Micro-class teaching method and system

Abstract

The invention provides a micro-class teaching method and system. The micro-class teaching method comprises the following steps: constructing a knowledge classification database; obtaining a demand of a target object, and determining course content according to the demand of the target object; performing knowledge point keyword extraction and scoring on course contents, forming index entries from keywords with scores, adding the index entries into a knowledge classification database, and establishing association between the knowledge classification database and teaching courseware; obtaining a target explanation knowledge point from the course content, and obtaining first teaching information, second teaching information, third teaching information and fourth teaching information according to the target explanation knowledge point; and obtaining final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and sending the final teaching information to a specified position. The system is low in cost, can meet the requirements of students under different conditions, and improves the learning efficiency of the students.

Description

Technical Field

[0001] The present invention relates to the field of online teaching, and in particular to a micro-class teaching method and system. Background Art

[0002] In recent years, Internet education has gradually become popular, and many schools and educational institutions have begun to implement Internet education, which breaks through the geographical and temporal limitations of education and helps to equalize educational resources. However, existing Internet education uses unified courseware for teaching, or one teacher teaches multiple students with the same textbooks and lesson preparation notes. Due to the obvious differences in learning ability, knowledge level, and ability to apply knowledge among different students, the existing undifferentiated knowledge infusion teaching method will inevitably lead to some students being unable to keep up with the teaching progress, while other students will not be able to learn the knowledge they are eager to learn. If one-to-one teaching and personalized training methods are adopted, the labor cost investment will be large, and the cost of education will increase exponentially.

[0003] In particular, for adult vocational education, many courses focus on explaining basic knowledge, ignoring the problems encountered by students in work scenarios and the need to use knowledge to solve problems. In the process of vocational education, it has the characteristics of limited learning time, differences in knowledge base, and emphasis on solving practical problems. Therefore, the present invention proposes a micro-class teaching method and system, which is not only low-cost, but also can carry out targeted teaching and training according to the learning needs of students, meet the needs of students in different situations, and improve students' learning efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a micro-course teaching method and system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a micro-class teaching method, comprising:

[0006] Construct a knowledge classification database;

[0007] Obtain the needs of the target audience and determine the course content based on the needs of the target audience;

[0008] Extract and score keywords of knowledge points in the course content, and form index items with scores, then add the index items to the knowledge classification database to establish the association between knowledge points in the course content and teaching courseware;

[0009] Obtaining target explanation knowledge points from the course content, and extracting case explanation information matching the target explanation knowledge points from a knowledge classification database according to the target explanation knowledge points, to obtain first teaching information;

[0010] Explain the knowledge points according to the target to obtain the teaching courseware and obtain the second teaching information;

[0011] According to the target explanation knowledge point, case explanation information corresponding to the target explanation knowledge point is extracted again from the knowledge classification database to obtain third teaching information;

[0012] According to the target knowledge points, test questions of different difficulty levels corresponding to the target knowledge points are randomly extracted from the knowledge classification database to obtain fourth teaching information;

[0013] Final teaching information is obtained according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and the final teaching information is sent to a designated location.

[0014] Furthermore, the construction of the knowledge classification database includes:

[0015] Determine the application scenario and determine the target knowledge points for the target object;

[0016] Conducting knowledge point evaluation on the target knowledge points to obtain knowledge point evaluation information;

[0017] Determine the target object's evaluation level and knowledge point target reception information based on the target's mastered knowledge points and knowledge point evaluation information;

[0018] According to the evaluation level of the target object and the target received information of the knowledge point, knowledge classification is constructed for the knowledge point to obtain a knowledge classification database.

[0019] Furthermore, the micro-class teaching method further adjusts the teaching information, determines whether to adjust the teaching content according to the fourth teaching information, obtains an adjustment analysis and judgment result, and adjusts the teaching information according to the adjustment analysis and judgment result, wherein determining whether to adjust the teaching content according to the fourth teaching information includes:

[0020] Acquire test parameters for testing the target object with respect to the fourth teaching information, and count the test time, test error rate, and number of repeated tests to obtain statistical results;

[0021] Determining parameter weights, and combining the parameter weights with test parameters to obtain test results;

[0022] The test results and the preset threshold are comprehensively judged to obtain the mastery degree of the target object;

[0023] Determine whether to adjust the teaching courseware according to the mastery level.

[0024] Further, when extracting case explanation information matching the target explanation knowledge point from the knowledge classification database according to the target explanation knowledge point, matching the case explanation information is performed in the knowledge classification database according to the target explanation knowledge point combined with the difficulty classification identifier, and case explanation information corresponding to the target explanation knowledge point of different difficulty levels is obtained to obtain the first case explanation extraction information, and then the course additional demand characteristics are obtained from the course content, and it is analyzed whether the course additional demand characteristics limit the application scenario and whether there is a targeted demand. When the course additional demand characteristics do not limit the application scenario and there is no targeted demand, the target number of cases are randomly selected according to the difficulty level for the first case explanation extraction information to obtain The first teaching information, when the application scenario and / or targeted demand are limited in the additional demand characteristics of the course, when the target application scenario and / or target targeted demand is obtained, the first case explanation extraction information is screened for the second time according to the target application scenario and / or target targeted demand to obtain the second case explanation extraction information, and then the number of cases in each difficulty level of the second case explanation extraction information is obtained, and it is analyzed whether the number of cases is greater than the target number. When the number of cases is not greater than the target number, the second case explanation extraction information is the first teaching information. When the number of cases is greater than the target number, the target number of cases are randomly screened according to the difficulty level for the second case explanation extraction information to obtain the first teaching information.

[0025] Furthermore, the fourth teaching information also performs a test record when the target object tests the test questions to generate a historical test record. After the target object tests the fourth teaching information and obtains the test results, the target object is tested again for confirmation. When the target object is tested again, the fourth teaching information is updated according to the test results, so that the target object is tested again using the updated fourth teaching information. When the fourth teaching information is updated according to the test results, the current test results are combined with the test results of the historical test records to perform a correctness analysis to obtain the comprehensive test correctness of the test questions. The current test questions are screened according to the comprehensive test correctness of the test questions to obtain the first test question screening result, and then the number of test questions in the first test question screening result is obtained, and the number of updated test questions is determined. At the same time, the question types of the test questions in the first test question screening result are obtained, and the updated test question types are determined. Then, the test questions are obtained again in the knowledge classification database according to the number of updated test questions combined with the updated test question types to obtain updated test questions, so as to obtain the updated fourth teaching information according to the first test question screening result and the updated test questions.

[0026] Furthermore, the determining of the parameter weights includes:

[0027] Determine a first reference weight in the fourth teaching information for the test question in combination with the capacity of the fourth teaching information;

[0028] In the fourth teaching information, the test questions are differentiated according to the degree of difficulty to determine the distribution of difficulty levels, the number of test questions of the first difficulty gradient, the number of test questions of the second difficulty gradient, the number of test questions of the third difficulty gradient, and the number of test questions of the fourth difficulty gradient are obtained from the distribution of difficulty levels, and the number of test questions of the first difficulty gradient, the number of test questions of the second difficulty gradient, the number of test questions of the third difficulty gradient, and the number of test questions of the fourth difficulty gradient are combined with the degree of difficulty to determine a second reference weight;

[0029] The first reference weight and the second reference weight are analyzed and determined to determine the parameter weight.

[0030] Furthermore, when extracting keywords from knowledge points, keyword extraction is performed on knowledge points through a keyword extraction model to obtain keywords of the knowledge points, and the keyword extraction model includes: a preprocessing unit, a conversion unit and an extraction unit; when using the keyword extraction model to extract keywords from knowledge points, the preprocessing unit analyzes the knowledge points, decomposes the knowledge points into multiple words, and obtains knowledge point word segmentation information; the conversion unit quantifies the knowledge point word segmentation information, and converts the knowledge point word segmentation information into a knowledge point text vector; the extraction unit uses a neural network classification model to output the knowledge point text vector into a target number of information clusters, analyzes the core information of the information cluster, determines the keywords of the information cluster, and integrates the keywords of each information cluster together to obtain the keywords of the knowledge point.

[0031] A micro-course teaching system, comprising: a database construction module, a course content production module, a knowledge point association module, a first teaching information generation module, a second teaching information generation module, a third teaching information generation module, a fourth teaching information generation module and a final teaching information generation module;

[0032] The database construction module is used to construct a knowledge classification database;

[0033] The module for making course content is used to obtain the needs of the target object and determine the course content according to the needs of the target object;

[0034] The knowledge point association module is used to extract and score knowledge point keywords for course content, and form index items with scores, and then add the index items to the knowledge classification database to establish the association between knowledge points in the course content and teaching courseware.

[0035] The first teaching information generation module is used to obtain target explanation knowledge points from the course content, and extract case explanation information matching the target explanation knowledge points from the knowledge classification database according to the target explanation knowledge points to obtain the first teaching information;

[0036] The second teaching information generating module is used to obtain teaching courseware according to the target explanation knowledge point to obtain the second teaching information;

[0037] The third teaching information generating module is used to extract case explanation information corresponding to the target explanation knowledge point from the knowledge classification database again according to the target explanation knowledge point to obtain the third teaching information;

[0038] The fourth teaching information generating module is used to randomly extract test questions of different difficulty levels corresponding to the target teaching knowledge points from the knowledge classification database according to the target teaching knowledge points to obtain the fourth teaching information;

[0039] The final teaching information generating module is used to obtain the final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and send the final teaching information to a designated location.

[0040] An electronic device comprises: a memory and a processor, wherein the memory is used to store a program, the program is a program generated according to a micro-class teaching method, and the processor is used to execute the program stored in the memory to implement any method in a micro-class teaching method.

[0041] A computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any method in a micro-class teaching method.

[0042] The present invention can configure teaching information according to the learning needs of different students, so that the target objects can realize learning through teaching information as needed, and can also carry out targeted teaching and training according to the learning needs of students, thereby providing convenience for students' learning, meeting the needs of students in different situations, improving students' learning efficiency and the satisfaction of target objects, and eliminating the need for teachers to spend time and effort to teach students in accordance with their aptitude, which not only can efficiently obtain teaching information, but also can effectively reduce cost consumption.

[0043] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0044] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0046] Figure 1 A schematic diagram of the steps of the micro-class teaching method of the present invention;

[0047] Figure 2 It is a schematic diagram of step S1 in the micro-class teaching method of the present invention;

[0048] Figure 3 It is a schematic diagram of the steps of adjusting the teaching courseware in the micro-class teaching method of the present invention;

[0049] Figure 4 It is a schematic diagram of the micro-class teaching system described in the present invention.

[0050] Icons: 1. Database construction module; 2. Course content production module; 3. Knowledge point association module; 4. First teaching information generation module; 5. Second teaching information generation module; 6. Third teaching information generation module; 7. Fourth teaching information generation module; 8. Final teaching information generation module. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0052] Example 1

[0053] like Figure 1 As shown, an embodiment of the present invention provides a micro-class teaching method, including:

[0054] S1. Build a knowledge classification database;

[0055] S2. Obtain the needs of the target audience and determine the course content based on the needs of the target audience;

[0056] S3. Extract and score keywords of knowledge points in the course content, form index items with scores, and then add the index items to the knowledge classification database to establish the association between knowledge points in the course content and teaching courseware;

[0057] S4, obtaining target explanation knowledge points from the course content, and extracting case explanation information matching the target explanation knowledge points from the knowledge classification database according to the target explanation knowledge points, to obtain first teaching information;

[0058] S5, obtaining teaching courseware according to the target knowledge point explanation, and obtaining the second teaching information;

[0059] S6, extracting case explanation information corresponding to the target explanation knowledge point from the knowledge classification database again according to the target explanation knowledge point to obtain third teaching information;

[0060] S7, randomly extracting test questions of different difficulty levels corresponding to the target knowledge points from the knowledge classification database according to the target knowledge points to obtain fourth teaching information;

[0061] S8. Obtain final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and send the final teaching information to a designated location.

[0062] In the above technical solution, the knowledge classification database includes: teaching courseware, application scenario information, case explanation information and test questions. There is a mapping relationship between the teaching courseware and the application scenario information and the knowledge points, and each knowledge point corresponds to a teaching courseware.

[0063] In the above technical solution, the target object is the object of micro-class teaching, that is, the students who are learning.

[0064] In the above technical solution, the course content includes: target knowledge points and course attachment demand characteristics. The course attachment demand characteristics include: whether the application scenario is limited, whether there is a targeted demand, etc.

[0065] In the above technical solution, the final teaching information includes: teaching courseware, application scenario information, case explanation information and test questions.

[0066] In the above technical solution, the case explanation information may be in the form of a document, or in the form of audio or video.

[0067] In the above technical solution, the needs of the target object can be to investigate the nature of its work to understand the knowledge that needs to be learned. The knowledge that needs to be learned here usually refers to the desire to improve in work or life, but not knowing what knowledge to learn to improve. It can also be judged based on the knowledge that the target object itself wants to learn, and the knowledge to be learned is determined in combination with the target object’s clear needs. If the target object does not know what knowledge to learn, its work situation can be obtained to recommend the knowledge that needs to be mastered in the corresponding work to facilitate its improvement of knowledge and skills.

[0068] In the above technical solution, after establishing the association between the knowledge points in the course content and the teaching courseware, the current target explanation knowledge point is obtained according to the learning order and progress during the target object's learning process for the final teaching information, and the current target explanation knowledge point is indexed based on the index item, so as to extract the teaching courseware corresponding to the target explanation knowledge point from the knowledge classification database, and then before explaining the target explanation knowledge point, the corresponding teaching courseware is displayed to the target object, so that the target object can watch and understand the corresponding teaching courseware before learning through the final teaching information, so that the target object can better grasp the key points of the explanation during the learning process, improve the learning efficiency of the target object, and ensure the learning effect of the target object.

[0069] In the above technical solution, the teaching courseware can be a recorded teaching video courseware or document courseware for explaining the target knowledge points, or it can be a course content produced according to the needs of the target object through understanding and analysis, and the teaching courseware associated with the target knowledge points can be one or more.

[0070] In the above technical solution, the content of the third teaching information is less than that of the first teaching information. The third teaching information may be the same as part of the content of the first teaching information, or may be information extracted from the knowledge analysis database that is different from but similar to the first teaching information.

[0071] In the above technical scheme, when the final teaching information is obtained according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, the first teaching information, the second teaching information, the third teaching information and the fourth teaching information are connected according to the teaching rules to obtain the final teaching information, and then sent to the designated location, so that the target object can start to learn the corresponding knowledge points according to the final teaching information, wherein the designated location refers to the terminal location where the target object learns.

[0072] The above technical solution can configure teaching information according to the learning needs of different students, so that the target objects can learn through teaching information as needed. It can also carry out targeted teaching and training according to the students' learning needs, which provides convenience for students' learning, meets the needs of students in different situations, improves students' learning efficiency and the satisfaction of the target objects, and eliminates the need for teachers to spend time and effort to teach students in accordance with their aptitude. Not only can teaching information be obtained efficiently, but cost consumption can also be effectively reduced. By constructing a knowledge classification database, the knowledge classification database contains all teaching courseware, application scenario information, case explanation information and test questions, so that when determining the teaching information according to the needs of the target object, it can be directly retrieved from the knowledge classification database, which not only provides convenience for determining the teaching information, but also effectively saves the time for determining the teaching information; by obtaining the needs of the target object, the final teaching information can meet the needs of different students, thereby realizing teaching students in accordance with their aptitude, so that the target object can learn in a targeted manner through the final teaching content according to its own needs, thereby improving the learning efficiency of the target object; by obtaining the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, the final teaching information can not only explain the knowledge point information, but also explain the application of the knowledge point, and can also test the knowledge point, so that the target object can learn better through the final teaching information and ensure the learning effect, and the third teaching information is to extract the case explanation information corresponding to the target explanation knowledge point from the knowledge classification database again, so that the target object can learn and understand the target explanation knowledge point through the case explanation information many times during the learning process, deepen the cognition of the target explanation knowledge point, and effectively improve the target object's understanding of the relevant knowledge points.

[0073] Example 2

[0074] Based on Example 1, Figure 2 As shown, in one embodiment provided by the present invention, the construction of a knowledge classification database includes:

[0075] S101, determine the application scenario and determine the target knowledge points for the target object;

[0076] S102, performing knowledge point evaluation on the target knowledge points to obtain knowledge point evaluation information;

[0077] S103, determining the evaluation level of the target object and the knowledge point target reception information according to the target mastered knowledge points and knowledge point evaluation information;

[0078] S104: construct knowledge classification for the knowledge points according to the evaluation level of the target object and the target received information of the knowledge points to obtain a knowledge classification database.

[0079] In the above technical scheme, when conducting knowledge point evaluation for the target knowledge points, the emphasis and difficulty levels of the target knowledge points are evaluated respectively to obtain first knowledge point evaluation information and second knowledge point evaluation information, and then the first knowledge point evaluation information and the second knowledge point evaluation information are integrated to obtain the knowledge point evaluation information.

[0080] In the above technical solution, the information received by the knowledge point target is the key information that needs to be mastered by the target object in the target knowledge point.

[0081] The above technical solution constructs a knowledge classification database so that when determining teaching information based on the needs of the target object, it can be directly retrieved from the knowledge classification database, which not only provides convenience for determining the teaching information, but also effectively saves the time for determining the teaching information. In the knowledge classification database, the knowledge classification database can have a clear level division through knowledge point evaluation, and orderly management of teaching courseware, application scenario information, case explanation information, test questions and other information can be achieved, which is convenient for extraction when the teaching information is determined, so that when extracting from the knowledge classification database, it can be obtained according to the actual situation of the target object. At the same time, it also ensures the comprehensiveness of the knowledge classification database, so that it can meet the needs of any level.

[0082] Example 3

[0083] Based on Example 1, Figure 3 As shown, in one embodiment provided by the present invention, the micro-class teaching method further adjusts the teaching information, determines whether to adjust the teaching content according to the fourth teaching information, obtains an adjustment analysis and judgment result, and adjusts the teaching information according to the adjustment analysis and judgment result, wherein determining whether to adjust the teaching content according to the fourth teaching information includes:

[0084] S901, obtaining test parameters for the target object to be tested on the fourth teaching information, and counting the test time, test error rate and number of repeated tests to obtain statistical results;

[0085] S902, determining parameter weights, and combining the parameter weights with test parameters to obtain test results;

[0086] S903, comprehensively judging the test result and the preset threshold value to obtain the mastery degree of the target object;

[0087] S904: Determine whether to adjust the teaching courseware according to the mastery level.

[0088] In the above technical scheme, when the teaching information is adjusted according to the adjustment analysis and judgment result, when the adjustment analysis and judgment result is to adjust the teaching content, the preset knowledge point evaluation tool is used to analyze the evaluation results of the target explanation knowledge points according to the test results of the fourth teaching information, and the target object’s mastery distribution of the target explanation knowledge points is determined to obtain the knowledge point mastery analysis data, and then the adjustment target and adjustment type are determined based on the knowledge point mastery analysis data combined with the judgment threshold, and the adjustment information is acquired in the knowledge classification database according to the adjustment target and adjustment type to obtain the adjustment increase information, so as to supplement the teaching content with the adjustment increase information. Among them, the adjustment goal is to master the relatively weak knowledge points. The adjustment types include: using teaching courseware for repeated learning, using case explanation information for optimized learning, and using test questions for consolidated learning. The judgment threshold is the boundary value limit of the adjustment type. For example: the knowledge point mastery analysis data is in the range of [A, B], and the adjustment type of the target knowledge point explanation is using teaching courseware for repeated learning. The knowledge point mastery analysis data is in the range of [C, D], and the adjustment type of the target knowledge point explanation is using case explanation information for optimized learning. The knowledge point mastery analysis data is in the range of [E, F], and the adjustment type of the target knowledge point explanation is using case explanation information for optimized learning. The knowledge point mastery analysis data is in the range of [G, H], and the target knowledge point explanation does not need to be adjusted.

[0089] In the above technical scheme, when the target explanation knowledge points are evaluated and analyzed by the preset knowledge point evaluation tool according to the test results of the fourth teaching information, the mastery of the target explanation knowledge points is evaluated according to the test results of the target object on the test questions in the fourth teaching information, so as to obtain the mastery distribution of the target explanation knowledge points. The target explanation knowledge points can also be listed so that the target object can be evaluated according to its own knowledge point mastery, so as to determine the mastery distribution of the target explanation knowledge points according to the evaluation results. When obtaining the mastery distribution of the target explanation knowledge points, either the mastery distribution of the target explanation knowledge points can be obtained by using any of the above methods, or the mastery distribution of two target explanation knowledge points can be obtained by using two methods, and then the difference between the mastery distribution of the two target explanation knowledge points is analyzed, and the test results of the target object on the test questions corresponding to the target object in the fourth teaching information for the difference part are retrieved for the difference part, and the target test results are obtained, and the target test results are analyzed to see whether there are phenomena such as guessing or missing answers, and abnormal analysis results are obtained, and the final data of the difference part is determined according to the abnormal analysis results, so as to obtain the final mastery distribution of the target explanation knowledge points.

[0090] In the above technical solution, the parameter value ranges of the test time, the test error rate and the number of repeated tests, as well as the weight value corresponding to each parameter are set in advance.

[0091] The above technical solution adjusts the teaching content by determining whether to adjust the teaching content according to the fourth teaching information, thereby adjusting the teaching content. It can not only clarify the distribution of the target object's mastery of the target explanation knowledge points, but also enable the target object to learn the target explanation knowledge points that are not well mastered by the target object again, ensuring that the target object can completely and better master the target explanation knowledge points, thereby improving the teaching effect of the teaching content. When adjusting the teaching information according to the adjustment analysis and judgment results, by determining the adjustment target and the adjustment type, the adjustment information can be obtained from the knowledge classification database according to the adjustment target and the adjustment type when determining the adjustment information, which facilitates the acquisition of the adjustment information. In addition, when the preset knowledge point evaluation tool is used to analyze the evaluation results of the target explanation knowledge points according to the test results of the fourth teaching information, the preset knowledge point evaluation tool can not only efficiently realize the evaluation of the mastery of the target explanation knowledge points, quickly obtain the mastery distribution of the target explanation knowledge points, but also can select the determination method of the mastery distribution of the target explanation knowledge points by itself, improve the flexibility of obtaining the mastery distribution of the target explanation knowledge points, and at the same time, it can also ensure the accuracy of the mastery distribution of the target explanation knowledge points, thereby ensuring the accuracy of the adjustment of the teaching information and ensuring the teaching effect.

[0092] Example 4

[0093] Based on Example 1, in one embodiment provided by the present invention, when extracting case explanation information matching the target explanation knowledge point from the knowledge classification database according to the target explanation knowledge point, the case explanation information is matched in the knowledge classification database according to the target explanation knowledge point combined with the difficulty classification identifier, and the case explanation information corresponding to the target explanation knowledge point of different difficulty levels is obtained to obtain the first case explanation extraction information, and then the course additional demand characteristics are obtained from the course content, and the course additional demand characteristics are analyzed to determine whether the application scenario is limited and whether there is a directional demand. When the course additional demand characteristics do not limit the application scenario and do not have a directional demand, the target number is randomly screened according to the difficulty level for the first case explanation extraction information. The first teaching information can be obtained by randomly selecting cases. When the application scenario and / or targeted demand are limited in the additional demand features of the course, the target application scenario and / or target targeted demand are obtained, and the first case explanation extraction information is screened for the second time according to the target application scenario and / or target targeted demand to obtain the second case explanation extraction information. Then, the number of cases in each difficulty level of the second case explanation extraction information is obtained, and it is analyzed whether the number of cases is greater than the target number. When the number of cases is not greater than the target number, the second case explanation extraction information is the first teaching information. When the number of cases is greater than the target number, the first teaching information can be obtained by randomly selecting the target number of cases according to the difficulty level for the second case explanation extraction information.

[0094] In the above technical solution, the target number is preset, and the target number for each difficulty level can be the same or different.

[0095] In the above technical scheme, the case explanation information is matched in the knowledge classification database according to the target explanation knowledge point combined with the difficulty classification mark, the keywords of the target explanation knowledge point are obtained, and each case explanation information is quickly searched and retrieved according to the target explanation knowledge point keywords combined with the difficulty classification mark, and the first case explanation information is extracted, and each case explanation information is set with corresponding knowledge point keywords and difficulty marks.

[0096] The above-mentioned technical scheme matches the case explanation information in the knowledge classification database according to the target explanation knowledge points combined with the difficulty classification identifier, so that the obtained first teaching information contains different difficulty levels, improves the comprehensiveness of the teaching information, and enables the target object to better learn the teaching information. In addition, when determining the first teaching information, it is also determined in combination with the additional demand characteristics of the course, so that the first teaching information can be adjusted according to the needs of the target object, so that the final teaching information can be targeted for teaching the target object according to limited application scenarios and directional needs, so that the target object can better apply the teaching information to work scenarios or practical problems, thereby improving the flexibility of the final teaching information, meeting the needs of the target object, and realizing teaching students in accordance with their aptitude.

[0097] Example 5

[0098] Based on Example 3, in an embodiment provided by the present invention, the fourth teaching information also performs a test record when the target object tests the test questions, and generates a historical test record. After the target object tests the fourth teaching information and obtains the test results, the target object is tested again for confirmation. When the target object is tested again, the fourth teaching information is updated according to the test results, so that the target object is tested again using the updated fourth teaching information. When the fourth teaching information is updated according to the test results, the current test results are combined with the test results of the historical test records to perform a correct rate analysis to obtain a comprehensive test correct rate of the test questions. The current test questions are screened according to the comprehensive test correct rate of the test questions to obtain a first test question screening result, and then the number of test questions in the first test question screening result is obtained, and the number of updated test questions is determined. At the same time, the question types of the test questions in the first test question screening result are obtained, and the updated test question types are determined. Then, according to the number of updated test questions combined with the updated test question types, the test questions are again obtained in the knowledge classification database to obtain updated test questions, so as to obtain the updated fourth teaching information according to the first test question screening result and the updated test questions.

[0099] In the above technical solution, the number of times the target object is tested on the fourth teaching information is not unique.

[0100] In the above technical solution, the number of test questions in the fourth teaching information is the same before and after updating.

[0101] In the above technical solution, when the target object does not undergo retesting, there is no need to update the fourth teaching information.

[0102] In the above technical solution, when determining the type of updated test questions, knowledge points with a lower degree of mastery are given priority, while knowledge points that have been mastered are intermittently considered to consolidate the corresponding knowledge points.

[0103] In the above technical solution, when test questions are obtained again in the knowledge classification database according to the number of updated test questions and the updated test question types, the test questions that have been extracted will no longer be extracted, which will avoid repeatedly extracting the same test question and affecting the test-taking effect of the target object.

[0104] The above technical scheme realizes the detection of the learning effect of the target explanation knowledge points through the fourth teaching information, and reflects the parts of the target object's lack of mastery of the target explanation knowledge points through the test results of the test questions, so that the target object's consolidation of the target explanation knowledge points can be enhanced through multiple tests. In addition, Rongguang's test records can not only reflect the changes in the test results of the target object, but also provide a reference when updating the fourth teaching information, avoiding repeated test questions for the target explanation knowledge points that are well mastered, improving the effectiveness of the test questions, and making the updated test questions more reasonable and of better value.

[0105] Example 6

[0106] Based on Example 3, in one embodiment provided by the present invention, the determining of parameter weights includes:

[0107] Determine a first reference weight in the fourth teaching information for the test question in combination with the capacity of the fourth teaching information;

[0108] In the fourth teaching information, the test questions are differentiated according to the degree of difficulty to determine the distribution of difficulty levels, the number of test questions of the first difficulty gradient, the number of test questions of the second difficulty gradient, the number of test questions of the third difficulty gradient, and the number of test questions of the fourth difficulty gradient are obtained from the distribution of difficulty levels, and the number of test questions of the first difficulty gradient, the number of test questions of the second difficulty gradient, the number of test questions of the third difficulty gradient, and the number of test questions of the fourth difficulty gradient are combined with the degree of difficulty to determine a second reference weight;

[0109] The first reference weight and the second reference weight are analyzed and determined to determine the parameter weight.

[0110] In the above technical solution, the sum of the parameter weights is 1.

[0111] The above technical solution determines the parameter weight by analyzing the first reference weight and the second reference weight comprehensively, so that the parameter weight can not only reflect the proportion of the test questions in the fourth teaching information, but also reflect the influence of the difficulty level, thereby making the test results more objective and better able to reflect the target object's feedback on the teaching information.

[0112] Example 7

[0113] Based on Example 1, in an embodiment provided by the present invention, when extracting keywords from knowledge points, keyword extraction is performed on the knowledge points through a keyword extraction model to obtain keywords of the knowledge points, and the keyword extraction model includes: a preprocessing unit, a conversion unit and an extraction unit; when using the keyword extraction model to extract keywords from knowledge points, the preprocessing unit analyzes the knowledge points, decomposes the knowledge points into multiple words, and obtains knowledge point segmentation information; the conversion unit quantifies the knowledge point segmentation information, and converts the knowledge point segmentation information into a knowledge point text vector; the extraction unit uses a neural network classification model to output the knowledge point text vector into a target number of information clusters, analyzes the core information of the information cluster, determines the keywords of the information cluster, and integrates the keywords of each information cluster together to obtain the keywords of the knowledge point.

[0114] In the above technical solution, in the keyword extraction model, the preprocessing unit, the conversion unit and the extraction unit are connected in sequence, the conversion unit obtains the knowledge point segmentation information obtained by the preprocessing unit, and inputs the knowledge point text vector to the neural network classification model in the extraction unit after quantization processing.

[0115] In the above technical solution, the target quantity is preset and can be adjusted appropriately according to demand.

[0116] The above technical solution uses the keyword extraction model to extract keywords from knowledge points to obtain keywords of knowledge points. It can not only quickly realize keyword extraction for multiple knowledge points, but also improve the accuracy of keywords by utilizing the characteristics of the model. It is not easy to make errors in the keyword extraction process of the keyword extraction model, and it can also limit the target number according to demand in the keyword extraction model, thereby improving the flexibility of the keyword extraction model.

[0117] Example 8

[0118] like Figure 4As shown, the present invention provides a micro-course teaching system, including: a database construction module 1, a course content production module 2, a knowledge point association module 3, a first teaching information generation module 4, a second teaching information generation module 5, a third teaching information generation module 6, a fourth teaching information generation module 7 and a final teaching information generation module 8;

[0119] The database construction module 1 is used to construct a knowledge classification database;

[0120] The course content making module 2 is used to obtain the needs of the target object and determine the course content according to the needs of the target object;

[0121] The knowledge point association module 3 is used to extract and score knowledge point keywords for the course content, and form index items with the scored keywords, and then add the index items to the knowledge classification database to establish the association between the knowledge points in the course content and the teaching courseware.

[0122] The first teaching information generating module 4 is used to obtain target explanation knowledge points from the course content, and extract case explanation information matching the target explanation knowledge points from the knowledge classification database according to the target explanation knowledge points, so as to obtain the first teaching information;

[0123] The second teaching information generating module 5 is used to obtain teaching courseware according to the target knowledge point to obtain the second teaching information;

[0124] The third teaching information generating module 6 is used to extract case explanation information corresponding to the target explanation knowledge point from the knowledge classification database again according to the target explanation knowledge point to obtain the third teaching information;

[0125] The fourth teaching information generating module 7 is used to randomly extract test questions of different difficulty levels corresponding to the target knowledge points from the knowledge classification database according to the target knowledge points to obtain the fourth teaching information;

[0126] The final teaching information generating module 8 is used to obtain the final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and send the final teaching information to a designated location.

[0127] The above technical solution is a micro-class teaching system corresponding to a micro-class teaching method, which can configure teaching information according to the learning needs of different students, so that the target objects can learn through teaching information on demand, and can also carry out targeted teaching and training according to the students' learning needs, which provides convenience for students' learning, meets the needs of students in different situations, improves students' learning efficiency and the satisfaction of target objects, and does not require teachers to spend time and effort to teach students in accordance with their aptitude, which can not only obtain teaching information efficiently, but also effectively reduce cost consumption. By constructing a knowledge classification database through the database construction module, all teaching courseware, application scenario information, case explanation information and test questions are included in the knowledge classification database, so that when the first teaching information generation module, the second teaching information generation module, the third teaching information generation module and the fourth teaching information generation module determine the teaching information according to the needs of the target object, they can be directly retrieved from the knowledge classification database, which not only provides convenience for determining the teaching information, but also effectively saves the time for determining the teaching information; by making the course content module to obtain the needs of the target object, the final teaching information can meet the needs of different students, thereby realizing teaching students in accordance with their aptitude, so that the target object can learn in a targeted manner through the final teaching content according to its own needs, thereby improving the learning efficiency of the target object; through the first teaching information generation module, the second teaching information generation module, the third teaching information generation module and the fourth teaching information generation module, the teaching information generation module can be directly retrieved from the knowledge classification database, which not only provides convenience for determining the teaching information, but also effectively saves the time for determining the teaching information; by making the course content module, the needs of the target object are obtained, so that the final teaching information can meet the needs of different students, thereby realizing teaching students in accordance with their aptitude, so that the target object can learn in a targeted manner through the final teaching content according to its own needs, thereby improving the learning efficiency of the target object; The learning information generation module, the third teaching information generation module, and the fourth teaching information generation module respectively obtain the first teaching information, the second teaching information, the third teaching information, and the fourth teaching information so that the final teaching information obtained by the final teaching information generation module can not only explain the knowledge point information, but also explain the application of the knowledge points. At the same time, the knowledge points can also be tested, so that the target objects can learn better through the final teaching information and ensure the learning effect. Moreover, when the third teaching information generation module obtains the third teaching information, it can extract the case explanation information corresponding to the target explanation knowledge point from the knowledge classification database again, so that the target objects can learn and understand the target explanation knowledge points through the case explanation information many times in the learning process, deepen the cognition of the target explanation knowledge points, and can effectively improve the target objects' understanding of the relevant knowledge points.

[0128] Example 9

[0129] The present invention provides an electronic device, comprising: a memory and a processor, wherein the memory is used to store a program, the program is a program generated according to a micro-class teaching method, and the processor is used to execute the program stored in the memory to implement any one of the micro-class teaching methods described in Examples 1-7.

[0130] In the above technical solution, the memory, processor and communication interface are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines. The memory can be used to store software programs and modules, such as program instructions / modules corresponding to a micro-class teaching system provided in an embodiment of the present application, and the processor executes various functional applications and data processing by executing the software programs and modules stored in the memory. The communication interface can be used to communicate signaling or data with other node devices.

[0131] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The processor may be an integrated circuit chip with signal processing capabilities, and the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0132] Example 10

[0133] The present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement any one of the micro-class teaching methods described in Examples 1-7.

[0134] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0135] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A micro-class teaching method, characterized in that: include: Construct a knowledge classification database; Obtain the needs of the target audience and determine the course content based on the needs of the target audience; Extract and score keywords of knowledge points in the course content, form index items with scores, and then add the index items to the knowledge classification database to establish the association between knowledge points in the course content and teaching courseware; Obtaining target explanation knowledge points from the course content, and extracting case explanation information matching the target explanation knowledge points from a knowledge classification database according to the target explanation knowledge points, to obtain first teaching information; Explain the knowledge points according to the target to obtain the teaching courseware and obtain the second teaching information; According to the target explanation knowledge point, case explanation information corresponding to the target explanation knowledge point is extracted again from the knowledge classification database to obtain third teaching information; According to the target knowledge points, test questions of different difficulty levels corresponding to the target knowledge points are randomly extracted from the knowledge classification database to obtain fourth teaching information; Final teaching information is obtained according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and the final teaching information is sent to a designated location.

2. The micro-class teaching method according to claim 1, characterized in that: The construction of the knowledge classification database comprises: Determine the application scenario and determine the target knowledge points for the target object; Conducting knowledge point evaluation on the target knowledge points to obtain knowledge point evaluation information; Determine the target object's evaluation level and knowledge point target reception information based on the target's mastered knowledge points and knowledge point evaluation information; According to the evaluation level of the target object and the target received information of the knowledge point, knowledge classification is constructed for the knowledge point to obtain a knowledge classification database.

3. The micro-class teaching method according to claim 1, characterized in that: The micro-class teaching method further adjusts the teaching information, determines whether to adjust the teaching content according to the fourth teaching information, obtains an adjustment analysis and judgment result, and adjusts the teaching information according to the adjustment analysis and judgment result, wherein determining whether to adjust the teaching content according to the fourth teaching information includes: Acquire test parameters for testing the target object with respect to the fourth teaching information, and count the test time, test error rate, and number of repeated tests to obtain statistical results; Determining parameter weights, and combining the parameter weights with test parameters to obtain test results; The test results and the preset threshold are comprehensively judged to obtain the mastery degree of the target object; Determine whether to adjust the teaching courseware according to the mastery level.

4. The micro-class teaching method according to claim 1, characterized in that: When extracting case explanation information matching the target explanation knowledge point from the knowledge classification database according to the target explanation knowledge point, matching the case explanation information in the knowledge classification database according to the target explanation knowledge point combined with the difficulty classification identifier is performed to obtain case explanation information corresponding to the target explanation knowledge point of different difficulty levels, and obtain the first case explanation extraction information, and then obtain the course additional demand characteristics from the course content, analyze whether the course additional demand characteristics limit the application scenario and whether there is a directional demand, when the course additional demand characteristics do not limit the application scenario and do not exist a directional demand, for the first case explanation extraction information, randomly select the target number of cases according to the difficulty level to obtain the first teaching When the application scenario and / or targeted demand are limited in the additional demand characteristics of the course, when the target application scenario and / or targeted demand are obtained, the first case explanation extraction information is screened for the second time according to the target application scenario and / or targeted demand to obtain the second case explanation extraction information, and then the number of cases in each difficulty level of the second case explanation extraction information is obtained, and it is analyzed whether the number of cases is greater than the target number. When the number of cases is not greater than the target number, the second case explanation extraction information is the first teaching information. When the number of cases is greater than the target number, the target number of cases are randomly screened according to the difficulty level for the second case explanation extraction information to obtain the first teaching information.

5. The micro-class teaching method according to claim 3, characterized in that: The fourth teaching information also performs a test record when the target object tests the test questions to generate a historical test record. After the target object tests the fourth teaching information and obtains the test results, the target object is tested again for confirmation. When the target object is tested again, the fourth teaching information is updated according to the test results, so that the target object is tested again using the updated fourth teaching information. When the fourth teaching information is updated according to the test results, the current test results are combined with the test results of the historical test records to perform a correctness analysis to obtain the comprehensive test correctness of the test questions. The current test questions are screened according to the comprehensive test correctness of the test questions to obtain the first test question screening result, and then the number of test questions in the first test question screening result is obtained, and the number of updated test questions is determined. At the same time, the question types of the test questions in the first test question screening result are obtained, and the updated test question types are determined. Then, according to the number of updated test questions combined with the updated test question types, the test questions are again obtained in the knowledge classification database to obtain updated test questions, so as to obtain the updated fourth teaching information according to the first test question screening result and the updated test questions.

6. The micro-class teaching method according to claim 3, characterized in that: The step of determining the parameter weights includes: Determine a first reference weight in the fourth teaching information for the test question in combination with the capacity of the fourth teaching information; In the fourth teaching information, the test questions are differentiated according to the degree of difficulty to determine the distribution of difficulty levels, the number of test questions of the first difficulty gradient, the number of test questions of the second difficulty gradient, the number of test questions of the third difficulty gradient, and the number of test questions of the fourth difficulty gradient are obtained from the distribution of difficulty levels, and the number of test questions of the first difficulty gradient, the number of test questions of the second difficulty gradient, the number of test questions of the third difficulty gradient, and the number of test questions of the fourth difficulty gradient are combined with the degree of difficulty to determine a second reference weight; The first reference weight and the second reference weight are analyzed and determined to determine the parameter weight.

7. The micro-class teaching method according to claim 1, characterized in that: When extracting keywords from knowledge points, keyword extraction is performed on knowledge points through a keyword extraction model to obtain keywords of the knowledge points, and the keyword extraction model includes: a preprocessing unit, a conversion unit and an extraction unit; when using the keyword extraction model to extract keywords from knowledge points, the preprocessing unit analyzes the knowledge points, decomposes the knowledge points into multiple words, and obtains knowledge point word segmentation information; the conversion unit quantifies the knowledge point word segmentation information and converts the knowledge point word segmentation information into a knowledge point text vector; the extraction unit uses a neural network classification model to output the knowledge point text vector into a target number of information clusters, analyzes the core information of the information cluster, determines the keywords of the information cluster, and integrates the keywords of each information cluster together to obtain the keywords of the knowledge point.

8. A micro-class teaching system, characterized in that: The micro-course teaching system comprises: a database construction module (1), a course content production module (2), a knowledge point association module (3), a first teaching information generation module (4), a second teaching information generation module (5), a third teaching information generation module (6), a fourth teaching information generation module (7) and a final teaching information generation module (8); The database construction module (1) is used to construct a knowledge classification database; The module for making course content (2) is used to obtain the needs of the target object and determine the course content according to the needs of the target object; The knowledge point association module (3) is used to extract and score knowledge point keywords for the course content, and form index items with the scored keywords, and then add the index items to the knowledge classification database to establish the association between the knowledge points in the course content and the teaching courseware. The first teaching information generating module (4) is used to obtain target explanation knowledge points from the course content, and extract case explanation information matching the target explanation knowledge points from the knowledge classification database according to the target explanation knowledge points, so as to obtain the first teaching information; The second teaching information generating module (5) is used to obtain teaching courseware according to the target knowledge point to obtain the second teaching information; The third teaching information generating module (6) is used to extract case explanation information corresponding to the target explanation knowledge point from the knowledge classification database again according to the target explanation knowledge point to obtain the third teaching information; The fourth teaching information generating module (7) is used to randomly extract test questions of different difficulty levels corresponding to the target teaching knowledge points from the knowledge classification database according to the target teaching knowledge points, so as to obtain the fourth teaching information; The final teaching information generating module (8) is used to obtain the final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information, and send the final teaching information to a designated location.

9. An electronic device, characterized in that: The electronic device includes: a memory and a processor, wherein the memory is used to store a program, the program is a program generated according to the micro-class teaching method, and the processor is used to execute the program stored in the memory to implement the micro-class teaching method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the micro-class teaching method according to any one of claims 1 to 7.

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