Education evaluation system based on artificial intelligence

Abstract

The invention provides an artificial intelligence-based education evaluation system, which establishes a multi-dimensional student evaluation standard feature set and a multi-dimensional teacher evaluation standard feature set by crawling historical data, and respectively substitutes collected data uploaded by a teacher terminal, a school terminal, a student terminal and a parent terminal into an education matching degree model for matching degree calculation. The multi-dimensional student evaluation matching degree and the multi-dimensional teacher evaluation matching degree are subjected to weighted calculation according to a preset weight value to obtain a student learning evaluation result and a teacher teaching evaluation result, and the results are fed back to the teacher terminal, the school terminal, the student terminal and the parent terminal. According to the invention, the evaluation of student learning and teacher teaching is more objective and reasonable, and students, parents, teachers and schools are all used as participants of evaluation, so that the students, parents, teachers and schools can know education conditions in time, close connection among the students, parents, teachers and schools is increased, and good sustainable development of education relationships is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of education, and in particular to an education evaluation system based on artificial intelligence. Background Art

[0002] By evaluating education, we can not only establish a close connection between students and teachers and improve teaching standards, but also promote the healthy development of students' physical and mental health, and help students establish a correct outlook on life, values ​​and world view.

[0003] Conventional education evaluation methods are mostly that teachers rate students based on their grades and daily performance, and schools and parents rate teachers based on students' feedback. This evaluation method is highly subjective, lacks a reasonable judgment mechanism, and cannot establish close links between students, parents, teachers, and schools, which is not conducive to the good and sustainable development of the education environment.

[0004] Therefore, there is an urgent need for an artificial intelligence-based education assessment system that can solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an education assessment system based on artificial intelligence to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides the following solutions: The present invention provides an education evaluation system based on artificial intelligence, comprising: The teacher side is used to upload teaching plans, teaching content, teaching progress, and view teacher teaching evaluation results and student learning evaluation results; The school side is used to upload student academic performance, teacher assessment results, student daily performance, teacher daily performance, student rewards and punishments, teacher rewards and punishments, and view teacher teaching evaluation results and student learning evaluation results; The student side is used to upload learning plans, learning content, learning progress, and view student learning evaluation results; The parent side is used to upload the student's family performance, the student's physical and mental health, and view the student's learning evaluation results; The artificial intelligence evaluation platform is used to obtain the original data source list data, use the preset data source crawling technology to crawl the original data source list data to obtain the original data set, conduct educational data quality review and screening on the original data set, and obtain multidimensional student evaluation standard features and multidimensional teacher evaluation standard features by performing multidimensional feature extraction on the original data set. The multidimensional student evaluation standard features and the multidimensional teacher evaluation standard features are trained with an associated coupling model to obtain an education matching model, and the collected data uploaded by the teacher side, school side, student side and parent side are respectively substituted into the education matching model for matching calculation to obtain multidimensional student evaluation matching and multidimensional teacher evaluation matching. The multidimensional student evaluation matching and the multidimensional teacher evaluation matching are weightedly calculated according to preset weight values ​​to obtain student learning evaluation results and teacher teaching evaluation results, and the results are fed back to the teacher side, school side, student side and parent side.

[0007] Preferably, the teacher side, school side, student side and parent side all include an identity authentication module, a data upload module and a result receiving module.

[0008] Preferably, the artificial intelligence evaluation platform includes a data crawling module, an evaluation standard feature extraction module, a model building analysis and calculation module, and a data distribution module.

[0009] The present invention also provides an education assessment method based on artificial intelligence, comprising the following steps: S1. The artificial intelligence evaluation platform collects data from the original data source list, crawls the data from the original data source list using the preset data source crawling technology, obtains the original data set, extracts multidimensional features from the original data set, obtains a multidimensional student evaluation standard feature set and a multidimensional teacher evaluation standard feature set, and presets evaluation weight values ​​for all features in the set; S2. Teachers, schools, students, and parents upload student education data and teacher education data through the teacher end, school end, student end, and parent end respectively. The uploaded data is classified and sorted to establish a mapping relationship with the feature items in the multidimensional student evaluation standard feature set and the multidimensional teacher evaluation standard feature set. The uploaded data is scored using the education matching model, and the score is divided by the highest score of the scoring standard to obtain the student evaluation matching degree and teacher evaluation matching degree; S3. Perform weighted calculation on the student evaluation matching degree and the teacher evaluation matching degree according to the preset weight value to obtain the student learning evaluation result and the teacher teaching evaluation result; S4. Send the student learning evaluation results to the teacher side, school side, student side and parent side, and send the teacher teaching evaluation results to the teacher side and school side.

[0010] Preferably, in step S1, crawling the original data source list data using a preset data source crawling technology includes: using a preset visual recognition model to identify and extract data from the augmented reality data source list data; using a preset natural language processing crawler to identify and extract natural language keywords from the text language data source list data; and performing semantic analysis and extraction on the natural language data source list data.

[0011] Preferably, in step S1, the educational data quality review and screening of the original data set includes: cleaning the data, and labeling the cleaned data to obtain preprocessed data, wherein: The formula for data cleaning is: ; y is the cleaning data, x is the attribute item of a specific item in the original data set, a is the cleaning base constant item, n is the number of specific item data in the original data set, f(x) is the first data cleaning function selected according to x, g(x) is the second data cleaning function selected according to x, h(x) is the third data cleaning function selected according to x, k(x) is the fourth data cleaning function selected according to x, c is the cleaning amplitude item, and b is the cleaning amplitude item.

[0012] Preferably, in step S2, the scoring method adopts a fuzzy comprehensive evaluation model, a hierarchical analysis model or a multi-attribute decision-making model.

[0013] Preferably, in step S3, the calculation method of the student learning evaluation result is: A=a1*p1+a2*p2+…+a n-1 *p n-1 +a n *p n ; Among them, A is the student learning evaluation result, a is the preset evaluation weight value of the student evaluation item, p is the student evaluation matching degree of the student evaluation item, and 1 to n are the 1st student evaluation item to the nth student evaluation item.

[0014] Preferably, in step S3, the calculation method of the teacher teaching evaluation result is: B=b1*q1+b2*q2+…+b n-1 *q n-1 +b n *q n ; Among them, B is the teacher's teaching evaluation result, b is the preset evaluation weight value of the student evaluation item, q is the student evaluation matching degree of the student evaluation item, and 1 to n are the 1st teaching evaluation item to the nth teaching evaluation item.

[0015] Preferably, it also includes: S5. Supplement historical education data with student learning assessment results and teacher teaching evaluation results.

[0016] Compared with the prior art, the present invention has achieved the following beneficial technical effects: The present invention provides an artificial intelligence-based education evaluation system, which establishes a multidimensional student evaluation standard feature set and a multidimensional teacher evaluation standard feature set by crawling historical data, and substitutes the collected data uploaded by the teacher side, school side, student side and parent side into the education matching model for matching calculation, thereby obtaining a multidimensional student evaluation matching degree and a multidimensional teacher evaluation matching degree, and performs weighted calculation on the multidimensional student evaluation matching degree and the multidimensional teacher evaluation matching degree according to preset weight values ​​to obtain a student learning evaluation result and a teacher teaching evaluation result, and feeds the results back to the teacher side, school side, student side and parent side, so that the evaluation of student learning and teacher teaching is more objective and reasonable, and students, parents, teachers and schools are all participants in the evaluation, so that all parties can understand the education situation in a timely manner, increase the close connection between the four parties, and are conducive to the good and sustainable development of educational relations. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. 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.

[0018] The purpose of the present invention is to provide an education evaluation system based on artificial intelligence to solve the problems existing in the prior art.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with specific implementation methods.

[0020] Embodiment 1: This embodiment provides an education evaluation system based on artificial intelligence, including: The teacher side is used to upload teaching plans, teaching content, teaching progress, and view teacher teaching evaluation results and student learning evaluation results; The school side is used to upload student academic performance, teacher assessment results, student daily performance, teacher daily performance, student rewards and punishments, teacher rewards and punishments, and view teacher teaching evaluation results and student learning evaluation results; The student side is used to upload learning plans, learning content, learning progress, and view student learning evaluation results; The parent side is used to upload the student's family performance, the student's physical and mental health, and view the student's learning evaluation results; The artificial intelligence evaluation platform is used to obtain the original data source list data, use the preset data source crawling technology to crawl the original data source list data to obtain the original data set, conduct educational data quality review and screening on the original data set, and obtain multidimensional student evaluation standard features and multidimensional teacher evaluation standard features by performing multidimensional feature extraction on the original data set. The multidimensional student evaluation standard features and the multidimensional teacher evaluation standard features are trained with an associated coupling model to obtain an education matching model, and the collected data uploaded by the teacher side, school side, student side and parent side are respectively substituted into the education matching model for matching calculation to obtain multidimensional student evaluation matching and multidimensional teacher evaluation matching. The multidimensional student evaluation matching and the multidimensional teacher evaluation matching are weightedly calculated according to preset weight values ​​to obtain student learning evaluation results and teacher teaching evaluation results, and the results are fed back to the teacher side, school side, student side and parent side.

[0021] The teacher side, school side, student side and parent side all include an identity authentication module, a data upload module and a result receiving module.

[0022] The artificial intelligence evaluation platform includes a data crawling module, an evaluation standard feature extraction module, a model building analysis and calculation module, and a data distribution module. The data crawling module is mainly used to collect data. It should be understood that after data collection, it should be stored in a database to facilitate subsequent calls and archiving.

[0023] The artificial intelligence-based educational assessment method using the above system includes the following steps: S1. The artificial intelligence evaluation platform collects data from the original data source list, crawls the data from the original data source list using the preset data source crawling technology, obtains the original data set, extracts multidimensional features from the original data set, obtains a multidimensional student evaluation standard feature set and a multidimensional teacher evaluation standard feature set, and presets evaluation weight values ​​for all features in the set; Using the preset data source crawling technology to crawl the original data source list data includes: using the preset visual recognition model to identify and extract data from the augmented reality data source list data; using the preset natural language processing crawler to identify and extract natural language keywords from the text language data source list data; and performing semantic analysis and extraction on the natural language data source list data.

[0024] The educational data quality review and screening of the original data set includes: cleaning the data, labeling the cleaned data, and obtaining pre-processed data, where: The formula for data cleaning is: ; y is the cleaned data, x is the attribute item of the specific item in the original data set, a is the cleaning base constant item, n is the number of specific item data in the original data set, f(x) is the first data cleaning function selected according to x, g(x) is the second data cleaning function selected according to x, h(x) is the third data cleaning function selected according to x, k(x) is the fourth data cleaning function selected according to x, c is the cleaning amplitude item, and b is the cleaning amplitude item; Taking the mathematics teaching evaluation of the second class of the third grade of a primary school as an example, the historical data collected can include the teaching plan of the semester in the past ten years, the teaching progress at each time point, the test scores, the final test scores, whether students have won competition awards, whether teachers have won outstanding subject awards, etc. S2. Teachers, schools, students, and parents upload student education data and teacher education data through the teacher end, school end, student end, and parent end respectively. The uploaded data is classified and sorted to establish a mapping relationship with the feature items in the multidimensional student evaluation standard feature set and the multidimensional teacher evaluation standard feature set. The uploaded data is scored using the education matching model, and the score is divided by the highest score of the scoring standard to obtain the student evaluation matching degree and teacher evaluation matching degree; For example, for the math midterm exam score, the lowest score for the third grade in the first semester in the historical data is 64 points and the highest is 100 points. So the score below 64 can be recorded as 1, 64-67 points as 2, 68-71 points as 3, 72-75 points as 4, 76-79 points as 5, 80-83 points as 6, 84-87 points as 7, 88-91 points as 8, 92-95 points as 9, and 96 points and above as 10. If a student scores 89 points, then his score in this evaluation item is 8, and the evaluation matching degree of this item is 0.8; Of course, for some project data that cannot be quantitatively analyzed, the scoring method in the fuzzy comprehensive evaluation model, hierarchical analysis model or multi-attribute decision-making model can be used for scoring; S3. Perform weighted calculation on the student evaluation matching degree and the teacher evaluation matching degree according to the preset weight value to obtain the student learning evaluation result and the teacher teaching evaluation result; The calculation method of student learning evaluation results is: A=a1*p1+a2*p2+…+a n-1 *p n-1 +a n *p n ; Among them, A is the student learning evaluation result, a is the preset evaluation weight value of the student evaluation project, p is the student evaluation matching degree of the student evaluation project, and 1 to n are the first student evaluation project to the nth student evaluation project; The calculation method of teacher teaching evaluation results is: B=b1*q1+b2*q2+…+b n-1 *q n-1 +b n *q n ; Among them, B is the teacher's teaching evaluation result, b is the preset evaluation weight value of the student evaluation project, q is the student evaluation matching degree of the student evaluation project, and 1 to n are the first teaching evaluation project to the nth teaching evaluation project; S4. Send the student learning evaluation results to the teacher side, school side, student side and parent side, and send the teacher teaching evaluation results to the teacher side and school side; S5. Supplement historical education data with student learning assessment results and teacher teaching evaluation results to enrich the data.

[0025] The artificial intelligence-based education evaluation system provided by the present invention makes the evaluation of student learning and teacher teaching more objective and reasonable, and students, parents, teachers, and schools are all participants in the evaluation, so that all parties can understand the education situation in a timely manner, increase the close connection between the four parties, and are conducive to the good and sustainable development of educational relations.

[0026] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An artificial intelligence-based education assessment system, characterized by: include: The teacher side is used to upload teaching plans, teaching content, teaching progress, and view teacher teaching evaluation results and student learning evaluation results; The school side is used to upload student academic performance, teacher assessment results, student daily performance, teacher daily performance, student rewards and punishments, teacher rewards and punishments, and view teacher teaching evaluation results and student learning evaluation results; The student side is used to upload learning plans, learning content, learning progress, and view student learning evaluation results; The parent side is used to upload student family performance information, student physical and mental health status, and view student learning assessment results; The artificial intelligence evaluation platform is used to obtain the original data source list data, use the preset data source crawling technology to crawl the original data source list data to obtain the original data set, conduct educational data quality review and screening on the original data set, and obtain multidimensional student evaluation standard features and multidimensional teacher evaluation standard features by performing multidimensional feature extraction on the original data set. The multidimensional student evaluation standard features and the multidimensional teacher evaluation standard features are trained with an associated coupling model to obtain an education matching model, and the collected data uploaded by the teacher side, school side, student side and parent side are respectively substituted into the education matching model for matching calculation to obtain multidimensional student evaluation matching and multidimensional teacher evaluation matching. The multidimensional student evaluation matching and the multidimensional teacher evaluation matching are weightedly calculated according to preset weight values ​​to obtain student learning evaluation results and teacher teaching evaluation results, and the results are fed back to the teacher side, school side, student side and parent side.

2. The artificial intelligence-based education assessment system according to claim 1, characterized in that: The teacher side, school side, student side and parent side all include an identity authentication module, a data upload module and a result receiving module.

3. The artificial intelligence-based education assessment system according to claim 1, characterized in that: The artificial intelligence evaluation platform includes a data crawling module, an evaluation standard feature extraction module, a model building analysis and calculation module, and a data distribution module.

4. The artificial intelligence-based educational assessment method according to any one of claims 1 to 3, characterized in that: The following steps are involved: S1. The artificial intelligence evaluation platform collects data from the original data source list, crawls the data from the original data source list using the preset data source crawling technology, obtains the original data set, extracts multidimensional features from the original data set, obtains a multidimensional student evaluation standard feature set and a multidimensional teacher evaluation standard feature set, and presets evaluation weight values ​​for all features in the set; S2. Teachers, schools, students, and parents upload student education data and teacher education data through the teacher end, school end, student end, and parent end respectively. The uploaded data is classified and sorted to establish a mapping relationship with the feature items in the multidimensional student evaluation standard feature set and the multidimensional teacher evaluation standard feature set. The uploaded data is scored using the education matching model, and the score is divided by the highest score of the scoring standard to obtain the student evaluation matching degree and teacher evaluation matching degree; S3. Perform weighted calculation on the student evaluation matching degree and the teacher evaluation matching degree according to the preset weight value to obtain the student learning evaluation result and the teacher teaching evaluation result; S4. Send the student learning evaluation results to the teacher side, school side, student side and parent side, and send the teacher teaching evaluation results to the teacher side and school side.

5. The artificial intelligence-based educational assessment method according to claim 4, characterized in that: In step S1, crawling the original data source list data using the preset data source crawling technology includes: using the preset visual recognition model to identify and extract data from the augmented reality data source list data; using the preset natural language processing crawler to identify and extract natural language keywords from the text language data source list data; and performing semantic analysis and extraction on the natural language data source list data.

6. The artificial intelligence-based educational assessment method according to claim 4, characterized in that: In step S1, the quality review and screening of the original data set includes: cleaning the data, labeling the cleaned data, and obtaining pre-processed data, wherein: The formula for data cleaning is: ; y is the cleaning data, x is the attribute item of a specific item in the original data set, a is the cleaning base constant item, n is the number of specific item data in the original data set, f(x) is the first data cleaning function selected according to x, g(x) is the second data cleaning function selected according to x, h(x) is the third data cleaning function selected according to x, k(x) is the fourth data cleaning function selected according to x, c is the cleaning amplitude item, and b is the cleaning amplitude item.

7. The artificial intelligence-based educational assessment method according to claim 4, characterized in that: In step S2, the scoring method adopts a fuzzy comprehensive evaluation model, a hierarchical analysis model or a multi-attribute decision-making model.

8. The artificial intelligence-based educational assessment method according to claim 4, characterized in that: In step S3, the calculation method of the student learning evaluation result is: A=a1*p1+a2*p2+…+a n-1 *p n-1 +a n *p n ; Among them, A is the student learning evaluation result, a is the preset evaluation weight value of the student evaluation item, p is the student evaluation matching degree of the student evaluation item, and 1 to n are the 1st student evaluation item to the nth student evaluation item.

9. The artificial intelligence-based educational assessment method according to claim 4, characterized in that: In step S3, the calculation method of the teacher teaching evaluation result is: B=b1*q1+b2*q2+…+b n-1 *q n-1 +b n *q n ; Among them, B is the teacher's teaching evaluation result, b is the preset evaluation weight value of the student evaluation item, q is the student evaluation matching degree of the student evaluation item, and 1 to n are the 1st teaching evaluation item to the nth teaching evaluation item.

10. The artificial intelligence-based educational assessment method according to claim 4, characterized in that: Also includes: S5. Supplement historical education data with student learning assessment results and teacher teaching evaluation results.