Micro-lecture teaching method and system

Abstract

The application provides a micro-class teaching method and system, wherein the micro-class teaching method comprises the following steps: constructing a knowledge classification database; obtaining the demand of a target object and determining the course content according to the demand of the target object; carrying out knowledge point keyword extraction and scoring on the course content, forming an index item with scores and adding the index item to the knowledge classification database, and establishing the association between the knowledge classification database and the teaching courseware; obtaining the target explanation knowledge point from the course content and obtaining the first teaching information, the second teaching information, the third teaching information and the fourth teaching information according to the target explanation knowledge point; and sending the final teaching information to a specified position after obtaining the final teaching information according to the first teaching information, the second teaching information, the third teaching information and the fourth teaching information. The application can meet the demand of students in different situations and improve the learning efficiency of students.

Description

Technical Field

[0001] This invention relates to the field of online teaching, and in particular to a micro-lesson teaching method and system. Background Technology

[0002] In recent years, online education has become increasingly popular, with many schools and educational institutions implementing it. This breaks down geographical and temporal limitations, contributing to the equalization of educational resources. However, current online education often uses standardized courseware or a single teacher for multiple students, employing the same textbooks and lesson plans. Because students differ significantly in their learning abilities, knowledge levels, and application skills, this undifferentiated, rote learning approach inevitably leads to some students falling behind while others fail to acquire the knowledge they desire. If one-on-one, personalized instruction were adopted, the high labor costs would exponentially increase the overall educational cost.

[0003] In particular, many courses in adult vocational education focus on explaining basic knowledge, neglecting the problems students encounter in work scenarios and their need to apply that knowledge to solve problems. Vocational education is characterized by limited learning time, varying levels of prior knowledge, and a strong emphasis on solving practical problems. Therefore, this invention proposes a micro-lecture teaching method and system that is not only low-cost but also allows for targeted teaching and training based on students' learning needs, meeting the requirements of students in different situations and improving their learning efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a micro-lesson teaching method and system to solve the problems mentioned in the background art.

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

[0006] Build a knowledge classification database;

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

[0008] The course content is analyzed by extracting and scoring keywords of knowledge points, and then indexing the keywords with scores. These indexed keywords are then added to the knowledge classification database to establish a connection between knowledge points in the course content and teaching materials.

[0009] The target knowledge points are extracted from the course content, and case study information matching the target knowledge points is extracted from the knowledge classification database to obtain the first teaching information.

[0010] Based on the target knowledge points, teaching materials are obtained to acquire secondary teaching information;

[0011] Based on the knowledge points to be explained, the case study information corresponding to the knowledge points to be explained is extracted again from the knowledge classification database to obtain the third teaching information;

[0012] Based on the knowledge points to be explained, test questions of different difficulty levels corresponding to the knowledge points to be explained are randomly selected from the knowledge classification database to obtain the fourth teaching information;

[0013] The final teaching information is obtained based on the first, second, third, and fourth teaching information, and then sent to the designated location.

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

[0015] Determine the application scenario and identify the target knowledge points for the target audience;

[0016] To assess the knowledge points mastered for the stated objectives, knowledge point evaluation information is obtained.

[0017] Based on the knowledge points and knowledge point assessment information acquired by the target, determine the assessment level of the target object and the knowledge point target reception information;

[0018] Based on the assessment level of the target object and the target information received for the knowledge points, a knowledge classification database is constructed for each knowledge point.

[0019] Furthermore, the micro-lesson teaching method also adjusts the teaching information, determines whether to adjust the teaching content based on the fourth teaching information, obtains the adjustment analysis and judgment result, and adjusts the teaching information based on the adjustment analysis and judgment result. Specifically, determining whether to adjust the teaching content based on the fourth teaching information includes:

[0020] The test parameters for the target object to test the fourth teaching information are obtained, and the test time, test error rate and number of repeated tests are statistically analyzed to obtain the statistical results.

[0021] Determine the parameter weights, and combine the parameter weights with the test parameters to obtain the test results;

[0022] The test results are combined with preset thresholds to determine the target object's level of mastery.

[0023] Whether to adjust the teaching materials depends on the level of mastery described.

[0024] Furthermore, when extracting case study information matching the target knowledge points from the knowledge classification database, the case study information is matched against the target knowledge points in combination with difficulty level classification labels to obtain case study information corresponding to the target knowledge points at different difficulty levels, thus obtaining the first case study extraction information. Then, additional course requirement features are obtained from the course content, and it is analyzed whether these features limit the application scenario or have targeted needs. When the additional course requirement features do not limit the application scenario and do not have targeted needs, a target number of cases are randomly selected from the first case study extraction information according to their difficulty level to obtain the desired results. The first teaching information is obtained when the course's additional requirements specify the application scenario and / or have targeted requirements. Based on the target application scenario and / or target targeted requirements, the information extracted from the first case study is further filtered to obtain the second case study information. Then, the number of cases in each difficulty level of the second case study information is obtained, and it is analyzed whether the number of cases exceeds the target number. If the number of cases does not exceed the target number, the second case study information is the first teaching information. If the number of cases exceeds the target number, the target number of cases are randomly selected from the second case study information according to their difficulty level to obtain the first teaching information.

[0025] Furthermore, the fourth teaching information is recorded during the target object's testing of the test questions, generating historical test records. After the target object tests the fourth teaching information and obtains the test results, a retest is conducted to confirm the results. When the target object retests, the fourth teaching information is updated based on the test results, allowing the target object to retest using the updated fourth teaching information. When updating the fourth teaching information based on the test results, the current test results are combined with the test results from the historical test records for accuracy analysis to obtain the comprehensive test accuracy of the test questions. Based on the comprehensive test accuracy of the test questions, the current test questions are filtered to obtain the first test question filtering results. Then, the number of test questions in the first test question filtering results is obtained, and the number of updated test questions is determined. Simultaneously, the question types of the test questions in the first test question filtering results are obtained, and the updated test question types are determined. Then, based on the number of updated test questions and the updated test question types, test questions are retrieved again from the knowledge classification database to obtain updated test questions. Thus, the updated fourth teaching information is obtained based on the first test question filtering results and the updated test questions.

[0026] Furthermore, determining the parameter weights includes:

[0027] The fourth teaching information is used to determine the first reference weight for the test questions in conjunction with the capacity of the fourth teaching information.

[0028] The fourth teaching information categorizes test questions according to their difficulty level to determine the difficulty level distribution. From the difficulty level distribution, the number of test questions in the first difficulty level, the second difficulty level, the third difficulty level, and the fourth difficulty level are obtained. The number of test questions in the first difficulty level, the second difficulty level, the third difficulty level, and the fourth difficulty level are combined with the difficulty level to determine the second reference weight.

[0029] The parameter weights are determined by combining the first reference weight and the second reference weight.

[0030] Furthermore, when extracting keywords from knowledge points, a keyword extraction model is used to extract keywords for each knowledge point. This keyword extraction model includes a preprocessing unit, a transformation unit, and an extraction unit. When using this model to extract keywords from knowledge points, the preprocessing unit analyzes the knowledge point, decomposing it into multiple words to obtain word segmentation information. The transformation unit quantifies the word segmentation information, converting it into a knowledge point text vector. The extraction unit uses a neural network classification model to output a target number of information clusters from the knowledge point text vector. It analyzes the core information of each information cluster, determines the keywords of each cluster, and integrates the keywords from each cluster to obtain the keywords of the knowledge point.

[0031] A micro-lesson teaching system includes: a database construction module, a course content creation 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 course content creation module is used to obtain the needs of the target audience and determine the course content based on those needs.

[0034] The knowledge point association module is used to extract and score knowledge point keywords in the course content, and to create index items from the scored keywords. These index items are then added to the knowledge classification database to establish a link between knowledge points in the course content and teaching materials.

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

[0036] The second teaching information generation module is used to obtain teaching courseware based on the target knowledge points and thus obtain the second teaching information;

[0037] The third teaching information generation module is used to extract case explanation information corresponding to the target knowledge points from the knowledge classification database to obtain the third teaching information.

[0038] The fourth teaching information generation module is used to randomly select test questions of different difficulty levels corresponding to the target knowledge points from the knowledge classification database based on the target knowledge points to be explained, and obtain the fourth teaching information.

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

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

[0041] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement any of the methods in a micro-lesson teaching method.

[0042] This invention can configure teaching information according to the learning needs of different students, enabling the target audience to learn through teaching information as needed. It can also provide targeted teaching and training based on students' learning needs, making learning more convenient for students, meeting the needs of students in different situations, improving students' learning efficiency and target audience satisfaction. Moreover, it eliminates the need for teachers to spend time and effort on individualized instruction for different students, not only efficiently obtaining teaching information but also effectively reducing cost consumption.

[0043] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a schematic diagram illustrating one step of the micro-lesson teaching method described in this invention;

[0047] Figure 2 This is a schematic diagram of step S1 in the micro-lesson teaching method described in this invention;

[0048] Figure 3 This is a schematic diagram illustrating the steps of adjusting the teaching materials in the micro-lesson teaching method described in this invention;

[0049] Figure 4 This is a schematic diagram of the micro-lesson teaching system described in this invention.

[0050] Icons: 1. Database building module; 2. Course content creation 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 Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments.

[0052] Example 1

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

[0054] S1. Construct a knowledge classification database;

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

[0056] S3. Extract and score the knowledge points keywords for the course content, and form index items for the keywords with scores. 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 materials.

[0057] S4. Extract the target knowledge points from the course content, and extract case study information that matches the target knowledge points from the knowledge classification database to obtain the first teaching information.

[0058] S5. Obtain teaching materials based on the knowledge points explained according to the objectives, and obtain secondary teaching information;

[0059] S6. Based on the target knowledge points, extract the case study information corresponding to the target knowledge points from the knowledge classification database to obtain the third teaching information.

[0060] S7. Based on the knowledge points to be explained, randomly select test questions of different difficulty levels corresponding to the knowledge points to be explained from the knowledge classification database to obtain the fourth teaching information;

[0061] S8. Obtain the final teaching information based on the first, second, third, and fourth teaching information, and send the final teaching information to the 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 application scenario information and the knowledge points, and each knowledge point corresponds to a teaching courseware.

[0063] In the above technical solution, the target audience is the students who are learning through micro-lessons.

[0064] In the above technical solution, the course content includes: target knowledge points and course supplementary requirements. The additional requirements include: whether the application scenario is limited, and whether there are targeted requirements.

[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 solutions, the case study information can be in the form of a document, or in the form of audio or video.

[0067] In the above technical solution, the target's needs can be understood by researching the nature of their work and the knowledge they need to learn. Here, the knowledge they need to learn usually refers to wanting to improve in their work or life but not knowing what knowledge to learn. Alternatively, it can be determined based on the knowledge the target wants to learn and combined with the target's specific needs. If the target does not know what knowledge to learn, their work situation can be obtained to recommend the knowledge that needs to be mastered in their work, so as to facilitate their knowledge and skill improvement.

[0068] In the above technical solution, after establishing the association between knowledge points in the course content and teaching courseware, the current target knowledge point is obtained according to the learning order and progress during the learning process of the target object for the final teaching information. Based on the current target knowledge point, the index is performed, thereby retrieving the teaching courseware corresponding to the target knowledge point from the knowledge classification database. Then, before explaining the target knowledge point, the corresponding teaching courseware is displayed to the target object, so that the target object can view and understand the corresponding teaching courseware before learning through the final teaching information. This allows the target object to better grasp the key points of the explanation during the learning process, improves the target object's learning efficiency, and ensures the target object's learning effect.

[0069] In the above technical solution, the teaching courseware can be pre-recorded teaching video courseware or document courseware that explains the knowledge points of the target, or it can be course content created based on the needs of the target audience through understanding and analysis. Moreover, the teaching courseware associated with the knowledge points explained by the target can be one or multiple.

[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 some of the content in the first teaching information, or it 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 solution, when the final teaching information is obtained based on the first, second, third, and fourth teaching information, the first, second, third, and fourth teaching information are connected according to the teaching rules to obtain the final teaching information. Then, the final teaching information is sent to the designated location, so that the target can begin to learn the corresponding knowledge points based on the final teaching information. The designated location refers to the terminal location where the target learns.

[0072] The above-mentioned technical solution can configure teaching information according to the learning needs of different students, enabling the target audience to learn through teaching information as needed. It can also provide targeted teaching and training based on the students' learning needs, making learning more convenient for students, meeting the needs of students in different situations, improving students' learning efficiency and target audience satisfaction. Moreover, it eliminates the need for teachers to spend time and effort on individualized instruction for different students, not only efficiently obtaining teaching information but also effectively reducing cost consumption. By constructing a knowledge classification database, which includes all teaching materials, application scenarios, case studies, and test questions, the information can be directly retrieved from the database when determining teaching information based on the needs of the target audience. This not only facilitates the determination of teaching information but also saves time. By understanding the needs of the target audience, the final teaching information can meet the needs of different students, thus achieving individualized instruction. This allows students to learn in a targeted manner based on their individual needs, improving their learning efficiency. Furthermore, by acquiring first, second, third, and fourth levels of teaching information, the final teaching information not only explains the knowledge points but also their applications, and provides tests to test these points. This allows students to learn better and ensures learning outcomes. The third level of teaching information, derived from case studies extracted from the knowledge classification database, allows students to repeatedly learn and understand the knowledge points through these case studies, deepening their understanding and effectively improving their comprehension of the relevant knowledge points.

[0073] Example 2

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

[0075] S101. Determine the application scenario and identify the target knowledge points for the target audience;

[0076] S102. Conduct a knowledge point assessment for the knowledge points mastered for the target, and obtain knowledge point assessment information;

[0077] S103. Based on the target knowledge points and knowledge point assessment information, determine the assessment level of the target object and the knowledge point target reception information;

[0078] S104. Based on the assessment level of the target object and the target information received for the knowledge points, knowledge classification is constructed to obtain a knowledge classification database.

[0079] In the above technical solution, when evaluating the knowledge points mastered by the target, the degree of importance and difficulty of the knowledge points mastered by the target are assessed separately to obtain the first assessment information and the second assessment information of the knowledge points. Then, the first assessment information and the second assessment information of the knowledge points are combined to obtain the knowledge point assessment information.

[0080] In the above technical solution, the knowledge point target receiving information is the key information in the knowledge points that the target object needs to master.

[0081] The aforementioned technical solution, by constructing a knowledge classification database, enables direct retrieval of teaching information when determining the needs of the target audience. This not only facilitates the determination of teaching information but also effectively saves time. Furthermore, through knowledge point evaluation, the knowledge classification database exhibits clear hierarchical divisions, achieving orderly management of information such as teaching materials, application scenario information, case explanations, and test questions. This facilitates the retrieval of teaching information and allows for extraction based on the actual situation of the target audience. It also ensures the comprehensiveness of the knowledge classification database, meeting needs at any level.

[0082] Example 3

[0083] Based on Example 1, such as Figure 3 As shown, in one embodiment of the present invention, the micro-lesson teaching method further adjusts the teaching information, determines whether to adjust the teaching content based on fourth teaching information, obtains an adjustment analysis and judgment result, and adjusts the teaching information based on the adjustment analysis and judgment result. The determination of whether to adjust the teaching content based on the fourth teaching information includes:

[0084] S901. Obtain the test parameters of the target object for testing the fourth teaching information, and statistically analyze the test time, test error rate and number of repeated tests to obtain statistical results;

[0085] S902. Determine the parameter weights and combine the parameter weights with the test parameters to obtain the test results;

[0086] S903. The test results are combined with preset thresholds to determine the target object's level of mastery.

[0087] S904. Determine whether to adjust the teaching materials based on the level of mastery.

[0088] In the above technical solution, when adjusting teaching information based on the adjustment analysis and judgment results, if the adjustment analysis and judgment results indicate that the teaching content needs to be adjusted, then based on the test results of the fourth teaching information, a pre-set knowledge point assessment tool is used to analyze the assessment results of the target teaching knowledge points, determine the distribution of the target audience's mastery of the target teaching knowledge points, obtain knowledge point mastery analysis data, and then determine the adjustment target and adjustment type based on the knowledge point mastery analysis data and the judgment threshold. Adjustment information is then retrieved from the knowledge classification database according to the adjustment target and adjustment type to obtain adjustment addition information, which is then added to the teaching content for supplementary teaching. The adjustment target is to master relatively weak knowledge points. The adjustment types include: repeated learning using teaching materials, optimized learning using case studies, and consolidation learning using test questions. The judgment threshold is a boundary limit for the adjustment type. For example: if the knowledge point mastery analysis data is in the range of [A, B], the adjustment type for this knowledge point is repeated learning using teaching materials; if the knowledge point mastery analysis data is in the range of [C, D], the adjustment type for this knowledge point is optimized learning using case studies; if the knowledge point mastery analysis data is in the range of [E, F], the adjustment type for this knowledge point is optimized learning using case studies; if the knowledge point mastery analysis data is in the range of [G, H], no adjustment is needed for this knowledge point.

[0089] In the above technical solution, when analyzing the assessment results of the target knowledge points using a pre-set knowledge point evaluation tool based on the test results of the fourth teaching information, the degree of mastery of the target knowledge points is assessed based on the test results of the target subjects on the test questions in the fourth teaching information, thus obtaining the distribution of mastery of the target knowledge points. Alternatively, the target knowledge points can be listed so that the target subjects can evaluate themselves based on their own knowledge point mastery, thereby determining the distribution of mastery of the target knowledge points based on the evaluation results. When obtaining the distribution of mastery of the target knowledge points, either the above-mentioned method can be used, or two methods can be used to obtain two distributions of mastery of the target knowledge points. Then, the differences between the two distributions are analyzed, and for the differences, the test results of the target subjects on the test questions corresponding to the differences in the fourth teaching information are retrieved to obtain the target test results. The analysis is then conducted to determine whether there are any instances of guessing correctly or missing answers, obtaining anomaly analysis results. Based on the anomaly analysis results, the final data for the differences is determined, thus obtaining the final distribution of mastery of the target knowledge points.

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

[0091] The aforementioned technical solution adjusts the teaching content by determining whether to do so based on the fourth teaching information. This not only clarifies the distribution of the target audience's mastery of the target knowledge points but also allows for relearning of knowledge points that the target audience struggles with, ensuring a more complete and effective understanding and improving the teaching content's effectiveness. When adjusting the teaching information based on the adjustment analysis results, the determination of the adjustment target and type allows for convenient retrieval of adjustment information from a knowledge classification database. Furthermore, when analyzing the results of the fourth teaching information test using a pre-built knowledge point assessment tool, the tool efficiently assesses the mastery level of the target knowledge points, quickly obtaining the distribution of mastery. It also allows for the selection of the method used to determine the distribution, increasing the flexibility and ensuring the accuracy of the distribution data. This, in turn, ensures the accuracy of adjustments to the teaching information and guarantees teaching effectiveness.

[0092] Example 4

[0093] Based on Embodiment 1, in one embodiment of the present invention, when extracting case explanation information matching the target knowledge point from a knowledge classification database, the case explanation information is matched in the knowledge classification database according to the target knowledge point and the difficulty level classification identifier to obtain case explanation information corresponding to the target knowledge point at different difficulty levels, thus obtaining the first case explanation extraction information. Then, course additional demand features are obtained from the course content, and it is analyzed whether the course additional demand features limit the application scenario and have targeted needs. When the course additional demand features do not limit the application scenario and do not have targeted needs, the target number is randomly selected according to the difficulty level for the first case explanation extraction information. The first teaching information can be obtained from a single case. When the course additional requirements feature limits the application scenario and / or there is a targeted requirement, and the target application scenario and / or target targeted requirement are obtained, the information extracted from the first case explanation is further filtered according to the target application scenario and / or target targeted requirement to obtain the second case explanation information. Then, the number of cases in each difficulty level of the second case explanation 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 information is the first teaching information. When the number of cases is greater than the target number, the target number of cases are randomly selected according to the difficulty level of the second case explanation information to obtain the first teaching information.

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

[0095] In the above technical solution, when matching case explanation information in the knowledge classification database according to the target knowledge points and difficulty level classification labels, the keywords of the target knowledge points are obtained. Based on the target knowledge point keywords and difficulty level classification labels, each case explanation information is quickly searched and retrieved. The first case explanation extracts information. Each case explanation information is set with corresponding knowledge point keywords and difficulty level labels.

[0096] The above technical solution matches case study information in the knowledge classification database based on the target knowledge points and difficulty level classification labels, resulting in first teaching information with different difficulty levels. This improves the comprehensiveness of the teaching information, enabling the target audience to learn better. Furthermore, the determination of the first teaching information is also based on the additional needs of the course, allowing it to be adjusted according to the needs of the target audience. This ensures that the final teaching information is targeted at specific application scenarios and specific needs, enabling the target audience to better apply the teaching information to work scenarios or practical problems. This increases the flexibility of the final teaching information, meets the needs of the target audience, and achieves individualized instruction.

[0097] Example 5

[0098] Based on Embodiment 3, in one embodiment of the present invention, the fourth teaching information is also recorded during the target object's testing of the test questions, generating historical test records. After the target object tests the fourth teaching information and obtains the test results, it undergoes a retest for confirmation. When the target object retests, the fourth teaching information is updated based on the test results, so that the updated fourth teaching information is used for the target object to retest. When the fourth teaching information is updated based on the test results, the current test results are combined with the test results of the historical test records for accuracy analysis to obtain the comprehensive test accuracy of the test questions. Based on the comprehensive test accuracy of the test questions, the current test questions are filtered to obtain the first test question filtering results. Then, the number of test questions in the first test question filtering results 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 filtering results are obtained, and the updated test question types are determined. Then, based on the number of updated test questions and the updated test question types, test questions are retrieved again from the knowledge classification database to obtain updated test questions. Thus, the updated fourth teaching information is obtained based on the first test question filtering results and the updated test questions.

[0099] In the above technical solution, the number of times the target object tests 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 the update.

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

[0102] In the above technical solution, when determining the types of test questions to be updated, priority is given to knowledge points with lower levels of mastery, while knowledge points that have already been mastered are considered intermittently to reinforce the corresponding knowledge points.

[0103] In the above technical solution, when retrieving test questions from the knowledge classification database based on the number and type of updated test questions, test questions that have already been extracted will not be extracted again. This can avoid repeatedly extracting the same test question, which would affect the target audience's performance.

[0104] The above technical solution uses fourth-level teaching information to detect the learning effect of the target knowledge points. The test results reflect the areas where the target learners are lacking in their understanding of the target knowledge points. Through multiple tests, the target learners can consolidate their understanding of the target knowledge points. Furthermore, the test records not only reflect changes in the test results of the target learners, but also provide a reference when updating the fourth-level teaching information. This avoids repeatedly testing the target knowledge points that have been well mastered, thus improving the effectiveness of the test questions and making the updated test questions more reasonable and valuable.

[0105] Example 6

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

[0107] The fourth teaching information is used to determine the first reference weight for the test questions in conjunction with the capacity of the fourth teaching information.

[0108] The fourth teaching information categorizes test questions according to their difficulty level to determine the difficulty level distribution. From the difficulty level distribution, the number of test questions in the first difficulty level, the second difficulty level, the third difficulty level, and the fourth difficulty level are obtained. The number of test questions in the first difficulty level, the second difficulty level, the third difficulty level, and the fourth difficulty level are combined with the difficulty level to determine the second reference weight.

[0109] The parameter weights are determined by combining the first reference weight and the second reference weight.

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

[0111] The above technical solution determines the parameter weights by comprehensively analyzing the first reference weight and the second reference weight. This allows the parameter weights to not only reflect the proportion of test questions in the fourth teaching information, but also the impact of difficulty. As a result, the test results are more objective and better reflect the target audience's feedback on the teaching information.

[0112] Example 7

[0113] Based on Embodiment 1, in one embodiment of the present invention, when extracting keywords from knowledge points, a keyword extraction model is used to extract keywords from the knowledge points. 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 and decomposes them into multiple words to obtain knowledge point word segmentation information. The conversion unit quantifies the knowledge point word segmentation information and converts it into knowledge point text vectors. The extraction unit uses a neural network classification model to output a target number of information clusters from the knowledge point text vectors, analyzes the core information of each information cluster, determines the keywords of the information clusters, and integrates the keywords of each information cluster to obtain the keywords of the knowledge point.

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

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

[0116] The above technical solution extracts keywords from knowledge points using a keyword extraction model. This not only enables rapid keyword extraction from multiple knowledge points, but also improves the accuracy of keywords by leveraging the characteristics of the model. Furthermore, the keyword extraction model is less prone to errors during the extraction process and allows for setting limits on the target number of keywords as needed, thus enhancing the flexibility of the keyword extraction model.

[0117] Example 8

[0118] like Figure 4As shown, the present invention provides a micro-lesson 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 creation module 2 is used to obtain the needs of the target audience and determine the course content based on the needs of the target audience.

[0121] The knowledge point association module 3 is used to extract and score knowledge point keywords for the course content, and to form index items for the keywords with scores. Then, the index items are added to the knowledge classification database to establish the association between knowledge points in the course content and teaching materials.

[0122] The first teaching information generation module 4 is used to obtain the target knowledge points from the course content, and extract case explanation information that matches the target knowledge points from the knowledge classification database to obtain the first teaching information.

[0123] The second teaching information generation module 5 is used to obtain teaching courseware based on the target knowledge points and thus obtain the second teaching information;

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

[0125] The fourth teaching information generation module 7 is used to randomly select test questions of different difficulty levels corresponding to the target knowledge points from the knowledge classification database based on the target knowledge points to be explained, and obtain the fourth teaching information.

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

[0127] The above technical solution is a micro-lesson teaching system corresponding to a micro-lesson teaching method. It can configure teaching information according to the learning needs of different students, enabling the target audience to learn through teaching information as needed. It can also provide targeted teaching and training based on students' learning needs, providing convenience for students' learning, meeting the needs of students in different situations, improving students' learning efficiency and target audience satisfaction. Moreover, it eliminates the need for teachers to spend time and effort on individualized instruction for different students, not only efficiently obtaining teaching information but also effectively reducing cost consumption. The knowledge classification database, constructed through a database building module, contains all teaching materials, application scenario information, case study information, and test questions. This allows the first, second, third, and fourth teaching information generation modules to directly retrieve teaching information from the knowledge classification database when determining the teaching information based on the needs of the target audience. This not only facilitates the determination of teaching information but also effectively saves time. The course content creation module, by acquiring the needs of the target audience, ensures that the final teaching information meets the needs of different students, thereby achieving personalized education. This allows students to learn in a targeted manner based on their individual needs, improving their learning efficiency. Furthermore, the first, second, and third teaching information generation modules... The three teaching information generation modules—the first, second, third, and fourth—address the acquisition of the first, second, third, and fourth teaching information, respectively. This ensures that the final teaching information generated by the final teaching information generation module not only explains the knowledge points but also their applications, and allows for testing. This enables the target audience to learn more effectively and ensures learning outcomes. Furthermore, the third teaching information generation module, when acquiring the third teaching information, extracts case studies corresponding to the target knowledge points from the knowledge classification database. This allows the target audience to learn and understand the target knowledge points multiple times through case studies, deepening their understanding and effectively improving their comprehension 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 generated according to a micro-lesson teaching method, and the processor is used to execute the program stored in the memory to implement any of the micro-lesson teaching methods described in embodiments 1-7.

[0130] In the above technical solution, the memory, processor, and communication interface are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components 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 the program instructions / modules corresponding to the micro-lesson teaching system provided in the embodiments of this application. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing. The communication interface can be used to communicate with other node devices for signaling or data.

[0131] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), 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 storing a computer program, wherein the computer program is executed by a processor using any of the micro-lesson teaching methods described in embodiments 1-7.

[0134] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0135] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A microlecture teaching method, characterized by, The method comprises the following steps: constructing a knowledge classification database, including: determining an application scenario and target grasping knowledge points for a target object; performing knowledge point evaluation on the target grasping knowledge points to obtain knowledge point evaluation information; determining an evaluation level and knowledge point target receiving information of the target object according to the target grasping knowledge points and the knowledge point evaluation information; and performing knowledge classification construction on the knowledge points according to the evaluation level and the knowledge point target receiving information of the target object to obtain the knowledge classification database; obtaining a requirement of the target object and determining a course content according to the requirement of the target object; performing keyword extraction and scoring on the knowledge points of the course content, forming an index item with the score, and then adding the index item to the knowledge classification database to establish an association between the knowledge points and teaching courseware in the course content; obtaining a target explanation knowledge point from the course content and extracting case explanation information matching the target explanation knowledge point from the knowledge classification database according to the target explanation knowledge point to obtain first teaching information; obtaining a teaching courseware according to the target explanation knowledge point to obtain second teaching information; extracting case explanation information corresponding to the target explanation knowledge point from the knowledge classification database according to the target explanation knowledge point to obtain third teaching information; randomly extracting test questions of different difficulty levels corresponding to the target explanation knowledge point from the knowledge classification database according to the target explanation knowledge point to obtain fourth teaching information; 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 designated location; The micro-lesson teaching method also adjusts the teaching information, determines whether to adjust the teaching content according to the fourth teaching information to obtain an adjustment analysis and judgment result, and adjusts the teaching information according to the adjustment analysis and judgment result, wherein the determination of whether to adjust the teaching content according to the fourth teaching information comprises: obtaining test parameters of the target object for testing the fourth teaching information, and counting test time, test error rate and repeated test times to obtain a statistical result; determining a parameter weight and obtaining a test result by combining the parameter weight with the test parameters; comprehensively judging the test result and a preset passing threshold to obtain a grasping degree of the target object; According to the degree of mastery, it is determined whether to adjust the teaching courseware; when the adjustment analysis and judgment result is to adjust the teaching content, the preset knowledge point evaluation tool is used to analyze the test result of the target explanation knowledge point according to the test result of the fourth teaching information, the distribution of the target object's mastery of the target explanation knowledge point is determined, the knowledge point mastery analysis data is obtained, then the adjustment target and the adjustment type are determined according to the knowledge point mastery analysis data combined with the judgment threshold, the adjustment information is obtained in the knowledge classification database according to the adjustment target and the adjustment type, the adjustment increase information is obtained, and the adjustment increase information is supplemented to the teaching content for supplementary teaching; moreover, when the preset knowledge point evaluation tool is used to analyze the test result of the target explanation knowledge point according to the test result of the fourth teaching information, the degree of mastery of the target explanation knowledge point is evaluated according to the test result of the target object to the test questions in the fourth teaching information, so as to obtain the distribution of the target explanation knowledge point, and the target explanation knowledge point can also be listed, so that the target object can evaluate according to the knowledge point mastery, so as to determine the distribution of the target object's mastery of the target explanation knowledge point according to the evaluation result; Wherein, after establishing the association between the knowledge points in the course content and the teaching courseware, the current target explanation knowledge point is obtained during the learning process of the target object to the final teaching information according to the learning order and progress, the current target explanation knowledge point is indexed based on the index item, then the teaching courseware corresponding to the target explanation knowledge point is extracted from the knowledge classification database, and the corresponding teaching courseware is displayed to the target object before explaining the target explanation knowledge point; When the target object tests the test questions, test records are also recorded to generate historical test records, the target object is tested again after testing the test result of the fourth teaching information, when the target object is tested again, the fourth teaching information is updated according to the test result, 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 result, the correct rate of the test questions is analyzed by combining the test results of the historical test records, the comprehensive test correct rate of the test questions is obtained, the current test questions are screened according to the comprehensive test correct rate of the test questions, the first test question screening result is obtained, then the number of test questions in the first test question screening result is obtained, and the number of updated test questions is determined, the type of test questions in the first test question screening result is obtained, and the type of updated test questions is determined, then the test questions are obtained again in the knowledge classification database according to the number of updated test questions combined with the type of updated test questions, the updated test questions are obtained, and the updated fourth teaching information is obtained according to the first test question screening result and the updated test questions.

2. The microlecture teaching method of claim 1, wherein, According to the target explanation knowledge point, the case explanation information matched with the target explanation knowledge point is extracted from the knowledge classification database. According to the target explanation knowledge point, the case explanation information matching is carried out in the knowledge classification database combined with the difficulty level classification mark, the case explanation information corresponding to the target explanation knowledge point of different difficulty levels is obtained, the first case explanation extraction information is obtained, then the course additional demand characteristics are obtained from the course content, whether the application scene is limited and whether there is a directional demand are analyzed, when the application scene is not limited and there is no directional demand in the course additional demand characteristics, the first teaching information can be obtained by randomly selecting target number of cases according to the difficulty level of the first case explanation extraction information, when the application scene is limited and / or there is a directional demand in the course additional demand characteristics, when the target application scene and / or the target directional demand are obtained, the second case explanation extraction information is obtained by carrying out secondary screening on the first case explanation extraction information according to the target application scene and / or the target directional demand, then the number of cases of the second case explanation extraction information in each difficulty level is obtained, and whether the number of cases is greater than the target number is analyzed, 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 target number of cases according to the difficulty level of the second case explanation extraction information.

3. The microlecture teaching method of claim 1, wherein, When determining the parameter weight, it includes: In the fourth teaching information, the first reference weight is determined by combining the capacity of the fourth teaching information for the test questions; In the fourth teaching information, the difficulty level distribution is determined by distinguishing the test questions according to the difficulty level, 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 in the difficulty level distribution, 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 difficulty level to determine the second reference weight; The first reference weight and the second reference weight are analyzed and determined to determine the parameter weight.

4. The microlecture teaching method of claim 1, wherein, When extracting keywords in knowledge points, keywords are extracted from knowledge points by a keyword extraction model to obtain keywords of knowledge points. The keyword extraction model includes a preprocessing unit, a conversion unit and an extraction unit. When the keyword extraction model is used 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 quantization processing unit quantizes 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 a target number of information clusters from the knowledge point text vector, analyzes the core information of the information cluster, determines the keywords of the information cluster, and integrates the keywords of each information cluster to obtain the keywords of the knowledge point.

5. A microlecture teaching system characterized by comprising: The micro-lesson 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 for constructing a knowledge classification database, comprising: determining an application scenario and target mastering knowledge points for a target object; performing knowledge point evaluation on the target mastering knowledge points to obtain knowledge point evaluation information; determining an evaluation level and knowledge point target receiving information of the target object according to the target mastering knowledge points and the knowledge point evaluation information; and performing knowledge classification construction on the knowledge points according to the evaluation level and the knowledge point target receiving information of the target object to obtain the knowledge classification database; The course content production module (2) is used for obtaining the requirements of the target object and determining the course content according to the requirements of the target object; The knowledge point association module (3) is used for extracting and scoring knowledge point keywords for the course content, forming index items with scores, and then adding the index items to the knowledge classification database to establish the association between the knowledge points and the teaching courseware in the course content; The first teaching information generation module (4) is used for 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; The second teaching information generation module (5) is used for obtaining teaching courseware according to the target explanation knowledge points to obtain second teaching information; The third teaching information generation module (6) is used for extracting case explanation information corresponding to the target explanation knowledge points from the knowledge classification database according to the target explanation knowledge points to obtain third teaching information; The fourth teaching information generation module (7) is used for randomly extracting test questions of different difficulty levels corresponding to the target explanation knowledge points from the knowledge classification database according to the target explanation knowledge points to obtain fourth teaching information; The final teaching information generation module (8) is used for 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 designated location; The micro-lesson teaching system further comprises: adjusting the teaching information, determining whether to adjust the teaching content according to the fourth teaching information to obtain an adjustment analysis and judgment result, and adjusting 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 comprises: obtaining test parameters of the target object for testing the fourth teaching information, and counting test time, test error rate and repeated test times to obtain a statistical result; determining a parameter weight and obtaining a test result by combining the parameter weight with the test parameters; comprehensively judging the test result and a preset passing threshold to obtain the mastering degree of the target object; According to the degree of mastery, it is determined whether to adjust the teaching courseware; when the adjustment analysis and judgment result is to adjust the teaching content, the preset knowledge point evaluation tool is used to analyze the test result of the target explanation knowledge point according to the test result of the fourth teaching information, the distribution of the target object's mastery of the target explanation knowledge point is determined, the knowledge point mastery analysis data is obtained, then the adjustment target and adjustment type are determined according to the knowledge point mastery analysis data combined with the judgment threshold, and the adjustment information is obtained in the knowledge classification database according to the adjustment target and adjustment type, and the adjustment increase information is obtained, so that the adjustment increase information is supplemented to the teaching content for supplementary teaching; moreover, when the preset knowledge point evaluation tool is used to analyze the test result of the target explanation knowledge point according to the test result of the fourth teaching information, the degree of mastery of the target explanation knowledge point is evaluated according to the test result of the target object to the test questions in the fourth teaching information, so that the distribution of the target explanation knowledge point is obtained, and the target explanation knowledge point can also be listed, so that the target object can evaluate according to the knowledge point mastery, so as to determine the distribution of the target object's mastery of the target explanation knowledge point according to the evaluation result; Wherein, after establishing the association between the knowledge points in the course content and the teaching courseware, the current target explanation knowledge point is obtained during the learning process of the target object to the final teaching information according to the learning order and progress, the current target explanation knowledge point is indexed based on the index item, and then the teaching courseware corresponding to the target explanation knowledge point is extracted from the knowledge classification database, so that the corresponding teaching courseware is displayed to the target object before explaining the target explanation knowledge point; When the target object tests the test questions, test records are also recorded, and historical test records are generated; after the target object tests the test result of the fourth teaching information, the target object is tested again, when the target object tests again, the fourth teaching information is updated according to the test result, so that the target object tests again using the updated fourth teaching information, when the fourth teaching information is updated according to the test result, the correct rate of the test questions is analyzed by combining the test results of the historical test records, the comprehensive test correct rate of the test questions is obtained, the current test questions are screened according to the comprehensive test correct rate of the test questions, the first test question screening result is obtained, then the number of test questions in the first test question screening result is obtained, and the number of updated test questions is determined, the type of test questions in the first test question screening result is obtained, and the type of updated test questions is determined, then the test questions are obtained again in the knowledge classification database according to the number of updated test questions combined with the type of updated test questions, the updated test questions are obtained, and the updated fourth teaching information is obtained according to the first test question screening result and the updated test questions.

6. An electronic device, comprising: The electronic device comprises a memory and a processor, wherein the memory is used for storing a program, the program is a program generated according to the micro course teaching method, and the processor is used for executing the program stored in the memory to realize the micro course teaching method in any one of claims 1-4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the micro course teaching method in any one of claims 1-4.

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