Method for automatically judging answer correctness

Abstract

The invention discloses a method for automatically judging whether an answer is right or wrong. The scheme comprises a data acquisition unit which supports multi-form answer input; the question analysis module can analyze questions to determine knowledge fields and extract key information; the intelligent detection core is used for detecting and accurately analyzing errors according to answer types; the knowledge storage library is used for storing massive mathematical knowledge and dynamically updating the mathematical knowledge; and the result feedback component is used for intuitively presenting the result, performing voice broadcast and giving personalized learning suggestions according to the answer history. According to the scheme, various mathematics answers can be accurately detected, errors can be deeply analyzed, the problems that traditional mathematics answer detection is low in efficiency, poor in accuracy, lack of personalized guidance and the like are effectively solved through multi-module collaborative operation, mathematics teaching and learning efficiency is greatly improved, and the method has extremely important application value and innovative significance in the technical field of education and is worthy of popularization and application. And the intelligent process of mathematics education is powerfully promoted.

Description

1. Technical Field

[0001] The present invention relates to the intersection of education and information technology, and particularly focuses on the innovation of mathematics learning auxiliary tools, aiming to provide an efficient, accurate and intelligent solution for automatically detecting mathematics answers to help optimize mathematics teaching and learning activities. 2. Background Technology

[0002] In traditional mathematics education scenarios, the answer detection process mostly relies on manual operations. Teachers have a heavy task of correcting homework and test papers, which not only consumes a lot of time and energy, but also makes it difficult to ensure the consistency of the evaluation criteria during the long correction process. At the same time, the feedback cycle of manual correction is long, and students cannot know the errors and correct them in time, which affects the learning effect and knowledge consolidation efficiency. With the rise of digital education, online learning platforms and intelligent education software continue to emerge, but the existing mathematics answer detection technology still has many limitations, such as the single answer format requirement, difficulty in understanding complex mathematical semantics, insufficient in-depth and accurate error analysis, and lack of personalized learning guidance. These problems cannot fully meet the needs of modern mathematics education for efficiency, intelligence, and personalization. III. Summary of the invention

[0003] 1. Purpose of the Invention

[0004] The present invention aims to construct a comprehensive and intelligent solution for automatically detecting mathematical answers, which can quickly and accurately detect various mathematical answers, accurately analyze the causes of errors and provide personalized learning feedback, thereby improving the overall efficiency and quality of mathematics learning and teaching, and promoting the digital and intelligent transformation of mathematics education.

[0005] 2. System composition and functions

[0006] -Data acquisition unit:

[0007] -Design a multi-channel data collection interface that supports direct text entry and adapts to common keyboard input operations to meet students' needs for text answer input on computers or mobile devices.

[0008] -Integrates advanced image recognition technology, uses high-precision character segmentation and recognition algorithms for images of students' handwritten answers, can process handwritten math characters of different colors, different pen tip thicknesses, and a variety of paper backgrounds, converts them into text data, and realizes the digital collection of handwritten answers.

[0009] -Equipped with a speech recognition engine, it has powerful voice signal processing capabilities and mathematical terminology recognition capabilities. It can accurately recognize mathematical voice expressions in different accents, speaking speeds and intonations, and convert voice commands or oral answers into text form, providing students with a convenient way to input answers. It is especially suitable for young students or special education scenarios.

[0010] - Question analysis module:

[0011] -Build a huge mathematical knowledge classification system, covering all branches from basic arithmetic to advanced mathematics, such as four operations, algebraic equations, functions, geometric figures, probability statistics, etc. Through in-depth semantic analysis of the text content of mathematical questions, using lexical analysis, syntactic analysis and semantic understanding algorithms in natural language processing technology, accurately determine the mathematical knowledge category to which the question belongs.

[0012] - Develop an intelligent algorithm for extracting key information from questions, which can automatically identify key elements such as numerical constants, variable symbols, mathematical operators, geometric figure feature descriptions, conditional restriction statements, etc., and establish a clear mathematical model representation. For example, for a question like "In a right triangle ABC, ∠C=90°, AB=5, AC=3, find the length of BC", it can accurately extract the conditions of the right triangle, the known side length information, and the required side length information, and build a mathematical model based on the Pythagorean theorem.

[0013] -Intelligent detection core:

[0014] -For numerical answers, we use high-precision numerical calculation and comparison algorithms, which can not only accurately match numerical values, but also set a reasonable error range for approximate comparison according to the requirements of the question and mathematical common sense. For example, in questions involving the calculation of pi, the rationality of the answer can be judged based on the accuracy requirements.

[0015] -For the answers to algebraic expressions, use symbolic calculation and simplification techniques to simplify the input expression into a standard form, and compare the simplified result with the standard answer symbol by symbol, while checking whether the derivation process of the expression conforms to the rules of algebraic operations. For example, when simplifying a polynomial expression, check whether the steps of combining like terms and factoring are correct.

[0016] -When solving equations, strictly follow the mathematical principles of solving equations, substitute the answer into the original equation to check the equations on both sides to ensure the correctness of the solution. And it can fully verify the solutions of complex equations such as multivariate equations and high-order equations.

[0017] -When faced with answers related to geometric figures, conduct a comprehensive check on the attribute descriptions of the figures, area and volume calculation results, angle measurement values, etc. based on geometric axioms, theorems, and formulas. For example, when checking the answer to the sum of the interior angles of a triangle, determine whether it is equal to 180°; when checking the answer to the calculation of the volume of a cone, verify whether the correct formula is used and the correct parameters are substituted.

[0018] -When an answer error is detected, the error analysis engine is started to deeply analyze the root cause of the error based on the error pattern library and mathematical knowledge reasoning mechanism. The error pattern library contains common calculation error types (such as four arithmetic errors, symbol errors, etc.), concept error types (such as misunderstanding of geometric concepts, neglect of function domain, etc.), formula application error types (such as incorrect use of area formula, Newton-Leibniz formula, etc.) and logical reasoning error types (such as reasoning step skipping in proof questions, assumption errors, etc.). Through matching with the error pattern library and mathematical logical reasoning, detailed error prompt information is generated, clearly indicating the location of the error, the cause of the error, and the ideas and methods to guide the correct answer.

[0019] -Knowledge Repository:

[0020] -Integrate a large amount of mathematical knowledge resources, including but not limited to mathematical questions and answers in textbooks of all grades and versions, mathematical competition questions and solutions, examples and explanations in classic mathematical works, etc. Categorize and store them according to the mathematical knowledge system for quick retrieval and call.

[0021] -Establish a dynamic update mechanism, maintain close cooperation with mathematics education research institutions and textbook compilation units, and obtain timely information on updated mathematics knowledge, adjusted teaching syllabus content, and new mathematics question types and problem-solving methods. Use web crawler technology to regularly collect the latest mathematics knowledge results from authoritative mathematics education websites and academic journals, and update and expand the knowledge repository in real time to ensure that the system always has the most cutting-edge and accurate mathematics knowledge and testing standards.

[0022] -Result feedback component:

[0023] -Design a visual result display interface to show students whether their answers are correct or not through intuitive charts, color markings, and text descriptions. For example, use green to indicate correct answers, red to indicate incorrect answers, and list error prompts in detail next to incorrect answers.

[0024] - Develop a voice feedback function, using speech synthesis technology to broadcast answer detection results and error prompts in clear and natural voice, which is convenient for young students or students with visual impairments to use.

[0025] -Build a user answer history database to record each student's answer, including question content, answer, answer time, test results and other information. Based on big data analysis technology, conduct in-depth mining of students' answer history, analyze students' learning weaknesses, easy-to-make mistakes, and learning progress trends. According to the analysis results, use intelligent recommendation algorithms to filter out personalized learning materials suitable for students from knowledge repositories and external learning resource libraries, such as targeted exercise sets, knowledge point explanation videos, online tutoring course recommendations, etc., and display these recommended information on the user interface to provide students with personalized learning path planning and learning resource support. 4. Specific implementation methods

[0026] In actual application scenarios, this solution for automatically detecting mathematical answers can be integrated into online education platforms, intelligent learning software, or the school's internal teaching management system.

[0027] 1. Teacher-side operation:

[0028] -After logging into the system, teachers can select or create math question sets based on teaching plans and course content, and set parameters such as question difficulty, question type distribution, and answering time limit.

[0029] -After students finish answering questions, teachers can start the answer detection function in the system with one click. The system will automatically batch detect all students' answers and generate a detailed detection report. The report includes statistical analysis of students' overall answer situation (such as correct rate, error rate distribution, scores of each question type, etc.), individual student answer details (answers to each question, detection results, error prompts), and class rankings and score distribution curves. Teachers can use these reports to fully understand students' learning situation, adjust teaching strategies in a timely manner, and conduct concentrated explanations and guidance on common problems faced by students.

[0030] 2. Student-side operation:

[0031] -Students can use the system to self-check at any time during the learning process. By selecting the corresponding learning chapter or knowledge point, the system will automatically generate exercises or students can enter extracurricular exercises to check their answers. -After students submit their answers, the system will quickly check and feedback the results. Students can carefully read the error prompts, understand the reasons for their mistakes, and click on the recommended learning materials link for targeted learning and practice. At the same time, students can view their answer history, track their learning progress and improvement, and adjust their learning methods and focus according to the system's suggestions.

[0032] Through the above specific implementation methods on the teacher side and the student side, this solution for automatically detecting mathematical answers can be effectively integrated into all aspects of mathematics teaching and learning, giving full play to its advantages of automatic detection, precise analysis, and personalized feedback, and providing strong support for the modernization of mathematics education.

Claims

1. A method for automatically judging whether an answer is right or wrong, characterized in that: include: -Data collection unit: used to collect the mathematical answer data to be tested, supporting multiple data formats and input methods, including text entry, image recognition conversion, and voice input converted into text form, so as to ensure that answer information from different sources can be widely accepted. - Question analysis module: It can conduct in-depth analysis of the math questions corresponding to the collected answers and determine the mathematical knowledge field to which they belong, such as arithmetic operations, solving algebraic equations, geometric figure measurement calculations, or statistical probability-related problems, etc. At the same time, it can accurately extract various key elements in the questions, including known values, variable settings, key parameters of graphics, and the internal operational logic relationship between elements. -Intelligent detection core: -Differentiated detection strategies are implemented for different types of math answers. For numerical answers, accurate numerical matching is performed or compared within a preset reasonable error range; for answers of algebraic expressions, they are simplified to the simplest form and consistency check is performed with the standardized simplified result, and the compliance and correctness of the calculation steps are checked at the same time; when faced with the solution of an equation, the original equation is strictly substituted for validity verification; and for answers related to geometric figures, based on geometric principles and theorems, a comprehensive check is made to see whether the graphic attributes, area, volume, angle and other elements described in the answer are consistent with the conditions of the question and geometric laws. -Once a deviation in the answer is detected, it has the function of accurately diagnosing the error type, and can distinguish different categories such as calculation errors, errors caused by deviation in concept understanding, incorrect application of formulas, and logical reasoning errors, and automatically generate targeted error prompts. The prompts should specify in detail the specific links where the error occurred, the root causes of the error, and the direction of thinking to guide the correct answer. -Knowledge repository: It has a massive amount of mathematical knowledge data resources, including a wealth of math problem examples and their corresponding standard answers, detailed problem-solving step analysis, in-depth explanations of relevant math knowledge points, etc. It also has a dynamic update mechanism. Based on the development of the math discipline, changes in educational and teaching concepts, and the update requirements of curriculum standards, it can timely expand, revise and optimize the knowledge content in the repository, thereby providing solid data and knowledge support for the answer detection process. -Result feedback component: responsible for presenting the final result of answer detection, i.e. the judgment information of whether the answer is correct or not, as well as the detailed detection feedback content, to the user in an intuitive and diversified way. It can be displayed in the form of a visual text report on the display screen, or the results and prompt information can be broadcast to the user through speech synthesis technology. At the same time, it can fully store and deeply analyze the user's answer history record. Based on the big data analysis of the user's answering behavior and error patterns, it can tailor personalized learning improvement suggestions and targeted practice recommendation plans for users.

2. A system for automatically detecting mathematical answers according to claim 1, characterized in that: When performing image recognition conversion, the data acquisition unit uses advanced optical character recognition (OCR) technology and intelligent image preprocessing algorithms, which can effectively identify handwritten mathematical content in a variety of font styles, different writing styles, and complex backgrounds, and convert them into editable text data with high precision. It also has good fault tolerance and error correction capabilities for blurred, tilted, and partially occluded handwritten content.

3. A system for automatically detecting mathematical answers according to claim 1, characterized in that: The question parsing module adopts a technical architecture that combines natural language processing with mathematical semantic understanding. In the process of parsing questions, it can semantically decompose and reconstruct mathematical models of complex mathematical problems with practical application backgrounds, convert real-world scenario descriptions into standardized mathematical expressions or model frameworks, and then implement the answer detection process.

4. A system for automatically detecting mathematical answers according to claim 1, characterized in that: When generating personalized learning improvement suggestions, the result feedback component comprehensively considers multiple dimensional factors such as the user's age, grade, learning stage, past academic performance, and current test performance, and uses an intelligent recommendation algorithm to select the content that best suits the user's current learning needs from the system's massive learning resource library, including but not limited to special exercise sets for specific knowledge points, detailed video tutorials, additional reading materials, and online learning course recommendations.