learning preference information to match the education program information with an education program suitable for the person being tested. 【0042】 The customized educational program provision unit 550 can provide a customized recommended educational program to the subject once the consulting unit 400 has generated a customized educational program suitable for the subject using the learning preference information. 【0043】 Figure 2 is a flowchart of a method for determining learning tendency information for a gene testing information infrastructure according to one embodiment of the present invention. Figure 3 is a flowchart showing in detail the learning tendency information generation stage of Figure 2. 【0044】 Referring to Figures 2 and 3, the method for determining learning tendency information for the genetic testing information infrastructure may include a genetic sample collection stage S110, a questionnaire information input stage S120, a genetic information generation stage S210, a basic information generation stage S220, a learning tendency information generation stage S300, a learning tendency information update stage S400, and a learning tendency information provision stage S500. 【0045】 In the gene sample collection step S110, a biosample of the person being tested is collected. The biosample refers to a biological sample containing the genetic information of the person being tested. The biological sample may be in the form of a biological fluid. In one specific example, the biosample may be saliva, a blood sample, a whole blood sample, etc. 【0046】 In the aforementioned questionnaire information input stage S120, the person being tested or their guardian can input various information about the person being tested. When a questionnaire is provided via a mobile device, internet app, or web, various questionnaire information can be input in response to it. For example, the questionnaire information may include personal information such as the person being tested's age, gender, and family, as well as information about their physical abilities, exercise level, learning environment such as study time, and learning outcomes. 【0047】 Furthermore, in the aforementioned questionnaire information input stage S120, responses to questionnaires regarding non-cognitive categories related to living environment, personality, and inclinations can be input in order to generate non-cognitive category learning tendency information, which will be described later. 【0048】 In the gene information generation step S210, gene information can be generated by performing a gene test on the collected biosample via a gene testing device. 【0049】 In the basic information generation stage S220, basic information of the subject can be generated using the questionnaire information. The basic information may include items related to cognitive categories and a variety of items related to non-cognitive categories. For example, the basic information may include objective indicators of the user's personal characteristics, physical abilities, learning ability, resilience, Big 5 personality traits, and multiple intelligences, as included in the questionnaire information. The Big 5 is a personality psychology model that explains personality as five interdependent factors, and these five factors correspond to neuroticism, extraversion, openness, agreeableness, and conscientiousness. 【0050】 In the learning tendency information generation step S300, cognitive category learning tendency information can be generated based on the genetic information, non-cognitive category learning tendency information can be generated based on the genetic information and the basic information, and learning tendency information can be generated by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information. 【0051】 The learning tendency information generation step S300 may specifically include a cognitive category learning tendency information generation step S310 based on genetic information, a non-cognitive category learning tendency information generation step S320 based on genetic information and basic information, and a learning tendency information generation step S330 that integrates the cognitive category learning tendency information and the non-cognitive category learning tendency information. 【0052】 In the cognitive category learning tendency information generation stage S310, detailed categories are classified for cognitive tendencies that influence learning, and papers related to genes that influence each detailed category can be collected (crawled). Based on the reliability (paper evidence level 2 or higher) and impact factor of the collected papers, genes are selected and weighted, and the cognitive category learning tendency information can be generated based on the gene information. Items that fall under the cognitive categories may include, but are not limited to, comprehension, thinking ability, creativity, and concentration. 【0053】 Furthermore, the algorithm for generating the aforementioned cognitive category learning tendency information can be generated using an artificial intelligence model. 【0054】 Based on the aforementioned genetic information and basic information, in the non-cognitive category learning tendency information generation stage S320, detailed categories are classified for non-cognitive tendencies that affect learning, and papers related to genes that affect each detailed category can be collected (crawled). By selecting and weighting genes based on the reliability (paper evidence level 2 or higher) and impact factor of the collected papers, the non-cognitive category learning tendency information can be generated based on the aforementioned genetic information. 【0055】 Using the aforementioned basic information, it is possible to generate learning tendency information categorized by non-cognitive categories based on environmental factors. 【0056】 The learning tendency information for non-cognitive categories based on the aforementioned genetic factors and the learning tendency information for non-cognitive categories based on the aforementioned environmental factors can be combined to generate the learning tendency information for non-cognitive categories. 【0057】 The aforementioned non-cognitive categories include, but are not limited to, physical tendencies such as endurance, physical strength, attention, and agility, mental tendencies such as resilience, self-regulation, self-esteem, and positivity, and the Big Five personality traits. 【0058】 In the learning tendency information generation stage S330, the cognitive category learning tendency information and the non-cognitive category learning tendency information can be combined to generate learning tendency information. 【0059】 When generating learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information, an artificial intelligence platform's judgment algorithm can be used to generate the learning tendency information. 【0060】 The judgment algorithm uses genetic information and basic information of multiple test subjects to classify types of combinations of cognitive category learning tendency information and non-cognitive category learning tendency information, uses learning outcome information included in the basic information to analyze differences in the types of the above for the same outcome for each test subject, analyzes differences in outcomes for each test subject for similar types of cognitive category learning tendency information, analyzes differences in outcomes for each test subject for similar types of non-cognitive category learning tendency information, and uses learning environment information included in the basic information to analyze reinforcement conditions for the non-cognitive category learning tendency information, and uses these correlations to generate the learning tendency information. 【0061】 For example, the judgment algorithm may be an AI-based judgment algorithm that grasps the correlation between each item of the cognitive category learning tendency information and the degree of learning achievement, and the correlation between each item of the non-cognitive category learning tendency information and the degree of learning achievement, and based on this, assigns weighted values ​​to each item that comprehensively affects the degree of learning achievement, thereby establishing a criterion for determining the learning tendency. In the learning tendency information update stage S400, after the learning tendency information has been provided, the learning tendency information can be updated to reflect the learning results of the person being tested. Specifically, the learning tendency information can be updated so that the learning results of the person being tested can be compared, re-predicted, and re-recommended. 【0062】 In this process, learning outcomes and information related to the living environment can be continuously updated to track the progress of the subject's learning outcomes. For example, learning outcomes such as test and exam scores in measuring outcomes related to learning and teaching methods, information on creating an environment for positive emotions and mental strength development (GRIT, meditation, exercise), achievement desires such as competitiveness, learning attitudes such as questioning during class and concentration, and evaluation information on academic perseverance can be continuously updated to track progress in academic outcomes. 【0063】 In the learning tendency information provision stage S500, a report containing the learning tendency information can be provided to the person being tested or the educational program provider. The report provides an understanding of the person being tested's genetic capabilities by analyzing the presence or absence of mutations in genes related to learning categories (cognitive, non-cognitive, multiple intelligences, neuroscience, etc.) in the genetic test result data, and can provide a customized report by selecting only the desired categories. In other words, the report can provide the person being tested or the educational program provider with information that combines basic information, questionnaire information, and genetic learning tendency information. Furthermore, the report can also provide basic information and tips regarding the person being tested's genetic characteristics related to the learning tendency information, methods for reinforcing and supplementing those genetic characteristics, etc., in the form of text and video clips. 【0064】 Figure 4 is a flowchart showing the consulting stage of a method for determining learning tendency information for a gene testing information infrastructure according to one embodiment of the present invention. 【0065】 Referring to Figure 4, the method for determining learning aptitude information of the gene testing information infrastructure may further include a consulting stage S600, an educational program provision stage S710, a customized educational program provision stage S720, a learning outcome collection stage S810, and a learning outcome analysis report provision stage S820. The consulting stage S600 may further include an educational program information input stage S605, an educational program matching stage S610, and a customized program generation stage S620. 【0066】 In the educational program information input stage S605, available educational programs can be input. In the educational program matching stage S610, the learning aptitude information can be used to match the educational program information with an educational program suitable for the test subject. In the customized program generation stage S620, the learning aptitude information can be used to generate a customized educational program suitable for the individual test subject. 【0067】 For example, it is possible to select and recommend educational programs (lectures) that can strengthen the strengths and compensate for the weaknesses of each item in the cognitive or non-cognitive categories of learning aptitude information. Furthermore, data (learning outcomes, learning attitudes, learning environment, etc.) can be tracked and collected while the student takes the recommended educational program, and the differences can be analyzed and verified by comparing this data with the data used to determine the student's learning aptitude. 【0068】 The aforementioned educational program or the customized educational program may include, for example, customized information on an artificial intelligence platform relating to a collaborative learning academy that can maximize learning effectiveness, or an educational program that can maximize learning effectiveness through an artificial intelligence algorithm. 【0069】 In the educational program provision stage S710 and the customized educational program provision stage S720, the educational institution can provide the educational program or the customized educational program to the person being tested. 【0070】 In the learning outcome collection stage S810, the learning activities of the subject can be monitored to measure the level of achievement for each type of learning method. The measured level of achievement can be used to train or update the judgment algorithm, and the optimal learning method according to the genetic learning tendency can be recommended. For example, the judgment algorithm on the AI ​​platform can be continuously improved by modifying and supplementing weighted values ​​according to the degree of each item that affects the learning achievement of the judgment algorithm. The level of achievement may include learning method / type data by school or learning method / type data by teacher. The level of achievement may also include information such as current lecture attendance status, progress, attendance status, and learning record information. 【0071】 In the learning outcome analysis report provision stage S820, a learning outcome analysis report can be provided to the examinee or the educational program provider based on the information regarding the level of achievement. 【0072】 For example, educational institutions, academies, and schools where the test subject has consented to providing information can summarize, extract, and group information from each test subject's learning aptitude result table (outcome level) for the purpose of establishing a customized educational direction for the test subject, implementing it, and managing learning outcomes, and then provide it to the academy or teacher. 【0073】 For example, based on the aforementioned learning preference information, educational programs and customized educational programs can be provided to school teachers, and the results of the solutions can be compared, classified, summarized, extracted, and grouped to provide information to administrators and teachers for the purpose of learning management. 【0074】 The aforementioned information may be provided or entered using a PC web system to record the examinee's current lecture attendance status, progress in lectures, attendance record, learning records, and materials, and then provided to the examinee and the consultant. 【0075】 According to this embodiment, the person being tested, their guardian, the provider of the educational program, etc., can continuously receive information about the recommended educational program, as well as information on the person being tested's current status of attending lectures, progress, attendance record, etc., and the person being tested can receive continuous feedback on learning consulting (explanation of the genetic analysis report and aspects related to improving the child's learning ability, such as the child's psychological state and the educational program). 【0076】 According to embodiments of the present invention, cognitive category learning tendency information and non-cognitive category learning tendency information are generated based on the genetic information and basic information of the person being tested. By integrating these, the innate biological temperament (genetic factors) and environment are understood, and learning tendency information is acquired separately for cognitive and non-cognitive categories, allowing for a more accurate determination of learning tendency information. Through this, a more effective educational program can be provided. 【0077】 As described above with reference to embodiments, a person skilled in the art will understand that the present invention can be modified and altered in various ways without departing from the spirit and scope of the invention as described in the following claims. [Explanation of Symbols] 【0078】 10. Persons being tested 100 Basic information generation section 200 Genetic Testing Information Generation Department 300 Learning tendency information judgment department 350 Learning Tendency Information Update Department 400 Consulting Department 500 Educational Programs Department 550 Customized Educational Program Delivery Department

Claims

[Claim 1] A basic information generation unit that receives the subject's information and generates basic information, A gene testing information generation unit that analyzes the genetic sample of the person being tested and generates genetic information, Includes a learning tendency information determination unit that generates cognitive category learning tendency information based on the genetic information, generates non-cognitive category learning tendency information based on the genetic information and the basic information, and generates learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information, The aforementioned cognitive category learning tendency information includes information on at least one of the following: comprehension, thinking ability, creativity, and concentration. The aforementioned non-cognitive category learning tendency information includes information on at least one of the following: endurance, physical fitness, attention, and agility. The aforementioned learning tendency information determination unit is: The cognitive tendencies that influence learning are classified into detailed categories to generate cognitive categories, and the cognitive category learning tendency information is generated using the genetic information. The system generates non-cognitive categories by classifying detailed categories based on non-cognitive tendencies that influence learning, generates learning tendency information for non-cognitive categories based on genetic factors using the genetic information, and generates learning tendency information for each non-cognitive category based on environmental factors using the basic information. The learning tendency information for each non-cognitive category based on the aforementioned genetic factors and the learning tendency information for each non-cognitive category based on the aforementioned environmental factors are combined to generate the learning tendency information for each non-cognitive category. When the learning tendency information determination unit generates learning tendency information by combining the cognitive category learning tendency information and the non-cognitive category learning tendency information, it uses the determination algorithm of the artificial intelligence platform for generating the learning tendency information. The aforementioned decision algorithm is: Using genetic and basic information of multiple test subjects, The types of combinations of the cognitive category learning tendency information and the non-cognitive category learning tendency information are classified, Using the learning outcome information included in the basic information, we analyze the differences in the aforementioned types for each test subject for the same outcome. We analyzed the differences in outcomes among test subjects for similar types of cognitive category learning tendency information. We analyzed the differences in outcomes among test subjects for similar types of non-cognitive category learning tendency information. A learning tendency information determination system for a genetic testing information infrastructure, characterized by using the learning environment information included in the basic information to analyze the reinforcement conditions for the non-cognitive category learning tendency information and using the correlations between them. [Claim 2] Including the consulting department, The system further includes an educational program provision department that provides educational program information, and the consulting department uses the learning preference information to match the educational program information with an educational program suitable for the person being tested. The learning tendency information determination system for a genetic testing information infrastructure according to claim 1, further comprising a customized educational program provision unit, wherein the consulting unit uses the learning tendency information to generate a customized educational program suitable for the person being tested. [Claim 3] The consulting department provides the subject of the examination or the provider of the educational program with a report containing the learning tendency information. The learning tendency information determination system for a gene testing information infrastructure according to claim 2, characterized in that the report further includes the basic information. [Claim 4] The learning tendency information determination system for a gene testing information infrastructure according to claim 3, further comprising a learning tendency information update unit that, after being provided with the learning tendency information, updates the learning tendency information by reflecting the learning results of the person being tested and comparing it with the learning tendency information. [Claim 5] When the learning tendency information determination unit generates cognitive category learning tendency information based on the genetic information, A learning tendency information determination system for a gene testing information infrastructure according to claim 1, characterized in that it collects papers on genes that affect each of the aforementioned cognitive categories, selects genes and assigns weights based on the reliability and impact factor of each paper, and determines the degree of association and influence between genes and learning tendency information for each cognitive category. [Claim 6] When the learning tendency information determination unit generates non-cognitive category learning tendency information based on the genetic information and the basic information, The learning tendency information determination system for a gene testing information infrastructure according to claim 5, characterized in that it collects papers on genes that affect each of the aforementioned non-cognitive categories, selects genes and assigns weights based on the reliability and impact factor of each paper, and determines the degree of association and influence between genes and learning tendency information for each of the aforementioned non-cognitive categories.

Images