An intelligent parsing method for junior high school English reading comprehension test questions based on a large model

Abstract

The present invention discloses a method for intelligent parsing of junior high school English reading comprehension test questions based on a large model. The method comprises the following steps: S1, obtaining the answer text and the corresponding test question text of the junior high school English reading comprehension test questions completed by students, wherein the answers are derived from the published standard answers; S2, preprocessing the input test question text and answer text; S3, performing a deep semantic level analysis of the test question text using a large model of deep context understanding; S4, analyzing the semantic matching degree of the student answer based on the test question text and the standard answer, combined with the understanding of the text context and logical relationship by the large model of logical reasoning, and generating a semantic and logical comprehensive evaluation result of the student answer based on the standard answer and the large model of logical reasoning; S5, dynamically generating learning suggestions based on the semantic and logical comprehensive evaluation result of the student answer; and S6, outputting a test question analysis report. The present invention parses student answers more accurately and provides targeted learning guidance, thereby significantly improving teaching effectiveness and student learning experience.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to a method for intelligently parsing English reading comprehension test questions for junior high schools based on a large model. Background Art

[0002] With the development of artificial intelligence technology, the field of education has gradually introduced intelligent tools to assist teaching and assessment. Junior high school English reading comprehension is an important part of English teaching. Its test results can directly reflect students' reading ability, semantic understanding level and logical thinking ability. However, in the traditional teaching and assessment system, there are many challenges in the correction and feedback of English reading comprehension test questions.

[0003] At present, most schools and teaching assistance institutions still use manual grading to evaluate English reading comprehension tests. Teachers need to review students' answers one by one, analyze their answer ideas by comparing them with standard answers, and give comments and scores. Although this can reflect the students' answering process, it has obvious limitations: due to the huge workload, it is difficult for teachers to efficiently grade a large number of test papers in a short period of time. In addition, teachers' subjective judgments inevitably lead to inconsistencies in grading standards, affecting the objectivity of the evaluation.

[0004] In recent years, some automatic grading systems have begun to be applied in the field of education. They usually automatically score students' answers based on keyword matching and simple grammatical analysis. However, the automatic grading systems have exposed the following problems in actual application: on the one hand, the keyword matching method cannot deeply understand the semantics of students' answers and easily ignores students' reasonable flexibility in expression; on the other hand, the automatic grading system has difficulty handling complex logical relationships and is insufficient in identifying students' errors in the reasoning chain. In addition, the existing automatic grading technology lacks personalized feedback function and can only provide simple correct and incorrect judgments, making it difficult to provide targeted learning suggestions for students' weaknesses.

[0005] To sum up, existing technologies have significant shortcomings in grading efficiency, assessment accuracy and personalized feedback, and are unable to meet the needs of modern educational scenarios for intelligent and efficient assessment. These technical defects directly affect students' learning experience and teachers' teaching quality. A new method is urgently needed to solve the above problems. Summary of the Invention

[0006] One purpose of the present invention is to propose an intelligent parsing method for junior high school English reading comprehension test questions based on a large model. The present invention can more accurately parse students' answers and provide targeted learning guidance, thereby significantly improving teaching effectiveness and students' learning experience.

[0007] According to an embodiment of the present invention, a method for intelligently parsing junior high school English reading comprehension test questions based on a large model includes the following steps:

[0008] S1. Obtain the answer texts and corresponding test texts of the junior high school English reading comprehension test completed by students. The answers are derived from the published standard answers.

[0009] S2. Preprocess the input test question text and answer text, including formatting, grammar normalization and noise removal, to generate standardized text;

[0010] S3. Utilize the deep context understanding model to conduct in-depth semantic analysis of the test text, analyzing the contextual relationships, implicit semantic clues, and logical structure within the test text, extracting the main idea of ​​the article, the central idea of ​​each paragraph, and key sentences. Perform semantic analysis on the student's answer text, identifying the logical reasoning process, answer ideas, and implicit information in the language expression within the student's answer, extracting its relevance to the test text, and conducting multi-level context understanding based on the semantic analysis results to generate semantic analysis results.

[0011] S4. Based on the test text and standard answers, combined with the logical reasoning model's understanding of the text context and logical relationships, analyze the semantic matching of the student's answers. Analyze the reasoning chain of the student's answers to identify logical errors or loopholes in the reasoning. Based on the standard answers and the logical reasoning model, generate a comprehensive semantic and logical evaluation of the student's answers, and mark any errors in the answers.

[0012] S5. Dynamically generate learning suggestions based on the semantic and logical comprehensive evaluation results of students' answers;

[0013] S6. Output the test question analysis report, which includes the standard answer, semantic analysis results, logical evaluation, student answer error marking and learning suggestions.

[0014] Optionally, S1 includes the following specific steps:

[0015] S11. Receive student's answer text A from a preset database or online system i And the corresponding test text Q i , where A i represents the answer text of the i-th student, Q i Indicates the reading comprehension test text corresponding to the answer;

[0016] S12, the received answer text A i and the test text Q i Perform matching association to make the answer text A i With the test text Q i One-to-one correspondence, if the match fails, an exception prompt will be returned:

[0017]

[0018] Among them, Match(A i ,Q i ) represents the matching result of the system between the answer text and the test text, θ is the matching threshold, when the similarity reaches or exceeds θ, the match is successful, otherwise an abnormal prompt is returned, Sim(A i ,Q i ) is to use cosine similarity to calculate the similarity between two texts:

[0019]

[0020] in, and is the word vector weight of the answer and question text, and the weight of the kth word in the text;

[0021] S13, according to the preset format rules, the received answer text A i And the test text Q i For storage, the identifier format is (A i ,Q i ).

[0022] Optionally, S2 includes the following specific steps:

[0023] S21, the received test text Q i and answer text A i Perform paragraph indentation, punctuation replacement, and space deletion to obtain the formatted test text Q′ i and answer text A′ i ;

[0024] S22, the formatted test text Q' i and answer text A′ i Perform grammatical standardization and automatically correct grammatical errors in the text, including corrections of tense inconsistency, subject-verb inconsistency, and incorrect punctuation.

[0025] S23, the formatted test text Q' i and answer text A′ i Perform denoising to remove meaningless symbols, redundant spaces and extra characters;

[0026] S24, the denoised test text Q′ i and answer text A′ i Perform standardization to make the vocabulary and expressions of the text conform to the teaching standards, replace all non-standard words with standard forms, and obtain the standardized test text and answer text

[0027] S25. Verify the standardized test text and answer text If the format, syntax or content does not meet the standards, an exception prompt will be returned and marked as Otherwise, the standardized test text and answer text Pass to the next processing step.

[0028] Optionally, S3 includes the following specific steps:

[0029] S31. Using the deep context understanding model to analyze the standardized test text Perform deep semantic level analysis to generate multi-level semantic representations, including word-level embedding vectors Sentence-level representation vector and the passage-level representation vector

[0030] S32. Analyze the test text The contextual relationship, implicit semantic clues and logical structure in the text are used to extract the main idea of ​​the article. i 、C. Paragraph main idea ij and key sentence K ij ;

[0031] Article Topic i Obtained by maximizing the similarity between the chapter-level representation vector and the topic vector T:

[0032]

[0033] Among them, Sim(·,·) is the similarity calculation function, T is the predefined topic vector set;

[0034] Paragraph Main Idea C ij Obtained by clustering analysis of sentence-level representation vectors within a paragraph;

[0035] Key sentence K ij By calculating the sentence-level representation vector and the article theme M i Determination of relevance:

[0036]

[0037] Where δ is the correlation threshold.

[0038] S33. Standardized student answer text Perform semantic analysis to generate semantic representation vectors of student answers Calculate word-level embedding using the same method as the test text Sentence-level representation and paragraph-level representation

[0039] S34. Identify the logical reasoning process, answer ideas and implicit information in the language expression in the student's answer, extract its relevance to the test text, and calculate the semantic matching degree S between the student's answer and the test text. i :

[0040]

[0041] S35. Based on the standardized test text and standard answers, use the logical reasoning model to generate a standard reasoning chain diagram:

[0042]

[0043] in, is the set of concept nodes in the standard answer, is the set of logical relationship edges between concepts;

[0044] Construct a reasoning chain diagram of the student's answer:

[0045]

[0046] in, is the set of concept nodes in the student’s answer, is the set of logical relationship edges between nodes;

[0047] S36. Comparison of reasoning chains of students' test questions and standard reasoning chain diagram Evaluate the completeness and correctness of students' reasoning process and calculate the structural similarity of the reasoning chain:

[0048]

[0049] in, is the similarity of the reasoning chain diagram structure between the student answer and the standard answer, Represents the set of matching relationship nodes between two graphs, The total number of nodes representing the standard reasoning chain graph;

[0050] S37, based on the results of semantic analysis of the test text and student answer text Perform multi-level contextual understanding to identify irony, metaphor, or simile in test questions and generate semantic analysis results, including the degree of semantic and logical match between student answers and the test text, as well as annotations of misunderstandings and reasoning errors in student answers:

[0051]

[0052] Among them, R iIt represents the semantic analysis result of the i-th test question, which is a multi-dimensional comprehensive evaluation result of the student's answer text and the test question text. Indicates the matching degree between the rhetoric recognition result in the test text and the total rhetoric set, represents the set of rhetorical devices identified in the i-th test text, R total represents the total set of predefined rhetorical devices, and λ1, λ2, and λ3 represent weighted coefficients of different dimensions, which are used to balance the importance of semantic matching, logical reasoning, reasoning chain matching, and rhetoric recognition in the parsing results.

[0053] Optionally, the word-level embedding vector Indicates the test text The embedding of the jth word in , the sentence-level representation vector is obtained by weighted averaging the word-level embeddings:

[0054]

[0055] Among them, N k is the number of words in the k-th sentence, is the attention weight of the j-th word, calculated as:

[0056]

[0057] Among them, u is the context global semantic vector;

[0058] Chapter-level representation vector Obtained by performing encoder processing on the sentence-level representation vector.

[0059] Optionally, S5 includes the following specific steps:

[0060] S51, according to the comprehensive evaluation results R i Identify students' weaknesses in semantic understanding and logical reasoning and generate a set of weaknesses:

[0061] B i ={b ij ∣Error ij =|S ij -L ij |≥∈};

[0062] Among them, Error ij Indicates that students have a semantic matching degree S ij Similarity to logic L ij The deviation between them, ∈ is the error threshold, and the aspects with errors greater than the threshold are marked as weak points b ij ;

[0063] S52. Retrieve the student’s weak points from the question bank ijRelated recommended test questions collection T ij , select suitable test questions by context similarity and matching degree of reasoning chain graph structure:

[0064]

[0065] in, is the set of test questions in the question bank, α1 and β1 are the weight coefficients of semantic and logical matching;

[0066] S53, based on the student's learning trajectory and semantic matching S i Generate personalized learning path P i :

[0067]

[0068] in, Represents finding the path P that makes the learning path the best i ,ω j Weak point b ij The weight coefficient in path optimization indicates the importance of the weak point in the learning path. For weak point b ij The average quality rating of the matched test set, For semantic similarity, calculate the question vector V t and weak point semantic vector The similarity between Represents the recommended test question reasoning chain graph G t Reasoning chain diagram with standard test questions The structural similarity between ∥G t ∥ represents the module length of the test question reasoning chain diagram, α2 and β2 are the weighted coefficients of semantic similarity and logical reasoning similarity.

[0069] Optionally, S6 includes the following specific steps:

[0070] S61. Generate a test analysis report that includes a comparison of the standard answer and the student's answer. The report shows the differences between the student's answer and the standard answer, and marks each error to highlight the student's understanding deviations and logical errors in the process of answering the questions.

[0071] S62, Semantic Analysis section describes the student's performance in understanding the main idea of ​​the text, the central idea of ​​the paragraph, and key sentences. The question analysis report highlights the student's scores and weaknesses in the semantic level, and indicates the areas of comprehension that need improvement.

[0072] S63: The logical assessment section analyzes whether the student's reasoning chain is complete, determines whether there are any jumps or unreasonable inferences in logical thinking, and presents the specific links where the logical problems are in the test question analysis report;

[0073] S64. Based on the results of semantic analysis and logical assessment, the test analysis report provides specific improvement directions and learning methods for students' weak points in semantic understanding and logical reasoning, and recommends exercises related to the weak points.

[0074] The beneficial effects of the present invention are:

[0075] (1) The present invention adopts a multi-level semantic embedding representation, constructs text representation from the word level, sentence level and paragraph level respectively, and generates a reasoning chain diagram in combination with a large logical reasoning model. In the semantic matching process, not only the similarity calculation of word vectors and sentence-level embeddings is used to improve the matching degree between the answer and the test text, but also the similarity of the reasoning chain structure is used to evaluate the logical process of students in answering questions. The traditional keyword matching method can only capture shallow semantics. The algorithm of the present invention significantly improves the parsing accuracy and logical reasoning ability of complex text semantics through the combination of deep semantic analysis and logical reasoning, ensuring a more comprehensive understanding of students' answers.

[0076] (2) The present invention combines the calculation results of semantic matching and reasoning chain structure similarity, and dynamically generates the optimal learning path according to the student's performance in different weak points. It not only considers the student's current knowledge mastery, but also dynamically sorts and adjusts the difficulty of the exercises in the learning path based on the similarity of the semantics and logical structure of the recommended test questions, ensuring that each student can obtain learning resources that match their knowledge level, avoiding the learning frustration caused by the inappropriate difficulty of the exercises in traditional methods, and achieving a more efficient learning experience.

[0077] (3) The present invention introduces three modules: semantic analysis, logical evaluation, and personalized learning suggestions in the generation of test analysis reports. Each incorrect item is accurately marked through a multi-dimensional comparison between the standard answer and the student's answer, and the analysis results at the semantic and logical levels are presented in detail in the report. Unlike the existing technology that only provides a simple judgment on whether the answer is correct or not, the analysis report of the present invention can not only point out the students' specific weaknesses but also generate targeted learning suggestions based on the reasoning chain analysis, further enhancing the students' room for improvement in semantic understanding and logical reasoning. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0079] Figure 1 This is a flowchart of a method for intelligent parsing of junior high school English reading comprehension test questions based on a large model proposed by the present invention;

[0080] Figure 2This is a schematic diagram of the personalized learning path generation process in the intelligent parsing method for junior high school English reading comprehension test questions based on a large model proposed by the present invention. DETAILED DESCRIPTION

[0081] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0082] refer to Figure 1-Figure 2 A method for intelligently parsing English reading comprehension test questions for junior high school students based on a large model includes the following steps:

[0083] S1. Obtain the answer texts and corresponding test texts of the junior high school English reading comprehension test completed by students. The answers are derived from the published standard answers.

[0084] S2. Preprocess the input test question text and answer text, including formatting, grammar normalization and noise removal, to generate standardized text;

[0085] S3. Utilize the deep context understanding model to conduct in-depth semantic analysis of the test text, analyzing the contextual relationships, implicit semantic clues, and logical structure within the test text, extracting the main idea of ​​the article, the central idea of ​​each paragraph, and key sentences. Perform semantic analysis on the student's answer text, identifying the logical reasoning process, answer ideas, and implicit information in the language expression within the student's answer, extracting its relevance to the test text, and conducting multi-level context understanding based on the semantic analysis results to generate semantic analysis results.

[0086] S4. Based on the test text and standard answers, combined with the logical reasoning model's understanding of the text context and logical relationships, analyze the semantic matching of the student's answers. Analyze the reasoning chain of the student's answers to identify logical errors or loopholes in the reasoning. Based on the standard answers and the logical reasoning model, generate a comprehensive semantic and logical evaluation of the student's answers, and mark any errors in the answers.

[0087] S5. Dynamically generate learning suggestions based on the semantic and logical comprehensive evaluation results of students' answers;

[0088] S6. Output the test question analysis report, which includes the standard answer, semantic analysis results, logical evaluation, student answer error marking and learning suggestions.

[0089] In this embodiment, S1 includes the following specific steps:

[0090] S11. Receive student's answer text A from a preset database or online system i And the corresponding test text Q i , where Ai represents the answer text of the i-th student, Q i Indicates the reading comprehension test text corresponding to the answer;

[0091] S12, the received answer text A i and the test text Q i Perform matching association to make the answer text A i With the test text Q i One-to-one correspondence, if the match fails, an exception prompt will be returned:

[0092]

[0093] Among them, Match(A i ,Q i ) represents the matching result of the system between the answer text and the test text, θ is the matching threshold, when the similarity reaches or exceeds θ, the match is successful, otherwise an abnormal prompt is returned, Sim(A i ,Q i ) is to use cosine similarity to calculate the similarity between two texts:

[0094]

[0095] in, and is the word vector weight of the answer and question text, and the weight of the kth word in the text;

[0096] S13, according to the preset format rules, the received answer text A i And the test text Q i For storage, the identifier format is (A i ,Q i ).

[0097] In this embodiment, S2 includes the following specific steps:

[0098] S21, the received test text Q i and answer text A i Perform paragraph indentation, punctuation replacement, and space deletion to obtain the formatted test text Q′ i and answer text A′ i ;

[0099] S22, the formatted test text Q' i and answer text A′ i Perform grammatical standardization and automatically correct grammatical errors in the text, including corrections of tense inconsistency, subject-verb inconsistency, and incorrect punctuation.

[0100] S23, the formatted test text Q'i and answer text A′ i Perform denoising to remove meaningless symbols, redundant spaces and extra characters;

[0101] S24, the denoised test text Q′ i and answer text A′ i Perform standardization to make the vocabulary and expressions of the text conform to the teaching standards, replace all non-standard words with standard forms, and obtain the standardized test text and answer text

[0102] S25. Verify the standardized test text and answer text If the format, syntax or content does not meet the standards, an exception prompt will be returned and marked as Otherwise, the standardized test text and answer text Pass to the next processing step.

[0103] In this embodiment, S3 includes the following specific steps:

[0104] S31. Using the deep context understanding model to analyze the standardized test text Perform deep semantic level analysis to generate multi-level semantic representations, including word-level embedding vectors Sentence-level representation vector and the passage-level representation vector

[0105] S32. Analyze the test text The contextual relationship, implicit semantic clues and logical structure in the text are used to extract the main idea of ​​the article. i 、C. Paragraph main idea ij and key sentence K ij ;

[0106] Article Topic i Obtained by maximizing the similarity between the chapter-level representation vector and the topic vector T:

[0107]

[0108] Among them, Sim(·,·) is the similarity calculation function, T is the predefined topic vector set;

[0109] Paragraph Main Idea C ij Obtained by clustering analysis of sentence-level representation vectors within a paragraph;

[0110] Key sentence K ijBy calculating the sentence-level representation vector and the article theme M i Determination of relevance:

[0111]

[0112] Where δ is the correlation threshold.

[0113] S33. Standardized student answer text Perform semantic analysis to generate semantic representation vectors of student answers Calculate word-level embedding using the same method as the test text Sentence-level representation and paragraph-level representation

[0114] S34. Identify the logical reasoning process, answer ideas and implicit information in the language expression in the student's answer, extract its relevance to the test text, and calculate the semantic matching degree S between the student's answer and the test text. i :

[0115]

[0116] S35. Based on the standardized test text and standard answers, use the logical reasoning model to generate a standard reasoning chain diagram:

[0117]

[0118] in, is the set of concept nodes in the standard answer, is the set of logical relationship edges between concepts;

[0119] Construct a reasoning chain diagram of the student's answer:

[0120]

[0121] in, is the set of concept nodes in the student’s answer, is the set of logical relationship edges between nodes;

[0122] S36. Comparison of reasoning chains of students' test questions and standard reasoning chain diagram Evaluate the completeness and correctness of students' reasoning process and calculate the structural similarity of the reasoning chain:

[0123]

[0124] in, is the similarity of the reasoning chain diagram structure between the student answer and the standard answer, Represents the set of matching relationship nodes between two graphs, The total number of nodes representing the standard reasoning chain graph;

[0125] S37, based on the results of semantic analysis of the test text and student answer text Perform multi-level contextual understanding to identify irony, metaphor, or simile in test questions and generate semantic analysis results, including the degree of semantic and logical match between student answers and the test text, as well as annotations of misunderstandings and reasoning errors in student answers:

[0126]

[0127] Among them, R i It represents the semantic analysis result of the i-th test question, which is a multi-dimensional comprehensive evaluation result of the student's answer text and the test question text. Indicates the matching degree between the rhetoric recognition result in the test text and the total rhetoric set, represents the set of rhetorical devices identified in the i-th test text, R total represents the total set of predefined rhetorical devices, and λ1, λ2, and λ3 represent weighted coefficients of different dimensions, which are used to balance the importance of semantic matching, logical reasoning, reasoning chain matching, and rhetoric recognition in the parsing results.

[0128] In this implementation, the word-level embedding vector Indicates the test text The embedding of the jth word in , the sentence-level representation vector is obtained by weighted averaging the word-level embeddings:

[0129]

[0130] Among them, N k is the number of words in the k-th sentence, is the attention weight of the j-th word, calculated as:

[0131]

[0132] Among them, u is the context global semantic vector;

[0133] Chapter-level representation vector Obtained by performing encoder processing on the sentence-level representation vector.

[0134] In this embodiment, S5 includes the following specific steps:

[0135] S51, according to the comprehensive evaluation results R i Identify students' weaknesses in semantic understanding and logical reasoning and generate a set of weaknesses:

[0136] B i={b ij |Error ij =|S ij -L ij |≥∈};

[0137] Among them, Error ij Indicates that students have a semantic matching degree S ij Similarity to logic L ij The deviation between them, ∈ is the error threshold, and the aspects with errors greater than the threshold are marked as weak points b ij ;

[0138] S52. Retrieve the student’s weak points from the question bank ij Related recommended test questions collection T ij , select suitable test questions by context similarity and matching degree of reasoning chain graph structure:

[0139]

[0140] in, is the set of test questions in the question bank, α1 and β1 are the weight coefficients of semantic and logical matching;

[0141] S53, based on the student's learning trajectory and semantic matching S i Generate personalized learning path P i :

[0142]

[0143] in, Represents finding the path P that makes the learning path the best i ,ω j Weak point b ij The weight coefficient in path optimization indicates the importance of the weak point in the learning path. For weak point b ij The average quality rating of the matched test set, For semantic similarity, calculate the question vector V t and weak point semantic vector The similarity between Represents the recommended test question reasoning chain graph G t Reasoning chain diagram with standard test questions The structural similarity between ∥G t ∥ represents the module length of the test question reasoning chain diagram, α2 and β2 are the weighted coefficients of semantic similarity and logical reasoning similarity.

[0144] In this embodiment, S6 includes the following specific steps:

[0145] S61. Generate a test analysis report that includes a comparison of the standard answer and the student's answer. The report shows the differences between the student's answer and the standard answer, and marks each error to highlight the student's understanding deviations and logical errors in the process of answering the questions.

[0146] S62, Semantic Analysis section describes the student's performance in understanding the main idea of ​​the text, the central idea of ​​the paragraph, and key sentences. The question analysis report highlights the student's scores and weaknesses in the semantic level, and indicates the areas of comprehension that need improvement.

[0147] S63: The logical assessment section analyzes whether the student's reasoning chain is complete, determines whether there are any jumps or unreasonable inferences in logical thinking, and presents the specific links where the logical problems are in the test question analysis report;

[0148] S64. Based on the results of semantic analysis and logical assessment, the test analysis report provides specific improvement directions and learning methods for students' weak points in semantic understanding and logical reasoning, and recommends exercises related to the weak points.

[0149] Example 1:

[0150] In March 2024, a key middle school in a certain city adopted the intelligent analysis system of the present invention in the final examination of the second grade of junior high school. The system was responsible for correcting the English reading comprehension questions of 250 students. Each test paper contained 5 reading comprehension articles with a total of 25 questions. The question types covered detail comprehension, reasoning and judgment, word meaning guessing and paragraph summary. Due to the huge amount of correction and the need for accurate and personalized feedback, the school decided to use this system for intelligent correction to verify its actual effect.

[0151] Teacher Wang uploaded the scanned copies of all 250 test papers to the intelligent parsing system starting at 14:30 on March 20, 2024. The system automatically read the text and formatted the test papers and answer texts.

[0152] One student's answer to a question that caught the system's attention was the following:

[0153] Questions:

[0154] You have the chance to stay at home by yourself! You may feelhappy.But you could also feel a bit worried 1._______.Don't be afraid you cando things to get away from danger when you are at home alone and also learn how to deal with an emergency if it happens.

[0155] Student answer: E. because you want to be independent;

[0156] Intelligent parsing process:

[0157] Time: The system started parsing the student's test questions at 14:37.

[0158] Semantic analysis: The system first identifies the key point of the question as "But you could also feel a bit worried," and based on the context, determines that the sentence needs to explain the reason for the worry rather than express an independent wish.

[0159] Logical reasoning: The system generated a chain of reasoning using a logical reasoning model. It discovered that the semantics of the student's answer did not align with the contextual logic. The system automatically flagged the error and noted in the report: "The student mistakenly interpreted the reason for their concern as a desire for independence. However, based on the context of the article, this should describe the difficulties the student might face being alone at home."

[0160] The system suggests the correct answer is:

[0161] F.because you may face with some difficulties that you never met before;

[0162] Personalized feedback and learning path generation:

[0163] The system generated a detailed personalized feedback report at 15:10, pointing out the student's weaknesses in reasoning and judgment questions, and recommended five related practice questions. The questions were all from the school question bank and were highly similar to the questions that the student answered incorrectly in terms of semantics and logical structure. The system also suggested that the student read the article "How to Be Safe When Home Alone" to further consolidate this type of semantic inference ability.

[0164] Marking time: 250 papers will be marked before 15:45, with a total time of 1 hour and 15 minutes.

[0165] Evaluation consistency: The intelligent system achieved a 98.5% consistency in semantic matching and logical inference.

[0166] Teacher feedback: Teacher Wang said that the system has greatly reduced the workload and saved nearly 80% of the time than expected.

[0167] Student feedback: After receiving the feedback reports, 73% of students had a clearer understanding of their misunderstandings and said that their weaknesses had been effectively improved through the recommended exercises.

[0168] On April 10, 2024, the school conducted another mock exam to assess students' progress in reasoning and word meaning guessing questions. The data showed that the class's overall accuracy rate for this type of question increased from 78% to 89%, and the students with the highest error rate increased their accuracy rate by 18 percentage points.

[0169] This example demonstrates the efficiency and accuracy of the intelligent analysis system in large-scale examination grading. The system not only rapidly completes grading tasks but also provides detailed, personalized feedback, helping students focus on weaknesses and improve. Furthermore, the system's high consistency ensures the objectivity of assessment results, significantly improving teacher efficiency and teaching effectiveness.

[0170] The present invention adopts multi-level semantic embedding representation, constructs text representation from the word level, sentence level and paragraph level respectively, and generates a reasoning chain diagram in combination with a large logical reasoning model. In the semantic matching process, not only the similarity calculation of word vectors and sentence-level embeddings is used to improve the matching degree between the answer and the test text, but also the similarity of the reasoning chain structure is used to evaluate the logical process of students in answering questions. The traditional keyword matching method can only capture shallow semantics. The algorithm of the present invention significantly improves the parsing accuracy and logical reasoning ability of complex text semantics through the combination of deep semantic analysis and logical reasoning, ensuring a more comprehensive understanding of students' answers.

[0171] The present invention combines the calculation results of semantic matching and reasoning chain structure similarity, and dynamically generates the optimal learning path according to the student's performance in different weak points. It not only takes into account the student's current knowledge mastery, but also dynamically sorts and adjusts the difficulty of the exercises in the learning path based on the similarity of the semantics and logical structure of the recommended test questions, ensuring that each student can obtain learning resources that match their knowledge level, avoiding the learning frustration caused by the inappropriate difficulty of the exercises in traditional methods, and achieving a more efficient learning experience.

[0172] The present invention introduces three modules: semantic analysis, logical evaluation and personalized learning suggestions in the generation of test analysis reports. Each incorrect item is accurately marked through a multi-dimensional comparison between the standard answer and the student's answer, and the analysis results at the semantic and logical levels are presented in detail in the report. Unlike the existing technology that only provides a simple judgment on whether the answer is correct or not, the analysis report of the present invention can not only point out the students' specific weaknesses but also generate targeted learning suggestions based on reasoning chain analysis, further enhancing students' room for improvement in semantic understanding and logical reasoning.

[0173] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for intelligent parsing of junior high school English reading comprehension test questions based on a large model, characterized by: The steps include: S1. Obtain the answer texts and corresponding test texts of the junior high school English reading comprehension test completed by students. The answers are derived from the published standard answers. S2. Preprocess the input test question text and answer text, including formatting, grammar normalization and noise removal, to generate standardized text; S3. Utilize the deep context understanding model to conduct in-depth semantic analysis of the test text, analyzing the contextual relationships, implicit semantic clues, and logical structure within the test text, extracting the main idea of ​​the article, the central idea of ​​each paragraph, and key sentences. Perform semantic analysis on the student's answer text, identifying the logical reasoning process, answer ideas, and implicit information in the language expression within the student's answer, extracting its relevance to the test text, and conducting multi-level context understanding based on the semantic analysis results to generate semantic analysis results. S4. Based on the test text and standard answers, combined with the logical reasoning model's understanding of the text context and logical relationships, analyze the semantic matching of the student's answers. Analyze the reasoning chain of the student's answers to identify logical errors or loopholes in the reasoning. Based on the standard answers and the logical reasoning model, generate a comprehensive semantic and logical evaluation of the student's answers, and mark any errors in the answers. S5. Dynamically generate learning suggestions based on the semantic and logical comprehensive evaluation results of students' answers; S6. Output the test question analysis report, which includes the standard answer, semantic analysis results, logical evaluation, student answer error marks and learning suggestions; The S5 includes the following specific steps: S51, according to the comprehensive evaluation results R i Identify students' weaknesses in semantic understanding and logical reasoning and generate a set of weaknesses: B i ={b ij ∣Error ij =|S ij -L ij |≥∈}; Among them, Error ij Indicates that students have a semantic matching degree S ij Similarity to logic L ij The deviation between them, ∈ is the error threshold, and the aspects with errors greater than the threshold are marked as weak points b ij ; S52. Retrieve the student’s weak points from the question bank ij Related recommended test questions collection T ij , select suitable test questions by context similarity and matching degree of reasoning chain graph structure: in, is the set of test questions in the question bank, α1 and β1 are the weight coefficients of semantic and logical matching; S53, based on the student's learning trajectory and semantic matching S i Generate personalized learning path P i : in, Represents finding the path P that makes the learning path the best i ,ω j Weak point b ij The weight coefficient in path optimization indicates the importance of the weak point in the learning path. For weak point b ij The average quality rating of the matched test set, For semantic similarity, calculate the question vector V t and weak point semantic vector The similarity between Represents the recommended test question reasoning chain graph G t Reasoning chain diagram with standard test questions The structural similarity between ∥G t ∥ represents the module length of the test question reasoning chain diagram, α2 and β2 are the weighted coefficients of semantic similarity and logical reasoning similarity.

2. The intelligent parsing method for junior high school English reading comprehension test questions based on a large model according to claim 1 is characterized in that: The S1 includes the following specific steps: S11. Receive student's answer text A from a preset database or online system i And the corresponding test text Q i , where A i represents the answer text of the i-th student, Q i Indicates the reading comprehension test text corresponding to the answer; S12, the received answer text A i and the test text Q i Perform matching association to make the answer text A i With the test text Q i One-to-one correspondence, if the match fails, an exception prompt will be returned: Among them, Match(A i ,Q i ) represents the matching result of the system between the answer text and the test text, θ is the matching threshold, when the similarity reaches or exceeds θ, the match is successful, otherwise an abnormal prompt is returned, Sim(A i ,Q i ) is to use cosine similarity to calculate the similarity between two texts: in, and is the word vector weight of the answer and question text, and the weight of the kth word in the text; S13, according to the preset format rules, the received answer text A i And the test text Q i For storage, the identifier format is (A i ,Q i ).

3. The intelligent parsing method for junior high school English reading comprehension test questions based on a large model according to claim 1 is characterized in that: The S2 includes the following specific steps: S21, the received test text Q i and answer text A i Perform paragraph indentation, punctuation replacement, and space deletion to obtain the formatted test text Q′ i and answer text A′ i ; S22, the formatted test text Q' i and answer text A′ i Perform grammatical standardization and automatically correct grammatical errors in the text, including corrections of tense inconsistency, subject-verb inconsistency, and incorrect punctuation. S23, the formatted test text Q' i and answer text A′ i Perform denoising to remove meaningless symbols, redundant spaces and extra characters; S24, the denoised test text Q′ i and answer text A′ i Perform standardization to make the vocabulary and expressions of the text conform to the teaching standards, replace all non-standard words with standard forms, and obtain the standardized test text and answer text S25. Verify the standardized test text and answer text If the format, syntax or content does not meet the standards, an exception prompt will be returned and marked as Otherwise, the standardized test text and answer text Pass to the next processing step.

4. The intelligent parsing method for junior high school English reading comprehension test questions based on a large model according to claim 1 is characterized in that: The S3 includes the following specific steps: S31. Using the deep context understanding model to analyze the standardized test text Perform deep semantic level analysis to generate multi-level semantic representations, including word-level embedding vectors Sentence-level representation vector and the passage-level representation vector S32. Analyze the test text The contextual relationship, implicit semantic clues and logical structure in the text are used to extract the main idea of ​​the article. i 、C. Paragraph main idea ij and key sentence K ij ; Article Topic i Obtained by maximizing the similarity between the chapter-level representation vector and the topic vector T: Among them, Sim(·,·) is the similarity calculation function, T is the predefined topic vector set; Paragraph Main Idea C ij Obtained by clustering analysis of sentence-level representation vectors within a paragraph; Key sentence K ij By calculating the sentence-level representation vector and the article theme M i Determination of relevance: Among them, δ is the correlation threshold; S33. Standardized student answer text Perform semantic analysis to generate semantic representation vectors of student answers Calculate word-level embedding using the same method as the test text Sentence-level representation and paragraph-level representation S34. Identify the logical reasoning process, answer ideas and implicit information in the language expression in the student's answer, extract its relevance to the test text, and calculate the semantic matching degree S between the student's answer and the test text. i : S35. Based on the standardized test text and standard answers, use the logical reasoning model to generate a standard reasoning chain diagram: in, is the set of concept nodes in the standard answer, is the set of logical relationship edges between concepts; Construct a reasoning chain diagram of the student's answer: in, is the set of concept nodes in the student’s answer, is the set of logical relationship edges between nodes; S36. Comparison of reasoning chains of students' test questions and standard reasoning chain diagram Evaluate the completeness and correctness of students' reasoning process and calculate the structural similarity of the reasoning chain: in, is the similarity of the reasoning chain diagram structure between the student answer and the standard answer, Represents the set of matching relationship nodes between two graphs, Represents the total number of nodes in the standard reasoning chain graph; S37, based on the results of semantic analysis of the test text and student answer text Perform multi-level contextual understanding to identify irony, metaphor, or simile in test questions and generate semantic analysis results, including the degree of semantic and logical match between student answers and the test text, as well as annotations of misunderstandings and reasoning errors in student answers: Among them, R i It represents the semantic analysis result of the i-th test question, which is a multi-dimensional comprehensive evaluation result of the student's answer text and the test question text. Indicates the matching degree between the rhetoric recognition result in the test text and the total rhetoric set, represents the set of rhetorical devices identified in the i-th test text, R total represents the total set of predefined rhetorical devices, and λ1, λ2, and λ3 represent weighted coefficients of different dimensions, which are used to balance the importance of semantic matching, logical reasoning, reasoning chain matching, and rhetoric recognition in the parsing results.

5. The intelligent parsing method for junior high school English reading comprehension test questions based on a large model according to claim 4 is characterized in that: The word-level embedding vector Indicates the test text The embedding of the jth word in , the sentence-level representation vector is obtained by weighted averaging the word-level embeddings: Among them, N k is the number of words in the k-th sentence, is the attention weight of the j-th word, calculated as: Among them, u is the context global semantic vector; Chapter-level representation vector Obtained by performing encoder processing on the sentence-level representation vector.

6. The intelligent parsing method for junior high school English reading comprehension test questions based on a large model according to claim 1 is characterized in that: The S6 comprises the following specific steps: S61. Generate a test analysis report that includes a comparison of the standard answer and the student's answer. The report shows the differences between the student's answer and the standard answer, and marks each error to highlight the student's understanding deviations and logical errors in the process of answering the questions. S62, Semantic Analysis section describes the student's performance in understanding the main idea of ​​the text, the central idea of ​​the paragraph, and key sentences. The question analysis report highlights the student's scores and weaknesses in the semantic level, and indicates the areas of comprehension that need improvement. S63: The logical assessment section analyzes whether the student's reasoning chain is complete, determines whether there are any jumps or unreasonable inferences in logical thinking, and presents the specific links where the logical problems are in the test question analysis report; S64. Based on the results of semantic analysis and logical assessment, the test analysis report provides specific improvement directions and learning methods for students' weak points in semantic understanding and logical reasoning, and recommends exercises related to the weak points.

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