System and method for measuring subject core literacy based on production technology

Abstract

The present invention discloses a system and method for measuring subject core literacy based on production technology, the system comprising: an acquisition module, acquiring subject core literacy standard type data of each subject; a disassembly module, acquiring a production content database; a first mapping module, acquiring a production label database; a second mapping module, acquiring a subject test question database with attribute labels; a first processing module, acquiring a test paper library with attribute labels for measuring the subject core literacy type of the user; a second processing module, acquiring a right-wrong ratio data set characterizing the subject core literacy type; an analysis module, analyzing and summarizing the right-wrong ratio data set, and outputting the subject core literacy measurement result of the user. The present invention can simply, quickly and accurately measure the subject core literacy of users (such as students), and quantify the measurement results, so as to provide a good and effective basis for users to optimize or improve their subject core literacy, and then facilitate improving the subject core literacy of users.

Description

Technical Field

[0001] The present invention relates to the field of educational application and data processing technology, and in particular to a system and method for measuring subject core literacy based on production technology. Background Art

[0002] The current test question structure mainly includes the following key elements: question stem, options, answers, analysis, knowledge point annotations, question types and scores. After users, such as students, answer the test questions, they can analyze whether the answers are correct, scores and students' weak knowledge points. However, it is impossible to measure the specific subject core literacy deficiencies that lead to students' failure to master the knowledge points.

[0003] Moreover, the current test structure contains knowledge points with too large a granularity, and there is no theoretical algorithm to reduce the granularity of knowledge points, which results in the inability to accurately link knowledge points to specific subject core competency types. The system is unable to collect accurate data related to subject core competencies, so it is impossible to accurately measure subject core competencies by answering test questions.

[0004] Therefore, the current relevant technologies are unable to accurately measure students' core subject literacy, resulting in the inability to accurately grasp the distribution of students' core subject literacy, which is not conducive to the improvement and enhancement of students' core subject literacy. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0006] To this end, one object of the present invention is to propose a system for measuring subject core literacy based on generative technology, which can simply, quickly and accurately measure the subject core literacy of users (such as students), and quantify the measurement results, thereby providing a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy.

[0007] To this end, the second purpose of the present invention is to propose a method for measuring subject core competencies based on production technology.

[0008] To this end, a third object of the present invention is to provide an electronic device.

[0009] To this end, a fourth object of the present invention is to provide a computer-readable storage medium.

[0010] To achieve the above-mentioned objectives, an embodiment of the first aspect of the present invention discloses a system for measuring subject core literacy based on production technology, including: an acquisition module, used to acquire subject core literacy standard type data of each subject, label and summarize the subject core literacy standard type data, and obtain a subject core literacy type database corresponding to each subject; a disassembly module, used to disassemble the minimum granularity knowledge points in the knowledge framework of each subject based on production technology, and obtain a production content database; a first mapping module, used to perform relationship mapping between the subject core literacy type database and the production content database, and obtain a production label database having identification subject knowledge attributes and subject core literacy type attributes; a second mapping module, used to map the attribute labels in the production label database to the subject test question database one by one, and obtain to a subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; a first processing module is used to group the subject test questions in the subject test question database with attribute labels to obtain an attribute label test paper library with a type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; a second processing module is used to provide the test papers in the attribute label test paper library to the user, and receive the user's answer information for the test paper, and map the answer information with the attribute label associated with the corresponding test question in the test paper to obtain a right-or-wrong ratio data set; an analysis module is used to analyze the right-or-wrong ratio data set and output the subject core literacy measurement result of the user.

[0011] According to the system for measuring subject core literacy based on production technology in an embodiment of the present invention, the subject core literacy standard type data of each subject is obtained through an acquisition module, and the subject core literacy standard type data is labeled and summarized to obtain a subject core literacy type database corresponding to each subject; the minimum granularity knowledge points in the knowledge framework of each subject are disassembled based on production technology through a disassembly module to obtain a production content database; the subject core literacy type database and the production content database are relationally mapped through a first mapping module to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database through a second mapping module to obtain a subject core literacy type database with identification of subject knowledge attributes and subject core literacy type attributes. A subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; the subject test questions in the subject test question database with attribute labels are grouped by a first processing module to obtain an attribute label test paper library with a type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; the test papers in the attribute label test paper library are provided to the user by a second processing module, and the user's answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed by the analysis module, and the user's subject core literacy measurement result is output. Therefore, the system uses the national subject core literacy classification standards to break down the natural subject knowledge points, micro-gridizes the knowledge points to only contain a single subject core literacy type, and then marks the micro-gridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through users' (such as students) answering actions. That is to say, the subject core literacy is quantitatively broken down in a generative way, and the attribute labels after the breakdown are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and level of attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, the system can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, which is conducive to providing a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy. At the same time, the system can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0012] In addition, the system for measuring subject core literacy based on production technology according to the above embodiment of the present invention may also have the following additional technical features:

[0013] In some examples, the disassembly module disassembles the smallest granularity knowledge points in each subject knowledge framework based on generative technology, including: using generative technology to decompose the thinking actions in the process of learning the smallest granularity knowledge points in each subject knowledge framework in a generative way, and making the disassembled subject production formulas map one or more subject core literacy standard type data, and forming the production content database based on the disassembled subject production formulas.

[0014] In some examples, the first mapping module performs a relational mapping between the subject core literacy type database and the production content database, including: marking the subject core literacy type labels for the subject productions one by one by matching one or more core literacy standard type data mapped by the disassembled subject productions, thereby forming the production label database having the attributes of identifying subject knowledge and subject core literacy types.

[0015] In some examples, the subject core literacy standard type data of each subject has a variety of different label types, and different label types correspond to different weight coefficients. Before the second processing module performs relationship mapping between the answer information and the attribute labels associated with the corresponding test questions in the test paper, the second processing module is also used to: split the score of each test question in the test paper according to the weight coefficients corresponding to the multiple subject core literacy types marked with the test question, so that the score of the test question and the weight coefficients of the corresponding multiple subject core literacy types have a corresponding relationship.

[0016] In some examples, the second processing module performs a relationship mapping between the answer information and the attribute labels associated with the corresponding test questions in the test paper, including: determining the actual score obtained by the user for the test question based on the answer information; determining the scores corresponding to multiple subject core literacy types based on the actual score and the weight coefficients of multiple subject core literacy types corresponding to the test question under the actual score.

[0017] In some examples, the measurement results of the user's subject core literacy include at least: the proportional relationship of the user's subject core literacy type dimensions in the current subject and the distribution status of the subject core literacy level in each subject core literacy type dimension.

[0018] In some examples, the analysis module is also used to generate a subject core literacy evaluation report based on the subject core literacy measurement results of the user, specifically including: inputting the distribution status of the user's subject core literacy level in each subject core literacy type dimension into a pre-trained subject core literacy evaluation model to obtain evaluation description information corresponding to the subject core literacy level, summarizing the evaluation description information to generate the subject core literacy evaluation report, wherein the subject core literacy evaluation model includes a corresponding mapping relationship between pre-calibrated subject core literacy levels and corresponding evaluation description information, the distribution status of the subject core literacy level includes level grade and level score, and the corresponding evaluation description information is obtained according to the level grade and the level score in the score range, and different level grades correspond to different evaluation description information in different score ranges.

[0019] To achieve the above-mentioned purpose, the embodiment of the second aspect of the present invention discloses a method for measuring subject core literacy based on production technology, comprising the following steps: obtaining subject core literacy standard type data of each subject, labeling and summarizing the subject core literacy standard type data, and obtaining a subject core literacy type database corresponding to each subject; based on production technology, disassembling the minimum granularity knowledge points in the knowledge framework of each subject, and obtaining a production content database; relationally mapping the subject core literacy type database with the production content database, and obtaining a production label database having identification of subject knowledge attributes and subject core literacy type attributes; mapping the attribute labels in the production label database one by one to the subject test question database, and obtaining a production label database with identification of subject knowledge attributes and subject core literacy type attributes; mapping the attribute labels in the production label database one by one to the subject test question database, and obtaining a production label database with identification of subject knowledge attributes and subject core literacy type attributes; A subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring a user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; the test papers in the attribute label test paper library are provided to users, and user answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-or-wrong ratio data set; the right-or-wrong ratio data set is analyzed, and the subject core literacy measurement results of the user are output.

[0020] According to the method for measuring subject core literacy based on production technology in an embodiment of the present invention, by obtaining the subject core literacy standard type data of each subject, the subject core literacy standard type data is labeled and summarized to obtain the subject core literacy type database corresponding to each subject; based on the production technology, the minimum granularity knowledge points in the knowledge framework of each subject are disassembled to obtain the production content database; the subject core literacy type database is relationally mapped with the production content database to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database to obtain a subject core literacy type database with attributes. A subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring user subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; test papers in the attribute label test paper library are provided to users, and user answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, this method uses the national subject core literacy classification standard to disassemble the natural subject knowledge points, micro-gridizes the knowledge points to only contain a single subject core literacy type, and then marks the micro-gridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through users' (such as students) answering actions. That is to say, the subject core literacy is quantitatively disassembled in a generative way, and the disassembled attribute labels are associated and mapped to the students' subject test questions. Through the answering of the subject test questions, the number and level of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. According to this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, this method can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, so as to provide a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy. At the same time, this method can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0021] To achieve the above-mentioned purpose, the third aspect of the present invention discloses an electronic device, which includes: the system for measuring subject core competencies based on production technology according to the first aspect of the present invention; or, the electronic device includes: a processor, a memory, and a program for measuring subject core competencies based on production technology stored in the memory and executable on the processor, wherein the program for measuring subject core competencies based on production technology, when executed by the processor, implements the method for measuring subject core competencies based on production technology according to the second aspect of the present invention.

[0022] According to the electronic device of the embodiment of the present invention, the subject core literacy standard type data of each subject is obtained, and the subject core literacy standard type data is labeled and summarized to obtain the subject core literacy type database corresponding to each subject; the minimum granularity knowledge points in the knowledge framework of each subject are disassembled based on the production technology to obtain the production content database; the subject core literacy type database is relationally mapped with the production content database to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database to obtain the subject test question database with attribute labels; The invention relates to a database of subject test questions with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring a user's subject core literacy, wherein the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; test papers in the attribute label test paper library are provided to users, and user answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, the electronic device breaks down the natural subject knowledge points by using the national subject core literacy classification standards, microgrids the knowledge points to include only a single subject core literacy type, and then marks the microgridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through the user's (such as students) answering actions. That is to say, the subject core literacy is quantitatively decomposed in a generative way, and the decomposed attribute labels are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and levels of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, the electronic device can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, so as to provide a good and effective basis for the user to optimize or improve his subject core literacy, thereby facilitating the improvement of the user's subject core literacy. At the same time, the electronic device can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0023] To achieve the above-mentioned objectives, an embodiment of the fourth aspect of the present invention discloses a computer-readable storage medium, on which is stored a program for measuring core competencies of a subject based on production technology. When the program for measuring core competencies of a subject based on production technology is executed by a processor, a method for measuring core competencies of a subject based on production technology as described in the embodiment of the second aspect of the present invention is implemented.

[0024] According to the computer-readable storage medium of an embodiment of the present invention, when the program stored thereon for measuring subject core literacy based on production technology is executed by a processor, the subject core literacy standard type data of each subject is obtained, the subject core literacy standard type data is labeled and summarized, and a subject core literacy type database corresponding to each subject is obtained; the minimum granularity knowledge points in the knowledge framework of each subject are disassembled based on production technology to obtain a production content database; the subject core literacy type database is relationally mapped with the production content database to obtain a production label database having identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database A subject test question database with attribute labels is obtained from the database, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; the subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; the test papers in the attribute label test paper library are provided to the user, and the user's answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, by using the national subject core literacy classification standards to decompose the natural subject knowledge points, the knowledge points are micro-gridified to only contain a single subject core literacy type, and then the micro-gridized knowledge points are marked on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through the user's (such as students) answering actions. That is to say, the subject core literacy is quantitatively decomposed in a generative way, and the decomposed attribute labels are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and level of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Thus, it is possible to simply, quickly and accurately measure the subject core literacy of users, and quantify the measurement results, so as to provide a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of the subject core literacy of users. At the same time, the present invention can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, and have high flexibility and applicability.

[0025] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0027] Figure 1 is a structural block diagram of a system for measuring subject core literacy based on production technology according to an embodiment of the present invention;

[0028] Figure 2 It is a schematic diagram of the overall execution flow of a system for measuring subject core literacy based on production technology according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of a user's subject core literacy measurement result according to an embodiment of the present invention;

[0030] Figure 4 It is a flowchart of a method for measuring subject core literacy based on production technology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] Embodiments of the present invention are described in detail below. The embodiments described with reference to the accompanying drawings are exemplary. Embodiments of the present invention are described in detail below.

[0032] The current test structure mainly includes the following key elements: question stem, options, answers, analysis, knowledge point annotation, question type and score. The inventor found that for the current test questions, after users, such as students, answer the test questions, they can analyze the correctness of the answers, scores and students' weak knowledge points through the answered test questions, but it is impossible to measure the specific lack of core literacy of the subject that causes the students to fail to master the knowledge point. In addition, the knowledge point granularity contained in the current test structure is too large, and there is no theoretical algorithm to reduce the granularity of the knowledge points, resulting in the inability to accurately associate the knowledge points with specific core literacy types of the subject. The system cannot collect accurate data related to the core literacy of the subject, so it is impossible to complete the accurate measurement of the core literacy of the subject through answering the test questions.

[0033] To this end, an embodiment of the present invention proposes a system, method, electronic device and computer-readable storage medium for measuring subject core literacy based on generative technology. The subject core literacy is quantitatively decomposed in a generative manner, and the attribute labels after decomposition are associated and mapped to the students' subject test questions. The number and level of various subject core literacy type attributes are summarized through the answers to the subject test questions, and weighted statistical calculations are performed to obtain quantitative measurement results, forming a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that the test questions have the function of measuring subject core literacy, thereby being able to simply, quickly and accurately measure the subject core literacy of users (such as students), and quantify the measurement results, thereby providing a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy.

[0034] Reference below Figure 1-Figure 4 A system, method, electronic device, and computer-readable storage medium for measuring core competencies of a subject based on production technology according to an embodiment of the present invention are described.

[0035] Before describing the system, method, electronic device and computer-readable storage medium for measuring subject core literacy based on production technology according to an embodiment of the present invention, for ease of understanding, the subject core literacy is first described.

[0036] Specifically, subject core literacy is a new definition proposed in the new Chinese language curriculum standards in my country. It is the most basic and important quality that students develop through subject professional learning. It is a combination of basic knowledge, professional skills, basic qualities and basic experience with the characteristics of the subject that students cultivate through professional learning and social practice activities.

[0037] Core competencies of a subject are comprehensive and essential qualities or abilities that are based on subject knowledge and skills and integrate emotions, attitudes or values. They can better reflect students' core competency levels in the process of dealing with complex problems. Each subject has a set of core competency standards issued by the state. For example: The core competencies of physics are the essential qualities and key abilities that students gradually improve in the process of receiving physics education and are needed for their lifelong development and socio-economic development. They are the qualities that students have inherent in physics education and are the key components of students' scientific knowledge.

[0038] In the embodiment of the present invention, the specific examples for the core literacy of the subject are as follows: the core literacy of the subject of high school mathematics is divided into 6 directions, namely mathematical abstraction, logical reasoning, mathematical modeling, intuitive imagination, mathematical operation and data analysis. For each direction, there are 3 levels, namely: mathematical abstraction I, mathematical abstraction II, mathematical abstraction III; logical reasoning I, logical reasoning II, logical reasoning III; mathematical modeling I, mathematical modeling II, mathematical modeling III; intuitive imagination I, intuitive imagination II, intuitive imagination III; mathematical operation I, mathematical operation II, mathematical operation III; mathematical analysis I, mathematical analysis II, mathematical analysis III.

[0039] Figure 1 1 is a structural block diagram of a system for measuring subject core literacy based on production technology according to an embodiment of the present invention. Figure 1 As shown, the system 100 for measuring core subject competencies based on production technology includes: an acquisition module 110, a disassembly module 120, a first mapping module 130, a second mapping module 140, a first processing module 150, a second processing module 160 and an analysis module 170.

[0040] Specifically, the acquisition module 110 is used to obtain the subject core literacy standard type data of each subject, label and summarize the subject core literacy standard type data, and obtain the subject core literacy type database corresponding to each subject.

[0041] Specifically, we obtain the subject core literacy standard type data released by the state, and label the subject core literacy standard type data into the database. For example, the high school mathematics mentioned above corresponds to 18 core literacy types (6 directions and 3 levels included in each direction), and other subjects also have different numbers of core literacy types. Among them, different label types have different weights. For example, the weight coefficient corresponding to mathematical abstraction I is 1.0, the weight coefficient corresponding to mathematical abstraction II is 2.0, and the weight coefficient corresponding to mathematical abstraction III is 3.0. Thus, a subject core literacy type database corresponding to each subject is formed, recorded as DB1.

[0042] The disassembly module 120 is used to disassemble the smallest granularity knowledge points in the knowledge framework of each subject based on the production technology to obtain a production content database.

[0043] Specifically, production technology is production expression, and its basic form is P→Q or IF P THEN Q. It is a computational model called Post machine proposed based on string substitution rules. Each rule in the model is called a production. Among them, P is the premise of the production, also known as the antecedent, which gives the prerequisite for whether the production can be used, and is composed of a logical combination of facts; Q is a set of conclusions or operations, also known as the consequent of the production, which indicates the conclusion that should be drawn or the action that should be performed when the premise P is satisfied. In other words, the meaning of the production is that if the premise P is satisfied, the conclusion Q can be drawn or the operation specified by Q can be performed.

[0044] In a specific embodiment of the present invention, a specific example of a production formula is as follows: according to the human cognitive model (19 types such as concept type, rule type, method type, principle type, abstract concept type, method application type, etc.), the peripheral knowledge points in the national curriculum standards are broken down into smaller granularity, which is called a "subject production formula."

[0045] For example, the present invention uses this method to break down the knowledge point of "domain of function" in high school mathematics, forming four subject production formulas, namely: "*domain of function", "find the domain of function", "domain of composite function" and "*given the domain of y=f(g(x)), find the domain of y=f(x)".

[0046] Take the first production rule of the subject "*domain of function" as an example:

[0047] Its basic form is: IF: ① The known function y=f(x) represents the function from non-empty number set A to non-empty number set B, ②x is the independent variable, x∈A; THEN: set A is called the domain of the function; Cognitive type: abstract concept class.

[0048] In an embodiment of the present invention, the disassembly module 120 performs production-based disassembly on the smallest granularity knowledge points in each subject knowledge framework based on production-based technology, and the disassembled subject productions form a production content database, recorded as DB2.

[0049] The first mapping module 130 is used to perform relationship mapping between the subject core literacy type database DB1 and the production content database DB2 to obtain a production label database having identification subject knowledge attributes and subject core literacy type attributes, recorded as DB3.

[0050] The second mapping module 140 is used to map the attribute tags in the production tag database to the subject test question database one by one to obtain the subject test question database with attribute tags, wherein each subject test question in the subject test question database with attribute tags corresponds to at least one attribute tag.

[0051] Specifically, the content of a subject test question is carefully prepared and summarized to form a subject test question database. The attribute labels in the production label database DB3 are mapped one by one to the test questions in the subject test question database. Each test question covers one or more attribute labels of DB3. Test questions with attribute labels have the ability to measure the user's subject core literacy. After multiple test questions in the subject test question database are labeled, a subject test question database with attribute labels that can measure the user's subject core literacy can be formed, which is recorded as DB4.

[0052] In one embodiment of the present invention, before mapping the attribute labels in the production label database to the subject test question database one by one, the second mapping module 140 is also used to: for the current subject, select a plurality of subject test questions corresponding to the current subject, and aggregate the plurality of subject test questions to form the subject test question database.

[0053] The first processing module 150 is used to group the subject test questions in the subject test question database with attribute labels to obtain an attribute label test paper library with the type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database.

[0054] Specifically, the labeled test questions are selected from the subject test question database DB4 with attribute labels and assembled manually or automatically according to the rules to form a test paper. The test paper assembled in this way with one or more test questions forms an attribute label test paper library that can accurately measure the user's subject core literacy, recorded as DB5.

[0055] The second processing module 160 is used to provide the user with the test paper in the attribute label test paper library, and receive the user's answer information for the test paper, and map the answer information with the attribute labels associated with the corresponding test questions in the test paper to obtain a right-or-wrong ratio data set for characterizing the core literacy types of the subject.

[0056] Specifically, the test papers in the attribute-labeled test paper library DB5 are provided to users, such as students, for the users to answer, and the answer information of the users is received, such as "√" or "×" answered by the users to the test questions in the test paper, and the answer information is mapped to the right or wrong of the subject core literacy type attribute label, that is, the answer information is relationally mapped with the attribute labels associated with the corresponding test questions in the test paper. Since each test question is marked with one or more types of subject core literacy type attribute labels, after collecting the answer information, a data set of right and wrong ratios of different types will be obtained, that is, a data set of right and wrong ratios used to characterize the subject core literacy type results will be obtained.

[0057] The analysis module 170 is used to analyze and summarize the right-wrong ratio data set according to the mapping relationship between the attribute labels and the subject core literacy types, and output the user's subject core literacy measurement results.

[0058] Specifically, the right-or-wrong ratio data set calculated by the second processing module 160 for characterizing the results of the subject core literacy type is analyzed and summarized according to the mapping relationship between the attribute label and the subject core literacy type to obtain the user's measurement data on the subject core literacy of the current subject, that is, the user's subject core literacy measurement result, and the subject core literacy measurement result is output for the user to view and refer to, thereby providing a good and effective basis for the user to optimize or improve his or her subject core literacy, thereby facilitating the improvement of the user's subject core literacy.

[0059] Thus, combined Figure 2As shown, the above-mentioned system 100 for measuring subject core literacy based on production technology obtains the subject core literacy standard type data of each subject through the acquisition module 110, labels and summarizes the subject core literacy standard type data, and obtains the subject core literacy type database corresponding to each subject; through the disassembly module 120, the minimum granularity knowledge points in the knowledge framework of each subject are disassembled based on the production technology to obtain the production content database; through the first mapping module 130, the subject core literacy type database and the production content database are relationally mapped to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; through the second mapping module 140, the attribute labels in the production label database are mapped one by one to the subject test question database to obtain a production content database. A subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped by a first processing module 150 to obtain an attribute label test paper library with a type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; the test papers in the attribute label test paper library are provided to the user by a second processing module 160, and the user's answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-or-wrong ratio data set; the right-or-wrong ratio data set is analyzed by an analysis module 170, and the user's subject core literacy measurement result is output. Therefore, the system 100 decomposes the natural subject knowledge points by using the national subject core literacy classification standard, microgrids the knowledge points to only contain a single subject core literacy type, and then marks the microgridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through users' (such as students) answering actions. That is to say, the subject core literacy is quantitatively decomposed in a generative manner, and the decomposed attribute labels are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and level of attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, the system 100 can simply, quickly and accurately measure the user's subject core literacy and quantify the measurement results, thereby providing a good and effective basis for the user to optimize or improve his or her subject core literacy, thereby facilitating the improvement of the user's subject core literacy.At the same time, the system 100 can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0060] In a specific embodiment, the system 100 for measuring core subject competencies based on production technology can be run based on electronic devices, such as computers, learning machines, tablet computers or smart phones, so as to be convenient for different users and improve ease of use and applicability.

[0061] In one embodiment of the present invention, the disassembly module 120 disassembles the smallest granularity knowledge points in each subject knowledge framework based on the generative technology, including: using the generative technology to decompose the thinking actions in the process in a generative way according to the thinking processing process when learning the smallest granularity knowledge points in the subject knowledge framework, and making the disassembled subject production formulas map one or more subject core literacy standard type data, and forming the production content database DB2 according to the disassembled subject production formulas.

[0062] In one embodiment of the present invention, the first mapping module 130 performs relational mapping between the subject core literacy type database and the production content database, including: marking the subject core literacy type labels for the subject productions one by one by matching one or more core literacy standard type data mapped by the disassembled subject productions, and forming the production label database DB3 having the attributes of identifying subject knowledge and subject core literacy types.

[0063] In one embodiment of the present invention, the subject core literacy standard type data of each subject has a variety of different label types, and different label types correspond to different weight coefficients. Specifically, when the subject core literacy standard type data of each subject is labeled into the database, the weights are different based on the different label types. For example, for high school mathematics, there are 18 subject core literacy types, and the weights corresponding to each subject core literacy type are also different. For example, the weight coefficient corresponding to mathematical abstraction I is 1.0, the weight coefficient corresponding to mathematical abstraction II is 2.0, and the weight coefficient corresponding to mathematical abstraction III is 3.0. In order to reduce redundancy, they are not listed one by one here. Furthermore, the core literacy types of each subject and their corresponding weight coefficients are summarized and mapped to form a core literacy type database DB1 of each subject.

[0064] Before mapping the answer information to the attribute tags associated with the corresponding test questions in the test paper, the second processing module 160 is also used to: split the score of each test question in the test paper according to the weight coefficients corresponding to the multiple subject core literacy types marked by the test question, so that the score of the test question and the weight coefficients of the multiple subject core literacy types corresponding to it have a corresponding relationship. The sum of the scores corresponding to the weight coefficients of the multiple subject core literacy types is equal to the score of the test question.

[0065] Specifically, the first processing module 150 selects labeled test questions from the subject test question database DB4 with attribute labels, and assembles the test questions manually or automatically according to the rules to obtain an attribute label test paper library with the type of measuring the subject core literacy of the user. The test papers in the attribute label test paper library are composed of at least one test question in the subject test question database. The second processing module 160 divides the score of each test question on the test paper according to the weight coefficients of the multiple subject core literacy types marked on the test question, so that the score of the test question and the weight coefficients of the multiple subject core literacy types corresponding to it have a corresponding relationship. For example: the total score of Question A is 5 points, and the question covers 2 productions: the first production is "*domain of function", and its mapped subject core literacy type is mathematical abstraction II, and the corresponding weight coefficient is 2.0; the second production is "*given the domain of y=f(g(x)), find the domain of y=f(x)", and its mapped subject core literacy type is mathematical operation III, and its corresponding weight coefficient is 3.0. Then the total score of Question A of 5 points will be automatically decomposed into mathematical abstraction II = 2 points and mathematical operation III = 3 points, that is, the weight coefficient corresponding to mathematical abstraction II is 2.0, and the weight coefficient corresponding to mathematical operation III is 3.0. Therefore, assembling a test paper with multiple test questions can accurately measure the user's subject core literacy type and its corresponding weight score.

[0066] In one embodiment of the present invention, the second processing module 160 performs relationship mapping between the answer information and the attribute labels associated with the corresponding test questions in the test paper, including: determining the actual score obtained by the user for the test question based on the answer information; determining the scores corresponding to multiple subject core literacy types based on the actual score and the weight coefficients of multiple subject core literacy types corresponding to the test question under the actual score.

[0067] Specifically, first, the actual score obtained by the user for the test question is determined based on the user's answer information for the test paper. For example, if a question is answered correctly, the score corresponding to the question is obtained. If a question is answered incorrectly, the score corresponding to the question is lost, that is, the score is 0. After obtaining the actual score of the question, the score corresponding to each subject core literacy type is determined according to the actual score and the weight coefficients of the multiple subject core literacy types corresponding to the actual score. For example, if a question is answered correctly, the question can obtain all the scores. According to the total score, the scores are allocated to each subject core literacy type according to the weight coefficients of the multiple subject core literacy types corresponding to the question. The larger the weight coefficient, the higher the corresponding score. The sum of the scores corresponding to the weight coefficients of the multiple subject core literacy types corresponding to the question is equal to the actual score of the question; conversely, if a question is answered incorrectly, the question will not be scored, that is, the actual score is 0, and the scores corresponding to the weight coefficients of the multiple subject core literacy types corresponding to the question will also be missing, that is, 0.

[0068] In one embodiment of the present invention, the subject core literacy measurement results of the user include at least: the proportional relationship of the subject core literacy type dimension of the current subject and the distribution status of the subject core literacy level in each subject core literacy type dimension. In other words, the proportional relationship of each subject core literacy type can be quantified through the subject core literacy measurement results of the user, and the distribution status of the subject core literacy of the students can be accurately measured through the students' answering actions. Since different users have different answering results, the system will automatically calculate and convert the corresponding subject science literacy level of each user, thereby achieving the purpose of accurately measuring the subject core literacy of the user, which is beneficial to provide a good and effective basis for users to optimize or improve their subject core literacy, and then facilitate the improvement of the subject core literacy of users.

[0069] In combination with the above embodiment, the answer information of the user to the test question, that is, the "√" or "×" for the answer to the test question is mapped to the right or wrong of the subject core literacy type label. Since each question is marked with one or more types of subject core literacy type labels, a data set of right and wrong ratios of different types will be obtained after the collection is completed, that is, a data set of right and wrong ratios for characterizing the results of subject core literacy types will be obtained. For example, a test paper is shown in Table 1 below:

[0070]

[0071] Table 1

[0072] For the test paper distribution example in Table 1, the answer information of user A is as follows: 1.√, 2.×, 3.√, 4.×, 5.√, 6.×, then the measurement results of the subject core literacy type for user A are shown in Table 2 below:

[0073]

[0074] Table 2

[0075] Specifically, in Table 1, the production formula is the subject core literacy type label, and different subject core literacy type labels correspond to different subject core literacy types.

[0076] For example, in Question 1, Production 1 corresponds to the subject core literacy type a, Production 2 corresponds to the subject core literacy type c, Production 3 corresponds to the subject core literacy type m, and Production 4 corresponds to the subject core literacy type n, where the weight coefficient of subject core literacy type a is 1.0, the weight coefficient of subject core literacy type c is 1.0, the weight coefficient of subject core literacy type m is 1.0, and the weight coefficient of subject core literacy type n is 2.0. Since the total score of Question 1 is 5 points, if Question 1 is answered correctly, the score is allocated according to the weight coefficient of each subject core literacy type, that is, the score corresponding to subject core literacy type a corresponding to Production 1 is 1 point, the score corresponding to subject core literacy type c corresponding to Production 2 is 1 point, the score corresponding to subject core literacy type m corresponding to Production 3 is 1 point, and the score corresponding to subject core literacy type n corresponding to Production 4 is 2 points. If Question 1 is answered incorrectly, the scores corresponding to each subject core literacy type are all 0.

[0077] In Question 2, Production 2 corresponds to the subject core literacy type c, Production 7 corresponds to the subject core literacy type m, and Production 13 corresponds to the subject core literacy type f. The weight coefficient of subject core literacy type c is 1.0, the weight coefficient of subject core literacy type m is 3.0, and the weight coefficient of subject core literacy type f is 1.0. Since the total score of Question 2 is 5 points, if Question 2 is answered correctly, the score is allocated according to the weight coefficient of each subject core literacy type, that is, the score corresponding to subject core literacy type c corresponding to Production 2 is 1 point, the score corresponding to subject core literacy type m corresponding to Production 7 is 3 points, and the score corresponding to subject core literacy type f corresponding to Production 13 is 1 point. If Question 2 is answered incorrectly, the scores corresponding to each subject core literacy type are all 0.

[0078] In Question 3, production 32 corresponds to the subject core literacy type g, and production 13 corresponds to the subject core literacy type f, where the weight coefficient of subject core literacy type g is 1.0, and the weight coefficient of subject core literacy type f is 1.0. Since the total score of Question 3 is 5 points, if Question 3 is answered correctly, the score is allocated according to the weight coefficient of each subject core literacy type, that is, the score corresponding to subject core literacy type g corresponding to production 32 is 2.5 points, and the score corresponding to subject core literacy type f corresponding to production 13 is 2.5 points. If Question 3 is answered incorrectly, the scores corresponding to each subject core literacy type are all 0.

[0079] In Question 4, production 4 corresponds to subject core literacy type n, and production 19 corresponds to subject core literacy type b, where the weight coefficient of subject core literacy type n is 2.0, and the weight coefficient of subject core literacy type b is 1.0. Since the total score of Question 4 is 5 points, if Question 4 is answered correctly, the score is allocated according to the weight coefficient of each subject core literacy type, that is, the score corresponding to subject core literacy type n corresponding to production 4 is 3.35 points, and the score corresponding to subject core literacy type b corresponding to production 19 is 1.65 points. If Question 4 is answered incorrectly, the scores corresponding to each subject core literacy type are all 0.

[0080] In Question 5, production formula 1 corresponds to subject core literacy type a, production formula 7 corresponds to subject core literacy type m, and production formula 19 corresponds to subject core literacy type b, among which the weight coefficient of subject core literacy type a is 1.0, the weight coefficient of subject core literacy type m is 3.0, and the weight coefficient of subject core literacy type b is 1.0. Since the total score of Question 5 is 5 points, if Question 5 is answered correctly, the score is allocated according to the weight coefficient of each subject core literacy type, that is, the score corresponding to subject core literacy type a corresponding to production formula 1 is 1 point, the score corresponding to subject core literacy type m corresponding to production formula 7 is 3 points, and the score corresponding to subject core literacy type b corresponding to production formula 19 is 1 point. If Question 5 is answered incorrectly, the scores corresponding to each subject core literacy type are all 0.

[0081] In Question 6, Production 2 corresponds to the subject core literacy type c, and Production 7 corresponds to the subject core literacy type m, where the weight coefficient of subject core literacy type c is 1.0, and the weight coefficient of subject core literacy type m is 3.0. Since the total score of Question 6 is 5 points, if Question 6 is answered correctly, the score is allocated according to the weight coefficient of each subject core literacy type, that is, the score corresponding to subject core literacy type c corresponding to Production 2 is 1.25 points, and the score corresponding to subject core literacy type m corresponding to Production 7 is 3.75 points. If Question 6 is answered incorrectly, the scores corresponding to each subject core literacy type are all 0.

[0082] Corresponding to Table 1, in Table 2, the score of subject core literacy type a is 2, that is, after Question 1 and Question 5 are answered correctly, subject core literacy type a each obtains 1 point. The score of subject core literacy type b is 1, that is, after Question 5 is answered correctly, subject core literacy type b obtains 1 point. The score of subject core literacy type c is 1, that is, after Question 1 is answered correctly, subject core literacy type c obtains 1 point. The score of subject core literacy type f is 2.5, that is, after Question 3 is answered correctly, subject core literacy type f obtains 2.5 points. The score of subject core literacy type g is 2.5, that is, after Question 3 is answered correctly, subject core literacy type g obtains 2.5 points. The score of subject core literacy type m is 4, that is, the sum of 1 point for subject core literacy type m after Question 1 is answered correctly and 3 points for subject core literacy type m after Question 5 is answered correctly. The score for subject core literacy type n is 2, that is, after question 1 is answered correctly, subject core literacy type n gets 2 points.

[0083] For the test paper distribution example in Table 1, the answer information of user B is as follows: 1.√, 2.×, 3.√, 4.√, 5.×, 6.√, then the measurement results of the subject core literacy type for user B are shown in Table 3 below:

[0084]

[0085] Table 3

[0086] Corresponding to Table 1, in Table 3, the score of subject core literacy type a is 1, that is, after question 1 is answered correctly, subject core literacy type a obtains 1 point. The score of subject core literacy type b is 1.65, that is, after question 4 is answered correctly, subject core literacy type b obtains 1.65 points. The score of subject core literacy type c is 2.25, that is, after question 1 is answered correctly, subject core literacy type c obtains 1 point and after question 6 is answered correctly, subject core literacy type c obtains 1.25 points, and the sum of the two is taken. The score of subject core literacy type f is 2.5, that is, after question 3 is answered correctly, subject core literacy type f obtains 2.5 points. The score of subject core literacy type g is 2.5, that is, after question 3 is answered correctly, subject core literacy type g obtains 2.5 points. The score of subject core literacy type m is 4.75, which is the sum of 1 point for subject core literacy m after question 1 is answered correctly and 3.75 points for subject core literacy type m after question 6 is answered correctly. The score of subject core literacy type n is 5.35, which is the sum of 2 points for subject core literacy m after question 1 is answered correctly and 3.35 points for subject core literacy type m after question 4 is answered correctly.

[0087] In one embodiment of the present invention, the analysis module 170 is also used to generate a subject core literacy evaluation report based on the user's subject core literacy measurement results, specifically including: inputting the distribution status of the user's subject core literacy level in each subject core literacy type dimension into a pre-trained subject core literacy evaluation model to obtain evaluation description information corresponding to the subject core literacy level, summarizing the evaluation description information, and generating the subject core literacy evaluation report, wherein the subject core literacy evaluation model includes a corresponding mapping relationship between pre-calibrated subject core literacy levels and corresponding evaluation description information, the distribution status of the subject core literacy level includes level grade and level score, and the corresponding evaluation description information is obtained according to the level grade and the level score in the score range, and different level grades correspond to different evaluation description information in different score ranges.

[0088] In a specific embodiment, in combination Figure 3 As shown, an example of generating a subject core literacy evaluation report is as follows: Different types of subject core literacy are divided differently for different academic years and subjects, so the corresponding subject core literacy score ranges and evaluation description information are also different for different academic years and subjects. For example: The college entrance examination mathematics subject includes six core literacy categories, including mathematical abstraction, logical reasoning, mathematical modeling, intuitive imagination, mathematical operations, and data analysis, and each core literacy includes three levels; and the college entrance examination Chinese subject includes four core literacy categories, including language accumulation and construction, thinking development and improvement, aesthetic appreciation and creation, and cultural understanding and inheritance, and each core literacy includes five levels. In order to reduce redundancy, they will not be listed one by one here. Based on this, the corresponding different core literacy and different levels are assigned corresponding evaluation description information according to different score ranges.

[0089] The following is a specific example of how the scores of the core competency of logical reasoning in college entrance examination mathematics at three levels correspond to different evaluation description information in different score ranges.

[0090] 1. When logical reasoning I, i.e. level 1, is in the score range of [0-50], the corresponding descriptive evaluation information is: not understanding the logical relationship between the conditions and conclusions of familiar mathematical propositions; not mastering the proofs of some basic propositions and theorems.

[0091] 2. When logical reasoning I, i.e. level one, is in the score range of [51-100], the corresponding descriptive evaluation information is: understanding the logical relationship between the conditions and conclusions of familiar mathematical propositions; mastering the proofs of some basic propositions and theorems.

[0092] 3. When logical reasoning II, i.e. level 2, is in the score range of [0-50], the corresponding descriptive evaluation information is: unable to choose appropriate argumentation methods to prove mathematical propositions related to learned knowledge; unable to use counterexamples to prove that certain mathematical conclusions are invalid.

[0093] 4. When logical reasoning II, i.e. level 2, is in the score range of [51-100], the corresponding descriptive evaluation information is: able to choose appropriate argumentation methods to prove mathematical propositions related to the learned knowledge; able to show that certain mathematical conclusions are not valid by giving counterexamples.

[0094] 5. When logical reasoning III, i.e. level three, is in the score range of [0-50], the corresponding descriptive evaluation information is: for more complex mathematical problems, transitional propositions cannot be constructed and it is difficult to explore the way of argumentation.

[0095] 6. When Logical Reasoning III, i.e. Level 32, is in the score range of [51-100], the corresponding descriptive evaluation information is: For more complex mathematical problems, one can explore ways of argumentation by constructing transitional propositions, solve problems, and express the argumentation process in rigorous mathematical language.

[0096] like Figure 3 In the example shown, in the core literacy measurement results of a student, the logical reasoning level 1, that is, the score corresponding to logical reasoning I is 16 (%), which matches the above 1st item, that is, not understanding the logical relationship between the conditions and conclusions of familiar mathematical propositions; not mastering the proof of some basic propositions and theorems. The reasoning level 2, that is, the score corresponding to logical reasoning II is 40 (%), which matches the above 3rd item, that is, not being able to choose the appropriate argumentation method to prove the mathematical propositions related to the knowledge learned; not being able to use counterexamples to prove that some mathematical conclusions are not valid.

[0097] The corresponding score value for reasoning level three, that is, logical reasoning III, is 28 (%), which matches the above item 5, that is, for more complex mathematical problems, transitional propositions cannot be constructed and it is difficult to explore ways of argumentation.

[0098] Based on this, by assembling dialogue templates and dialogue vocabulary, we can come up with adjustment suggestions for the development of the student's core competencies in each subject, and achieve personalized customized measurement and improvement suggestions, which will help provide a good and effective basis for the student to optimize or improve his or her subject core competencies, thereby facilitating the improvement of the user's subject core competencies.

[0099] In summary, according to the system for measuring subject core literacy based on production technology in an embodiment of the present invention, the subject core literacy standard type data of each subject is obtained through the acquisition module, and the subject core literacy standard type data is labeled and summarized to obtain the subject core literacy type database corresponding to each subject; the minimum granularity knowledge points in the knowledge framework of each subject are disassembled by the disassembly module based on the production technology to obtain the production content database; the subject core literacy type database and the production content database are relationally mapped through the first mapping module to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database through the second mapping module to obtain a production content database with identification of subject knowledge attributes and subject core literacy type attributes. A subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped by a first processing module to obtain an attribute label test paper library with a type of measuring a user's subject core literacy, wherein the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; test papers in the attribute label test paper library are provided to users by a second processing module, and user answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-or-wrong ratio data set; the right-or-wrong ratio data set is analyzed by an analysis module, and a measurement result of the user's subject core literacy is output. Therefore, the system uses the national subject core literacy classification standards to break down the natural subject knowledge points, micro-gridizes the knowledge points to only contain a single subject core literacy type, and then marks the micro-gridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through users' (such as students) answering actions. That is to say, the subject core literacy is quantitatively broken down in a generative way, and the attribute labels after the breakdown are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and level of attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, the system can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, which is conducive to providing a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy. At the same time, the system can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0100] A further embodiment of the present invention also proposes a method for measuring core subject literacy based on production technology. The method is used in the system for measuring core subject literacy based on production technology described in any one of the above embodiments of the present invention.

[0101] Figure 4 1 is a flow chart of a method for measuring subject core literacy based on production technology according to an embodiment of the present invention. Figure 4 As shown in the figure, the method for measuring core subject literacy based on production technology includes the following steps:

[0102] Step S1: Obtain the subject core literacy standard type data of each subject, label and summarize the subject core literacy standard type data, and obtain the subject core literacy type database corresponding to each subject.

[0103] Specifically, we need to obtain the subject core literacy standard type data released by the state, and label the subject core literacy standard type data into the database. For example, the high school mathematics mentioned above has 18 core literacy types (6 directions and 3 levels in each direction), and other subjects also have different numbers of core literacy types. Different label types have different weights. For example, the weight coefficient corresponding to Mathematical Abstract I is 1.0, the weight coefficient corresponding to Mathematical Abstract II is 2.0, and the weight coefficient corresponding to Mathematical Abstract III is 3.0. Thus, a subject core literacy type database corresponding to each subject is formed.

[0104] Step S2: Based on the production technology, the minimum granularity knowledge points in the knowledge framework of each subject are disassembled to obtain a production content database.

[0105] Specifically, production technology is production expression, and its basic form is P→Q or IF P THEN Q. It is a computational model called Post machine proposed based on string substitution rules. Each rule in the model is called a production. Among them, P is the premise of the production, also known as the antecedent, which gives the prerequisite for whether the production can be used, and is composed of a logical combination of facts; Q is a set of conclusions or operations, also known as the consequent of the production, which indicates the conclusion that should be drawn or the action that should be performed when the premise P is satisfied. In other words, the meaning of the production is that if the premise P is satisfied, the conclusion Q can be drawn or the operation specified by Q can be performed.

[0106] In a specific embodiment of the present invention, a specific example of a production formula is as follows: according to the human cognitive model (19 types such as concept type, rule type, method type, principle type, abstract concept type, method application type, etc.), the peripheral knowledge points in the national curriculum standards are broken down into smaller granularity, which is called a "subject production formula."

[0107] For example, the present invention uses this method to break down the knowledge point of "domain of function" in high school mathematics, forming four subject production formulas, namely: "*domain of function", "find the domain of function", "domain of composite function" and "*given the domain of y=f(g(x)), find the domain of y=f(x)".

[0108] Take the first production rule of the subject "*domain of function" as an example:

[0109] Its basic form is: IF: ① The known function y=f(x) represents the function from non-empty number set A to non-empty number set B, ②x is the independent variable, x∈A; THEN: set A is called the domain of the function; Cognitive type: abstract concept class.

[0110] In an embodiment of the present invention, based on the production technology, the minimum granularity knowledge points in the knowledge framework of each subject are decomposed in a production manner, and the decomposed subject production formulas form a production content database.

[0111] Step S3: Map the relationship between the subject core competency type database and the production content database to obtain a production tag database that identifies subject knowledge attributes and subject core competency type attributes.

[0112] Step S4: Map the attribute tags in the production tag database to the subject test question database one by one to obtain the subject test question database with attribute tags, wherein each subject test question in the subject test question database with attribute tags corresponds to at least one attribute tag.

[0113] Specifically, the content of a subject test question is carefully prepared and summarized to form a subject test question database. The attribute labels in the production label database are mapped one by one to the test questions in the subject test question database. Each test question covers one or more attribute labels. Test questions with attribute labels have the ability to measure the user's subject core literacy. After multiple test questions in the subject test question database are labeled, a subject test question database with attribute labels that can measure the user's subject core literacy can be formed.

[0114] In one embodiment of the present invention, before mapping the attribute labels in the production label database to the subject test question database one by one, it also includes: for the current subject, selecting multiple subject test questions corresponding to the current subject, and aggregating the multiple subject test questions to form the subject test question database.

[0115] Step S5: grouping the subject test questions in the subject test question database with attribute labels to obtain an attribute label test paper library with the type of measuring the user's subject core literacy, wherein the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database.

[0116] Specifically, the labeled test questions are selected from the subject test question database with attribute labels and assembled manually or automatically according to the rules to form a test paper. The test paper assembled by one or more test questions forms an attribute label test paper library that can accurately measure the user's subject core literacy.

[0117] Step S6: Provide the test paper in the attribute label test paper library to the user, receive the user's answer information for the test paper, map the answer information with the attribute label associated with the corresponding test question in the test paper, and obtain a right-or-wrong ratio data set for characterizing the core literacy type of the subject.

[0118] Specifically, it means providing users, such as students, with test papers in a test paper library with attribute labels for them to answer, and receiving answer information from users, such as "√" or "×" answered by users to test questions in the test papers, and mapping the answer information to the right or wrong of the subject core literacy type attribute labels, that is, mapping the answer information with the attribute labels associated with the corresponding test questions in the test paper. Since each test question is marked with one or more types of subject core literacy type attribute labels, after collecting the answer information, a data set of right and wrong ratios of different types will be obtained, that is, a right and wrong ratio data set used to characterize the subject core literacy type results will be obtained.

[0119] Step S7: According to the mapping relationship between the attribute labels and the subject core literacy types, the right-wrong ratio data set is analyzed and summarized, and the subject core literacy measurement results of the user are output.

[0120] Specifically, the right-or-wrong ratio data set calculated in step S6 for characterizing the results of the subject core literacy type is analyzed and summarized according to the mapping relationship between the attribute label and the subject core literacy type to obtain the user's measurement data on the subject core literacy of the current subject, that is, the user's subject core literacy measurement result, and the subject core literacy measurement result is output for the user to view and refer to, thereby providing a good and effective basis for the user to optimize or improve his or her subject core literacy, thereby facilitating the improvement of the user's subject core literacy.

[0121] Therefore, the above-mentioned method of measuring subject core literacy based on generative technology obtains the subject core literacy standard type data of each subject, labels and summarizes the subject core literacy standard type data, and obtains the subject core literacy type database corresponding to each subject; based on generative technology, the minimum granularity knowledge points in the knowledge framework of each subject are disassembled to obtain a generative content database; the subject core literacy type database is relationally mapped with the generative content database to obtain a generative label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the generative label database are mapped one by one to the subject test question database to obtain a database with attribute labels A subject test question database, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; the test papers in the attribute label test paper library are provided to the user, and the user's answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, this method uses the national subject core literacy classification standard to disassemble the natural subject knowledge points, micro-gridizes the knowledge points to only contain a single subject core literacy type, and then marks the micro-gridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through users' (such as students) answering actions. That is to say, the subject core literacy is quantitatively disassembled in a generative way, and the disassembled attribute labels are associated and mapped to the students' subject test questions. Through the answering of the subject test questions, the number and level of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. According to this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, this method can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, so as to provide a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy. At the same time, this method can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0122] In one embodiment of the present invention, in step S2, the minimum granularity knowledge points in each subject knowledge framework are disassembled based on the generative technology, including: using the generative technology to decompose the thinking actions in the process of learning the minimum granularity knowledge points in each subject knowledge framework in a generative way, and making the disassembled subject production formulas map one or more subject core literacy standard type data, and forming the production content database according to the disassembled subject production formulas.

[0123] In one embodiment of the present invention, the subject core literacy type database is relationally mapped with the production content database, including: marking the subject core literacy type labels for the subject productions one by one by matching one or more core literacy standard type data mapped by the disassembled subject productions, thereby forming the production label database having the attributes of identifying subject knowledge and subject core literacy types.

[0124] In one embodiment of the present invention, the subject core literacy standard type data of each subject has a variety of different label types, and different label types correspond to different weight coefficients. Specifically, when the subject core literacy standard type data of each subject is labeled into the database, the weights are different based on the different label types. For example, for high school mathematics, there are 18 subject core literacy types, and the weights corresponding to each subject core literacy type are also different. For example, the weight coefficient corresponding to mathematical abstraction I is 1.0, the weight coefficient corresponding to mathematical abstraction II is 2.0, and the weight coefficient corresponding to mathematical abstraction III is 3.0. In order to reduce redundancy, they are not listed one by one here. Furthermore, the core literacy types of each subject and their corresponding weight coefficients are summarized and mapped to form a core literacy type database for each subject.

[0125] In step S6, before mapping the answer information to the attribute labels associated with the corresponding test questions in the test paper, it is also used to: split the score of each test question in the test paper according to the weight coefficients corresponding to the multiple subject core literacy types marked by the test question, so that the score of the test question and the weight coefficients of the multiple subject core literacy types corresponding to it have a corresponding relationship. The sum of the scores corresponding to the weight coefficients of the multiple subject core literacy types is equal to the score of the test question.

[0126] Specifically, the labeled test questions are selected from the subject test question database with attribute labels, and the test questions are manually or automatically assembled according to the rules to obtain the attribute label test paper library with the type of measuring the subject core literacy of the user. The test papers in the attribute label test paper library are composed of at least one test question in the subject test question database. For the test papers in the attribute label test paper library, the score of each test question on the test paper is split according to the weight coefficients of the multiple subject core literacy types marked by the test question, so that the score of the test question and the weight coefficients of the multiple subject core literacy types corresponding to it have a corresponding relationship. For example: the total score of Question A is 5 points, and the question covers 2 productions: the first production is "*domain of function", and its mapped subject core literacy type is mathematical abstraction II, and the corresponding weight coefficient is 2.0; the second production is "*given the domain of y=f(g(x)), find the domain of y=f(x)", and its mapped subject core literacy type is mathematical operation III, and its corresponding weight coefficient is 3.0. Then the total score of Question A of 5 points will be automatically decomposed into mathematical abstraction II = 2 points and mathematical operation III = 3 points, that is, the weight coefficient corresponding to mathematical abstraction II is 2.0, and the weight coefficient corresponding to mathematical operation III is 3.0. Therefore, assembling a test paper with multiple test questions can accurately measure the user's subject core literacy type and its corresponding weight score.

[0127] In one embodiment of the present invention, a relationship mapping is performed between the answer information and the attribute labels associated with the corresponding test questions in the test paper, including: determining the actual score obtained by the user for the test question based on the answer information; determining the scores corresponding to the multiple subject core literacy types based on the actual score and the weight coefficients of the multiple subject core literacy types corresponding to the test question under the actual score.

[0128] Specifically, first, the actual score obtained by the user for the test question is determined based on the user's answer information for the test paper. For example, if a question is answered correctly, the score corresponding to the question is obtained. If a question is answered incorrectly, the score corresponding to the question is lost, that is, the score is 0. After obtaining the actual score of the question, the score corresponding to each subject core literacy type is determined according to the actual score and the weight coefficients of the multiple subject core literacy types corresponding to the actual score. For example, if a question is answered correctly, the question can obtain all the scores. According to the total score, the scores are allocated to each subject core literacy type according to the weight coefficients of the multiple subject core literacy types corresponding to the question. The larger the weight coefficient, the higher the corresponding score. The sum of the scores corresponding to the weight coefficients of the multiple subject core literacy types corresponding to the question is equal to the actual score of the question; conversely, if a question is answered incorrectly, the question will not be scored, that is, the actual score is 0, and the scores corresponding to the weight coefficients of the multiple subject core literacy types corresponding to the question will also be missing, that is, 0.

[0129] In one embodiment of the present invention, the subject core literacy measurement results of the user include at least: the proportional relationship of the subject core literacy type dimension of the current subject and the distribution status of the subject core literacy level in each subject core literacy type dimension. In other words, the proportional relationship of each subject core literacy type can be quantified through the subject core literacy measurement results of the user, and the distribution status of the subject core literacy of the students can be accurately measured through the students' answering actions. Since different users have different answering results, the system will automatically calculate and convert the corresponding subject science literacy level of each user, thereby achieving the purpose of accurately measuring the subject core literacy of the user, which is beneficial to provide a good and effective basis for users to optimize or improve their subject core literacy, and then facilitate the improvement of the subject core literacy of users.

[0130] In one embodiment of the present invention, after step S7, it also includes: generating a subject core literacy evaluation report based on the subject core literacy measurement results of the user, specifically including: inputting the distribution state of the subject core literacy level of the user in each subject core literacy type dimension into a pre-trained subject core literacy evaluation model to obtain evaluation description information corresponding to the subject core literacy level, summarizing the evaluation description information to generate the subject core literacy evaluation report, wherein the subject core literacy evaluation model includes a corresponding mapping relationship between pre-calibrated subject core literacy levels and corresponding evaluation description information, the distribution state of the subject core literacy level includes level grade and level score, according to the level grade and the level score in the score range, the corresponding evaluation description information is obtained, and different level grades correspond to different evaluation description information in different score ranges.

[0131] It should be noted that when measuring core competencies of a subject, the specific implementation method of the method for measuring core competencies of a subject based on production technology is similar to the specific implementation method of the system 100 for measuring core competencies of a subject based on production technology in any of the above-mentioned embodiments of the present invention. Therefore, for a detailed exemplary description of the method for measuring core competencies of a subject based on production technology, please refer to the aforementioned relevant description part of the system 100 for measuring core competencies of a subject based on production technology. In order to reduce redundancy, it will not be repeated here.

[0132] According to the method for measuring subject core literacy based on production technology in an embodiment of the present invention, by obtaining the subject core literacy standard type data of each subject, the subject core literacy standard type data is labeled and summarized to obtain the subject core literacy type database corresponding to each subject; based on the production technology, the minimum granularity knowledge points in the knowledge framework of each subject are disassembled to obtain the production content database; the subject core literacy type database is relationally mapped with the production content database to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database to obtain a subject core literacy type database with attributes. A subject test question database with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring user subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; test papers in the attribute label test paper library are provided to users, and user answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, this method uses the national subject core literacy classification standard to disassemble the natural subject knowledge points, micro-gridizes the knowledge points to only contain a single subject core literacy type, and then marks the micro-gridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through users' (such as students) answering actions. That is to say, the subject core literacy is quantitatively disassembled in a generative way, and the disassembled attribute labels are associated and mapped to the students' subject test questions. Through the answering of the subject test questions, the number and level of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. According to this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, this method can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, so as to provide a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of users' subject core literacy. At the same time, this method can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0133] A further embodiment of the present invention provides an electronic device.

[0134] In some embodiments, the electronic device includes: a system 100 for measuring core subject competencies based on production technology according to any one of the above embodiments of the present invention.

[0135] In other embodiments, the electronic device includes: a processor, a memory, and a program for measuring core competencies of a subject based on production technology, which is stored in the memory and can be run on the processor. When the program for measuring core competencies of a subject based on production technology is executed by the processor, it implements any one of the methods for measuring core competencies of a subject based on production technology described above in the present invention.

[0136] It should be noted that when the electronic device is measuring subject core competencies, its specific implementation method is similar to the specific implementation method of the system or method for measuring subject core competencies based on production technology in any of the above-mentioned embodiments of the present invention. Therefore, for a detailed exemplary description of the process of measuring subject core competencies by the electronic device, please refer to the relevant description part of the system or method for measuring subject core competencies based on production technology. In order to reduce redundancy, it will not be repeated here.

[0137] In a specific embodiment, the electronic device is, for example, a computer, a learning machine, a tablet computer or a smart phone, so as to facilitate use by different users and improve the convenience and applicability of use.

[0138] According to the electronic device of the embodiment of the present invention, the subject core literacy standard type data of each subject is obtained, and the subject core literacy standard type data is labeled and summarized to obtain the subject core literacy type database corresponding to each subject; the minimum granularity knowledge points in the knowledge framework of each subject are disassembled based on the production technology to obtain the production content database; the subject core literacy type database is relationally mapped with the production content database to obtain a production label database with identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database to obtain the subject test question database with attribute labels; The invention relates to a database of subject test questions with attribute labels, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring a user's subject core literacy, wherein the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; test papers in the attribute label test paper library are provided to users, and user answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, the electronic device breaks down the natural subject knowledge points by using the national subject core literacy classification standards, microgrids the knowledge points to include only a single subject core literacy type, and then marks the microgridized knowledge points on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through the user's (such as students) answering actions. That is to say, the subject core literacy is quantitatively decomposed in a generative way, and the decomposed attribute labels are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and levels of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Therefore, the electronic device can simply, quickly and accurately measure the user's subject core literacy, and quantify the measurement results, so as to provide a good and effective basis for the user to optimize or improve his subject core literacy, thereby facilitating the improvement of the user's subject core literacy. At the same time, the electronic device can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, with high flexibility and applicability.

[0139] A further embodiment of the present invention also discloses a computer-readable storage medium, on which is stored a program for measuring core competencies of a subject based on production technology. When the program for measuring core competencies of a subject based on production technology is executed by a processor, the method for measuring core competencies of a subject based on production technology as described in any one of the above embodiments of the present invention is implemented.

[0140] That is, when the program for measuring subject core literacy based on production technology stored in the computer-readable storage medium is executed by the processor, the following steps are specifically performed:

[0141] Step 1: Obtain the subject core literacy standard type data of each subject, label and summarize the subject core literacy standard type data, and obtain the subject core literacy type database corresponding to each subject.

[0142] Step 2: Based on the generative technology, the smallest granularity knowledge points in the knowledge framework of each subject are disassembled to obtain the generative content database.

[0143] Step 3: Map the relationship between the subject core literacy type database and the production content database to obtain a production label database that identifies subject knowledge attributes and subject core literacy type attributes.

[0144] Step 4: Map the attribute tags in the production tag database to the subject test question database one by one to obtain the subject test question database with attribute tags, wherein each subject test question in the subject test question database with attribute tags corresponds to at least one attribute tag.

[0145] Step 5: Group the subject test questions in the subject test question database with attribute labels to obtain an attribute label test paper library with the type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database.

[0146] Step 6: Provide the user with the test paper in the attribute label test paper library, receive the user's answer information for the test paper, map the answer information with the attribute label associated with the corresponding test question in the test paper, and obtain a right-wrong ratio data set.

[0147] Step 7: Analyze the right-or-wrong ratio data set and output the subject core literacy measurement results of the user.

[0148] According to the computer-readable storage medium of an embodiment of the present invention, when the program stored thereon for measuring subject core literacy based on production technology is executed by a processor, the subject core literacy standard type data of each subject is obtained, the subject core literacy standard type data is labeled and summarized, and a subject core literacy type database corresponding to each subject is obtained; the minimum granularity knowledge points in the knowledge framework of each subject are disassembled based on production technology to obtain a production content database; the subject core literacy type database is relationally mapped with the production content database to obtain a production label database having identification of subject knowledge attributes and subject core literacy type attributes; the attribute labels in the production label database are mapped one by one to the subject test question database A subject test question database with attribute labels is obtained from the database, wherein each subject test question in the subject test question database with attribute labels corresponds to at least one attribute label; the subject test questions in the subject test question database with attribute labels are grouped to obtain an attribute label test paper library with a type of measuring the user's subject core literacy, and the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; the test papers in the attribute label test paper library are provided to the user, and the user's answer information for the test papers is received, and the answer information is mapped with the attribute labels associated with the corresponding test questions in the test papers to obtain a right-wrong ratio data set; the right-wrong ratio data set is analyzed, and the user's subject core literacy measurement result is output. Therefore, by using the national subject core literacy classification standards to decompose the natural subject knowledge points, the knowledge points are micro-gridified to only contain a single subject core literacy type, and then the micro-gridized knowledge points are marked on the test questions for users to answer. The correctness of the user's answer results is converted through mapping relationships and data classification and summary, and a right-wrong ratio data of different subject core literacy types will be obtained. This data can quantitatively reflect the proportional relationship of the dimensions of each subject core literacy type, and accurately measure the distribution status of students' subject core literacy types through the user's (such as students) answering actions. That is to say, the subject core literacy is quantitatively decomposed in a generative way, and the decomposed attribute labels are associated and mapped to the students' subject test questions. Through the answers to the subject test questions, the number and level of the attributes of various subject core literacy types are summarized, and weighted statistical calculations are performed to obtain quantitative measurement results. Based on this measurement result, students can be provided with a better basis for improving or correcting their learning direction and methods. Thus, it is possible to simply, quickly and accurately measure the subject core literacy of users, and quantify the measurement results, so as to provide a good and effective basis for users to optimize or improve their subject core literacy, thereby facilitating the improvement of the subject core literacy of users. At the same time, the present invention can form a complete set of subject core literacy measurement labels and subject core literacy measurement algorithms, which can be marked on other test questions, so that other test questions also have the function of measuring students' subject core literacy, and have high flexibility and applicability.

[0149] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example.

[0150] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A system for measuring core subject literacy based on production technology, characterized by: include: An acquisition module is used to acquire the subject core literacy standard type data of each subject, label and summarize the subject core literacy standard type data, and obtain the subject core literacy type database corresponding to each subject; The disassembly module is used to disassemble the thinking actions in the process of learning the smallest granularity knowledge points in the knowledge framework of each subject in a generative way based on the generative technology, and map the disassembled subject production formula to one or more subject core literacy standard type data, and form a production content database based on the disassembled subject production formula; The first mapping module is used to mark the subject production formulas with subject core literacy type labels one by one by matching one or more core literacy standard type data mapped by the disassembled subject production formulas, so as to form a production formula label database with identification subject knowledge attributes and subject core literacy type attributes; A second mapping module is used to map the attribute tags in the production tag database to the subject test question database one by one to obtain the subject test question database with attribute tags, wherein each subject test question in the subject test question database with attribute tags corresponds to at least one attribute tag; The first processing module is used to group the subject test questions in the subject test question database with attribute labels to obtain an attribute label test paper library with a type of measuring the subject core literacy of the user, wherein the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; The second processing module is used to provide the test paper in the attribute label test paper library to the user, receive the user's answer information for the test paper, and perform relationship mapping between the answer information and the attribute label associated with the corresponding test question in the test paper to obtain a right-wrong ratio data set; The analysis module is used to analyze the right-or-wrong ratio data set and output the subject core literacy measurement results of the user.

2. The system for measuring subject core literacy based on production technology according to claim 1 is characterized in that: The subject core literacy standard type data of each subject has a plurality of different label types, and different label types correspond to different weight coefficients. Before the second processing module performs relationship mapping between the answer information and the attribute label associated with the corresponding test question in the test paper, it is also used to: The score of each test question in the test paper is split according to the weight coefficients corresponding to the multiple subject core literacy types marked with the test question, so that the score of the test question and the weight coefficients of the corresponding multiple subject core literacy types have a corresponding relationship.

3. The system for measuring subject core literacy based on production technology according to claim 2 is characterized in that: The second processing module performs relationship mapping between the answer information and the attribute labels associated with the corresponding test questions in the test paper, including: Determine the actual score obtained by the user for the test question according to the answer information; The scores corresponding to the multiple subject core literacy types are determined based on the actual score and the weight coefficients of the multiple subject core literacy types corresponding to the test question under the actual score.

4. The system for measuring subject core literacy based on production technology according to claim 1 is characterized in that: The measurement results of the user's subject core literacy include at least: the proportional relationship of the user in the subject core literacy type dimensions of the current subject and the distribution status of the subject core literacy level in each subject core literacy type dimension.

5. The system for measuring subject core literacy based on production technology according to claim 4 is characterized in that: The analysis module is also used to generate a subject core literacy evaluation report based on the subject core literacy measurement results of the user, specifically including: The distribution status of the user's subject core literacy level in each subject core literacy type dimension is input into a pre-trained subject core literacy evaluation model to obtain evaluation description information corresponding to the subject core literacy level, and the evaluation description information is summarized to generate the subject core literacy evaluation report, wherein the subject core literacy evaluation model includes a corresponding mapping relationship between pre-calibrated subject core literacy levels and corresponding evaluation description information, and the distribution status of the subject core literacy level includes level grade and level score, and the corresponding evaluation description information is obtained according to the level grade and the level score in the score range, and different level grades correspond to different evaluation description information in different score ranges.

6. A method for measuring subject core literacy based on production technology, characterized in that: The following steps are involved: Obtain the subject core literacy standard type data of each subject, label and summarize the subject core literacy standard type data, and obtain the subject core literacy type database corresponding to each subject; Based on production technology, the thinking process of learning the smallest granularity knowledge points in the knowledge framework of each subject is disassembled in a production way, and the disassembled subject production formulas are mapped to one or more subject core literacy standard type data, and a production content database is formed based on the disassembled subject production formulas; By matching one or more core literacy standard type data mapped by the disassembled subject production formula, the subject core literacy type labels are marked on the subject production formulas one by one, so as to form a production formula label database with identification subject knowledge attributes and subject core literacy type attributes; Mapping the attribute tags in the production tag database to the subject test question database one by one to obtain the subject test question database with attribute tags, wherein each subject test question in the subject test question database with attribute tags corresponds to at least one attribute tag; Performing grouping of subject test questions in the subject test question database with attribute labels to obtain an attribute label test paper library with a type of measuring the subject core literacy of the user, wherein the test papers in the attribute label test paper library are composed of at least one test question in the subject test question database; Providing the user with the test paper in the attribute label test paper library, receiving the user's answer information for the test paper, and mapping the answer information with the attribute label associated with the corresponding test question in the test paper to obtain a right-wrong ratio data set; The right-or-wrong ratio data set is analyzed, and the subject core literacy measurement results of the user are output.

7. An electronic device, characterized in that: include: A system for measuring subject core literacy based on production technology as described in any one of claims 1 to 5; or, A processor, a memory, and a program for measuring core competencies of a subject based on production technology, which is stored in the memory and can be run on the processor. When the program for measuring core competencies of a subject based on production technology is executed by the processor, the method for measuring core competencies of a subject based on production technology as described in claim 6 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for measuring core competencies of a subject based on production technology. When the program for measuring core competencies of a subject based on production technology is executed by a processor, the method for measuring core competencies of a subject based on production technology as described in claim 6 is implemented.

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