Intelligent teaching evaluation method, system and equipment based on data analysis and medium

Abstract

The invention relates to the technical field of big data, in particular to an intelligent teaching evaluation method, system and device based on data analysis and a medium. According to the method, the initial evaluation index is obtained by responding to the evaluation trigger instruction, and the standardization of the evaluation process is ensured; secondly, historical learning data of students are collected, knowledge point mastering coefficients are calculated, and quantitative analysis of individual learning conditions is achieved; calculating the overall grasp degree of the class, setting an early warning threshold value, and timely discovering a universal problem in learning; further identifying weak knowledge points and analyzing pre-association and subsequent influence of the weak knowledge points, and deeply understanding the root of learning disorder and possibly caused chain reaction; and finally, dynamically updating an evaluation index according to an analysis result, generating a teaching suggestion report, and providing a targeted teaching optimization scheme for teachers. The learning problem can be found and intervened as soon as possible, more effective teaching suggestions can be provided according to the relevance between the knowledge points, and the scientificity and practicability of teaching evaluation are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of big data, and in particular to intelligent teaching evaluation methods, systems, devices and media based on data analysis. Background Art

[0002] With the rapid development of educational informatization and the widespread application of big data technology, intelligent teaching evaluation has become an important means of improving teaching quality. Scientific and objective teaching evaluation can not only promptly identify problems in the learning process, but also provide data support for teaching decision-making, which is of great significance for implementing individualized teaching and precision teaching.

[0003] Currently, a variety of teaching evaluation systems based on data analysis are being applied in educational practice. These systems collect data such as students' homework completion and test scores, use statistical analysis methods to calculate the degree of knowledge mastery, and generate evaluation reports to provide teaching references for teachers.

[0004] However, the existing teaching evaluation system often only focuses on the mastery of a single knowledge point, fails to fully consider the correlation between knowledge points, and lacks an early warning mechanism for the overall learning situation of the class. This situation needs further improvement. Summary of the Invention

[0005] To address the problem that existing teaching evaluation systems often only focus on the mastery of a single knowledge point, fail to fully consider the correlation between knowledge points, and lack an early warning mechanism for the overall learning status of the class, this application provides an intelligent teaching evaluation method, system, device, and medium based on data analysis, using the following technical solutions: In a first aspect, the present application provides an intelligent teaching evaluation method based on data analysis, characterized in that it includes the following steps: Respond to the knowledge point evaluation trigger instruction and obtain the initial evaluation indicators of the course knowledge point system; Obtain each student's historical learning data of knowledge points and calculate the knowledge point mastery coefficient based on the historical learning data; Calculate the overall mastery of the class based on the mastery coefficient of the knowledge points, and provide graded warnings for the overall mastery of the class based on preset warning thresholds; Identify weak knowledge points where the overall mastery of the class falls below the warning threshold, and analyze the preceding connections and subsequent impacts of these weak knowledge points; The initial evaluation indicators are updated according to the weak knowledge point analysis results, and a teaching suggestion report is generated.

[0006] By adopting the above technical solution, this application first obtains initial evaluation indicators by responding to evaluation trigger instructions to ensure the standardization of the evaluation process; then collects students' historical learning data and calculates the knowledge point mastery coefficient to achieve quantitative analysis of individual learning conditions; then calculates the overall mastery of the class and sets early warning thresholds to promptly discover common problems in learning; further identifies weak knowledge points and analyzes their prior associations and subsequent impacts, and deeply understands the root causes of learning disabilities and the possible chain reactions; finally, dynamically updates evaluation indicators based on the analysis results and generates a teaching suggestion report to provide teachers with targeted teaching optimization plans; not only can it detect and intervene in learning problems early, but it can also provide more effective teaching suggestions based on the correlation between knowledge points, significantly improving the scientificity and practicality of teaching evaluation.

[0007] Optionally, in response to a knowledge point evaluation trigger instruction, obtaining initial evaluation indicators for the course knowledge point system includes the following steps: Preprocessing the original data of the course knowledge point system to obtain processed knowledge point system data; Building a knowledge point association graph based on the processed knowledge point system data to generate a hierarchical relationship between knowledge points; Calculating the importance of knowledge points according to the knowledge point association graph, and generating the difficulty coefficient and application frequency of each knowledge point based on the knowledge point importance; The knowledge point evaluation index is initialized and set based on the difficulty coefficient and the application frequency.

[0008] By adopting the above technical solution, this application first performs pre-processing such as standardization and denoising on the original data of the course knowledge point system to ensure data quality; then constructs a knowledge point association map to display the hierarchy and dependency relationship between knowledge points in a visual way; then analyzes the connectivity and influence range of each knowledge point in the map, calculates the importance of the knowledge point, and generates a difficulty coefficient reflecting the cognitive law and the application frequency of actual teaching needs based on this; finally, the difficulty coefficient and application frequency are used as key parameters to initialize the knowledge point evaluation indicators; so that the evaluation standards are more in line with the actual teaching characteristics, providing a scientific basis for subsequent teaching evaluation.

[0009] Optionally, obtaining each student's historical learning data of a knowledge point and calculating the knowledge point mastery coefficient based on the historical learning data specifically includes the following steps: The historical learning data includes classroom exercise scores, completion of homework, unit test scores, classroom interaction frequency and knowledge application performance; Divide the learning process into time periods, determine all learning data related to a single knowledge point, assign a value to each time period of the single knowledge point based on historical learning data, and obtain a stage-by-stage mastery score; The weighted average of the staged mastery scores of all time periods involved in a single knowledge point is calculated to obtain a comprehensive evaluation score for the single knowledge point, and the knowledge point mastery coefficient is obtained using the comprehensive evaluation score and standardization processing.

[0010] By adopting the above technical solution, this application first comprehensively collects learning process data, including information in multiple dimensions such as classroom exercise scores, completion of homework, unit test scores, classroom interaction frequency and knowledge application performance; then divides the learning process into multiple time periods according to the teaching progress, and comprehensively assigns values ​​to the learning data in each time period to obtain a mastery score that can reflect the stage-by-stage learning effect; finally, by taking a weighted average of the mastery scores of different time periods, the comprehensive evaluation score is calculated, and the final knowledge point mastery coefficient is obtained after standardization; not only does it take into account multiple dimensions of the learning process, but it also captures the dynamic changes in learning effects through time period division, making the evaluation results more objective and accurate.

[0011] Optionally, calculating the overall mastery of the class based on the knowledge point mastery coefficient includes the following steps: Acquiring historical teaching data, and acquiring knowledge point attainment rate data based on the historical teaching data; The overall mastery of the class is calculated using the knowledge point mastery coefficient and the knowledge point attainment rate data.

[0012] By adopting the above technical solution, this application first obtains historical teaching data, including the learning performance of each class on the same knowledge points in previous years, and obtains the standard-reaching rate data of each knowledge point through data analysis. These standard-reaching rate data reflect the difficulty level and learning rules of different knowledge points; then the knowledge point mastery coefficient of the current class students and the historical standard-reaching rate data are comprehensively calculated to obtain the overall mastery degree that can objectively reflect the class learning level; by introducing historical teaching data as a reference benchmark, the calculation of the overall mastery degree of the class is more scientific and has more reference value.

[0013] Optionally, obtaining historical teaching data and obtaining knowledge point attainment rate data based on the historical teaching data specifically includes the following steps: Obtaining student performance distribution data and knowledge point assessment frequency data based on the historical teaching data; The knowledge point attainment rate data is determined based on the student score distribution data and the knowledge point assessment frequency data.

[0014] By adopting the above technical solution, this application first extracts the distribution of student scores from historical teaching data, including statistical indicators such as average score, standard deviation, and the proportion of people in each score range, and at the same time counts the frequency and difficulty level of the knowledge point in various question types; then combines the score distribution data with the assessment frequency data, and calculates a more accurate knowledge point attainment rate through a mathematical model; it more objectively reflects the students' true mastery of the knowledge points.

[0015] Optionally, analyzing the preceding associations and subsequent impacts of the weak knowledge points specifically includes the following steps: The analysis factors of the preceding association and subsequent impact include the difficulty level of the knowledge point, the degree of knowledge transfer, the length of learning investment, the distribution of error types and the knowledge application scenario; Determine, based on the knowledge point association graph, the preceding associated knowledge points and subsequent impact knowledge points involved in a single weak knowledge point, and assign values ​​to each preceding associated knowledge point and subsequent impact knowledge point of the single weak knowledge point according to analysis factors to obtain an association score; The arithmetic mean of all associated scores involved in a single weak knowledge point is extracted to calculate the analysis factor discrimination score of the single weak knowledge point. The analysis factor discrimination score is multiplied by the preset factor weight and the sum is taken to obtain the knowledge point association impact data.

[0016] By adopting the above technical solution, this application first determines five core analysis factors, including the difficulty level of knowledge points, the degree of knowledge transfer, the length of learning investment, the distribution of error types and the knowledge application scenarios. These factors comprehensively cover all aspects of knowledge point learning; then, based on the knowledge point association map, the preceding knowledge points and subsequent knowledge points directly related to the weak knowledge points are identified, and each related knowledge point is quantitatively scored according to the five analysis factors to obtain a score reflecting the strength of the association; finally, the analysis factor discrimination score is obtained by calculating the arithmetic mean of the association score, and then the weighted sum is performed in combination with the pre-set factor weights, and finally data that can quantitatively represent the degree of influence of the knowledge point association is obtained; through multi-dimensional factor evaluation and systematic association analysis, not only can the root cause of the learning problem be accurately located, but also the chain reaction that the problem may cause can be predicted.

[0017] Optionally, update the initial evaluation indicators based on the weak knowledge point analysis results and generate a teaching suggestion report, including: Acquiring teaching condition data, wherein the teaching condition data includes the number of available class hours and the teacher's teaching schedule; Update the initial evaluation indicators based on the overall class mastery and the correlation between previous and next knowledge points for each of the weak knowledge points; According to the teaching condition data, a teaching focus adjustment plan that conforms to the existing teaching time is determined, and based on the teaching focus adjustment plan, a teaching suggestion report including a teaching schedule, key explanation content and targeted exercise suggestions is generated.

[0018] By adopting the above technical solution, this application first obtains specific teaching condition data, including the number of available class hours and the teacher's teaching time arrangement. These data reflect the actual constraints of teaching resources; then, based on the overall class mastery of each weak knowledge point and the strength of the connection with other knowledge points, the initial evaluation indicators are dynamically updated to ensure that the evaluation standards are consistent with the actual situation; finally, based on the available teaching time, a practical and feasible teaching focus adjustment plan is formulated, and a detailed teaching suggestion report is generated accordingly, including specific teaching progress arrangements, content that needs to be emphasized, and targeted practice suggestions; by combining the evaluation results with actual teaching conditions, the operability of the teaching suggestions is ensured, enabling teachers to achieve the best teaching effect with limited teaching resources.

[0019] In a second aspect, the present application provides an intelligent teaching evaluation system based on data analysis, comprising: The initial evaluation indicator acquisition module is used to respond to the knowledge point evaluation trigger instruction and obtain the initial evaluation indicators of the course knowledge point system; The knowledge point mastery coefficient calculation module is used to obtain each student's knowledge point historical learning data and calculate the knowledge point mastery coefficient based on the historical learning data; A graded warning module is used to calculate the overall mastery of the class based on the mastery coefficient of the knowledge point, and to provide graded warnings for the overall mastery of the class based on a preset warning threshold; The correlation impact analysis module is used to identify weak knowledge points where the overall mastery of the class is below the warning threshold, and analyze the previous correlation and subsequent impact of the weak knowledge points; The teaching suggestion report generation module is used to update the initial evaluation indicators according to the weak knowledge point analysis results and generate a teaching suggestion report.

[0020] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent teaching evaluation method based on data analysis when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned intelligent teaching evaluation method based on data analysis.

[0022] In summary, this application includes at least one of the following beneficial technical effects: 1. This application first obtains initial assessment indicators by responding to assessment trigger instructions to ensure the standardization of the assessment process; then collects students' historical learning data and calculates knowledge point mastery coefficients to achieve quantitative analysis of individual learning situations; then calculates the overall mastery of the class and sets warning thresholds to promptly identify common learning problems; further identifies weak knowledge points and analyzes their pre-relevance and subsequent impacts to gain a deeper understanding of the root causes of learning barriers and the potential chain reactions; finally, based on the analysis results, dynamically updates assessment indicators and generates teaching recommendation reports, providing teachers with targeted teaching optimization plans. This not only enables early detection and intervention of learning problems, but also provides more effective teaching suggestions based on the correlation between knowledge points, significantly improving the scientific nature and practicality of teaching evaluation; 2. This application first performs preprocessing, such as standardization and denoising, on the raw data of the course knowledge point system to ensure data quality. It then constructs a knowledge point association map, visually displaying the hierarchy and dependency relationships between knowledge points. It then analyzes the connectivity and influence range of each knowledge point in the map, calculates the knowledge point importance, and, based on this, generates a difficulty coefficient reflecting cognitive laws and an application frequency for actual teaching needs. Finally, using the difficulty coefficient and application frequency as key parameters, it initializes the knowledge point evaluation indicators, making the evaluation criteria more consistent with actual teaching characteristics and providing a scientific basis for subsequent teaching evaluation. 3. This application first comprehensively collects learning process data, including information on multiple dimensions such as classroom exercise scores, completion of homework, unit test scores, classroom interaction frequency and knowledge application performance; then divides the learning process into multiple time periods according to the teaching progress, and comprehensively assigns values ​​to the learning data in each time period to obtain a mastery score that can reflect the stage-by-stage learning effect; finally, the comprehensive evaluation score is calculated by taking a weighted average of the mastery scores of different time periods, and the final knowledge point mastery coefficient is obtained after standardization; not only does it take into account multiple dimensions of the learning process, but it also captures the dynamic changes in learning effects through time period division, making the evaluation results more objective and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 2 This is a flow chart of step S100 in an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 3 This is a flow chart of step S200 in an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 4 This is a flow chart of step S300 in an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 5 This is a flow chart of step S310 in an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 6 This is a flow chart of step S400 in an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 7 This is a flow chart of step S500 in an intelligent teaching evaluation method based on data analysis according to an embodiment of the present application; Figure 8 This is a module diagram of an intelligent teaching evaluation system based on data analysis according to an embodiment of the present application; Figure 9 This is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0026] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0027] In the first aspect, this application provides an intelligent teaching evaluation method based on data analysis, referring to Figure 1 , including the following steps: S100: Respond to a knowledge point evaluation trigger instruction and obtain initial evaluation indicators of the course knowledge point system.

[0028] In this embodiment, the knowledge point evaluation trigger instruction refers to an instruction for evaluating the mastery of a knowledge point, which is triggered by the teacher or periodically. The initial evaluation indicator refers to the benchmark parameter determined for the course knowledge point system last time, which is used to quantify the importance of the knowledge point and the basic evaluation standard.

[0029] Specifically, the system responds to the evaluation instruction triggered by the teacher or periodically, and obtains the benchmark parameters determined last time for the course knowledge point system to obtain the importance of the knowledge points and the basic evaluation criteria.

[0030] S200: Obtain historical learning data of each student's knowledge points, and calculate the knowledge point mastery coefficient based on the historical learning data.

[0031] Specifically, historical learning data for knowledge points includes classroom exercise scores, homework completion, unit test scores, classroom interaction frequency, and knowledge application performance. The system calculates each student's knowledge point mastery coefficient based on the scores of each student in various exercises and homework, as collected from the database. It is understandable that a database is needed to store students' scores for various exercises and homework. The database can be statistically analyzed based on knowledge points. This not only facilitates the calculation of the overall score of each knowledge point, but also allows teachers to re-formulate questions and tests based on the classified knowledge points in the database.

[0032] S300: Calculate the overall mastery of the class based on the knowledge point mastery coefficient, and perform graded warnings on the overall mastery of the class based on a preset warning threshold.

[0033] Specifically, the overall mastery of the class is calculated based on the knowledge point mastery coefficient of each student, and a graded warning is given to the overall mastery of the class based on a preset threshold.

[0034] S400. Identify weak knowledge points where the overall mastery of the class is below the warning threshold, and analyze the pre-correlation and subsequent impact of the weak knowledge points.

[0035] Specifically, when the class mastery level is lower than the warning threshold, it is determined to be a weak knowledge point, and the pre-correlation and subsequent impact of the weak knowledge point are analyzed. The pre-correlation and subsequent impact refer to the previously related knowledge points obtained based on the course knowledge point system. By analyzing the emphasis and basic evaluation standards of the previously related knowledge points, the adjustment weight of the current weak knowledge point is determined.

[0036] S500. Update the initial evaluation indicators based on the weak knowledge point analysis results and generate a teaching suggestion report.

[0037] Specifically, the initial evaluation indicators are updated based on the analysis results of weak knowledge points, and a teaching suggestion report is generated, including teaching progress schedule, key explanation content and targeted practice suggestions.

[0038] Furthermore, based on the analysis of weak knowledge points, the system implements personalized practice push notifications by constructing a question classification database. This database labels and categorizes each question according to the knowledge point coverage, difficulty level, question type, and common error points. The system also creates a student profiling tool that includes knowledge point mastery coefficients, error type distribution, problem-solving speed, question type adaptability, and an ability improvement curve. Based on these two data dimensions, the system employs different push strategies: For knowledge points with a mastery coefficient below 0.6, it primarily pushes basic questions focusing on a single knowledge point, along with specialized training questions targeting common student error types. For knowledge points with a mastery coefficient between 0.6 and 0.8, it prioritizes comprehensive application of knowledge points, with a difficulty distribution of 30% for basic, 50% for intermediate, and 20% for advanced. For knowledge points with a mastery coefficient above 0.8, it primarily focuses on comprehensive questions covering multiple knowledge points, incorporating innovative and open-ended questions. The system dynamically updates the question set weekly based on student performance, adjusting question difficulty and knowledge point distribution in real time to ensure targeted and adaptable practice. This helps students effectively address their weaknesses while maintaining learning motivation and improving practice efficiency.

[0039] In addition, the system establishes a knowledge point association analysis mechanism. It first obtains the knowledge point number of the next lesson's teaching content, and then calculates the correlation between each knowledge point in the next lesson and the identified weak knowledge points. The calculation process comprehensively considers three dimensions: the weight of the direct dependency relationship, the number of common prerequisite knowledge points, and the frequency of co-occurrence of historical test questions. When the correlation exceeds the preset threshold, the system marks the knowledge point as "needing associated review" and generates teaching connection suggestions. For example, when explaining a new knowledge point, it is recommended that teachers spend 5-10 minutes reviewing the associated weak knowledge points. For example, before explaining "the application of vectors in analytic geometry", they should review the weak knowledge point "vector operation rules". Through typical examples, the basic methods of vector addition and subtraction and scalar product should be strengthened to ensure that students understand the geometric meaning of vector operations; then, when explaining new knowledge, help students establish the intrinsic connection between new and old knowledge. The system also pushes 2-3 targeted examples, adds these suggestions to the teaching suggestion report and prominently prompts them on the teaching system interface to achieve effective review of weak knowledge points and smooth connection with new knowledge.

[0040] In one embodiment, referring to Figure 2 In step S100, in response to the knowledge point evaluation trigger instruction, the initial evaluation indicators of the course knowledge point system are obtained, which specifically includes the following steps: S110: Pre-process the original data of the course knowledge point system to obtain processed knowledge point system data.

[0041] In this embodiment, the raw data of the course knowledge point system includes the knowledge point name, teaching objectives, assessment requirements, textbook chapter location, and recommended teaching duration. Preprocessing refers to the process of standardizing, denoising, and structuring the raw data to make it easier to store and analyze.

[0042] Specifically, preprocessing first removes redundant text from the raw data, including repeated descriptions, modifiers, and punctuation. Then, structured processing is performed, converting the unstructured text into structured data according to pre-set field specifications. The processed data is stored in a MySQL relational database, with physical partitions established for elementary, middle, and high school grades, and logical partitions established for four knowledge point types: concepts, calculations, experiments, and applications, to facilitate rapid subsequent retrieval and analysis.

[0043] S120. Construct a knowledge point association graph based on the processed knowledge point system data to generate a hierarchical relationship between knowledge points.

[0044] In this embodiment, the knowledge point association map is used to display the hierarchical relationship and dependency relationship between knowledge points. The hierarchical relationship includes vertical superior-subordinate relationships and horizontal association relationships.

[0045] Specifically, a knowledge point relationship mapping table is first established, recording the relationship types between pairs of knowledge points. Then, a directed graph structure is constructed based on the mapping table, with nodes representing knowledge points and edges representing relationship types. A graph layout algorithm is then used to automatically generate a knowledge point association graph. For example, in mathematics, "fraction addition and subtraction" and "fraction multiplication and division" form a parallel relationship, while "fraction arithmetic operations" and "fraction word problems" form a progressive relationship.

[0046] S130. Calculate the importance of the knowledge points according to the knowledge point association graph, and generate the difficulty coefficient and application frequency of each knowledge point based on the knowledge point importance.

[0047] In this embodiment, the importance of a knowledge point is a quantitative indicator that measures the importance of the knowledge point in the entire curriculum system. The difficulty coefficient reflects the cognitive difficulty required to learn the knowledge point, and the application frequency indicates how often the knowledge point appears in actual applications.

[0048] S140: Initialize and set the knowledge point evaluation indicators based on the difficulty coefficient and application frequency.

[0049] Specifically, we established an evaluation indicator configuration table, using the difficulty coefficient as the baseline value for the mastery threshold and the application frequency as the adjustment factor for the scoring weight. For knowledge points with high importance, their scoring weights were increased; for knowledge points with high difficulty coefficients, the mastery threshold was appropriately lowered.

[0050] In one embodiment, referring to Figure 3In step S200, the historical learning data of each student's knowledge points are obtained, and the knowledge point mastery coefficient is calculated based on the historical learning data, which specifically includes the following steps: S210: Divide the learning process into time periods, determine all learning data involved in a single knowledge point, assign a value to each time period of the single knowledge point based on historical learning data, and obtain a stage-by-stage mastery score.

[0051] Among them, historical learning data includes classroom exercise scores, homework completion rate, unit test scores, classroom interaction frequency and knowledge application performance.

[0052] In this embodiment, time period division refers to dividing the entire learning process into multiple assessment intervals based on the teaching progress. Historical learning data refers to student performance data at different learning stages within each time period. The staged mastery score refers to the assessment score derived from the comprehensive learning data within a single time period.

[0053] Specifically, we first create a time period division table, dividing the semester into several teaching units according to the teaching plan, with each teaching unit as a time period. Then, we construct a data collection table to record each student's learning data in each time period.

[0054] S220. Perform a weighted average of the stage-by-stage mastery scores of all time periods involved in a single knowledge point to calculate a comprehensive evaluation score for the single knowledge point, and use the comprehensive evaluation score and standardization processing to obtain a knowledge point mastery coefficient.

[0055] In this embodiment, weighted average refers to assigning different weights to the stage-by-stage mastery scores of different time periods for calculation. The comprehensive evaluation score refers to the final evaluation score obtained by weighted average.

[0056] Specifically, a time-weighted distribution table is established, assigning different weights to different time periods, with higher weights for recent periods and lower weights for longer periods. To calculate the comprehensive assessment score, the mastery score for each time period is multiplied by the corresponding weight and the sum is calculated. Then, using the maximum and minimum value normalization method, the comprehensive assessment score is converted into a knowledge point mastery coefficient between 0 and 1.

[0057] In one embodiment, referring to Figure 4 In step S300, the overall mastery of the class is calculated based on the knowledge point mastery coefficient, which specifically includes the following steps: S310: Obtain historical teaching data, and obtain knowledge point attainment rate data based on the historical teaching data.

[0058] In this example, historical teaching data refers to the teaching records of each class in the same subject over the past three years, including student performance records, assessment records, and teaching feedback records. Knowledge point attainment rate data refers to the expected attainment level for each knowledge point, calculated based on historical teaching data, and serves as a reference benchmark for assessing the overall mastery of the current class.

[0059] S320. Calculate the overall mastery of the class using the knowledge point mastery coefficient and knowledge point attainment rate data.

[0060] In this embodiment, the overall mastery of the class refers to the overall mastery level of a certain knowledge point by the current class students, and requires comprehensive consideration of the individual students' knowledge point mastery coefficient and the achievement rate performance in historical teaching data.

[0061] Specifically, a mastery calculation rule table was established, combining the knowledge point mastery coefficients and standard attainment rates in a weighted manner. Mastery calculations were performed using the following method: First, the average of the class's knowledge point mastery coefficients was calculated as the baseline mastery. This baseline mastery was then compared with the historical standard attainment rates. If the current mastery was higher than the historical standard attainment rate, the final mastery assessment value was increased; otherwise, the assessment value was decreased. The specific calculation formula is: Overall class mastery = Baseline mastery × 0.7 + Historical standard attainment rate × 0.3.

[0062] In one embodiment, referring to Figure 5 In step S310, historical teaching data is obtained, and knowledge point attainment rate data is obtained based on the historical teaching data, specifically including the following steps: S311. Obtain student performance distribution data and knowledge point assessment frequency data based on historical teaching data.

[0063] In this embodiment, student score distribution data refers to the statistics of student scores corresponding to each knowledge point in the historical teaching data; knowledge point assessment frequency data refers to the detailed records of the knowledge point being assessed in classroom exercises, unit tests, midterm and final exams.

[0064] S312. Determine the knowledge point attainment rate data based on the student score distribution data and the knowledge point assessment frequency data.

[0065] In this embodiment, the knowledge point achievement rate data refers to the proportion of knowledge points that have reached the standard, which is obtained by analyzing the distribution of students' scores and the frequency of assessments. The achievement rate data needs to balance the two dimensions of performance and assessment difficulty.

[0066] Specifically, for example, if the grade distribution weight is 0.6, the calculation is: (A-grade percentage × 1.0 + B-grade percentage × 0.8 + C-grade percentage × 0.6 + D-grade percentage × 0.4) / total number of students. The assessment frequency weight is 0.4, calculated by setting a coefficient based on the difficulty of the assessment: 0.6 for multiple-choice questions, 0.8 for fill-in-the-blank questions, and 1.0 for essay questions. Then, multiply the average score rate for each question type by the corresponding coefficient. The final achievement rate is the weighted sum of these two components.

[0067] Furthermore, the system sets differentiated attainment rate assessment standards for different types of student groups. First, a student ability stratification table is established, and students are divided into three levels according to their recent three comprehensive test scores: Level A, Level B, and Level C. Then, a attainment rate adjustment matrix is ​​constructed, and different attainment reference values ​​are set for students at different levels: the knowledge point attainment benchmark value for Level A students is increased by 0.1 on the basis of the original attainment rate, requiring them to reach a higher level; the original attainment rate is used as the benchmark value for Level B students; the attainment benchmark value for Level C students is reduced by 0.1 on the basis of the original attainment rate, and a step-by-step improvement target is set. The system regularly and dynamically adjusts the attainment rate requirements based on the actual performance of each type of student. When Level C students reach the existing goals, the requirements are gradually raised until they reach the original attainment rate level, achieving teaching according to students' aptitude and precise teaching.

[0068] In one embodiment, referring to Figure 6 In step S400, the preceding association and subsequent impact of the weak knowledge points are analyzed, specifically including the following steps: S410. Determine the preceding related knowledge points and subsequent influencing knowledge points involved in a single weak knowledge point based on the knowledge point association map, assign values ​​to each preceding related knowledge point and subsequent influencing knowledge point of the single weak knowledge point based on analysis factors, and obtain an association score.

[0069] The analysis factors of pre-association and subsequent influence include the difficulty level of knowledge points, the degree of knowledge transfer, the length of learning investment, the distribution of error types and the knowledge application scenarios.

[0070] S420. Extract the arithmetic mean of all associated scores involved in a single weak knowledge point, calculate the analysis factor discrimination score of the single weak knowledge point, multiply the analysis factor discrimination score by the preset factor weight and sum them to obtain the knowledge point association impact data.

[0071] In one embodiment, referring to Figure 7 In step S500, the initial evaluation indicators are updated according to the weak knowledge point analysis results, and a teaching suggestion report is generated, which specifically includes: S510: Acquire teaching condition data, which includes the number of available class hours and the teacher's teaching schedule.

[0072] In this embodiment, teaching condition data refers to the objective environmental factors that influence the implementation of teaching. The number of available class hours refers to the number of class hours that can be used for key lectures and supplementary exercises, while ensuring that the syllabus requirements are met. Teacher teaching schedules refer to the time teachers can devote to after-class tutoring and answering questions.

[0073] S520. Update the initial evaluation indicators based on the class's overall mastery of each weak knowledge point and the correlation between previous and subsequent knowledge.

[0074] In this embodiment, updating the evaluation indicators refers to dynamically adjusting the original evaluation criteria based on the latest mastery analysis and knowledge association analysis results. The updated indicators need to balance the learning difficulty and teaching progress.

[0075] Specifically, establish an assessment indicator adjustment table and set threshold ranges for mastery and relevance. When the overall mastery of the class falls below 0.6, lower the requirements for that knowledge point and increase the weight of basic questions. When the relevance of the current and subsequent knowledge exceeds 0.8, increase the importance of that knowledge point and strengthen the process assessment.

[0076] S530. Determine a teaching focus adjustment plan that is consistent with the existing teaching time based on the teaching condition data, and generate a teaching suggestion report based on the teaching focus adjustment plan, which includes a teaching schedule, key explanation content, and targeted practice suggestions.

[0077] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0078] On the second aspect, the present application provides an intelligent teaching evaluation system based on data analysis. The intelligent teaching evaluation system based on data analysis of the present application is described below in combination with the above-mentioned intelligent teaching evaluation method based on data analysis.

[0079] Reference Figure 8 , an intelligent teaching evaluation system based on data analysis, comprising: The initial evaluation indicator acquisition module is used to respond to the knowledge point evaluation trigger instruction and obtain the initial evaluation indicators of the course knowledge point system; The knowledge point mastery coefficient calculation module is used to obtain each student's knowledge point historical learning data and calculate the knowledge point mastery coefficient based on the historical learning data; The graded warning module is used to calculate the overall mastery of the class based on the knowledge point mastery coefficient, and to provide graded warnings for the overall mastery of the class based on the preset warning threshold; The correlation impact analysis module is used to identify weak knowledge points where the overall mastery of the class is below the warning threshold, and analyze the previous correlation and subsequent impact of the weak knowledge points; The teaching suggestion report generation module is used to update the initial evaluation indicators based on the analysis results of weak knowledge points and generate a teaching suggestion report.

[0080] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an intelligent teaching evaluation method based on data analysis is implemented.

[0081] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0082] In one embodiment, an electronic device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0083] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The above-described computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0084] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An intelligent teaching evaluation method based on data analysis, characterized in that: The steps include: Respond to the knowledge point evaluation trigger instruction and obtain the initial evaluation indicators of the course knowledge point system; Obtain each student's historical learning data of knowledge points and calculate the knowledge point mastery coefficient based on the historical learning data; Calculate the overall mastery of the class based on the mastery coefficient of the knowledge points, and provide graded warnings for the overall mastery of the class based on preset warning thresholds; Identify weak knowledge points where the overall mastery of the class is below the warning threshold, and analyze the preceding connections and subsequent impacts of these weak knowledge points; The initial evaluation indicators are updated according to the weak knowledge point analysis results, and a teaching suggestion report is generated.

2. The intelligent teaching evaluation method based on data analysis according to claim 1 is characterized in that: Responding to the knowledge point evaluation trigger instruction, obtaining the initial evaluation indicators of the course knowledge point system includes the following steps: Preprocessing the original data of the course knowledge point system to obtain processed knowledge point system data; Building a knowledge point association graph based on the processed knowledge point system data to generate a hierarchical relationship between knowledge points; Calculating the importance of knowledge points according to the knowledge point association graph, and generating the difficulty coefficient and application frequency of each knowledge point based on the knowledge point importance; The knowledge point evaluation index is initialized and set based on the difficulty coefficient and the application frequency.

3. The intelligent teaching evaluation method based on data analysis according to claim 1 is characterized in that: Obtaining each student's historical learning data of a knowledge point and calculating the mastery coefficient of the knowledge point based on the historical learning data specifically includes the following steps: The historical learning data includes classroom exercise scores, completion of homework, unit test scores, classroom interaction frequency and knowledge application performance; Divide the learning process into time periods, determine all learning data related to a single knowledge point, assign a value to each time period of the single knowledge point based on historical learning data, and obtain a stage-by-stage mastery score; The weighted average of the staged mastery scores of all time periods involved in a single knowledge point is calculated to obtain a comprehensive evaluation score for the single knowledge point, and the knowledge point mastery coefficient is obtained using the comprehensive evaluation score and standardization processing.

4. The intelligent teaching evaluation method based on data analysis according to claim 1 is characterized in that: Calculating the overall mastery of the class based on the mastery coefficient of the knowledge points specifically includes the following steps: Acquiring historical teaching data, and acquiring knowledge point attainment rate data based on the historical teaching data; The overall mastery of the class is calculated using the knowledge point mastery coefficient and the knowledge point attainment rate data.

5. The intelligent teaching evaluation method based on data analysis according to claim 4 is characterized in that: Obtaining historical teaching data, and obtaining knowledge point attainment rate data based on the historical teaching data, specifically includes the following steps: Obtaining student performance distribution data and knowledge point assessment frequency data based on the historical teaching data; The knowledge point attainment rate data is determined based on the student score distribution data and the knowledge point assessment frequency data.

6. The intelligent teaching evaluation method based on data analysis according to claim 2 is characterized in that: Analyze the preceding associations and subsequent impacts of the weak knowledge points, specifically including the following steps: The analysis factors of the preceding association and subsequent impact include the difficulty level of the knowledge point, the degree of knowledge transfer, the length of learning investment, the distribution of error types and the knowledge application scenario; Determine, based on the knowledge point association graph, the preceding associated knowledge points and subsequent impact knowledge points involved in a single weak knowledge point, and assign values ​​to each preceding associated knowledge point and subsequent impact knowledge point of the single weak knowledge point according to analysis factors to obtain an association score; The arithmetic mean of all associated scores involved in a single weak knowledge point is extracted to calculate the analysis factor discrimination score of the single weak knowledge point. The analysis factor discrimination score is multiplied by the preset factor weight and the sum is taken to obtain the knowledge point association impact data.

7. The intelligent teaching evaluation method based on data analysis according to claim 1 is characterized in that: Based on the analysis results of weak knowledge points, the initial evaluation indicators are updated and a teaching suggestion report is generated, including: Acquiring teaching condition data, wherein the teaching condition data includes the number of available class hours and the teacher's teaching schedule; Update the initial evaluation indicators based on the overall class mastery and the correlation between previous and next knowledge points for each of the weak knowledge points; According to the teaching condition data, a teaching focus adjustment plan that conforms to the existing teaching time is determined, and based on the teaching focus adjustment plan, a teaching suggestion report including a teaching schedule, key explanation content and targeted exercise suggestions is generated.

8. An intelligent teaching evaluation system based on data analysis, characterized in that: include: The initial evaluation indicator acquisition module is used to respond to the knowledge point evaluation trigger instruction and obtain the initial evaluation indicators of the course knowledge point system; The knowledge point mastery coefficient calculation module is used to obtain each student's knowledge point historical learning data and calculate the knowledge point mastery coefficient based on the historical learning data; A graded warning module is used to calculate the overall mastery of the class based on the mastery coefficient of the knowledge point, and to provide graded warnings for the overall mastery of the class based on a preset warning threshold; The correlation impact analysis module is used to identify weak knowledge points where the overall mastery of the class is below the warning threshold, and analyze the previous correlation and subsequent impact of the weak knowledge points; The teaching suggestion report generation module is used to update the initial evaluation indicators according to the weak knowledge point analysis results and generate a teaching suggestion report.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the intelligent teaching evaluation method based on data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent teaching evaluation method based on data analysis described in any one of claims 1 to 7 are implemented.

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