Hybrid teaching effect evaluation method, system and storage medium

Abstract

The present application discloses a hybrid teaching effect evaluation method, system and storage medium, which relates to the field of indicator evaluation technology, including: determining the evaluation result of the teaching effect based on the teacher's teaching content and the student's teaching performance, wherein the types of teaching content include historical teaching content and current teaching content, and the types of teaching performance include historical teaching performance and current teaching performance. The evaluation result determined based on the historical teaching content and historical teaching performance is the first evaluation result, and the evaluation result determined based on the current teaching content and current teaching performance is the second evaluation result; comparing the first evaluation result and the second evaluation result to determine the improvement status of the teaching effect; and evaluating the teacher's teaching effect based on the improvement status. The present application conducts multiple teaching collections and evaluates the teaching content and learning ability separately, thereby making a comprehensive judgment on the teacher's teaching ability, making the evaluation of the teacher's teaching effect more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of indicator evaluation, and in particular to a hybrid teaching effect evaluation method, system and storage medium. Background Art

[0002] In current educational assessment techniques, the common practice is to measure the effectiveness of teachers' teaching by evaluating and statistically analyzing teachers' classroom performance and students' test scores. However, this assessment method has certain limitations, the main problem being its lack of controllability.

[0003] Because individual students have significant differences in learning abilities, even for the same course, taught by the same teacher using different lesson plans, different students will absorb and understand the information differently. This can lead to biased evaluation results, making it difficult to accurately reflect the teacher's teaching level and effectiveness. This single evaluation system makes it difficult to comprehensively and objectively assess the teaching effectiveness of teachers. To improve the accuracy and fairness of evaluations, it is necessary to optimize and adjust the existing evaluation system. Summary of the Invention

[0004] The main purpose of this application is to provide a hybrid teaching effectiveness evaluation method, system and storage medium, aiming to solve the technical problem of how to improve the accuracy of teacher teaching effectiveness evaluation.

[0005] To achieve the above objectives, this application proposes a hybrid teaching effect evaluation method, which includes:

[0006] Determine an evaluation result of the teaching effect based on the teacher's teaching content and the student's teaching performance, wherein the teaching content includes historical teaching content and current teaching content, the teaching performance includes historical teaching performance and current teaching performance, and the evaluation result includes a first evaluation result determined based on the historical teaching content and the historical teaching performance, and a second evaluation result determined based on the current teaching content and the current teaching performance;

[0007] Comparing the first evaluation result with the second evaluation result to determine the improvement of the teaching effect;

[0008] The teaching effectiveness of the teacher is evaluated based on the improvement status.

[0009] In one embodiment, the step of determining the evaluation result of the teaching effect based on the teacher's teaching content and the student's teaching performance includes:

[0010] Determine the corresponding teaching knowledge points according to the teacher's teaching content, and perform relevance evaluation on the teaching knowledge points to obtain content evaluation data;

[0011] Classifying the students' classes based on their teaching performance to obtain class grade data, and classifying each student in any grade class based on the historical teaching performance and the class grade data to obtain student grade data;

[0012] Counting the number of students in each grade in the student grade data to obtain grade data values ​​corresponding to the grades, and comparing the grade data values ​​with preset standard data values ​​as ability assessment data, wherein the degree of fit between the teaching content and the students is positively correlated with the ability assessment data;

[0013] An evaluation result of the teaching effect is calculated based on the content evaluation data and the ability evaluation data.

[0014] In one embodiment, the steps of determining corresponding teaching knowledge points according to the teacher's teaching content, and performing relevance evaluation on the teaching knowledge points to obtain content evaluation data include:

[0015] Identify the knowledge points of the teacher's teaching content to obtain the corresponding teaching knowledge point data, and identify the knowledge points of the preset teaching syllabus to obtain the corresponding knowledge point range;

[0016] Comparing the teaching knowledge point data with the knowledge point range, and canceling the step of determining the teaching effect evaluation result if the teaching knowledge point data is outside the knowledge point range;

[0017] In the case where the teaching knowledge point is within the knowledge point range, determining the attribute category of each knowledge point in the teaching knowledge point data based on the teaching syllabus, and for each knowledge point of any attribute category, determining the teaching time of each knowledge point based on the teaching content;

[0018] Evaluating the continuity of each knowledge point at each teaching moment to obtain first content evaluation sub-data, and evaluating the progressive difficulty of each knowledge point at each teaching moment to obtain second content evaluation sub-data;

[0019] Content evaluation data is calculated based on the first content evaluation sub-data and the second content evaluation sub-data.

[0020] In one embodiment, after the step of evaluating the continuity of each knowledge point according to each teaching moment, the method further includes:

[0021] In the case where the teaching moments corresponding to the knowledge points are not continuous, determining the correlation between the interspersed knowledge points that interrupt the continuity of the knowledge points and the knowledge points to obtain correlation data;

[0022] Adjusting the association data based on the progressive difficulty between the interspersed knowledge point and the subsequent knowledge point, wherein the subsequent knowledge point is a knowledge point that is adjacent to the interspersed knowledge point at the teaching time and is located after the interspersed knowledge point;

[0023] The first content evaluation sub-data is determined based on the adjusted relevance data.

[0024] In one embodiment, the step of evaluating the progressive difficulty of each knowledge point at each teaching moment to obtain the second content evaluation sub-data includes:

[0025] Determining the teaching duration and teaching sequence of each knowledge point based on each teaching moment, and performing a teaching difficulty assessment on each knowledge point based on each teaching duration to obtain first difficulty assessment data, and performing a teaching difficulty assessment on each knowledge point based on the teaching sequence to obtain second difficulty assessment data;

[0026] Comparing the collection of teaching hours and teaching sequences of each teacher for each knowledge point, determining a correlation between the teaching hours and teaching sequences of each knowledge point, and performing a teaching difficulty assessment on each knowledge point based on the correlation to obtain third difficulty assessment data;

[0027] The difficulty data of each knowledge point is calculated based on the first difficulty evaluation data, the second difficulty evaluation data and the third difficulty evaluation data, and the second content evaluation sub-data is determined according to the progressiveness of each difficulty data.

[0028] In one embodiment, the first evaluation result and the second evaluation result are both numerical values, and the step of comparing the first evaluation result and the second evaluation result to determine the improvement of the teaching effect includes:

[0029] determining a difference relationship between the first evaluation result and the second evaluation result;

[0030] If the difference relationship is that the second evaluation result is less than the first evaluation result, determining a first achievement degree of the first evaluation result relative to a preset standard teaching effect, and determining an improvement status of the teaching effect based on the first achievement degree and a preset standard fluctuation range;

[0031] When the difference relationship is that the second evaluation result is greater than or equal to the first evaluation result, the second achievement degree of the second evaluation result relative to the preset standard teaching effect is determined, and the improvement status of the teaching effect is determined based on the second achievement degree and the preset standard fluctuation range.

[0032] In one embodiment, the first evaluation result and the second evaluation result are both numerical values, and the step of evaluating the teacher's teaching effectiveness based on the improvement status includes:

[0033] When the improvement condition is that there is improvement, comparing the improvement condition with a preset standard teaching effect;

[0034] When the improvement condition is within a standard fluctuation range preset for the standard teaching effect, performing difference calculation between the first evaluation result and the second evaluation result and the numerical values ​​corresponding to the standard teaching effect, respectively, to obtain a first evaluation difference and a second evaluation difference;

[0035] The teaching effectiveness of the teacher is determined based on the ratio of the first evaluation difference to the second evaluation difference.

[0036] In one embodiment, after the step of evaluating the teacher's teaching effect based on the improvement status, the step further includes:

[0037] Comparing the ability assessment data in the second assessment result with the preset standard ability data to obtain corresponding ability matching data;

[0038] Comparing the content evaluation data in the second evaluation result with a preset standard teaching effect to obtain corresponding teaching data;

[0039] The ability matching data, the teaching data and the teaching effect are output in association.

[0040] In addition, to achieve the above objectives, the present application also proposes a hybrid teaching effect evaluation system, which includes:

[0041] an analysis module, configured to determine an evaluation result of the teaching effect based on the teacher's teaching content and the student's teaching performance, wherein the teaching content includes historical teaching content and current teaching content, the teaching performance includes historical teaching performance and current teaching performance, and the evaluation result includes a first evaluation result determined based on the historical teaching content and the historical teaching performance, and a second evaluation result determined based on the current teaching content and the current teaching performance;

[0042] A comparison module, configured to compare the first evaluation result with the second evaluation result to determine the improvement of the teaching effect;

[0043] An evaluation module is used to evaluate the teaching effect of the teacher based on the improvement status.

[0044] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the hybrid teaching effect evaluation method described above are implemented.

[0045] One or more technical solutions proposed in this application have at least the following technical effects:

[0046] This application first determines the evaluation results of teaching effectiveness based on the teacher's teaching content and the student's teaching performance, wherein the teaching content includes historical teaching content and current teaching content, the teaching performance includes historical teaching performance and current teaching performance, and the evaluation results include a first evaluation result determined based on the historical teaching content and the historical teaching performance, and a second evaluation result determined based on the current teaching content and the current teaching performance, so as to divide the evaluation of teaching effectiveness into the evaluation of teaching content and the evaluation of students' learning outcomes, so as to separate the teacher's teaching content and the students' learning ability, thereby making the evaluation of the teacher's teaching effectiveness more accurate, and using the previous teaching results as the basis for judging this teaching, thereby effectively reducing uncontrollable variables; then compare the first evaluation result and the second evaluation result to determine the improvement status of the teaching effect, so as to compare the results of the two evaluations with each other to determine whether the teacher's grasp of the students' learning situation has improved; finally, evaluate the teacher's teaching effectiveness based on the improvement status to obtain a more accurate teaching effectiveness evaluation result.

[0047] In summary, this application conducts multiple teaching collections, evaluates the teacher's teaching content and the student's learning ability separately, and then compares the results of the two evaluations. Based on the teacher's grasp of the student's learning situation, a comprehensive judgment is made on the teacher's teaching ability, making the evaluation of the teacher's teaching effect more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0050] Figure 1 A flowchart of the first embodiment of the hybrid teaching effect evaluation method provided in this application;

[0051] Figure 2A flow chart of the second embodiment of the hybrid teaching effect evaluation method provided in this application;

[0052] Figure 3 A flowchart of the third embodiment of the hybrid teaching effect evaluation method provided in this application;

[0053] Figure 4 A schematic diagram of a brief flow chart of the hybrid teaching effect evaluation method provided in Example 3 of the present application;

[0054] Figure 5 This is a schematic diagram of the module structure of the hybrid teaching effect evaluation system according to an embodiment of the present application; DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The main solution of the embodiment of the present application is: determine the evaluation results of teaching effectiveness based on the teacher's teaching content and the students' teaching performance, wherein the teaching content includes historical teaching content and current teaching content, the teaching performance includes historical teaching performance and current teaching performance, and the evaluation results include a first evaluation result determined based on the historical teaching content and the historical teaching performance, and a second evaluation result determined based on the current teaching content and the current teaching performance; compare the first evaluation result and the second evaluation result to determine the improvement status of the teaching effect; and evaluate the teaching effectiveness of the teacher based on the improvement status.

[0058] Because individual students have significant differences in learning abilities, even for the same course, taught by the same teacher using different lesson plans, different students will absorb and understand the information differently. This can lead to biased evaluation results, making it difficult to accurately reflect the teacher's teaching level and effectiveness. This single evaluation system makes it difficult to comprehensively and objectively assess the teaching effectiveness of teachers. To improve the accuracy and fairness of evaluations, it is necessary to optimize and adjust the existing evaluation system.

[0059] This application provides a solution by collecting teaching data multiple times, evaluating the teacher's teaching content and the students' learning ability separately, and then comparing the results of the two evaluations. In this way, a comprehensive judgment of the teacher's teaching ability can be made based on the teacher's understanding of the students' learning situation, making the evaluation of the teacher's teaching effect more accurate.

[0060] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions. The following uses electronic devices as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, the embodiment of the present application provides a hybrid teaching effect evaluation method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the hybrid teaching effect evaluation method of this application.

[0062] In this embodiment, the hybrid teaching effect evaluation method includes steps S10 to S30:

[0063] Step S10, determining a teaching effectiveness evaluation result based on the teacher's teaching content and the student's teaching performance, wherein the teaching content includes historical teaching content and current teaching content, the teaching performance includes historical teaching performance and current teaching performance, and the evaluation result includes a first evaluation result determined based on the historical teaching content and the historical teaching performance, and a second evaluation result determined based on the current teaching content and the current teaching performance;

[0064] It should be noted that teaching content refers to the knowledge system, teaching materials, course design and implementation process taught by teachers in the classroom, including specific teaching elements such as course objectives, knowledge point distribution, teaching methods, and teaching activities; historical teaching content refers to the course design, knowledge point arrangement, and teaching strategies used by teachers in the previous teaching cycle (such as the previous class); current teaching content refers to the teaching plan currently being implemented by teachers, including the latest curriculum adjustments, knowledge point updates, or teaching method improvements; teaching performance refers to the quantitative results obtained by students after course learning through tests, homework, classroom performance, etc., reflecting the students' mastery of the teaching content; historical teaching performance refers to students' performance data in the past teaching cycle (such as the test scores in the previous class); current teaching performance refers to students' real-time performance data in the current teaching cycle (such as the latest test results); evaluation results are quantitative or qualitative indicators derived from a comprehensive analysis of teaching content and teaching performance, used to measure teachers' teaching effectiveness; the first evaluation result is the evaluation result based on historical teaching content and historical teaching performance, reflecting the teacher's past teaching level; the second evaluation result is the evaluation result based on current teaching content and current teaching performance, reflecting the teacher's current teaching level.

[0065] It is understandable that due to the great limitations of traditional evaluation methods that rely only on single data (such as current teaching performance or current assessment results), step S10 is performed to build a multi-dimensional evaluation model by integrating historical and current teaching content and performance data. This can avoid misjudgment of teacher ability caused by isolated evaluation. For example, poor performance of a class may be caused by weak students' foundation rather than problems with the teacher's level, that is, it ignores the problems of individual differences among students and dynamic changes in teaching content, thereby providing teaching ability tracking in the time dimension, enhancing the longitudinal comparison ability of the evaluation, and providing an effective positioning basis for more accurately locating teaching problems (such as unreasonable knowledge point design leading to a decline in performance).

[0066] For example, the system first collects teaching materials such as the teacher's teaching plans, lesson plans, and courseware from two consecutive sessions, as well as data on the corresponding students' test scores and homework scores from two consecutive sessions. Next, by analyzing these historical and current teaching content and scores, and applying statistical analysis methods such as regression analysis or analysis of variance, it calculates the teaching effectiveness scores for the two phases, namely the first and second evaluation results. These evaluation results will be quantified based on the key knowledge points taught by the teacher and the students' mastery of the knowledge.

[0067] In a feasible implementation, step S10 may include steps S11 to S14:

[0068] Step S11, determining corresponding teaching knowledge points according to the teacher's teaching content, and performing relevance evaluation on the teaching knowledge points to obtain content evaluation data;

[0069] It should be noted that content evaluation data refers to the data obtained by evaluating the interrelationships between various knowledge points in the teacher's teaching content. This data reflects the logical relationship between knowledge points and the systematic nature of the teaching content.

[0070] It is understandable that since the teaching knowledge points set by teachers in the teaching process often have strong or weak correlations, this has a great impact on the scientific level of teaching and the degree of learning difficulty of students. Therefore, performing step S11 can avoid ignoring the problem that the teaching content may lack systematicity and logic, resulting in poor learning effects for students. By evaluating the correlation of knowledge points in the teaching content, the evaluation of the teaching content is included in the evaluation scope, so as to improve the comprehensiveness and accuracy of the evaluation of the teacher's teaching effect.

[0071] For example, by constructing a concept map or knowledge graph to visualize the connections between knowledge points, these connections are then quantitatively scored to form content evaluation data. For example, if knowledge point A is the basis of knowledge point B, then the correlation between A and B will be given a higher score. The higher the score, the more reasonable the teaching content design, and the more positive the content evaluation data.

[0072] In a feasible implementation, step S11 may include steps S111 to S115:

[0073] Step S111: performing knowledge point recognition on the teacher's teaching content to obtain corresponding teaching knowledge point data, and performing knowledge point recognition on the preset teaching syllabus to obtain the corresponding knowledge point range;

[0074] For example, using natural language processing technology and a knowledge base built on expert knowledge in the field of education, we conduct text analysis on teaching materials such as lesson plans, handouts, or courseware provided by teachers to identify and extract teaching knowledge points, forming a teaching knowledge point dataset. Simultaneously, we perform the same processing on the syllabus to identify and determine the scope of the knowledge points specified in the syllabus. This process may involve techniques such as keyword extraction and semantic analysis to ensure consistency between teaching knowledge points and the syllabus.

[0075] It should be noted that knowledge point recognition can be performed by a pre-trained large language model to improve recognition accuracy. The large language model is trained by a preset deep learning model. The training process of the deep learning model includes: obtaining a teaching text set, wherein the teaching text set is annotated with knowledge point information, and the teaching text set includes a training set and a verification set; inputting the training set into the deep learning model to train the deep learning model and obtain a candidate large language model; evaluating the candidate large language model according to the verification set to obtain a model evaluation result; if the model evaluation result does not meet the preset indicators, fine-tune the model parameters of the candidate large language model according to the model evaluation result, and return to the step of inputting the training set into the deep learning model to train the deep learning model based on the fine-tuned candidate large language model; if the model evaluation meets the preset indicators, use the candidate large language model as the large language model.

[0076] Step S112, comparing the teaching knowledge point data with the knowledge point range, and canceling the step of determining the teaching effect evaluation result when the teaching knowledge point data is outside the knowledge point range;

[0077] It is understandable that since teachers often include some additional teaching content during the teaching process, performing step S112 can avoid evaluating invalid teaching content that does not conform to the teaching syllabus, as well as the impact of invalid teaching content on normal evaluation, thereby improving the efficiency and accuracy of effective evaluation.

[0078] For example, the identified teaching knowledge point data is compared with the knowledge point range specified in the teaching syllabus. If it is found that the teacher's teaching content contains additional knowledge points outside the syllabus, or lacks necessary knowledge points required by the syllabus, the system will automatically cancel the subsequent teaching effectiveness evaluation steps, thereby avoiding the evaluation of teaching content that does not meet the syllabus requirements.

[0079] Step S113, when the teaching knowledge point is within the knowledge point range, determining the attribute category of each knowledge point in the teaching knowledge point data based on the teaching syllabus, and for each knowledge point of any attribute category, determining the teaching time of each knowledge point based on the teaching content;

[0080] It should be noted that the attribute category of each knowledge point refers to the category into which the knowledge point is divided in the teaching syllabus, such as basic knowledge, advanced concepts, etc.; the teaching time of each knowledge point refers to the specific time period arranged in the teaching process.

[0081] It is understandable that in order to classify knowledge points according to the syllabus and consider whether the teacher arranges reasonable teaching time according to the categories, step S113 is performed, which can avoid ignoring the problems of misplacement or omission of teachers in the teaching process, thereby providing an effective and comprehensive evaluation basis for subsequent evaluation.

[0082] For example, after confirming that the teaching knowledge point data meets the syllabus scope, each knowledge point is classified into attribute categories according to the teaching syllabus, such as basic knowledge, advanced knowledge, practical skills, etc. Then, the teaching time of each knowledge point is determined based on the arrangement record of the teaching content, that is, the specific teaching time point in the teaching plan.

[0083] Step S114, evaluating the continuity of each knowledge point at each teaching moment to obtain first content evaluation sub-data, and evaluating the progressive difficulty of each knowledge point at each teaching moment to obtain second content evaluation sub-data;

[0084] It should be noted that the first content evaluation sub-data refers to the evaluation results of the continuity and logical sequence of knowledge points in the teaching process; the second content evaluation sub-data refers to the evaluation results of the progressive difficulty of knowledge points in the teaching process.

[0085] It is understandable that since teachers often have problems with consistency and unreasonable arrangement of difficulty levels during the teaching process, performing step S114 can avoid ignoring the lack of logic in the teaching content and improper setting of the difficulty level of the teaching content during the evaluation process. It realizes that whether the teaching content is consistent and whether the progressive difficulty level is reasonably arranged is included in the evaluation scope, further improving the comprehensiveness and accuracy of the evaluation of the teacher's teaching effectiveness.

[0086] Exemplarily, the continuity of knowledge points of the same category is evaluated according to the teaching moments to find out whether the teaching sequence between knowledge points of the same category is reasonable, thereby obtaining the first content evaluation sub-data. For example, when the teaching moments corresponding to each knowledge point are continuous, that is, when the teaching sequence corresponding to each knowledge point of the same attribute category is not interrupted, the preset standard content evaluation value is used as the first content evaluation sub-data, and the standard content evaluation value is used to indicate that the evaluation result of the first content evaluation sub-data is an ideal result. At the same time, the progressive difficulty of the knowledge points in the teaching process will also be evaluated to find out whether the difficulty of the teaching content is gradually increasing, thereby obtaining the second content evaluation sub-data. These evaluations are usually carried out through data analysis methods.

[0087] Step S115 : Calculate content evaluation data based on the first content evaluation sub-data and the second content evaluation sub-data.

[0088] Exemplarily, the first content evaluation sub-data and the second content evaluation sub-data are combined, and comprehensive content evaluation data is calculated through weight distribution, which reflects the overall quality of the teaching content, including the continuity of knowledge points and the progressive difficulty level.

[0089] In this implementation, by identifying knowledge points in the teacher's teaching content and comparing them with the teaching syllabus, the compliance of the teaching content is ensured; by determining the attribute categories and teaching moments of the knowledge points, the systematic and logical consideration of the teaching content is guaranteed; by evaluating the continuity and progressive difficulty of the knowledge points, content evaluation sub-data are obtained; finally, the content evaluation data calculated based on these sub-data avoids problems such as the inability to identify deviations of teaching content from the teaching syllabus, incomplete coverage of knowledge points, unreasonable teaching sequence, and inappropriate difficulty of teaching content, and achieves a comprehensive and objective evaluation of the quality and suitability of the teaching content, thereby improving the accuracy and comprehensiveness of the teaching effect evaluation.

[0090] Step S12: classifying the students' classes based on their teaching performance to obtain class grade data, and classifying each student in any grade class based on the historical teaching performance and the class grade data to obtain student grade data;

[0091] It should be noted that class grade data refers to data that divides classes into different grades based on students' teaching performance, which is used to indicate the overall learning level of the class; student grade data refers to data that divides students into different grades within each class based on their teaching performance, which is used to indicate the students' individual learning level.

[0092] It is understandable that when the same teacher teaches a better class and a worse class, the teaching method of the better class cannot be directly applied to the worse class. Similarly, when teaching students in the same class, the content that the same teacher cannot explain can only be understood by the better students in the class, but not by the worse students. Therefore, step S12 is performed to grade the classes and students accordingly. This can avoid ignoring whether the teacher ignores the differences between classes, resulting in a one-size-fits-all teaching strategy, which is not conducive to teaching students in accordance with their aptitude, and whether the teacher cannot identify individual differences among students, which is not conducive to providing personalized learning support for students. Therefore, an evaluation basis is constructed based on the complex environmental distribution factors in the real environment to improve the comprehensiveness and accuracy of the evaluation of the teacher's teaching effectiveness.

[0093] For example, students' academic performance, such as test scores and homework completion, is first collected. Based on these performances, statistical methods such as mean scores or medians are used to categorize classes into different levels, such as advanced classes, regular classes, and remedial classes, thereby obtaining class grade data. Next, within each class, students are further categorized into different levels, such as advanced students, regular students, and remedial students, based on their academic performance, thereby obtaining student grade data. The role of class grade data in this categorization process is to avoid uniformly categorizing students by specific numerical scores. For example, a student with a score of 80 may be considered a poor student in an advanced class, but an advanced student in a regular class.

[0094] Step S13: Counting the number of students at each level in the student grade data to obtain a grade data value corresponding to the level, and comparing the grade data value with a preset standard data value as ability assessment data, wherein the degree of fit between the teaching content and the students is positively correlated with the ability assessment data;

[0095] It should be noted that ability assessment data refers to the assessment results obtained by comparing the number of students of each level in the class with the preset standard data values, which is used to measure the overall learning ability of the class.

[0096] It can be understood that in order to evaluate the overall learning ability of the class and compare it with the expected standards, step S13 is performed, which can avoid the problem of lack of accurate quantitative evaluation of the overall learning ability of the class, thereby accurately quantifying the learning ability of the class, and providing effective key indicators for accurately and comprehensively evaluating the teaching effect.

[0097] For example, student grade data within each class is collected, and the number of students in each grade (e.g., top students, average students, and remedial students) is calculated to generate grade data values. These grade data values ​​are then compared with pre-set standard data values. Finally, the comparison results are used as ability assessment data, reflecting the degree of fit between the teaching content and students' learning abilities. For example, if 90% of students score 80 points and 10% score full marks, this indicates that the teaching content meets the requirements for extended content but falls short in advanced and basic content. This results in students with average learning abilities not fully learning, resulting in a medium ability assessment data value. If 100% of students score 80 points and no one scores full marks, this indicates that the extended content is too difficult and the distinction between advanced and basic content is insufficient. This results in students with average and excellent learning abilities not fully learning, also resulting in a medium ability assessment data value. Similarly, this can be used to infer the teacher's understanding of the students' learning status.

[0098] Step S14: Calculate the teaching effectiveness evaluation result based on the content evaluation data and the ability evaluation data.

[0099] It is understandable that in order to comprehensively consider the quality of teaching content and students' learning ability and obtain a comprehensive teaching effectiveness evaluation, step S14 is performed to avoid the problem that a single-dimensional evaluation cannot fully reflect the teaching effect, resulting in deviation in the evaluation results. Through comprehensive evaluation, a more accurate and comprehensive teaching effectiveness evaluation can be obtained.

[0100] For example, the content assessment data and ability assessment data obtained are comprehensively analyzed. For example, a weighted average or other complex algorithm can be used to combine the data from these two dimensions to produce a comprehensive teaching effectiveness evaluation score or grade. This evaluation result not only considers the rationality and systematicness of the teaching content, but also the degree to which the teaching content matches the students' actual learning abilities, thereby providing a comprehensive evaluation of teaching effectiveness.

[0101] This implementation ensures the effectiveness and consistency of teaching content by evaluating the relevance of teaching knowledge points and generating content evaluation data. By grading students' classes and individual students, class and student grade data are generated, more accurately reflecting students' actual learning levels. By compiling student grade data and comparing it with preset standard values, ability assessment data is generated, reflecting the fit between the teaching content and students' learning abilities. Ultimately, the teaching effectiveness evaluation results calculated from the combination of content and ability assessment data achieve a comprehensive, objective, and systematic evaluation of teachers' teaching effectiveness, providing a scientific basis for teaching improvement.

[0102] Step S20, comparing the first evaluation result and the second evaluation result to determine the improvement of the teaching effect;

[0103] It is understandable that in order to quantify the improvement or decline trend of teachers' teaching effectiveness rather than just focusing on absolute values, step S20 is performed. By analyzing the differences between historical and current evaluation results, it is possible to avoid ignoring the problem of dynamic changes in teachers' teaching abilities, achieve a relative evaluation based on the extent of teachers' own progress, and reduce external interference to accurately identify whether teachers have the ability to make continuous improvements.

[0104] For example, the first and second evaluation results are compared and analyzed. This can be done by creating a comparison table or using statistical software to calculate the difference between the two evaluation results. For example, if the second evaluation result is higher than the first by a certain percentage, it can be considered that the teaching effect has improved; conversely, if the second evaluation result is lower than the first, it indicates that the teaching effect may have declined. Through this comparison, the degree of improvement in teaching effect can be quantified.

[0105] In a feasible implementation, step S20 may include steps S21 to S23:

[0106] Step S21, determining the difference relationship between the first evaluation result and the second evaluation result;

[0107] It is understandable that in order to compare the teaching effect evaluation results at two different time points to determine the changing trend of the teaching effect, step S21 is performed, which can avoid the problem of not being able to accurately judge whether the teaching effect has improved or declined, thereby realizing the quantification of the changes in teaching effect.

[0108] Exemplarily, the numerical values ​​of the first evaluation result and the second evaluation result are first obtained, which may be a set of scores, percentages or other quantitative indicators, and then the difference relationship is determined by calculating the difference between the two numerical values, that is, subtracting the first evaluation result from the second evaluation result.

[0109] Step S22: if the difference relationship is that the second evaluation result is less than the first evaluation result, determining a first achievement degree of the first evaluation result relative to a preset standard teaching effect, and determining an improvement status of the teaching effect based on the first achievement degree and a preset standard fluctuation range;

[0110] It should be noted that the first achievement degree refers to the degree of achievement between the first evaluation result and the preset standard teaching effect. This value is a ratio or percentage, which indicates the degree of closeness between the actual teaching effect and the standard effect; the standard fluctuation range refers to the allowable fluctuation range of the evaluation results in the teaching effect evaluation, which is used to judge the stability of the teaching effect.

[0111] It is understandable that in order to evaluate whether the teaching effect has been improved, step S22 is performed, which can avoid the problem of being unable to reasonably judge the improvement of the teaching effect, thereby achieving the effect of identifying and confirming the degree of improvement of the teaching effect.

[0112] For example, if the second evaluation result is found to be lower than the first, the first evaluation result is first compared with a preset standard teaching effect to calculate a first achievement degree. This is typically calculated by dividing the first evaluation result by the standard teaching effect to obtain a ratio. Then, based on a preset standard fluctuation range (i.e., an allowable fluctuation range for the evaluation results), the first achievement degree is determined to indicate a significant improvement in teaching effect. For example, if the standard teaching effect is 10 and the standard fluctuation range is 1, if the first evaluation result has a value of 5 and the second evaluation result has a value of 4, this indicates that the teacher's understanding of the students has decreased. Since the larger value is 5 in the first evaluation data, and 5 is less than 9, it can be directly determined that the second teaching result has not improved. On the other hand, if the first evaluation result has a value of 9 and the second evaluation result has a value of 8, since 9>8, it indicates that the teacher's understanding of the students' learning has decreased. However, since the first teaching result of 9 falls within the standard fluctuation range, it indicates that the teacher's understanding of the students' learning situation is standard. Therefore, the decrease in the second evaluation result is determined to be due to normal teaching fluctuations, and the difference between 9 and 8 is considered as the teaching improvement.

[0113] Step S23, when the difference relationship is that the second evaluation result is greater than or equal to the first evaluation result, determine the second achievement degree of the second evaluation result relative to the preset standard teaching effect, and determine the improvement status of the teaching effect based on the second achievement degree and the preset standard fluctuation range.

[0114] It should be noted that the second achievement degree refers to the degree of achievement between the second evaluation result and the preset standard teaching effect. This value is a ratio or percentage, indicating the degree of closeness between the actual teaching effect and the standard effect.

[0115] It is understandable that in order to evaluate whether the teaching effect remains stable or declines, step S23 is performed, which can avoid the problem of being unable to effectively identify whether the teaching effect is stable or declining, thereby achieving the effect of accurately judging the changing trend of the teaching effect.

[0116] For example, when the second assessment result is not lower than the first assessment result, the second assessment result is compared with the standard teaching effect to calculate a second achievement degree. This step involves converting the second assessment result into a ratio relative to the standard teaching effect. The second achievement degree is then determined based on the standard fluctuation range to determine whether the teaching effect remains stable or has declined. If the second achievement degree is within the standard fluctuation range, the teaching effect is considered stable; if it is outside the range, it may indicate a decline in teaching effect. For example, if the value corresponding to the first assessment is 8 and the value corresponding to the second assessment is 9, then since 8 is less than 9, it indicates that the teacher's understanding of the student's learning situation has improved. Since the value corresponding to the second assessment is 9 within the standard fluctuation range, it indicates that the teacher's understanding of the student's learning situation is at the standard level. Therefore, the value 9 corresponding to the second assessment is directly used as the improvement in teaching effect. On the other hand, if the value corresponding to the first assessment is 5 and the value corresponding to the second assessment is 6, since the value corresponding to the second assessment is 6 outside the standard fluctuation range, it indicates that the teacher's understanding of the student's learning situation is below the standard level. Therefore, the improvement in teaching effect is determined to be 9-8=1.

[0117] In this embodiment, the second evaluation data is compared with the first evaluation data to determine the magnitude relationship between the second and first evaluation data. If the second evaluation data is less than the first evaluation data, it indicates that the teacher's understanding of the student's learning status has decreased. Therefore, the first evaluation data with a higher level of understanding is compared with the built-in standard teaching effect to determine the cause of the decreased understanding. When the first achievement degree is within the standard fluctuation range, it indicates that the teacher's understanding of the student's learning status has reached the standard level during the first teaching session. Therefore, the second evaluation data of the second teaching session is used as the fluctuation of the teaching process. Conversely, when the first achievement degree exceeds the standard fluctuation range, it indicates that the teacher's understanding of the student's learning status is low. Since the second evaluation data is less than the first evaluation data, it is determined that the teacher's second teaching session has not made any progress based on the first teaching session. Similarly, when the second evaluation data is greater than the first evaluation data, it indicates that the teaching effect of the second teaching session is better than that of the first teaching session. Therefore, by comparing the second evaluation data with the standard teaching effect, it is clear whether the second teaching session has reached the standard level and making a corresponding judgment based on the achievement level, thereby ensuring the effectiveness of the judgment on the improvement of teaching effect.

[0118] Step S30: Evaluate the teacher's teaching effectiveness based on the improvement status.

[0119] For example, based on the determined teaching effectiveness improvement, a comprehensive assessment of the teacher's teaching effectiveness can be conducted by combining multi-dimensional data such as the teacher's teaching behavior, student feedback, and peer evaluation. For example, if the improvement status shows a significant improvement in teaching effectiveness, the teacher can be given a positive evaluation, and the specific aspects of their teaching improvement can be pointed out in the teacher evaluation report. If the improvement is not obvious or has declined, further analysis of the reasons is required, and targeted improvement suggestions are made accordingly to help the teacher improve the quality of teaching.

[0120] In a feasible implementation, step S30 may include steps A31 to A33:

[0121] Step A31, when the improvement condition is improvement, comparing the improvement condition with a preset standard teaching effect;

[0122] It is understandable that in order to quantify the degree of improvement in teaching effectiveness and to determine whether such improvement reaches or exceeds the preset standard, step A31 is performed. This can avoid the problem of being unable to reasonably determine whether the teaching effect has actually been improved. By comparing the improvement status with the preset standard, the degree of improvement in teaching effectiveness can be accurately measured.

[0123] Step A32: When the improvement status is within a standard fluctuation range preset for the standard teaching effect, performing difference calculation between the first evaluation result and the second evaluation result and the numerical values ​​corresponding to the standard teaching effect, respectively, to obtain a first evaluation difference and a second evaluation difference;

[0124] It should be noted that the first evaluation difference refers to the difference between the numerical value corresponding to the first evaluation result and the standard teaching effect, and the second evaluation difference refers to the difference between the numerical value corresponding to the second evaluation result and the standard teaching effect.

[0125] It is understandable that in order to analyze the changes in teaching effects more carefully, even if the improvement status is within the standard fluctuation range, it is necessary to understand the specific numerical differences. Therefore, performing step A32 can avoid the problem of ignoring small but potentially important changes in teaching effects, thereby achieving an in-depth understanding of the changing trends in teaching effects and providing more accurate data support for teaching improvements.

[0126] Step A33: Determine the teacher's teaching effectiveness based on the ratio of the first evaluation difference to the second evaluation difference.

[0127] It should be noted that the ratio of the first evaluation difference to the second evaluation difference reflects the relative change in the teacher's teaching effectiveness between the two evaluations and is an important indicator for measuring the improvement in the teacher's teaching effectiveness.

[0128] It can be understood that since the stability or degree of improvement of the teacher's teaching effect can be evaluated by comparing the ratio of the two evaluation differences, performing step A33 can avoid judging the teacher's teaching effect based on only a single evaluation result, which may lead to subjective and random problems in the evaluation, thereby objectively and systematically evaluating the teacher's teaching effect.

[0129] Exemplarily, after calculating the ratio of the first evaluation difference and the second evaluation difference, the teacher's teaching effectiveness is judged according to the preset evaluation criteria or ratio range. For example, if the ratio is 1 or close to 1, it may indicate that the teacher's teaching effectiveness is stable during the two evaluations; if the ratio is greater than 1, it may indicate that the teacher's teaching effectiveness has improved; if the ratio is less than 1, it may indicate that the teaching effectiveness has declined. The teaching effectiveness evaluation process can be implemented by an algorithm that corresponds the ratio to different teaching effectiveness levels and outputs the final evaluation result. For example, the calculation formula for the teaching effectiveness data is y=a+kx, where y is the authorized effect data, a is the data value corresponding to the second evaluation result, k is the weighting coefficient, and x is the ratio of the first evaluation difference to the second evaluation difference.

[0130] This implementation, through data comparison and difference analysis, avoids the subjectivity and lack of quantitative indicators in traditional evaluations, achieving the technical effect of accurately and objectively evaluating teachers' teaching effectiveness. Specifically, by comparing actual improvement with pre-set standard teaching results and calculating the difference in evaluation results within the standard fluctuation range, this method avoids subjective bias in the evaluation and the randomness of a single evaluation result, thereby ensuring the reliability and fairness of the evaluation results and effectively monitoring and evaluating teaching quality.

[0131] In another feasible implementation, step S30 may further include step B31:

[0132] Step B31: When the improvement status is no improvement, the second evaluation result is used as the teaching effect of the teacher.

[0133] It is understandable that, since when a teacher's teaching fails to bring about a significant improvement, a direct evaluation result based on the most recent data is needed to reflect the teacher's teaching effectiveness, so performing step B31 can avoid the problem of still trying to determine the teacher's teaching effectiveness through complex comparisons and ratio calculations when the teacher's teaching effectiveness lacks improvement, thereby reducing unnecessary calculations and possible misunderstandings, simplifying the evaluation process, and quickly providing feedback on the teacher's teaching effectiveness, so that education administrators can take corresponding measures based on the latest evaluation results.

[0134] This embodiment provides a hybrid teaching effectiveness evaluation method, which collects teaching data multiple times, evaluates the teacher's teaching content and the student's learning ability separately, and then compares the results of the two evaluations. In this way, a comprehensive judgment is made on the teacher's teaching ability based on the teacher's understanding of the student's learning situation, making the evaluation of the teacher's teaching effectiveness more accurate.

[0135] In a feasible implementation, after the step of evaluating the continuity of each knowledge point according to each teaching moment in step S114, steps S401 to S403 may be further included:

[0136] Step S401, when the teaching moments corresponding to the knowledge points are not continuous, determining the correlation between the interspersed knowledge points that interrupt the continuity of the knowledge points and the knowledge points, and obtaining correlation data;

[0137] It should be noted that interspersed knowledge points refer to knowledge points of different attribute categories that exist between knowledge points of the same attribute category. The teaching of this knowledge point may help to master subsequent knowledge points; correlation data refers to quantitative information that describes the logical relationship between interspersed knowledge points and other knowledge points.

[0138] It is understandable that, since some knowledge points may not have type consistency due to teaching arrangements or content characteristics during the teaching process, it is necessary to specifically identify the logical relationship between these interspersed knowledge points and the remaining knowledge points. Therefore, performing step S401 can avoid the problem of inaccurate teaching content evaluation caused by lack of category consistency between knowledge points, thereby ensuring the comprehensiveness and accuracy of teaching content evaluation and better understanding the intrinsic connection between knowledge points.

[0139] For example, when the teaching moments corresponding to each knowledge point are not continuous, that is, when the teaching sequence corresponding to each knowledge point of the same attribute category is interrupted, the knowledge points of different attribute categories that exist between the knowledge points of the same attribute category are identified, that is, interspersed knowledge points. For example, the order of the knowledge points of this teaching content is knowledge point 1, knowledge point 2, knowledge point 3, knowledge point 4, knowledge point 5, which means that the knowledge points corresponding to category A: knowledge point 1, knowledge point 3, are not continuous, so the correlation judgment is made on the interspersed knowledge point: knowledge point 2. By constructing a knowledge graph or using a correlation analysis algorithm, the correlation between these interspersed knowledge points and other knowledge points is quantified to generate correlation data. This data may include the dependency relationship, logical order or concept similarity between knowledge points.

[0140] Step S402: adjusting the association data based on the progressive difficulty level between the interspersed knowledge point and subsequent knowledge points, wherein the subsequent knowledge point is a knowledge point that is adjacent to the interspersed knowledge point at the teaching time and is located after the interspersed knowledge point.

[0141] It should be noted that the progressive difficulty level refers to the gradual increase or decrease in the difficulty of learning knowledge points; subsequent knowledge points refer to the knowledge points that follow the interspersed knowledge points in the teaching sequence and are adjacent to them in time.

[0142] It is understandable that since the progressive difficulty between knowledge points will affect students' learning effects and understanding, the correlation data needs to be adjusted according to this progressiveness. Therefore, performing step S402 can avoid the problem of bias in evaluation results caused by not considering the progressive difficulty of knowledge points, thereby more accurately reflecting the logical relationship and learning difficulty between knowledge points, so as to optimize the teaching content and sequence.

[0143] For example, it is first necessary to determine the adjacent knowledge points after each interspersed knowledge point, that is, the subsequent knowledge points. Then, based on the preset difficulty coefficient of each knowledge point, determine the difficulty progression between the interspersed knowledge point and the subsequent knowledge point. Then, based on this progression, adjust the previously obtained association data. For example, if there is a correlation between the interspersed knowledge point 2 and category A, and the difficulty coefficient is low, that is, the difficulty progression is increasing, then it indicates that the knowledge point 2 plays an auxiliary role in understanding the knowledge point 3, and the corresponding association data is increased; conversely, if the difficulty coefficient is greater than or equal to the difficulty coefficient of knowledge point 3, that is, the difficulty progression is decreasing, then the corresponding association data is reduced. This adjustment can be achieved through an algorithm that modifies the weight of the association data according to a preset rule or model.

[0144] Step S403: determining the first content evaluation sub-data based on the adjusted relevance data.

[0145] It is understandable that since the adjusted correlation data is needed to evaluate the effectiveness and suitability of the teaching content, performing step S403 can avoid ignoring the importance of the correlation between knowledge points and the rationality of the setting of interspersed knowledge points in content evaluation, thereby providing a more accurate and targeted teaching content evaluation.

[0146] For example, the adjusted relevance data is used as input, and the evaluation model is used to calculate the first content evaluation sub-data, thereby providing a quantitative basis for the overall teaching content evaluation. Specifically, the evaluation model can combine the relevance data with indicators such as teaching objectives and student learning outcomes, and through a series of algorithmic processing, output the first content evaluation sub-data. This data may include a comprehensive consideration of the importance of knowledge points, the expected value of teaching effects, and students' perception of learning difficulty.

[0147] This implementation, through knowledge point relevance analysis and relevant data adjustment based on the progressive difficulty level, avoids the problems of inaccurate teaching content evaluation and neglect of logical relationships between knowledge points caused by discontinuous knowledge point teaching. It accurately identifies and evaluates the relevance between knowledge points in the teaching content, and optimizes the teaching sequence and content design. Specifically, this method determines the relevance between interspersed knowledge points and other knowledge points, and adjusts the relevance data based on the progressive difficulty level between knowledge points. This ensures the comprehensiveness and effectiveness of teaching content evaluation, thereby helping to better understand and optimize the teaching structure and improve teaching quality.

[0148] In a feasible implementation, the step of evaluating the progressive difficulty of each knowledge point according to each teaching moment to obtain the second content evaluation sub-data in step S114 may include steps S410 to S430:

[0149] Step S410, determining the teaching duration and teaching sequence of each knowledge point based on each teaching moment, and performing a teaching difficulty assessment on each knowledge point based on each teaching duration to obtain first difficulty assessment data, and performing a teaching difficulty assessment on each knowledge point based on the teaching sequence to obtain second difficulty assessment data;

[0150] It should be noted that the first difficulty assessment data refers to the assessment result of the difficulty of the knowledge point based on the teaching time, and the second difficulty assessment data refers to the assessment result of the difficulty of the knowledge point based on the teaching sequence.

[0151] It is understandable that in order to quantify the difficulty of each knowledge point in the teaching process and consider the impact of teaching time and sequence on the difficulty, step S410 is performed to avoid the problem of being unable to accurately assess the objective difficulty of the knowledge points, thereby more accurately assessing the teaching difficulty of each knowledge point and providing effective and reliable data support for subsequent evaluations.

[0152] For example, data on the start and end of each knowledge point's teaching time are first collected to calculate the duration of each knowledge point's teaching. The teaching order of each knowledge point is then determined based on the teaching content. A statistical model is then used to analyze the relationship between teaching duration and knowledge point difficulty, generating first difficulty assessment data. The longer the teaching time, the higher the difficulty of the corresponding knowledge point. Simultaneously, by analyzing the correlation between the teaching order and the difficulty of the knowledge point, second difficulty assessment data is generated. The later the teaching order, the higher the difficulty of the corresponding knowledge point.

[0153] Step S420: Compare the collection of teaching hours and teaching sequences of each teacher for each knowledge point to determine the correlation between the teaching hours and teaching sequences of each knowledge point, and perform a teaching difficulty assessment on each knowledge point based on the correlation to obtain third difficulty assessment data;

[0154] It should be noted that the correlation between the teaching time and teaching sequence of each knowledge point refers to the consistency or difference in the length and sequence arrangement of knowledge points in actual teaching by different teachers; the third difficulty assessment data refers to the assessment results of the difficulty of the knowledge points based on this correlation.

[0155] It is understandable that in order to analyze the differences in teaching time and sequence of knowledge points among different teachers, as well as the impact of these differences on teaching difficulty, step S420 is performed. This can avoid ignoring the problem of the impact of teaching differences between teachers on difficulty assessment. By comparing teaching methods, the impact of teaching time and sequence on teaching difficulty is revealed, thereby promoting teaching standardization.

[0156] For example, based on the big data model, the teaching time and teaching sequence of each teacher on each knowledge point in the same category are collected and counted, and the collection of teaching time and teaching sequence of different teachers for each knowledge point is obtained. The teaching time set and the teaching sequence set are compared with each other to determine the correlation between the teaching time and teaching sequence of each knowledge point of the same attribute category. For example, when each teacher teaches the knowledge points of this category, the teaching time of a certain knowledge point will be longer, and the teaching sequence will be earlier. Then, the teaching situation of this knowledge point is judged to meet the teaching needs, so the difficulty of this knowledge point is processed so that the difficulty is between two adjacent knowledge points.

[0157] Step S430: Calculate the difficulty data of each knowledge point based on the first difficulty assessment data, the second difficulty assessment data, and the third difficulty assessment data, and determine the second content assessment sub-data according to the progressiveness of each difficulty data.

[0158] It is understandable that in order to integrate multiple data sources to comprehensively evaluate the difficulty of knowledge points, step S430 is performed. By providing a multi-dimensional teaching difficulty assessment model, the problem of incomplete assessment caused by a single assessment dimension can be avoided, and a comprehensive evaluation of the difficulty of knowledge points can be achieved, providing a more scientific basis for the adjustment and optimization of teaching content.

[0159] For example, the first, second, and third difficulty assessment data are input into a comprehensive assessment model. This model combines these three sets of data and calculates the comprehensive difficulty data for each knowledge point through weighted averaging or other synthesis methods. The model then determines the second content assessment sub-data based on the progressive nature of these difficulty data, i.e., the trend of increasing difficulty of the knowledge points. This process can be implemented using an algorithm that outputs a difficulty score for each knowledge point based on preset weights and standards.

[0160] Exemplarily, after step S420, the teaching difficulty of each knowledge point can also be evaluated by a pre-trained difficulty evaluation model to obtain fourth difficulty evaluation data, so as to calculate the difficulty data of each knowledge point based on the first difficulty evaluation data, the second difficulty evaluation data, the third difficulty evaluation data and the fourth difficulty evaluation data, and determine the second content evaluation sub-data according to the progressiveness of each difficulty data. Wherein, the pre-trained difficulty evaluation model is obtained by training a preset deep learning model, and the training process of the deep learning model includes: obtaining a teaching assessment text set, wherein any teaching assessment text in the teaching assessment text set includes knowledge point annotations and correctness judgment results corresponding to the knowledge point annotations; inputting the teaching assessment text set into the deep learning model, so as to learn the distribution of wrong questions of each knowledge point based on the knowledge point annotations and the corresponding correctness judgment results in the teaching assessment text set by the deep learning model, and obtain the pre-trained difficulty evaluation model. Therefore, the process of the pre-training difficulty assessment model performing teaching difficulty assessment on each of the knowledge points is that the pre-training difficulty assessment model performs teaching difficulty assessment on each of the knowledge points based on the learned distribution of wrong questions. For example, if a certain knowledge point accounts for a higher proportion in the learned distribution of wrong questions, then the teaching difficulty assessment result of the knowledge point corresponds to a more difficult knowledge point; if a certain knowledge point accounts for a smaller proportion or does not exist in the learned distribution of wrong questions, then the teaching difficulty assessment result of the knowledge point corresponds to a simpler knowledge point.

[0161] In this implementation, by determining the teaching duration and sequence, and conducting difficulty assessments separately, and then combining the teaching differences between teachers, multi-dimensional difficulty assessment data is obtained. This avoids the problem of traditional teaching that judges the difficulty of knowledge points based solely on subjective experience and ignores the impact of teaching duration and sequence on difficulty. It achieves a quantitative, objective and comprehensive assessment of the difficulty of knowledge points, thereby ensuring the accuracy and comprehensiveness of the assessment results.

[0162] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 After step S30, the hybrid teaching effect evaluation method further includes steps S40 to S60:

[0163] Step S40, comparing the capability assessment data in the second assessment result with preset standard capability data to obtain corresponding capability matching data;

[0164] It should be noted that standard ability data refers to the degree of match between the teacher's teaching content and the student's learning ability, which is pre-set based on education standards or curriculum requirements; ability fit data refers to the degree of match between the teaching content and the student's learning ability.

[0165] It can be understood that in order to quantify the degree of match between the teacher's relevant ability level and the preset standard, step S40 is performed, which can avoid the problem of being unable to objectively identify the degree of match between the teacher's teaching content and the student's learning ability, and achieve an objective evaluation of the teacher's ability.

[0166] For example, the ability assessment data in the second assessment result is that the number of better students accounts for 20%, the number of average students accounts for 60%, and the number of poor students accounts for 20%. The built-in standard ability data is that the number of better students accounts for 20%, the number of average students accounts for 60%, and the number of poor students accounts for 20%. By comparing the ability assessment data with the built-in standard data value, since the ability assessment data and the standard data value are the same, it is determined that the teaching content is consistent with the students' learning ability, and the corresponding ability fit data is 1. Otherwise, the ratio of the corresponding items is calculated to determine the corresponding ability fit data.

[0167] Step S50, comparing the content evaluation data in the second evaluation result with a preset standard teaching effect to obtain corresponding teaching data;

[0168] It should be noted that teaching data refers to the comparison results between teachers’ actual teaching effects and preset standard teaching effects.

[0169] It is understandable that in order to evaluate the gap between the teacher's teaching content and the expected teaching effect, step S50 is performed, which can avoid the problem of lack of quantitative indicators in teaching effect evaluation, thereby providing a method to measure the effectiveness of the teacher's teaching content, which helps to optimize teaching design and improve teaching effects.

[0170] For example, if the content evaluation data in the second evaluation result is 8, and the built-in standard teaching effect is 10, the corresponding teaching data is 8÷10=0.8.

[0171] Step S60: Correlate and output the ability matching data, the teaching data, and the teaching effect.

[0172] It is understandable that in order to combine the teacher's ability assessment, content assessment and teaching effect to obtain a comprehensive teaching evaluation result, step S60 is performed to avoid the problem of scattered and unsystematic evaluation results, thereby achieving a comprehensive evaluation of the teacher's comprehensive teaching level, and then providing a comprehensive teaching evaluation report to help education managers make more comprehensive and accurate decisions.

[0173] For example, competency fit data, teaching data, and teaching effectiveness data can be integrated into a comprehensive assessment report. This process can be achieved through a data management platform that aggregates data from different assessment dimensions and presents it through visualization tools. For example, the platform might generate a report containing charts and key indicators to demonstrate the teacher's competency fit, the effectiveness of the teaching content, and the overall teaching effectiveness.

[0174] In this embodiment, by implementing a multi-dimensional teaching evaluation plan, the ability evaluation data is compared with the standard ability data to obtain ability fit data, the content evaluation data is compared with the standard teaching effect to obtain teaching data, and these data are associated with the teaching effect and output, thereby avoiding the problem of relying only on a single indicator or subjective judgment in traditional evaluation, ensuring the accuracy and completeness of the evaluation results, and achieving a comprehensive, objective and quantitative evaluation of teachers' teaching ability, thereby helping education managers better understand teachers' teaching performance, optimize teacher training plans, and improve overall teaching quality.

[0175] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments above can be referred to the above introduction and will not be described in detail later. Figure 3 , step S402 may further include steps S01 to S03:

[0176] Step S01, when the difficulty level between the interspersed knowledge point and the subsequent knowledge point is progressively decreasing, performing the step of determining the first content evaluation sub-data based on the adjusted relevance data based on the relevance data;

[0177] It is understandable that since there is no need to adjust the correlation data when the interspersed knowledge points and the subsequent knowledge points show "decreasing difficulty" (for example, first teaching advanced calculus and then returning to basic algebra), step S01 is performed to objectively reflect the correlation data between the interspersed knowledge points and the remaining knowledge points by not adjusting the correlation data.

[0178] Step S02: When the difficulty level of the interspersed knowledge points and the subsequent knowledge points is progressively increasing or equal, matching the interspersed knowledge points with the teacher's history teaching content collection;

[0179] Step S03: if the interspersed knowledge point does not exist in the history teaching content collection, then performing the step of determining the first content evaluation sub-data based on the adjusted relevance data based on the relevance data;

[0180] It is understandable that when the difficulty of the interspersed knowledge points increases or remains the same as that of the subsequent knowledge points (such as the transition from basic algebra to geometric proofs), and the interspersed knowledge points have never appeared in history teaching, there is no need to adjust the correlation data, so step S03 is performed, and by not adjusting the correlation data, objective reflection of the correlation data between the interspersed knowledge points and the remaining knowledge points is achieved.

[0181] Step S04: If the interspersed knowledge points exist in the history teaching content collection, weighted adjustment is performed on the relevance data based on a preset interference coefficient.

[0182] It is understandable that since this interspersed knowledge point has appeared in history teaching, it is necessary to additionally consider that this interspersed knowledge point is easier for students to understand, so step S04 is performed. By making corresponding adjustments to the correlation data when this interspersed knowledge point has appeared in history teaching, it is possible to avoid evaluation deviations caused by inaccurate grasp of the interspersed knowledge points, thereby achieving accurate grasp of the impact of the interspersed knowledge points on the evaluation of teaching content.

[0183] For example, if the interspersed knowledge point exists in the collection of historical teaching content, indicating that the interspersed knowledge point has historical teaching conditions, then the difficulty coefficient of the interspersed knowledge point for students in the current state is reduced, thereby making the interspersed knowledge point play an auxiliary role in the understanding of subsequent knowledge points, thereby increasing the corresponding correlation data.

[0184] In this embodiment, by only adjusting the correlation data between the interspersed knowledge points and the remaining knowledge points when the difficulty level between the interspersed knowledge points and the subsequent knowledge points is increasing or remaining the same, and the interspersed knowledge points have appeared in history teaching, so as to reasonably reflect the impact of the interspersed knowledge points on the evaluation of teaching content, thereby further improving the accuracy and comprehensiveness of the evaluation of teachers' teaching effectiveness.

[0185] For example, to help understand the implementation process of the hybrid teaching effect evaluation method obtained by combining the above-mentioned embodiment 1, embodiment 2 and embodiment 3, please refer to Figure 4 , Figure 4 This paper provides a brief flow chart of the hybrid teaching effect evaluation method, specifically:

[0186] Teaching content and teaching results are collected twice: the history teaching content and teaching results obtained in the first collection, and the current teaching content and current teaching results obtained in the second collection. During the first collection process, the history teaching content is continuously evaluated to obtain the first content evaluation sub-data. The history teaching content is progressively evaluated in terms of difficulty to obtain the second content evaluation sub-data. The first and second content evaluation sub-data are then combined to obtain content evaluation data. History teaching results are then evaluated by class and individual to obtain ability evaluation data. Finally, the evaluated content evaluation data and ability evaluation data are combined to obtain the first evaluation result corresponding to the first collection process. The process of the current teaching content and current teaching results during the second collection process is indicated by a dotted line in the figure, indicating that the process is consistent with the data processing process of the first collection process, ultimately obtaining the second evaluation result, and therefore is not further described. The first and second evaluation results are then compared to determine the improvement in teaching effectiveness, and the teacher's teaching effectiveness is evaluated based on this improvement. In addition, based on the ability assessment data and content assessment data in the second assessment result, corresponding ability fit data and teaching data are generated, and the teaching effect, ability fit data and teaching data are output together.

[0187] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the hybrid teaching effect evaluation method of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

[0188] This application also provides a hybrid teaching effect evaluation system, please refer to Figure 5 , the hybrid teaching effect evaluation system includes:

[0189] An analysis module 10 is configured to determine a teaching effectiveness evaluation result based on the teacher's teaching content and the student's teaching performance, wherein the types of the teaching content include historical teaching content and current teaching content, and the types of the teaching performance include historical teaching performance and current teaching performance. The evaluation result determined based on the historical teaching content and the historical teaching performance is a first evaluation result, and the evaluation result determined based on the current teaching content and the current teaching performance is a second evaluation result.

[0190] A comparison module 20 is used to compare the first evaluation result with the second evaluation result to determine the improvement of the teaching effect;

[0191] The evaluation module 30 is used to evaluate the teaching effect of the teacher based on the improvement status.

[0192] Optionally, the analysis module 10 is further configured to:

[0193] Determine the corresponding teaching knowledge points according to the teacher's teaching content, and perform relevance evaluation on the teaching knowledge points to obtain content evaluation data;

[0194] Classifying the students' classes based on their teaching performance to obtain class grade data, and classifying each student in any grade class based on the teaching performance of each student and the class grade data of the grade class to obtain student grade data;

[0195] Counting the number of students in each grade in the student grade data to obtain grade data values ​​corresponding to the grades, and comparing the grade data values ​​with preset standard data values ​​as ability assessment data, wherein the degree of conformity of the comparison results is positively correlated with the ability assessment data;

[0196] An evaluation result of the teaching effect is calculated based on the content evaluation data and the ability evaluation data.

[0197] Optionally, the analysis module 10 is further configured to:

[0198] Identify the knowledge points of the teacher's teaching content to obtain the corresponding teaching knowledge point data, and identify the knowledge points of the preset teaching syllabus to obtain the corresponding knowledge point range;

[0199] Comparing the teaching knowledge point data with the knowledge point range, and canceling the step of determining the teaching effect evaluation result if the teaching knowledge point data is outside the knowledge point range;

[0200] In the case where the teaching knowledge point is within the knowledge point range, determining the attribute category of each knowledge point in the teaching knowledge point data based on the teaching syllabus, and for each knowledge point of any attribute category, determining the teaching time of each knowledge point based on the teaching content;

[0201] Evaluating the continuity of each knowledge point at each teaching moment to obtain first content evaluation sub-data, and evaluating the progressive difficulty of each knowledge point at each teaching moment to obtain second content evaluation sub-data;

[0202] Content evaluation data is calculated based on the first content evaluation sub-data and the second content evaluation sub-data.

[0203] Optionally, the analysis module 10 is further configured to:

[0204] In the case where the teaching moments corresponding to the knowledge points are not continuous, determining the correlation between the interspersed knowledge points that interrupt the continuity of the knowledge points and the knowledge points to obtain correlation data;

[0205] Adjusting the association data based on the progressive difficulty between the interspersed knowledge point and the subsequent knowledge point, wherein the subsequent knowledge point is a knowledge point that is adjacent to the interspersed knowledge point at the teaching time and is located after the interspersed knowledge point;

[0206] The first content evaluation sub-data is determined based on the adjusted relevance data.

[0207] Optionally, the analysis module 10 is further configured to:

[0208] Determining the teaching duration and teaching sequence of each knowledge point based on each teaching moment, and performing a teaching difficulty assessment on each knowledge point based on each teaching duration to obtain first difficulty assessment data, and performing a teaching difficulty assessment on each knowledge point based on the teaching sequence to obtain second difficulty assessment data;

[0209] Comparing the collection of teaching hours and teaching sequences of each teacher for each knowledge point, determining a correlation between the teaching hours and teaching sequences of each knowledge point, and performing a teaching difficulty assessment on each knowledge point based on the correlation to obtain third difficulty assessment data;

[0210] The difficulty data of each knowledge point is calculated based on the first difficulty evaluation data, the second difficulty evaluation data and the third difficulty evaluation data, and the second content evaluation sub-data is determined according to the progressiveness of each difficulty data.

[0211] Optionally, the comparison module 20 is further configured to:

[0212] determining a difference relationship between the first evaluation result and the second evaluation result;

[0213] If the difference relationship is that the second evaluation result is less than the first evaluation result, determining a first achievement degree of the first evaluation result relative to a preset standard teaching effect, and determining an improvement status of the teaching effect based on the first achievement degree and a preset standard fluctuation range;

[0214] When the difference relationship is that the second evaluation result is greater than or equal to the first evaluation result, the second achievement degree of the second evaluation result relative to the preset standard teaching effect is determined, and the improvement status of the teaching effect is determined based on the second achievement degree and the preset standard fluctuation range.

[0215] Optionally, the evaluation module 30 is further configured to:

[0216] When the improvement condition is that there is improvement, comparing the improvement condition with a preset standard teaching effect;

[0217] When the improvement condition is within a standard fluctuation range preset for the standard teaching effect, performing difference calculation between the first evaluation result and the second evaluation result and the numerical values ​​corresponding to the standard teaching effect, respectively, to obtain a first evaluation difference and a second evaluation difference;

[0218] The teaching effectiveness of the teacher is determined based on the ratio of the first evaluation difference to the second evaluation difference.

[0219] Optionally, the evaluation module 30 is further configured to:

[0220] Comparing the ability assessment data in the second assessment result with the preset standard ability data to obtain corresponding ability matching data;

[0221] Comparing the content evaluation data in the second evaluation result with a preset standard teaching effect to obtain corresponding teaching data;

[0222] The ability matching data, the teaching data and the teaching effect are output in association.

[0223] Optionally, the analysis module 10 is further configured to:

[0224] In a case where the difficulty level between the interspersed knowledge point and the subsequent knowledge point decreases progressively, performing the step of determining the first content evaluation sub-data based on the adjusted relevance data based on the relevance data;

[0225] In the case where the difficulty level of the interspersed knowledge points and the subsequent knowledge points is progressively increasing or equal, matching the interspersed knowledge points with the teacher's history teaching content collection;

[0226] If the interspersed knowledge point does not exist in the history teaching content collection, then performing the step of determining the first content evaluation sub-data based on the adjusted relevance data based on the relevance data;

[0227] If the interspersed knowledge points exist in the history teaching content collection, the relevance data is weightedly adjusted based on a preset interference coefficient.

[0228] The hybrid teaching effectiveness evaluation system provided in this application, which employs the hybrid teaching effectiveness evaluation method described in the aforementioned embodiment, can address the technical problem of improving the accuracy of teacher teaching effectiveness evaluations. Compared to the prior art, the beneficial effects of the hybrid teaching effectiveness evaluation system provided in this application are the same as those of the hybrid teaching effectiveness evaluation method described in the aforementioned embodiment. Other technical features of the hybrid teaching effectiveness evaluation system are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0229] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the hybrid teaching effect evaluation method in the above-mentioned embodiment.

[0230] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof.

[0231] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device is enabled to: determine the evaluation results of the teaching effect based on the teacher's teaching content and the student's teaching performance, wherein the types of the teaching content include historical teaching content and current teaching content, and the types of the teaching performance include historical teaching performance and current teaching performance, the evaluation result determined based on the historical teaching content and the historical teaching performance is the first evaluation result, and the evaluation result determined based on the current teaching content and the current teaching performance is the second evaluation result; compare the first evaluation result and the second evaluation result to determine the improvement status of the teaching effect; and evaluate the teaching effect of the teacher based on the improvement status.

[0232] Computer program code for carrying out operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​and conventional procedural programming languages.

[0233] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function.

[0234] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned hybrid teaching effectiveness evaluation method. This computer-readable storage medium can address the technical problem of improving the accuracy of teacher teaching effectiveness evaluations. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the hybrid teaching effectiveness evaluation method provided in the aforementioned embodiments and are not further elaborated here.

Claims

1. A hybrid teaching effect evaluation method, characterized in that: The hybrid teaching effect evaluation method includes: Determine an evaluation result of the teaching effect based on the teacher's teaching content and the student's teaching performance, wherein the types of the teaching content include historical teaching content and current teaching content, and the types of the teaching performance include historical teaching performance and current teaching performance. The evaluation result determined based on the historical teaching content and the historical teaching performance is a first evaluation result, and the evaluation result determined based on the current teaching content and the current teaching performance is a second evaluation result. Comparing the first evaluation result with the second evaluation result to determine the improvement of the teaching effect; Evaluate the teaching effectiveness of the teacher based on the improvement status; The step of determining the evaluation result of the teaching effect according to the teacher's teaching content and the student's teaching performance includes: Through the preset knowledge base and pre-trained large language model, the teacher's teaching content is analyzed to identify and extract the corresponding teaching knowledge point data. The preset knowledge base and pre-trained large language model are used to perform text analysis on the preset teaching outline to identify and extract the corresponding knowledge point range. Comparing the teaching knowledge point data with the knowledge point range, and if the teaching knowledge point is within the knowledge point range, determining the attribute category of each knowledge point in the teaching knowledge point data based on the teaching syllabus, and for each knowledge point in any attribute category, determining the teaching time of each knowledge point based on the teaching content; Based on the teaching moments of each knowledge point in the teacher's teaching content, the continuity of each knowledge point is evaluated. If the teaching moments corresponding to each knowledge point are not continuous, the interspersed knowledge points that interrupt the continuity of each knowledge point are determined, and the correlation between the interspersed knowledge points and each knowledge point is quantified by constructing a knowledge graph or using a preset correlation analysis algorithm to obtain correlation data; In the case where the difficulty level of the interspersed knowledge point and the subsequent knowledge point is progressively increasing or equal, the interspersed knowledge point is matched based on the teacher's history teaching content collection. If the interspersed knowledge point exists in the history teaching content collection, the correlation data is weighted and adjusted based on a preset interference coefficient, wherein the subsequent knowledge point is a knowledge point among the knowledge points that is adjacent to the interspersed knowledge point in teaching time and is located after the interspersed knowledge point; determining first content evaluation sub-data based on the adjusted relevance data; Evaluate the progressive difficulty of each knowledge point at each teaching moment to obtain second content evaluation sub-data; calculating content-evaluation data based on the first content-evaluation sub-data and the second content-evaluation sub-data; Ability evaluation data is determined based on the student's teaching performance, and an evaluation result of the teaching effect is calculated based on the content evaluation data and the ability evaluation data.

2. The hybrid teaching effect evaluation method according to claim 1, characterized in that: The step of determining the ability assessment data based on the student's teaching performance includes: Classifying the students' classes based on their teaching performance to obtain class grade data, and classifying each student in any grade class based on the teaching performance of each student and the class grade data of the grade class to obtain student grade data; The number of students in each grade in the student grade data is counted to obtain the grade data value of the corresponding grade, and the comparison result of each grade data value with the preset standard data value is used as the ability assessment data, wherein the degree of conformity of the comparison result is positively correlated with the ability assessment data.

3. The hybrid teaching effect evaluation method according to claim 1, characterized in that: The first evaluation result and the second evaluation result are both numerical values, and the step of comparing the first evaluation result and the second evaluation result to determine the improvement of the teaching effect includes: determining a difference relationship between the first evaluation result and the second evaluation result; If the difference relationship is that the second evaluation result is less than the first evaluation result, determining a first achievement degree of the first evaluation result relative to a preset standard teaching effect, and determining an improvement status of the teaching effect based on the first achievement degree and a preset standard fluctuation range; When the difference relationship is that the second evaluation result is greater than or equal to the first evaluation result, the second achievement degree of the second evaluation result relative to the preset standard teaching effect is determined, and the improvement status of the teaching effect is determined based on the second achievement degree and the preset standard fluctuation range.

4. The hybrid teaching effect evaluation method according to claim 1, characterized in that: The step of evaluating the progressive difficulty of each knowledge point according to each teaching moment to obtain second content evaluation sub-data includes: Determining the teaching duration and teaching sequence of each knowledge point based on each teaching moment, and performing a teaching difficulty assessment on each knowledge point based on each teaching duration to obtain first difficulty assessment data, and performing a teaching difficulty assessment on each knowledge point based on the teaching sequence to obtain second difficulty assessment data; Comparing the collection of teaching hours and teaching sequences of each teacher for each knowledge point, determining a correlation between the teaching hours and teaching sequences of each knowledge point, and performing a teaching difficulty assessment on each knowledge point based on the correlation to obtain third difficulty assessment data; The difficulty data of each knowledge point is calculated based on the first difficulty evaluation data, the second difficulty evaluation data and the third difficulty evaluation data, and the second content evaluation sub-data is determined according to the progressiveness of each difficulty data.

5. The hybrid teaching effect evaluation method according to claim 1, characterized in that: The first evaluation result and the second evaluation result are both numerical values, and the step of evaluating the teacher's teaching effect based on the improvement status includes: When the improvement condition is that there is improvement, comparing the improvement condition with a preset standard teaching effect; When the improvement condition is within a standard fluctuation range preset for the standard teaching effect, performing difference calculation between the first evaluation result and the second evaluation result and the numerical values ​​corresponding to the standard teaching effect, respectively, to obtain a first evaluation difference and a second evaluation difference; The teaching effectiveness of the teacher is determined based on the ratio of the first evaluation difference to the second evaluation difference.

6. The hybrid teaching effect evaluation method according to claim 1, characterized in that: After the step of evaluating the teacher's teaching effect based on the improvement status, the method further includes: Comparing the ability assessment data in the second assessment result with the preset standard ability data to obtain corresponding ability matching data; Comparing the content evaluation data in the second evaluation result with a preset standard teaching effect to obtain corresponding teaching data; The ability matching data, the teaching data and the teaching effect are output in association.

7. A hybrid teaching effect evaluation system, characterized by: The hybrid teaching effect evaluation system includes: an analysis module, configured to determine an evaluation result of the teaching effect based on the teacher's teaching content and the student's teaching performance, wherein the types of the teaching content include historical teaching content and current teaching content, and the types of the teaching performance include historical teaching performance and current teaching performance, the evaluation result determined based on the historical teaching content and the historical teaching performance is a first evaluation result, and the evaluation result determined based on the current teaching content and the current teaching performance is a second evaluation result; The analysis module is also used to identify knowledge points of the teacher's teaching content through a preset knowledge base and a pre-trained large language model to obtain corresponding teaching knowledge point data, and to identify knowledge points of the preset teaching syllabus through a preset knowledge base and a pre-trained large language model to obtain a corresponding knowledge point range; compare the teaching knowledge point data with the knowledge point range, and when the teaching knowledge point data is outside the knowledge point range, cancel the step of determining the evaluation result of the teaching effect; when the teaching knowledge point is within the knowledge point range, determine the attribute category of each knowledge point in the teaching knowledge point data based on the teaching syllabus, and for each knowledge point of any attribute category, determine the teaching time of each knowledge point based on the teaching content; evaluate the continuity of each knowledge point according to the teaching time of each knowledge point in the teacher's teaching content, and when the teaching time corresponding to each knowledge point is not continuous, determine the interspersed knowledge points that interrupt the continuity of each knowledge point, and construct a knowledge graph or A preset correlation analysis algorithm is used to quantify the correlation between the interspersed knowledge point and each of the knowledge points to obtain correlation data; when the difficulty progression between the interspersed knowledge point and the subsequent knowledge point is increasing or remaining the same, the interspersed knowledge point is matched based on the teacher's history teaching content collection, and if the interspersed knowledge point exists in the history teaching content collection, the correlation data is weighted and adjusted based on a preset interference coefficient, wherein the subsequent knowledge point is a knowledge point among the knowledge points that is adjacent to the interspersed knowledge point at the teaching moment and is located after the interspersed knowledge point; first content evaluation sub-data is determined based on the adjusted correlation data; the difficulty progression of each knowledge point is evaluated according to each teaching moment to obtain second content evaluation sub-data; content evaluation data is calculated based on the first content evaluation sub-data and the second content evaluation sub-data; ability evaluation data is determined based on the student's teaching performance, and an evaluation result of the teaching effect is calculated based on the content evaluation data and the ability evaluation data; A comparison module, configured to compare the first evaluation result with the second evaluation result to determine the improvement of the teaching effect; An evaluation module is used to evaluate the teaching effect of the teacher based on the improvement status.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the hybrid teaching effect evaluation method according to any one of claims 1 to 6 are implemented.

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