Teaching evaluation methods and related equipment based on knowledge graph and semantic analysis

Abstract

The embodiment of the present application provides a teaching evaluation method and related equipment based on knowledge graph and semantic analysis, which can solve the problem that it is impossible to accurately evaluate students' mastery or the teaching situation of the subject. The method includes: performing semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data to construct a target knowledge graph; generating online test questions for the content of the first subject textbook based on the target knowledge graph, and the test questions are used to evaluate the students' mastery of at least one target knowledge point in the teaching content of the target subject; when the answer data received from the students does not match the standard answer data of the online test questions, the question stem information of the online test questions is semantically analyzed to generate a question stem comprehension test to conduct teaching evaluation on the students.

Description

Technical Field

[0001] This application relates to the field of smart education, and in particular to a teaching evaluation method and related equipment based on knowledge graph and semantic analysis. Background Art

[0002] Smart education is an innovative educational model that deeply integrates modern information technology with education. Its goal is to improve educational quality, promote educational equity, and enable personalized learning and teaching. Traditional, one-size-fits-all education models fail to meet students' individual learning needs. Smart education can provide personalized teaching plans based on each student's learning situation. With the accelerating pace of knowledge update, society's demand for lifelong learning is increasing. Smart education can support flexible learning methods and meet the learning needs of people of different ages. Smart education leverages big data analysis and artificial intelligence technologies to provide personalized learning content and paths based on students' learning behavior, progress, and characteristics, helping them achieve optimal learning outcomes. Through intelligent teaching systems, teachers can access student learning data and feedback, understand their progress, and promptly adjust teaching plans and methods to improve teaching effectiveness.

[0003] However, there is still a lack of accuracy in assessing students' mastery or the teaching of the subject. Summary of the Invention

[0004] The embodiment of the present application provides a teaching evaluation method and related equipment based on knowledge graph and semantic analysis, which can solve the problem that when students take tests such as physics and mathematics application questions, they often misunderstand the meaning of the information expressed in the question stem. Even if the students have mastered the corresponding physics or mathematics subject knowledge points, they still give incorrect answers, resulting in the inability to accurately evaluate the students' mastery level or the teaching situation of the subject.

[0005] A first aspect of an embodiment of the present application provides a teaching evaluation method based on knowledge graph and semantic analysis, comprising:

[0006] Perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct the target knowledge graph;

[0007] Generate online test questions for the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material;

[0008] In the event that the answer data received from the student feedback does not match the standard answer data of the online test questions, a semantic analysis is performed on the stem information of the online test questions to generate a stem comprehension test to conduct a teaching evaluation of the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

[0009] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to conduct a teaching evaluation on the student, including:

[0010] When the answer data received from the student feedback does not match the standard answer data of the online test question, performing semantic analysis on the question stem information of the online test question to obtain a second subject knowledge point in the question stem information that is unrelated to the at least one target knowledge point;

[0011] The implicit information of the second subject knowledge point that is not disclosed in the question stem information as implicit information replaces the corresponding question stem information part to reorganize the question stem information of the online test questions to conduct a teaching evaluation of the first subject for the student.

[0012] Optionally, it also includes:

[0013] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0014] The second subject knowledge points and the student's information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject.

[0015] Optionally, it also includes:

[0016] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0017] The teaching content of the second subject knowledge points is generated based on the knowledge graph of the second subject and synchronized to the student end of the student.

[0018] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question to generate a stem comprehension test to conduct a teaching evaluation on the student, including:

[0019] When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question, and the stem information is broken down into a plurality of semantic confirmation points, where the plurality of semantic confirmation points are used to determine the semantics of the stem information;

[0020] Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the learners' actual understanding of each semantic confirmation point;

[0021] Comparing data on answers to the online test questions based on the actual understanding information and the target knowledge points;

[0022] When the answer comparison data matches the question answer data, the student is assessed to have mastered the target knowledge point.

[0023] Optionally, also include:

[0024] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0025] The word meaning and grammar associated with the target semantic confirmation point are synchronized to the student end of the student as teaching content.

[0026] Optionally, also include:

[0027] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0028] The word meaning and grammar associated with the target semantic confirmation point are synchronized as cross-disciplinary subject information data to the teacher's terminal associated with the student.

[0029] A second aspect of the embodiments of the present application provides a teaching evaluation device based on knowledge graph and semantic analysis, including:

[0030] A construction unit is used to perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct a target knowledge graph;

[0031] A generating unit, configured to generate online test questions of the first subject teaching content based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching content;

[0032] The evaluation unit is used to perform semantic analysis on the stem information of the online test questions to generate a stem comprehension test when the answer data received from the student feedback does not match the standard answer data of the online test questions, so as to conduct teaching evaluation on the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

[0033] A third aspect of an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the processor is configured to implement the steps of the above-mentioned teaching evaluation method based on knowledge graph and semantic analysis when executing a computer program stored in the memory.

[0034] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned teaching evaluation method based on knowledge graph and semantic analysis are implemented.

[0035] In summary, the teaching evaluation method based on knowledge graph and semantic analysis provided by the embodiment of the present application generates knowledge point data and relationship data between knowledge points by semantic analysis of the content of the first subject textbook to construct a target knowledge graph; generates online test questions of the content of the first subject textbook based on the target knowledge graph, and the test questions are used to evaluate the students' mastery of at least one target knowledge point in the teaching content of the target subject; when the answer data fed back by the students does not match the standard answer data of the online test questions, the stem information of the online test questions is semantically analyzed to generate a stem comprehension test to evaluate the students' teaching, and the comprehension test is used to evaluate the students' understanding of the stem information of the online test questions. Thus, through the knowledge graph, the system comprehensively covers all knowledge points in the textbook, ensuring the comprehensiveness of the evaluation content. Through semantic analysis and comprehension test, the system can distinguish between answer errors caused by weak grasp of knowledge points and incorrect understanding of the stem, thereby improving the accuracy of the evaluation. Using semantic analysis technology, the system can deeply analyze students' understanding of questions and refine assessment metrics. The system automatically detects and marks incorrect answers due to misunderstandings in the question stem, providing detailed error analysis to help teachers and students identify the root causes of the problems. This results in more accurate assessment results. By analyzing each student's answer data, it identifies individual weaknesses in knowledge points and question comprehension, enabling personalized assessments. Based on students' learning progress and assessment results, test questions and teaching strategies are adjusted in real time to meet their individual learning needs. Detailed analysis of student errors generates a question comprehension test, helping students identify their understanding gaps and improve learning efficiency. Assessment reports allow students to clearly understand their learning status, clarify learning goals, and conduct targeted review and consolidation. Teachers can use the system's assessment reports to understand students' mastery and comprehension of each knowledge point, enabling data-driven teaching improvements. Based on students' personalized assessment results, teachers can optimize teaching strategies, adjust content and methods, and improve overall teaching effectiveness. The system automatically extracts knowledge points, generates questions, analyzes data, and produces evaluation reports, significantly improving the efficiency of teaching evaluation and reducing teachers' workload. Through this intelligent evaluation system, schools and educational institutions can comprehensively monitor and manage teaching quality, identifying and resolving teaching problems in a timely manner.

[0036] Correspondingly, the teaching evaluation device, electronic device and computer-readable storage medium based on knowledge graph and semantic analysis provided by the embodiments of the present invention also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1A flowchart of a possible teaching evaluation method based on knowledge graph and semantic analysis provided in an embodiment of the present application;

[0038] Figure 2 A schematic structural block diagram of a possible teaching evaluation device based on knowledge graph and semantic analysis provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of the hardware structure of a possible teaching evaluation device based on knowledge graph and semantic analysis provided in an embodiment of the present application;

[0040] Figure 4 A schematic structural block diagram of a possible electronic device provided in an embodiment of the present application;

[0041] Figure 5 A schematic structural block diagram of a possible computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The embodiment of the present application provides a teaching evaluation method and related equipment based on knowledge graph and semantic analysis, which can solve the problem that when students take tests such as physics and mathematics application questions, they often misunderstand the meaning of the information expressed in the question stem. Even if the students have mastered the corresponding physics or mathematics subject knowledge points, they still give incorrect answers, resulting in the inability to accurately evaluate the students' mastery level or the teaching situation of the subject.

[0043] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only some embodiments of the present application, not all embodiments.

[0044] See also Figure 1, is a flowchart of a teaching evaluation method based on knowledge graph and semantic analysis provided in an embodiment of the present application, the teaching evaluation method based on knowledge graph and semantic analysis includes: S110-S130,

[0045] S110, performing semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct a target knowledge graph.

[0046] For example, natural language processing (NLP) is performed on the textbook content to extract knowledge points and their relationships, such as definitions, formulas, theorems, and examples. The extracted knowledge points and relationship data are constructed into a knowledge graph, forming a network structure of nodes and edges, showing the connections between the knowledge points.

[0047] S120, generating online test questions of the first subject teaching material content based on the target knowledge graph, wherein the test questions are used to evaluate the learner's mastery of at least one target knowledge point in the target subject teaching material.

[0048] For example, based on the knowledge points in the knowledge graph, test questions covering these knowledge points are designed, including multiple-choice questions, fill-in-the-blank questions, short-answer questions, etc. Standard answers are set for each test question to facilitate the comparison and analysis of subsequent answer data.

[0049] S130, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to conduct a teaching evaluation on the student, and the comprehension test is used to evaluate the student's understanding of the question stem information of the online test question.

[0050] For example, student response data is collected through an online platform, recording information such as answers and response time. Student responses are compared with standard answers to identify their mastery of each knowledge point. Questions with mismatched response data undergo further semantic analysis to identify key information and common error points within the question stem. The primary purpose of comprehension tests is to help the system or teachers distinguish between errors caused by a student's weak grasp of the knowledge point and errors in question stem comprehension. Through detailed semantic analysis, comprehension tests can pinpoint specific issues with students' understanding of the question stem, thereby preventing misattribution of comprehension errors to insufficient knowledge mastery. Comprehension tests enable the system to accurately assess students' mastery of the knowledge point. For errors caused by misunderstanding the question stem, the system can eliminate the risk of weak knowledge mastery, improving assessment accuracy. This avoids misjudgments caused by misunderstanding the question stem, making assessment results more valuable and helping teachers accurately understand students' true learning progress. The results of comprehension tests provide interdisciplinary teachers with specific information on student comprehension errors, enabling them to adjust teaching strategies and improve teaching methods to address student comprehension issues. Comprehension test results provide the system with a basis for adjusting students' learning paths, ensuring that students improve both their understanding of the questions and their mastery of key knowledge points. Based on each assessment result, the system dynamically adjusts students' learning paths to ensure that the direction and focus of their learning align with their actual needs.

[0051] Take a math word problem as an example. The problem description is: "A train travels at a speed of 60 kilometers per hour for 3 hours. How many kilometers did the train travel?"

[0052] Student C: miscalculated, thinking the distance traveled was 60 × 3 = 180 km (correct). Student D: misunderstood the question and answered that the train's speed was 60 km / h (incorrect, not understanding the need to calculate the total distance).

[0053] Comprehension Test Design and Effectiveness: Identifying Comprehension Errors: The system detected Student D's error and analyzed his response data, confirming that he did not understand the requirement to calculate the total distance. A comprehension test was designed: "Question: What is the total distance traveled in 3 hours at a speed of 60 kilometers per hour?" Options: A) 60 kilometers, B) 120 kilometers, C) 180 kilometers. Through the comprehension test, the system confirmed that Student D's error stemmed from a misunderstanding of the question, not from an inability to calculate distance. The system attributed Student D's error to a misunderstanding of the question, rather than a lack of knowledge. The system provided Student D with more similar word problems to strengthen his ability to extract information from the question stem.

[0054] According to the teaching evaluation method based on knowledge graph and semantic analysis provided by the above embodiment, the target knowledge graph is constructed by performing semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points; based on the target knowledge graph, an online test question of the first subject textbook content is generated, and the test question is used to evaluate the student's mastery of at least one target knowledge point in the target subject teaching content; when the answer data received from the student feedback does not match the standard answer data of the online test question, the question stem information of the online test question is semantically analyzed to generate a question stem comprehension test to evaluate the student's teaching, and the comprehension test is used to evaluate the student's understanding of the question stem information of the online test question. Thus, through the knowledge graph, the system comprehensively covers all knowledge points in the textbook, ensuring the comprehensiveness of the evaluation content. Through semantic analysis and comprehension test, the system can distinguish between answer errors caused by weak grasp of knowledge points and incorrect understanding of the question stem, thereby improving the accuracy of the evaluation. Using semantic analysis technology, the system can deeply analyze students' understanding of questions and refine assessment metrics. The system automatically detects and marks incorrect answers due to misunderstandings in the question stem, providing detailed error analysis to help teachers and students identify the root causes of the problems. This results in more accurate assessment results. By analyzing each student's answer data, it identifies individual weaknesses in knowledge points and question comprehension, enabling personalized assessments. Based on students' learning progress and assessment results, test questions and teaching strategies are adjusted in real time to meet their individual learning needs. Detailed analysis of student errors generates a question comprehension test, helping students identify their understanding gaps and improve learning efficiency. Assessment reports allow students to clearly understand their learning status, clarify learning goals, and conduct targeted review and consolidation. Teachers can use the system's assessment reports to understand students' mastery and comprehension of each knowledge point, enabling data-driven teaching improvements. Based on students' personalized assessment results, teachers can optimize teaching strategies, adjust content and methods, and improve overall teaching effectiveness. The system automatically extracts knowledge points, generates questions, analyzes data, and produces evaluation reports, significantly improving the efficiency of teaching evaluation and reducing teachers' workload. Through this intelligent evaluation system, schools and educational institutions can comprehensively monitor and manage teaching quality, identifying and resolving teaching problems in a timely manner.

[0055] According to some embodiments, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to conduct a teaching evaluation on the student, including:

[0056] When the answer data received from the student feedback does not match the standard answer data of the online test question, performing semantic analysis on the question stem information of the online test question to obtain a second subject knowledge point in the question stem information that is unrelated to the at least one target knowledge point;

[0057] The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information. The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information to reorganize the question stem information of the online test questions and conduct a teaching evaluation of the first subject for the students.

[0058] For example, when a student's answer data does not match the standard answer data, the system automatically identifies these answer data and enters the next step of the semantic analysis process. Semantic analysis is performed on the stem information of the online test questions corresponding to the unmatched answer data to obtain all the knowledge points in the stem information. During the semantic analysis process, second-subject knowledge points in the stem information that are unrelated to the target knowledge points are identified. For example, in a physics question, the mathematical calculation part involved may be a second-subject knowledge point. The implicit information of the second-subject knowledge point in the stem information is replaced with other corresponding second-subject knowledge points to reorganize the stem information. The purpose of this step is to ensure that the expression of the stem information is more clear and reduce the misunderstanding caused by implicit information. Through semantic analysis and replacement of implicit information, the expression of the stem information is made clearer, helping students better understand the requirements of the question and reducing the number of incorrect answers caused by vague or unclear stem information. By reorganizing the stem information, the system and teachers can distinguish whether the reason for the student's incorrect answer is a lack of solid grasp of the knowledge points or a misunderstanding of the stem, thereby improving the accuracy of the assessment. Based on the students' understanding of the reorganized question information, a personalized comprehension test is generated, and targeted learning suggestions and guidance are provided to help students make up for the deficiencies in knowledge points and understanding.

[0059] Take the math problem "Chickens and rabbits in the same cage" as an example. The question stem involves biology knowledge (the number of legs of chickens and rabbits). Even if young students master the knowledge of data calculation, they may still be unable to answer the question correctly due to misunderstanding the information in the question stem if they don't understand the number of legs of different animals. For example, the question is, "In a cage, there are several chickens and rabbits, with a total of 35 heads and 94 legs. How many chickens and rabbits are there?" Student A miscalculates, thinking there are 25 chickens and 10 rabbits (incorrect). Student B misunderstands the question, thinking chickens and rabbits have the same number of legs, and answers 30 chickens and 5 rabbits (incorrect). The system detects that Student A and Student B's answers do not match the standard answer. Semantic analysis of the question stem identifies the relevant knowledge points, including mathematical calculations (setting up and solving equations) and biological knowledge (the number of legs of chickens and rabbits). During the semantic analysis, it is determined that the biological knowledge point in the question stem is a secondary subject unrelated to the target knowledge point (mathematical calculation). The system restructures the question stem by replacing implicit information about a second subject (biology) with explicit information. The original question stem reads: "In a cage, there are several chickens and rabbits. They have 35 heads and 94 legs. How many chickens and rabbits are there?" The replaced question stem reads: "In a cage, there are several chickens and rabbits. The chickens have 2 legs, and the rabbits have 4 legs. There are 35 heads and 94 legs. How many chickens and rabbits are there?" By replacing the implicit information, the question stem becomes clearer, helping students better understand the question and reducing errors caused by ambiguous or unclear information. By reorganizing the question stem, the system can distinguish whether student errors stem from a lack of solid knowledge or a misunderstanding of the question stem. For example, the system can identify that Student B's error stems from a misunderstanding of the number of legs of chickens and rabbits, rather than an inability to solve the equation. Based on the student's understanding of the reorganized question stem, a personalized comprehension test is generated, providing targeted learning suggestions and guidance. For example, Student B is provided with additional learning materials and exercises on the number of legs of chickens and rabbits. Based on the results of the comprehension test, the system dynamically adjusts the student's learning path to ensure that the student improves both understanding the questions and mastering the key knowledge points.

[0060] According to some embodiments, further comprising:

[0061] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0062] The second subject knowledge points and the student's information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject.

[0063] For example, when a student's answer data does not match the standard answer data for the original online test question, the system automatically identifies this answer data and enters the next step of the semantic analysis process. Semantic analysis is performed on the stem information of the online test question corresponding to the unmatched answer data to obtain all knowledge points in the stem information. During the semantic analysis process, second-subject knowledge points in the stem information that are unrelated to the target knowledge points are identified. The implicit information of the second-subject knowledge points in the stem information is replaced with other corresponding second-subject knowledge points to reorganize the stem information. Based on the reorganized stem information, a comprehension test is generated to help students clarify the important information and logical structure in the stem. If the answer data received from the student for the stem information of the reorganized online test question matches the standard answer data for the online test question, the student's performance in understanding and applying the second-subject knowledge points is recorded. The recorded second-subject knowledge points and student information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject, helping the relevant teacher understand the student's performance in other subjects. By semantically analyzing and replacing implicit information, the information in the question stem is expressed more clearly, helping students better understand the requirements of the question and reducing answer errors caused by vague or unclear question stem information. By reorganizing the question stem information, the system and teachers can distinguish whether the reason for the student's incorrect answer is a weak grasp of the knowledge points or a misunderstanding of the question stem, thereby improving the accuracy of the assessment. Based on the student's understanding of the reorganized question stem information, a personalized comprehension test is generated, and targeted learning suggestions and guidance are provided to help students make up for the deficiencies in knowledge points and understanding. When the student correctly understands and applies the knowledge points of the second subject, this data is recorded and synchronized to the teacher side associated with the second subject to promote collaboration and improvement in interdisciplinary teaching. Based on the student's performance in the comprehension test and cross-disciplinary situation data, the student's learning path is dynamically adjusted to ensure that they can improve both in understanding the question stem and mastering the knowledge points.

[0064] According to some embodiments, further comprising:

[0065] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0066] The teaching content of the second subject knowledge points is generated based on the knowledge graph of the second subject and synchronized to the student end of the student.

[0067] For example, when a student's answer data does not match the standard answer data for the original online test question, the system automatically identifies this answer data and proceeds to the next step of the semantic analysis process. Semantic analysis is performed on the stem information of the online test question corresponding to the unmatched answer data to obtain all knowledge points in the stem information. During the semantic analysis process, second-subject knowledge points in the stem information that are unrelated to the target knowledge point are identified. Implicit information in the second-subject knowledge points in the stem information is replaced with other corresponding second-subject knowledge points to reorganize the stem information. Based on the reorganized stem information, a comprehension test is generated to help students clarify the important information and logical structure of the stem. If the student's feedback on the stem information of the reorganized online test question matches the standard answer data for the online test question, the student's performance in understanding and applying the second-subject knowledge points is recorded. Based on the knowledge graph of the second subject, teaching content related to the recorded second-subject knowledge points is generated and synchronized to the student's end. Based on the student's understanding of the reorganized stem information, a personalized comprehension test can be generated, providing targeted learning suggestions and guidance to help students address deficiencies in knowledge points and understanding. In addition, the learning path of students can be dynamically adjusted based on their performance in comprehension tests and cross-disciplinary learning data to ensure that they can improve both in understanding the questions and mastering the knowledge points.

[0068] According to some embodiments, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to perform teaching evaluation on the student, including:

[0069] When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question, and the stem information is broken down into a plurality of semantic confirmation points, where the plurality of semantic confirmation points are used to determine the semantics of the stem information;

[0070] Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the learners' actual understanding of each semantic confirmation point;

[0071] Comparing data on answers to the online test questions based on the actual understanding information and the target knowledge points;

[0072] When the answer comparison data matches the question answer data, the student is assessed to have mastered the target knowledge point.

[0073] It is understandable that test questions can be used to confirm the student's actual understanding of the question. If the answer calculated by the system based on the student's actual understanding and knowledge points is the same as the student's previous incorrect answer, it can be determined that the student has misunderstood the question rather than not mastered the knowledge points.

[0074] For example, when the student's answer data does not match the standard answer data of the online test questions, the system automatically identifies these answer data and enters the next step of the semantic analysis process. Perform semantic analysis on the stem information of the online test questions corresponding to the unmatched answer data, and break down the stem information into multiple semantic confirmation points. Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the student's actual understanding information of each semantic confirmation point. Collect the student's answers to each comprehension test question to confirm the student's actual understanding of each semantic confirmation point. Based on the student's actual understanding information, combined with the target knowledge points, recalculate and compare the answers to the online test questions. If the recalculated answer is the same as the student's previous incorrect answer, it is determined that the student misunderstood the stem rather than not mastered the knowledge point. When it is confirmed that the student has misunderstood, evaluate the student's mastery of the target knowledge point, and generate corresponding teaching feedback and suggestions.

[0075] Take the word problem "A car travels at 60 kilometers per hour. After 2 hours, calculate the total distance traveled." Student A thinks the total distance is 120 miles (incorrect, not noting that the total distance is in miles, while the speed is in kilometers per hour). Student B thinks the total distance is 240 miles (correct). The system detects that Student A's answer does not match the standard answer. Semantic analysis of the question stem, breaking it down into multiple semantic confirmation points, reveals that Student A actually understood the total distance to be in kilometers. The calculated result, based on this understanding, is 120, which matches the answer data. Therefore, it can be determined that the student's error stems from a misunderstanding of the question stem, rather than a lack of knowledge.

[0076] According to some embodiments, further comprising:

[0077] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0078] The word meaning and grammar associated with the target semantic confirmation point are synchronized to the student end of the student as teaching content.

[0079] According to some embodiments, further comprising:

[0080] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0081] The word meaning and grammar associated with the target semantic confirmation point are synchronized as cross-disciplinary subject information data to the teacher's terminal associated with the student.

[0082] The above describes the teaching evaluation method based on knowledge graph and semantic analysis in the embodiment of the present application. The following describes the teaching evaluation device based on knowledge graph and semantic analysis in the embodiment of the present application.

[0083] See also Figure 2 In the embodiments of the present application, an embodiment of a teaching evaluation device based on knowledge graph and semantic analysis is described, which may include:

[0084] A construction unit 201 is configured to perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct a target knowledge graph;

[0085] A generating unit 202 is configured to generate online test questions for the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material;

[0086] The evaluation unit 203 is used to perform semantic analysis on the stem information of the online test questions to generate a stem comprehension test when the answer data received from the student feedback does not match the standard answer data of the online test questions, so as to conduct teaching evaluation on the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

[0087] According to the teaching evaluation device based on knowledge graph and semantic analysis provided by the above embodiment, the target knowledge graph is constructed by performing semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points; based on the target knowledge graph, an online test question of the first subject textbook content is generated, and the test question is used to evaluate the student's mastery of at least one target knowledge point in the target subject teaching content; when the answer data received from the student feedback does not match the standard answer data of the online test question, the question stem information of the online test question is semantically analyzed to generate a question stem comprehension test to evaluate the student's teaching, and the comprehension test is used to evaluate the student's understanding of the question stem information of the online test question. Thus, through the knowledge graph, the system comprehensively covers all knowledge points in the textbook, ensuring the comprehensiveness of the evaluation content. Through semantic analysis and comprehension test, the system can distinguish between answer errors caused by weak grasp of knowledge points and incorrect understanding of the question stem, thereby improving the accuracy of the evaluation. Using semantic analysis technology, the system can deeply analyze students' understanding of questions and refine assessment metrics. The system automatically detects and marks incorrect answers due to misunderstandings in the question stem, providing detailed error analysis to help teachers and students identify the root causes of the problems. This results in more accurate assessment results. By analyzing each student's answer data, it identifies individual weaknesses in knowledge points and question comprehension, enabling personalized assessments. Based on students' learning progress and assessment results, test questions and teaching strategies are adjusted in real time to meet their individual learning needs. Detailed analysis of student errors generates a question comprehension test, helping students identify their understanding gaps and improve learning efficiency. Assessment reports allow students to clearly understand their learning status, clarify learning goals, and conduct targeted review and consolidation. Teachers can use the system's assessment reports to understand students' mastery and comprehension of each knowledge point, enabling data-driven teaching improvements. Based on students' personalized assessment results, teachers can optimize teaching strategies, adjust content and methods, and improve overall teaching effectiveness. The system automatically extracts knowledge points, generates questions, analyzes data, and produces evaluation reports, significantly improving the efficiency of teaching evaluation and reducing teachers' workload. Through this intelligent evaluation system, schools and educational institutions can comprehensively monitor and manage teaching quality, identifying and resolving teaching problems in a timely manner.

[0088] above Figure 2 The teaching evaluation device based on knowledge graph and semantic analysis in the embodiment of the present application is described from the perspective of modular functional entities. The following describes the teaching evaluation device based on knowledge graph and semantic analysis in the embodiment of the present application in detail from the perspective of hardware processing. Please refer to Figure 3An embodiment of a teaching evaluation device 300 based on knowledge graph and semantic analysis in the embodiment of the present application includes:

[0089] Input device 301, output device 302, processor 303 and memory 304, wherein the number of processor 303 can be one or more, Figure 3 In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 may be connected via a bus or other means, wherein: Figure 3 The bus connection is taken as an example.

[0090] By calling the operation instructions stored in the memory 304, the processor 303 is configured to perform the following steps:

[0091] Perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct the target knowledge graph;

[0092] Generate online test questions for the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material;

[0093] In the event that the answer data received from the student feedback does not match the standard answer data of the online test questions, a semantic analysis is performed on the stem information of the online test questions to generate a stem comprehension test to conduct a teaching evaluation of the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

[0094] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to conduct a teaching evaluation on the student, including:

[0095] When the answer data received from the student feedback does not match the standard answer data of the online test question, performing semantic analysis on the question stem information of the online test question to obtain a second subject knowledge point in the question stem information that is unrelated to the at least one target knowledge point;

[0096] The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information. The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information to reorganize the question stem information of the online test questions and conduct a teaching evaluation of the first subject for the students.

[0097] Optionally, it also includes:

[0098] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0099] The second subject knowledge points and the student's information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject.

[0100] Optionally, it also includes:

[0101] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0102] The teaching content of the second subject knowledge points is generated based on the knowledge graph of the second subject and synchronized to the student end of the student.

[0103] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question to generate a stem comprehension test to conduct a teaching evaluation on the student, including:

[0104] When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question, and the stem information is broken down into a plurality of semantic confirmation points, where the plurality of semantic confirmation points are used to determine the semantics of the stem information;

[0105] Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the learners' actual understanding of each semantic confirmation point;

[0106] Comparing data on answers to the online test questions based on the actual understanding information and the target knowledge points;

[0107] When the answer comparison data matches the question answer data, the student is assessed to have mastered the target knowledge point.

[0108] Optionally, also include:

[0109] Taking the semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees as the target semantic confirmation point;

[0110] The word meaning and grammar associated with the target semantic confirmation point are synchronized to the student terminal of the student as teaching content.

[0111] Optionally, also include:

[0112] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0113] The word meaning and grammar associated with the target semantic confirmation point are synchronized as cross-disciplinary subject information data to the teacher's terminal associated with the student.

[0114] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0115] See also Figure 4 , Figure 4 Schematic diagram of an electronic device according to an embodiment of the present application.

[0116] like Figure 4 As shown, an embodiment of the present application provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0117] Perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct the target knowledge graph;

[0118] Generate online test questions for the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material;

[0119] In the event that the answer data received from the student feedback does not match the standard answer data of the online test questions, a semantic analysis is performed on the stem information of the online test questions to generate a stem comprehension test to conduct a teaching evaluation of the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

[0120] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to conduct a teaching evaluation on the student, including:

[0121] When the answer data received from the student feedback does not match the standard answer data of the online test question, performing semantic analysis on the question stem information of the online test question to obtain a second subject knowledge point in the question stem information that is unrelated to the at least one target knowledge point;

[0122] The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information. The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information to reorganize the question stem information of the online test questions and conduct a teaching evaluation of the first subject for the students.

[0123] Optionally, it also includes:

[0124] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0125] The second subject knowledge points and the student's information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject.

[0126] Optionally, it also includes:

[0127] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0128] The teaching content of the second subject knowledge points is generated based on the knowledge graph of the second subject and synchronized to the student end of the student.

[0129] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question to generate a stem comprehension test to conduct a teaching evaluation on the student, including:

[0130] When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question, and the stem information is broken down into a plurality of semantic confirmation points, where the plurality of semantic confirmation points are used to determine the semantics of the stem information;

[0131] Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the learners' actual understanding of each semantic confirmation point;

[0132] Comparing data on answers to the online test questions based on the actual understanding information and the target knowledge points;

[0133] When the answer comparison data matches the question answer data, the student is assessed to have mastered the target knowledge point.

[0134] Optionally, also include:

[0135] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0136] The word meaning and grammar associated with the target semantic confirmation point are synchronized to the student end of the student as teaching content.

[0137] Optionally, also include:

[0138] The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point;

[0139] The word meaning and grammar associated with the target semantic confirmation point are synchronized as cross-disciplinary subject information data to the teacher's terminal associated with the student.

[0140] In the specific implementation process, when the processor 420 executes the computer program 411, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0141] Since the electronic device introduced in this embodiment is a device used to implement a teaching evaluation device based on knowledge graph and semantic analysis in the embodiment of this application, based on the method introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of this application is no longer introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of this application falls within the scope of protection of this application.

[0142] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application.

[0143] like Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented:

[0144] Perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct the target knowledge graph;

[0145] Generate online test questions for the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material;

[0146] In the event that the answer data received from the student feedback does not match the standard answer data of the online test questions, a semantic analysis is performed on the stem information of the online test questions to generate a stem comprehension test to conduct a teaching evaluation of the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

[0147] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the question stem information of the online test question to generate a question stem comprehension test to conduct a teaching evaluation on the student, including:

[0148] When the answer data received from the student feedback does not match the standard answer data of the online test question, performing semantic analysis on the question stem information of the online test question to obtain a second subject knowledge point in the question stem information that is unrelated to the at least one target knowledge point;

[0149] The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information. The implicit information of the second subject knowledge point that is not disclosed in the question stem information is replaced with the corresponding part of the question stem information to reorganize the question stem information of the online test questions and conduct a teaching evaluation of the first subject for the students.

[0150] Optionally, it also includes:

[0151] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0152] The second subject knowledge points and the student's information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject.

[0153] Optionally, it also includes:

[0154] Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question;

[0155] The teaching content of the second subject knowledge points is generated based on the knowledge graph of the second subject and synchronized to the student end of the student.

[0156] Optionally, when the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question to generate a stem comprehension test to conduct a teaching evaluation on the student, including:

[0157] When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question, and the stem information is broken down into a plurality of semantic confirmation points, where the plurality of semantic confirmation points are used to determine the semantics of the stem information;

[0158] Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the learners' actual understanding of each semantic confirmation point;

[0159] Comparing data on answers to the online test questions based on the actual understanding information and the target knowledge points;

[0160] When the answer comparison data matches the question answer data, the student is assessed to have mastered the target knowledge point.

[0161] Optionally, also include:

[0162] Taking the semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees as the target semantic confirmation point;

[0163] The word meaning and grammar associated with the target semantic confirmation point are synchronized to the student terminal of the student as teaching content.

[0164] Optionally, also include:

[0165] Taking the semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees as the target semantic confirmation point;

[0166] The word meaning and grammar associated with the target semantic confirmation point are synchronized as cross-disciplinary subject information data to the teacher's terminal associated with the student.

[0167] In a specific implementation process, the computer program 511 can be implemented when executed by a processor. Figure 1 Any implementation manner in the corresponding embodiments.

[0168] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0169] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0170] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0173] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process in the teaching evaluation method based on knowledge graph and semantic analysis in the corresponding embodiment.

[0174] The computer program product includes one or more computer instructions. When loaded and executed on a computer, the computer program instructions fully or partially perform the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium capable of computer storage or a data storage device such as a server or data center that integrates one or more available media. The available medium may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0175] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0176] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0178] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0179] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0180] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A teaching evaluation method based on knowledge graph and semantic analysis, characterized in that: include: Perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct the target knowledge graph; Generate online test questions for the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material; If the answer data received from the student does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question to generate a stem comprehension test to conduct teaching evaluation on the student; The step of performing semantic analysis on the stem information of the online test questions to generate a stem comprehension test to conduct teaching evaluation on the students includes: Performing semantic analysis on the stem information of the online test question to obtain a second subject knowledge point in the stem information that is unrelated to the at least one target knowledge point; The corresponding part of the question stem information is replaced by the second subject knowledge point that is not disclosed in the question stem information as implicit information, so as to reorganize the question stem information of the online test question and conduct a teaching evaluation of the first subject on the student, wherein the comprehension test is used to evaluate the student's understanding of the question stem information of the online test question, so as to distinguish based on the understanding level whether the student's incorrect answer is due to insufficient mastery of the at least one target knowledge point or incorrect understanding of the question stem information, so as to avoid attributing the problem of question stem comprehension ability to insufficient mastery of the first subject knowledge point.

2. The method according to claim 1, characterized in that Also includes: Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question; The second subject knowledge points and the student's information are synchronized as cross-disciplinary information data to the teacher's end associated with the student's second subject.

3. The method according to claim 1, wherein Also includes: Recording the second subject knowledge point when the answer data received from the student for the reorganized question stem information of the online test question matches the standard answer data of the online test question; The teaching content of the second subject knowledge points is generated based on the knowledge graph of the second subject and synchronized to the student end of the student.

4. The method according to claim 1, wherein When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question to generate a stem comprehension test to conduct teaching evaluation on the student, including: When the answer data received from the student feedback does not match the standard answer data of the online test question, semantic analysis is performed on the stem information of the online test question, and the stem information is broken down into a plurality of semantic confirmation points, where the plurality of semantic confirmation points are used to determine the semantics of the stem information; Generate corresponding comprehension test questions based on each semantic confirmation point to confirm the learners' actual understanding of each semantic confirmation point; Calculating answer comparison data for the online test questions based on the actual understanding information and the target knowledge points; When the answer comparison data matches the question answer data, the student is assessed to have mastered the target knowledge point.

5. The method according to claim 3, wherein Also includes: The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point; The word meaning and grammar associated with the target semantic confirmation point are synchronized to the student end of the student as teaching content.

6. The method according to claim 4, wherein: Also includes: The semantic confirmation point where the theoretical semantics differ from the actual understanding of the trainees is used as the target semantic confirmation point; The word meaning and grammar associated with the target semantic confirmation point are synchronized as cross-disciplinary subject information data to the teacher's terminal associated with the student.

7. A teaching evaluation device based on knowledge graph and semantic analysis, characterized in that: According to the method according to any one of claims 1 to 6, the device comprises: A construction unit is used to perform semantic analysis on the content of the first subject textbook to generate knowledge point data and relationship data between knowledge points to construct a target knowledge graph; A generating unit, configured to generate online test questions of the first subject teaching material based on the target knowledge graph, wherein the test questions are used to assess the learner's mastery of at least one target knowledge point in the target subject teaching material; The evaluation unit is used to perform semantic analysis on the stem information of the online test questions to generate a stem comprehension test when the answer data received from the student feedback does not match the standard answer data of the online test questions, so as to conduct teaching evaluation on the student. The comprehension test is used to evaluate the student's understanding of the stem information of the online test questions.

8. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the teaching evaluation method based on knowledge graph and semantic analysis as described in any one of claims 1 to 6 when executing the computer program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the teaching evaluation method based on knowledge graph and semantic analysis as described in any one of claims 1 to 6 is implemented.

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