Intelligent evaluation method for teaching effect of ideological and political classroom

Abstract

The invention relates to the technical field of big data analysis, in particular to an intelligent evaluation method for the teaching effect of an ideological and political classroom. The method comprises the following steps: S1, selecting key indexes reflecting teaching effects; s2, acquiring specific score data of each index through an educational administration system; s3, carrying out standardization processing on the collected scoring data to eliminate dimensional influence among different indexes; s4, multiplying the standardized score by a corresponding weight and summing to obtain a final teaching effect score of each student; and S5, calculating and comparing the average score of each class, and performing data analysis based on the score result to obtain the teaching effect difference of different classes. And multiplying the standardized score by a corresponding weight and summing to obtain a final teaching effect score of each student. The weight distribution is adjusted according to the actual demand and importance, so that the evaluation result is more reasonable and comprehensive. And the average score of each class is calculated and compared, so that the teaching effect difference of different classes can be known.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and specifically, to an intelligent evaluation method for the teaching effect of ideological and political classes. Background Art

[0002] The intelligent evaluation method of ideological and political classroom teaching effect is an innovative solution that combines modern information technology and educational theory. It aims to conduct a comprehensive, objective and dynamic evaluation of the teaching quality and student learning effect of ideological and political education classrooms through advanced technical means such as big data analysis, artificial intelligence, and cloud computing. The method first relies on extensive data collection, including data from multiple dimensions such as students' classroom participation, homework submission, test scores, and online learning behavior. These data are uploaded to the cloud server through the Internet or campus network. Then, the collected data is deeply analyzed using machine learning algorithms to identify the key factors affecting the teaching effect, and a prediction model is constructed to provide teachers with personalized teaching suggestions. In addition, with the help of natural language processing technology, the system can also automatically correct student homework and test papers, and even perform sentiment analysis on students' answers to further enrich the evaluation dimensions. Finally, all analysis results will be presented to teachers and managers in an intuitive and easy-to-understand form, helping them to understand the current teaching situation in a timely manner, discover potential problems, and take corresponding measures to optimize the teaching process. This evaluation method not only improves the efficiency and accuracy of the evaluation, but also provides strong support for promoting the modernization of ideological and political education.

[0003] In the existing intelligent evaluation methods of ideological and political classroom teaching effects, relying too much on technical means may ignore the emotional communication between teachers and students and personalized teaching needs, resulting in evaluation results that are too mechanical and lack flexibility; at the same time, there may be privacy protection issues in the process of data collection and processing, which may cause concerns among students or parents if not handled properly; in addition, the existing evaluation models and technologies may not fully cover the complexity and diversity of ideological and political education, especially when evaluating students' deep-level goals such as value formation and moral cognition, it is difficult to fully and accurately reflect the true effect of teaching. Therefore, an intelligent evaluation method for ideological and political classroom teaching effects is provided. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent evaluation method for the teaching effect of ideological and political classrooms, so as to solve the problems raised in the above-mentioned background technology that in the existing intelligent evaluation methods for the teaching effect of ideological and political classrooms, the reliance on technical means may ignore the emotional communication between teachers and students and the personalized teaching needs, resulting in the evaluation results being too mechanical and lacking in flexibility; at the same time, there may be privacy protection issues in the process of data collection and processing, which may cause concerns among students or parents if not handled properly; in addition, the existing evaluation models and technologies may not fully cover the complexity and diversity of ideological and political education, especially when evaluating students' deep-level goals such as value formation and moral cognition, it is difficult to fully and accurately reflect the true effect of teaching.

[0005] To achieve the above object, the present invention aims to provide an intelligent evaluation method for the teaching effect of ideological and political classroom, comprising the following steps: S1. Select key indicators that reflect teaching effectiveness; S2. Obtain the specific scoring data of each indicator through the academic affairs system; S3. Standardize the collected scoring data to eliminate the dimensional effects between different indicators; S4, multiply the standardized scores by the corresponding weights and sum them up to obtain the final teaching effect score for each student; S5. Calculate the average score of each class and compare them. Perform data analysis based on the score results to obtain the differences in teaching effectiveness among different classes.

[0006] As a further improvement of the present technical solution, in S1, the key indicators reflecting the teaching effect are selected to specifically include paper scores, class participation, homework completion quality, group cooperation and discussion performance.

[0007] As a further improvement of the technical solution, in S2, the specific process of obtaining the specific scoring data of each indicator through the teaching affairs system is as follows: S2.1. Obtain students’ test scores through the academic affairs system; S2.2, obtain the teacher's record of the number and quality of students' speeches in each class from the teaching system; S2.3, obtain the teacher's score based on the accuracy and completeness of the assignment from the teaching system; S2.4. Obtain teachers’ records of students’ performance in group activities from the academic affairs system.

[0008] As a further improvement of the technical solution, in S3, the specific steps of standardizing the collected scoring data are: S3.1. Sort the data of each indicator and find the minimum and maximum values; S3.2, use the maximum and minimum normalization algorithm to standardize the score data of each student; S3.3. Store the standardized scoring data in the database for subsequent calculation and analysis.

[0009] As a further improvement of the technical solution, in S3.2, the specific expression of the maximum and minimum normalization algorithm is: ; in, Indicates The raw scores of the indicators; Indicates The minimum value of an indicator; Indicates The maximum value of the indicators; Represents the normalized score.

[0010] As a further improvement of the technical solution, in S4, the specific steps of multiplying the standardized score by the corresponding weight and summing them to obtain the final teaching effect score of each student are: S4.1. Extract the standardized scoring data of each student from the database; S4.2. Calculate the final score of each student using a weighted sum algorithm; S4.3. Store the final score in the database.

[0011] As a further improvement of the technical solution, in S4.2, the specific expression of the weighted summation algorithm is: ; in, represents the final score of each student; Indicates the weight of the paper score; represents the weight of class participation; The weight representing the quality of job completion; Indicates the weight of group work and discussion performance; Indicates the standardized score of the test paper; A standardized score indicating class participation; A standardized score that indicates the quality of the work completed; A standardized score representing group cooperation and discussion performance.

[0012] As a further improvement of the technical solution, in S5, the specific steps of calculating and comparing the average scores of each class and visually displaying the differences in teaching effects of different classes are: S5.1. Calculate the average of all students’ final scores to get the overall score of the class; S5.2. Use charts to display the overall scores of different classes to visually compare the teaching effects of each class; S5.3. Analyze the differences between classes through the variance analysis algorithm to find out the classes with excellent performance and those that need improvement.

[0013] As a further improvement of the technical solution, in S5.1, the average of the final scores of all students is calculated to obtain the specific expression of the overall score of the class: ; in, Indicates the overall class score; Indicates Final score of each student; Indicates the number of students in the class.

[0014] As a further improvement of the technical solution, in S5.3, the specific steps of analyzing the differences between classes by the variance analysis algorithm are: Compute the total sum of squares: ; in, Indicates the number of classes; Indicates Number of students in a class; Indicates The first Final score of each student; represents the average rating of all students; Compute the between-group sum of squares: ; in, Indicates The average rating of the class; represents the sum of squares between groups; Compute the within-group sum of squares: ; in, represents the within-group sum of squares; Calculate the mean square error: ; ; in, Represents the total number of all students; represents the mean square error between groups; represents the within-group mean square error; Calculate the degrees of freedom: ; ; in, represents the degrees of freedom between groups; represents the within-group degrees of freedom; Calculate statistics: ; in, represents a statistic; The significance level chosen is 0.05; According to the degree of freedom and Look up the F distribution table to get the critical value ; , it is considered that there are significant differences in the scores between different classes; , it is considered that there is no significant difference in the scores between different classes.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the intelligent evaluation method of the teaching effect of ideological and political classroom, key indicators that can reflect the teaching effect are selected through scientific methods to ensure the rationality and scientificity of the evaluation system. The scoring data is obtained through the teaching system to ensure the accuracy and reliability of the data and reduce human errors. The scoring data of different indicators are standardized to eliminate the dimensional influence between different indicators, so that the data of different indicators can be directly compared and comprehensively evaluated, which improves the fairness and consistency of the evaluation.

[0016] 2. In the intelligent evaluation method of the teaching effect of ideological and political classroom, the standardized score is multiplied by the corresponding weight and summed to obtain the final teaching effect score of each student. The distribution of weights is adjusted according to actual needs and importance, making the evaluation results more reasonable and comprehensive. By calculating the average score of each class and comparing them, we can understand the differences in teaching effects between different classes. This helps school management and teachers to find problems in time and take targeted improvement measures. Data analysis based on the scoring results can not only find the differences in teaching effects between different classes, but also further explore the factors affecting teaching effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, this embodiment provides an intelligent evaluation method for ideological and political classroom teaching effects, including the following steps: S1. Select key indicators that reflect teaching effectiveness; In this example, the key indicators selected to reflect teaching effectiveness include paper scores, class participation, homework completion quality, and group cooperation and discussion performance.

[0020] Specifically, the selection of key indicators that reflect teaching effectiveness (such as paper scores, class participation, homework completion quality, group cooperation and discussion performance) plays a vital role. These indicators not only cover students' performance in knowledge mastery, classroom interaction, task completion and teamwork, but also can comprehensively and multi-dimensionally evaluate teaching effectiveness. Through these key indicators, the comprehensive quality and development status of students can be more accurately reflected, and scientific feedback can be provided to teachers to help them adjust teaching strategies in a timely manner, thereby improving teaching quality. In addition, these indicators also provide students with clear learning goals and improvement directions, promoting their all-round development.

[0021] S2. Obtain the specific scoring data of each indicator through the academic affairs system; In this example, the specific process of obtaining the specific scoring data of each indicator through the teaching affairs system is as follows: S2.1. Obtain students’ test scores through the academic affairs system; S2.2, obtain the teacher's record of the number and quality of students' speeches in each class from the teaching system; S2.3, obtain the teacher's score based on the accuracy and completeness of the assignment from the teaching system; S2.4. Obtain teachers’ records of students’ performance in group activities from the academic affairs system.

[0022] Specifically, the educational system includes students' test scores, teachers' classroom records, teachers' homework grading records, and teachers' performance records of students.

[0023] The specific scoring data of each indicator is obtained through the teaching system to ensure the objectivity and accuracy of the evaluation. The acquisition of this data not only reduces the errors and workload of manual recording, but also improves the integrity and timeliness of the data. It provides reliable data support for subsequent standardization processing, weighted summation and comparison between classes, making the evaluation of teaching effectiveness more scientific and efficient. This helps teachers to fully understand the multi-dimensional performance of students and adjust teaching strategies in a timely manner, thereby improving teaching quality.

[0024] S3. Standardize the collected scoring data to eliminate the dimensional effects between different indicators; In this example, the specific steps for standardizing the collected scoring data are: S3.1. Sort the data of each indicator and find the minimum and maximum values; S3.2, use the maximum and minimum normalization algorithm to standardize the score data of each student; In this example, the specific expression of the maximum and minimum normalization algorithm is: ; in, Indicates The raw scores of the indicators; Indicates The minimum value of an indicator; Indicates The maximum value of the indicators; Represents the normalized score.

[0025] S3.3. Store the standardized scoring data in the database for subsequent calculation and analysis.

[0026] Specifically, by sorting the data of each indicator, finding the minimum and maximum values, and then using the maximum and minimum normalization algorithm to standardize the scoring data, the dimensional influence between different indicators is eliminated, and the comparability and consistency of the data are improved. The standardized scoring data is stored in the database, providing a reliable basis for subsequent weighted summation, inter-class comparison and comprehensive analysis, ensuring the objectivity and scientificity of the teaching effect evaluation.

[0027] S4, multiply the standardized scores by the corresponding weights and sum them up to obtain the final teaching effect score for each student; In this example, the specific steps of multiplying the standardized scores by the corresponding weights and summing them up to obtain the final teaching effect score of each student are as follows: S4.1. Extract the standardized scoring data of each student from the database; S4.2. Calculate the final score of each student using a weighted sum algorithm; In this example, the specific expression of the weighted summation algorithm is: ; in, represents the final score of each student; Indicates the weight of the paper score; represents the weight of class participation; The weight representing the quality of job completion; Indicates the weight of group work and discussion performance; Indicates the standardized score of the test paper; A standardized score indicating class participation; A standardized score that indicates the quality of the work completed; A standardized score representing group cooperation and discussion performance.

[0028] S4.3. Store the final score in the database.

[0029] Specifically, this process combines the standardized scores of multiple evaluation indicators through a weighted summation algorithm, eliminates the dimensional effects between different indicators, and ensures the comparability and consistency of the data. The final score fully reflects the overall performance of the students, provides scientific and accurate data support for teachers and managers, helps them fully understand the learning situation of each student, adjust teaching strategies in a timely manner, and improve teaching quality. At the same time, the final score can also be used to provide personalized feedback to students, point out strengths and weaknesses, help students improve their learning methods, and promote all-round development. In short, this process ensures the scientificity and comprehensiveness of the teaching effect evaluation, and provides strong support for teaching improvement and student development.

[0030] S5. Calculate the average score of each class and compare them. Perform data analysis based on the score results to obtain the differences in teaching effectiveness among different classes.

[0031] In this example, the specific steps to calculate and compare the average scores of each class and visually display the differences in teaching effectiveness between different classes are as follows: S5.1. Calculate the average of all students’ final scores to get the overall score of the class; In this example, the specific expression for calculating the average of all students' final scores and obtaining the overall score of the class is: ; in, Indicates the overall class score; Indicates Final score of each student; Indicates the number of students in the class.

[0032] S5.2. Use charts to display the overall scores of different classes to visually compare the teaching effects of each class; S5.3. Analyze the differences between classes through the variance analysis algorithm to find out the classes with excellent performance and those that need improvement.

[0033] In this example, the specific steps for analyzing the differences between classes using the variance analysis algorithm are: Compute the total sum of squares: ; in, Indicates the number of classes; Indicates Number of students in a class; Indicates The first Final score of each student; represents the average rating of all students; Compute the between-group sum of squares: ; in, Indicates The average rating of the class; represents the sum of squares between groups; Compute the within-group sum of squares: ; in, represents the within-group sum of squares; Calculate the mean square error: ; ; in, Represents the total number of all students; represents the mean square error between groups; represents the within-group mean square error; Calculate the degrees of freedom: ; ; in, represents the degrees of freedom between groups; represents the within-group degrees of freedom; Calculate statistics: ; in, represents a statistic; The significance level chosen is 0.05; According to the degree of freedom and Look up the F distribution table to get the critical value ; , it is considered that there are significant differences in the scores between different classes; , it is considered that there is no significant difference in the scores between different classes.

[0034] Specifically, the implementation examples are: Suppose we have two classes A and B, each with 5 students: Significance level : ; Calculate the degrees of freedom: Degrees of freedom between groups : ; Within degrees of freedom : ; In the F distribution table, find the significance level , degrees of freedom and The critical value . Assume that we get .

[0035] Python example: from scipy.stats import f # Set significance level and degrees of freedom alpha = 0.05 df1 = 1 df2 = 8 # Get the critical value F_critical = f.ppf(1 - alpha, df1, df2) print(F_critical) Assume that the critical value obtained by the above method is ,So: , it is considered that there are significant differences in the scores between different classes; , it is considered that there is no significant difference in the scores between different classes.

[0036] By calculating the overall score of each class and using charts to visually display it, teachers and managers can quickly identify classes with excellent performance and those that need improvement. Further, the variance analysis algorithm is used to scientifically test whether there are significant differences in the scores between different classes, providing a scientific basis for teaching improvement, ensuring the scientific and comprehensive evaluation of teaching effects, and helping teachers to fully understand the teaching situation, adjust teaching strategies in a timely manner, and improve teaching quality.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent evaluation method for the teaching effect of ideological and political classroom, characterized by: The following steps are involved: S1. Select key indicators that reflect teaching effectiveness; S2. Obtain the specific scoring data of each indicator through the academic affairs system; S3. Standardize the collected scoring data to eliminate the dimensional effects between different indicators; S4, multiply the standardized scores by the corresponding weights and sum them up to obtain the final teaching effect score for each student; S5. Calculate the average score of each class and compare them. Perform data analysis based on the score results to obtain the differences in teaching effectiveness among different classes.

2. According to claim 1, the intelligent evaluation method for the ideological and political classroom teaching effect is characterized by: In S1, the key indicators reflecting the teaching effectiveness include paper scores, class participation, homework completion quality, group cooperation and discussion performance.

3. According to claim 1, the intelligent evaluation method for the ideological and political classroom teaching effect is characterized by: In S2, the specific process of obtaining the specific scoring data of each indicator through the teaching affairs system is as follows: S2.

1. Obtain students’ test scores through the academic affairs system; S2.2, obtain the teacher's record of the number and quality of students' speeches in each class from the teaching system; S2.3, obtain the teacher's score based on the accuracy and completeness of the assignment from the teaching system; S2.

4. Obtain teachers’ records of students’ performance in group activities from the academic affairs system.

4. According to claim 1, the intelligent evaluation method for the teaching effect of ideological and political classroom is characterized by: In S3, the specific steps of standardizing the collected scoring data are as follows: S3.

1. Sort the data of each indicator and find the minimum and maximum values; S3.2, use the maximum and minimum normalization algorithm to standardize the score data of each student; S3.

3. Store the standardized scoring data in the database for subsequent calculation and analysis.

5. According to claim 4, the intelligent evaluation method for the teaching effect of ideological and political classroom is characterized by: In S3.2, the specific expression of the maximum and minimum normalization algorithm is: ； in, Indicates The raw scores of the indicators; Indicates The minimum value of an indicator; Indicates The maximum value of the indicators; Represents the normalized score.

6. The intelligent evaluation method for the ideological and political classroom teaching effect according to claim 1 is characterized by: In S4, the specific steps of multiplying the standardized score by the corresponding weight and summing them to obtain the final teaching effect score of each student are: S4.

1. Extract the standardized scoring data of each student from the database; S4.

2. Calculate the final score of each student using a weighted sum algorithm; S4.

3. Store the final score in the database.

7. The intelligent evaluation method for the ideological and political classroom teaching effect according to claim 6 is characterized by: In S4.2, the specific expression of the weighted summation algorithm is: ； in, represents the final score of each student; Indicates the weight of the paper score; represents the weight of class participation; The weight representing the quality of job completion; Indicates the weight of group work and discussion performance; Indicates the standardized score of the test paper; A standardized score indicating class participation; A standardized score that indicates the quality of the work completed; A standardized score representing group cooperation and discussion performance.

8. The intelligent evaluation method for the ideological and political classroom teaching effect according to claim 1 is characterized by: In S5, the specific steps of calculating and comparing the average scores of each class and visually displaying the differences in teaching effects of different classes are: S5.

1. Calculate the average of all students’ final scores to get the overall score of the class; S5.

2. Use charts to display the overall scores of different classes to visually compare the teaching effects of each class; S5.

3. Analyze the differences between classes through the variance analysis algorithm to find out the classes with excellent performance and those that need improvement.

9. The intelligent evaluation method for the ideological and political classroom teaching effect according to claim 8 is characterized by: In S5.1, the average of the final scores of all students is calculated to obtain the specific expression of the overall score of the class: ； in, Indicates the overall grade of the class; Indicates Final score of each student; Indicates the number of students in the class.

10. The intelligent evaluation method for the ideological and political classroom teaching effect according to claim 8 is characterized by: In S5.3, the specific steps of analyzing the differences between classes by the variance analysis algorithm are: Compute the total sum of squares: ； in, Indicates the number of classes; Indicates Number of students in each class; Indicates The first Final score of each student; represents the average rating of all students; Compute the between-group sum of squares: ； in, Indicates The average rating of the class; represents the sum of squares between groups; Compute the within-group sum of squares: ； in, represents the within-group sum of squares; Calculate the mean square error: ； ； in, Represents the total number of all students; represents the mean square error between groups; represents the within-group mean square error; Calculate the degrees of freedom: ； ； in, represents the degrees of freedom between groups; represents the within-group degrees of freedom; Calculate statistics: ； in, represents a statistic; The significance level chosen is 0.05; According to the degree of freedom and Look up the F distribution table to get the critical value ; , it is considered that there are significant differences in the scores between different classes; , it is considered that there is no significant difference in the scores between different classes.

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