An estimation question exercise method, electronic device, and medium

Abstract

The embodiment provides an estimation exercise method, an electronic device and a medium. The method is applied to the electronic device, and the electronic device comprises a display screen. The method comprises the following steps: obtaining estimation data of a user; analyzing the estimation data to obtain estimation analysis data; displaying a result area and a plurality of estimation areas corresponding to a plurality of estimation calculation factor items on the display screen according to the estimation analysis data; arranging the plurality of estimation areas according to positions corresponding to each estimation calculation factor item; the estimation area has a numerical sequence; the value range of each numerical sequence is related to the value of the corresponding estimation calculation factor item; and in response to the selection of a numerical value in each numerical sequence by the user, displaying an operation result between the alternative numerical values in the plurality of numerical sequences on the result area. The embodiment can intuitively display the estimation process, help the user understand the logical process of estimation in a guided manner, and improve the interest of the user.

Description

Technical Field

[0001] This application relates to the field of data processing-assisted learning technology, and includes, but is not limited to, a method for practicing estimation problems, an electronic device, and a medium. Background Technology

[0002] For students and other groups with learning needs, doing exercises is an efficient way to practice, verify, and consolidate knowledge during the learning process. Reviewing and correcting mistakes is a key method for identifying gaps in knowledge, mastering new concepts, and discovering weaknesses, helping learners address their weaknesses and improve learning efficiency and quality. Currently, the practice and answering modes for mathematical estimation problems in existing educational smart applications and smart learning devices are highly homogenized, forming fixed traditional technical implementation schemes. Specifically, electronic devices directly display estimation problems on the screen, such as the typical mathematical estimation problem: "312 × 9 ≈ ?".

[0003] Existing learning apps rely on built-in OCR image recognition or formula recognition technology to recognize, parse, and format users' handwritten numerical answers. The recognized answers are then compared with pre-set standard estimated answers for verification, thus automating grading and determining correctness. However, this approach has the following drawbacks: First, the estimation problem-solving process is completely opaque, lacking step-by-step logical guidance, making it impossible for students to grasp the core estimation principles. Traditional technical solutions only assess the final estimation result, completely omitting the complete thought process specific to estimation problems: "identifying operational elements, selecting approximate values, rounding to simplify calculations, and deriving approximate results." There is no process visualization or step-by-step guidance mechanism. During practice, students can only passively rely on intuition to answer mentally or mechanically memorize standard answers, unable to understand the underlying core logic of "why round the original number, how to select an approximate value that fits the operational rules, and what impact different approximate values ​​have on the final estimation result." Under this model, students can only memorize answers to individual questions, failing to develop a general estimation problem-solving mindset. They are prone to repeating mistakes when faced with similar estimation problems with variations in question type and numerical adjustments, failing to truly master estimation knowledge points, and resulting in extremely low conversion rates for specialized practice.

[0004] Secondly, the purely independent mental calculation answering mode has a high threshold, placing a heavy psychological burden on students and resulting in poor initiative. Traditional estimation exercises rely entirely on students' instantaneous mental calculation ability to independently complete deductions, without any equipment assistance for parameter adjustment, step-by-step calculation, or dynamic trial calculation functions. For students with weak number sense and poor estimation foundation, high-intensity, unassisted pure mental calculation answers are difficult and have a low error tolerance rate, easily leading to feelings of difficulty and learning frustration. Over time, this can foster a resistance to estimation questions, significantly reducing the willingness to practice actively. At the same time, traditional answering only supports a single interactive form of handwriting and keyboard input, making the practice process tedious, boring, and lacking in interest, failing to mobilize students' subjective initiative. This results in the review of wrong questions and specific exercises becoming mere formalities, failing to achieve the teaching effect of identifying and consolidating knowledge gaps.

[0005] Third, traditional solutions suffer from drawbacks such as one-sided feedback and a lack of space for independent exploration. The equipment can only determine the correctness of the final answer, failing to reveal core issues like flaws in the solution process or deviations in numerical selection. Students only know the result is wrong but cannot pinpoint the specific error, resulting in extremely poor error correction and review efficiency. Furthermore, the lack of dynamic trial calculations and adjustable numerical interactive mechanisms prevents students from independently trying different approximate values, observing changes in calculation results, and actively summarizing estimation and calculation patterns. They are forced to passively accept knowledge, hindering inquiry-based deep learning. Summary of the Invention

[0006] In view of this, the estimation practice method, electronic device, and medium provided in this application can intuitively demonstrate the estimation process to the user, helping the user clearly understand the logical process of estimation in a guided manner, and enhancing the user's interest in practice. The estimation practice method, electronic device, and medium provided in this application are implemented as follows: A first aspect of this application provides a method for practicing estimation problems. This method is applied to an electronic device, which includes a display screen. The method includes: acquiring estimation problem data from a user; parsing the estimation problem data to obtain estimation problem parsing data, the estimation problem parsing data including multiple estimation calculation factors, the position of each estimation calculation factor, and the operation relationship; displaying a result area and multiple estimation areas corresponding to the multiple estimation calculation factors on the display screen based on the estimation problem parsing data; arranging the multiple estimation areas according to the position corresponding to each estimation calculation factor; each estimation area having a numerical sequence; the value range of each numerical sequence being related to the value of the corresponding estimation calculation factor; and, in response to the user's selection of a value in each numerical sequence, displaying the operation result between candidate values ​​in the multiple numerical sequences on the result area, the operation being performed based on the position and operation relationship of all estimation calculation factors.

[0007] In this embodiment, the solution uses students' individual estimation errors as the data source for practice, abandoning the generalized practice mode of randomized questions and standardized practice in traditional estimation exercises. It can precisely target students' weaknesses in estimation knowledge, ensuring that the practice content matches individual learning shortcomings and effectively reducing ineffective practice. Simultaneously, this solution breaks down and visualizes the operational structure and relationships of estimation problems, providing separate estimation operation and result display areas, along with selectable number sequences tailored to the questions. This breaks away from the closed practice mode of traditional estimation exercises that rely solely on mental calculation without process demonstration. Students can independently select estimation values ​​and complete calculation derivations in real time, transforming the abstract estimation thinking process into an intuitive practical process. This facilitates students' understanding of the operational logic and numerical relationships of estimation, helping them gradually establish standardized estimation problem-solving strategies and effectively improving their mastery of estimation knowledge.

[0008] In some possible embodiments, the value range of each of the above-mentioned number sequences is related to the value of the corresponding estimation calculation factor. Specifically, each number sequence takes the value of the corresponding estimation calculation factor as the sequence center value and takes values ​​within a predetermined value range to obtain the number sequence.

[0009] In this embodiment, by using the original calculated value of the question as the center value and then expanding upwards and downwards to generate the corresponding numerical sequence, it is ensured that all candidate values ​​revolve around the core calculation value of the original question. This value selection method aligns with the core learning logic of rounding down to the nearest value in elementary school mathematics estimation, ensuring that all generated candidate values ​​are suitable for the estimation test points of the current question, effectively avoiding invalid options with excessive numerical deviations or irrelevant estimation logic of the original question. At the same time, the value selection rule centered on the original value can standardize students' estimation thinking, guiding them to develop the habit of answering questions by taking the nearest approximation value around the original value, abandoning the erroneous methods of random selection and blind rounding, so that each value selection practice can accurately train students' core estimation ability, steadily improving their proficiency and accuracy in numerical estimation.

[0010] In some possible embodiments, the range of values ​​is determined according to the estimation rules of the estimation problem.

[0011] In this embodiment, the numerical range is determined based on the standard estimation rules corresponding to the estimation questions, ensuring that the generated candidate values ​​perfectly align with the classroom teaching standards and question types in elementary school mathematics. Different types of estimation questions have different rounding standards, value selection logic, and testing focuses. This solution does not use a uniform, fixed value range but dynamically adjusts the range according to the specific estimation rules for each question, effectively avoiding problems such as excessively large value ranges leading to chaotic values, or excessively small value ranges resulting in a single practice dimension. Simultaneously, the numerical range generated according to the standard estimation rules perfectly matches the learning difficulty of the corresponding grade level, preventing values ​​beyond the syllabus. This ensures that every value selection exercise for students aligns with the estimation problem-solving approaches stipulated in the textbook, precisely training the standard problem-solving abilities for the corresponding question types. This makes specialized practice more standardized and aligned with teaching requirements, further enhancing the professionalism and effectiveness of estimation practice.

[0012] In some possible embodiments, each of the above-mentioned number sequences is displayed in a list form in the corresponding estimation area; in response to the user's selection of a value in each number sequence, the method includes: in response to the user's drag gesture on the number sequence in the corresponding estimation area, determining that the value in the number sequence dragged to the target position is the value selected by the user. In this embodiment, the number sequences are displayed centrally in a list form in the corresponding estimation area, with a clear and organized interface layout, allowing students to intuitively see all available approximate values, quickly browse and filter suitable estimated values, and avoid the problem of chaotic value selection. At the same time, the drag-and-drop interaction method replaces the traditional manual input and keyboard click operation mode, making the operation simple and convenient, conforming to the touch operation habits of primary school students, and significantly reducing the operation threshold. The simple drag-and-drop interaction allows students to focus more on estimation value thinking and understanding of calculation logic, without spending too much time on tedious input operations, effectively improving the fluency of estimation practice. In addition, the fun touch drag-and-drop operation changes the boring experience of traditional problem-solving, further improving students' practice focus and willingness to practice independently, allowing students to repeatedly train their estimation value ability and consolidate estimation knowledge points in a relaxed interactive process.

[0013] In some possible embodiments, the calculation results in the result area are dynamically updated as the user updates the selected values ​​in the numerical sequence. In this embodiment, the device supports real-time changes in the calculation results following the estimated values ​​selected by the user, changing the static mode of traditional problem-solving where manual submission of answers is required to view the results. Every time the user changes or adjusts the alternative values ​​in the estimation area, the result area immediately refreshes the corresponding calculation results, eliminating the need for cumbersome submission operations and providing timely and intuitive feedback. Students can freely try different combinations of approximate values, observe the impact of numerical changes on the final calculation results in real time, actively explore the rules of estimation and rounding, and conduct independent inquiry-based learning. This dynamic and interconnected display method makes abstract numerical calculation relationships visual and perceptible, helping students quickly understand the estimation logic, break free from reliance on rote mental calculation, gradually cultivate numerical sensitivity through repeated trial calculations, solidly grasp the core problem-solving rules of estimation, and effectively improve practice results.

[0014] In some possible embodiments, the method further includes: in response to the user's submission of the calculation result, displaying different visual feedback screens on the display screen according to the correctness of the calculation result. In this embodiment, the solution adds a result submission and dedicated visual feedback link, making up for the shortcomings of traditional estimation exercises that lack a standardized answer loop and have rudimentary feedback forms. After students complete all operations of numerical selection and dynamic trial calculation, they can actively submit the calculation result. The device will automatically verify the correctness of the answer and match the corresponding visual feedback content, forming a complete practice loop of "value selection trial calculation - result submission - correctness feedback". Unlike the traditional mode of random answers and no standardized submission steps, this setting can standardize the student's estimation answer process, help students develop complete and rigorous problem-solving habits, and avoid perfunctory answers and random trial calculations. At the same time, the dedicated visual feedback mechanism makes the answer results clearer and more explicit, allowing students to receive effective feedback for each practice, no longer blindly doing more problems, effectively improving the completeness and standardization of estimation error review, and continuously accumulating standardized estimation problem-solving experience.

[0015] In some possible embodiments, the above-mentioned display of different visual feedback screens to the user on the display screen according to the correctness of the calculation result includes: when the calculation result is consistent with the correct answer, the visual feedback screen is marked in green; when the calculation result is inconsistent with the correct answer, the visual feedback screen is marked in red and displays the correct answer. In this embodiment, this solution adopts a red-green distinguishing visual feedback mode, replacing the monotonous text-based correct / incorrect prompts of traditional exercises. The visual feedback is intuitive and easy to understand, conforming to the cognitive habits of primary school students. The green marking of correct feedback can give students positive learning encouragement, enhance their sense of accomplishment in solving problems, alleviate students' resistance and fear of difficulty in estimation exercises, and make students more willing to actively carry out estimation-specific exercises. The red marking combined with the standard answer of incorrect feedback allows students to quickly know their mistakes and directly compare their values ​​and calculation processes with the standard results without having to consult additional analysis materials, greatly reducing the difficulty of reviewing incorrect questions. Through this clear differentiated feedback method, students can quickly locate their estimation value problems and calculation loopholes, correct incorrect problem-solving ideas in a timely manner, efficiently complete the knowledge gap filling, continuously standardize their estimation answering habits, and steadily improve the accuracy of estimation problem solving.

[0016] In some possible embodiments, the above-mentioned acquisition of user estimation question data includes: obtaining structured text information from user's incorrect questions, the structured text information including the text content of the user's incorrect questions; determining the question type of the user's incorrect question data based on the structured text information; and when the user's incorrect question type is an estimation question, acquiring the corresponding estimation question data. In this embodiment, this solution, through structured analysis and intelligent question type identification of student's incorrect questions, can automatically and accurately filter estimation question data from various mixed incorrect questions, effectively distinguishing estimation questions from other question types, avoiding the problems of mixed practice materials and impure specialized practice. Compared with the method of students manually sorting and filtering estimation incorrect questions, this automatic filtering process is more efficient and accurate, saving the time and effort of manual classification and sorting. At the same time, only the estimation incorrect questions of individual students are selected and retained as practice materials, focusing entirely on the students' errors in estimation knowledge points, further ensuring the pertinence of estimation-specific practice, eliminating irrelevant question types from interfering with the practice rhythm, and allowing subsequent visual estimation practice and dynamic trial calculation consolidation to accurately target individual learning weaknesses, effectively improving the practice quality of estimation incorrect question review.

[0017] Secondly, embodiments of this application also provide an electronic device with learning functionality, which includes a processor. The processor is used to execute an estimation problem practice method as described in the first aspect above. Exemplarily, the electronic device can be a learning machine, mobile phone, tablet computer, or smartwatch, etc.

[0018] Thirdly, embodiments of this application also provide a computer-readable storage medium storing instructions that, when executed on a processor, cause the processor to perform an estimation problem practice method as described in the first aspect above.

[0019] Regarding the technical principles and effects of the second and third aspects, please refer to the technical principles and effects of the first aspect mentioned above, which will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an estimation problem practice method provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating an estimation problem practice method provided in this application embodiment. Figure 2 ; Figure 3 A schematic diagram of the operation interface of an estimation problem practice method provided in this application embodiment. Figure 1 ; Figure 4 A schematic diagram of the operation interface of an estimation problem practice method provided in this application embodiment. Figure 2 ; Figure 5 A flowchart illustrating an estimation problem practice method provided in this application embodiment. Figure 3 ; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0024] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0025] It should be noted that the terms "first, second, third" used in the embodiments of this application are used to distinguish similar or different objects and do not represent a specific order of objects. It can be understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0026] First, some basic concepts involved in the embodiments of this application will be explained: In the specialized learning process of estimation in primary and secondary school mathematics, reviewing incorrect answers is a core learning method for students to identify and fill gaps in their knowledge, solidify their estimation problem-solving abilities, and improve their mathematical number sense. It possesses a personalized teaching value that cannot be replaced by conventional practice exercises. Estimation errors made in students' daily practice can realistically and intuitively reflect their individual knowledge weaknesses, accurately exposing loopholes and operational errors in core areas such as approximation selection, application of rounding rules, and logical deduction. Compared to indiscriminate practice with entirely new problems, targeted review of incorrect answers eliminates ineffective repetitive practice, focusing on students' common mistakes and accurately pinpointing the root causes of learning problems. Through systematic review of incorrect answers, students can effectively correct erroneous estimation problem-solving thinking and poor answering habits, deeply analyze the reasons for errors, thoroughly understand the general problem-solving logic for similar question types, and gradually build a standardized and complete estimation knowledge system.

[0027] Currently, the practice and answering modes for mathematical estimation problems in existing educational smart applications and smart learning devices are highly homogenized, forming fixed traditional technical implementation schemes. Specifically, electronic devices directly display estimation problems on the screen, such as the typical mathematical estimation problem: "312 × 9 ≈ ?"

[0028] Existing learning apps rely on built-in OCR image recognition or formula recognition technology to recognize, parse, and format users' handwritten numerical answers. The recognized answers are then compared with pre-set standard estimated answers for verification, thus automating grading and determining correctness. However, this approach has the following drawbacks: First, the estimation problem-solving process is completely opaque, lacking step-by-step logical guidance, making it impossible for students to grasp the core estimation principles. Traditional technical solutions only assess the final estimation result, completely omitting the complete thought process specific to estimation problems: "identifying operational elements, selecting approximate values, rounding to simplify calculations, and deriving approximate results." There is no process visualization or step-by-step guidance mechanism. During practice, students can only passively rely on intuition to answer mentally or mechanically memorize standard answers, unable to understand the underlying core logic of "why round the original number, how to select an approximate value that fits the operational rules, and what impact different approximate values ​​have on the final estimation result." Under this model, students can only memorize answers to individual questions, failing to develop a general estimation problem-solving mindset. They are prone to repeating mistakes when faced with similar estimation problems with variations in question type and numerical adjustments, failing to truly master estimation knowledge points, and resulting in extremely low conversion rates for specialized practice.

[0029] Secondly, the purely independent mental calculation answering mode has a high threshold, placing a heavy psychological burden on students and resulting in poor initiative. Traditional estimation exercises rely entirely on students' instantaneous mental calculation ability to independently complete deductions, without any equipment assistance for parameter adjustment, step-by-step calculation, or dynamic trial calculation functions. For students with weak number sense and poor estimation foundation, high-intensity, unassisted pure mental calculation answers are difficult and have a low error tolerance rate, easily leading to feelings of difficulty and learning frustration. Over time, this can foster a resistance to estimation questions, significantly reducing the willingness to practice actively. At the same time, traditional answering only supports a single interactive form of handwriting and keyboard input, making the practice process tedious, boring, and lacking in interest, failing to mobilize students' subjective initiative. This results in the review of wrong questions and specific exercises becoming mere formalities, failing to achieve the teaching effect of identifying and consolidating knowledge gaps.

[0030] Third, traditional solutions suffer from drawbacks such as one-sided feedback and a lack of space for independent exploration. The equipment can only determine the correctness of the final answer, failing to reveal core issues like flaws in the solution process or deviations in numerical selection. Students only know the result is wrong but cannot pinpoint the specific error, resulting in extremely poor error correction and review efficiency. Furthermore, the lack of dynamic trial calculations and adjustable numerical interactive mechanisms prevents students from independently trying different approximate values, observing changes in calculation results, and actively summarizing estimation and calculation patterns. They are forced to passively accept knowledge, hindering inquiry-based deep learning.

[0031] To address the aforementioned problems, firstly, embodiments of this application provide a method for practicing estimation problems, such as... Figure 1 As shown, the method includes the following steps S100-S400: S100: Obtain the user's estimation question data.

[0032] In some possible implementations, such as Figure 2 As shown, step S100 further includes the following sub-operations: steps S110-S130: S110. Obtain structured text information from the user's incorrect questions. For example, the structured text information includes the text content of the user's incorrect questions.

[0033] Step S110 is used to adapt to two main sources of incorrect answers: automatic collection by the system and manual upload by students. Through conventional image optimization and text parsing technologies, the system ensures the completeness, standardization, and accuracy of the incorrect answer data. For incorrect answers automatically collected by the system, after determining that a student's answer is incorrect, the device automatically captures the complete question, answer record, corresponding knowledge points, and other original information and completes basic organization. For paper-based incorrect answer images taken by students or imported from their albums, the device first performs image optimization processing, correcting tilt, adjusting brightness, reducing noise, and removing handwritten corrections to avoid interference from the shooting environment, image quality, and handwriting on text recognition accuracy.

[0034] After image preprocessing, the device uses high-precision text recognition technology to extract all information from the image, including text, operators, and layout structure, generating standardized structured text that includes text position, paragraph layout, and regional relationships. Compared to traditional plain text extraction methods, this solution's structured text can completely preserve the correspondence between page context, individual question content, and the image. It also organizes multiple data fields such as subject, grade level, calculation elements, and question type features, facilitating subsequent subject identification, question splitting, and question type matching. The system globally matches text content using a built-in subject keyword database, statistically analyzing the frequency of keywords for different subjects to preliminarily determine the subject of the page, thus solving the problems of traditional recognition methods ignoring page context and being prone to misjudgment when multiple subjects are mixed.

[0035] Building upon this foundation, this step leverages image vision technologies such as edge detection, connected component analysis, and layout recognition to identify distinguishing features of the page, including question numbers, blank areas, and dividing lines. The entire page is then divided into multiple independent question regions, and the location information, local images, and dedicated structured text for each question are extracted separately. This achieves a two-tiered, refined acquisition of both the overall page context and individual question information. Simultaneously, an OCR text recognition error-tolerant mechanism is implemented to automatically correct for similar-looking characters, misspellings, and missing characters, allowing for reasonable text differences and effectively improving the accuracy and usability of structured text recognition.

[0036] S120. Determine the question type of the user's incorrect question data based on the structured text information.

[0037] This step abandons the traditional simple judgment method of single keyword matching and adopts a collaborative judgment logic of multi-source information fusion. It combines three dimensions: question type keyword matching, question bank text similarity comparison, and image layout visual recognition. With the addition of weighted ranking and intelligent conflict handling rules, it accurately distinguishes the subdivided question types of math mistakes. It effectively solves the problem that single recognition methods are easily affected by incomplete text, recognition errors, and question type variations, which can lead to judgment errors or omissions.

[0038] In practice, the system performs two layers of matching and verification on the split structured text of each question: The first layer is keyword fuzzy matching, which calls the keyword library specifically for primary and secondary school math questions. Under the premise of being tolerant of slight recognition errors, it identifies characteristic words of question types such as estimation, mental arithmetic, and word problems, calculates the matching score and credibility, and outputs the keyword matching results; The second layer is question bank similarity matching. First, it standardizes and cleans up extra spaces, line breaks, and invalid punctuation in the question text, and then compares it with the standard question bank text through a professional algorithm. Differentiated matching standards are set for long and short texts. Short texts are strictly controlled to prevent misjudgment, while long texts are relaxed to prevent omissions, and corresponding question bank matching results are generated.

[0039] To adapt to various complex scenarios, this step incorporates comprehensive result processing and fallback recognition logic. If the keyword matching matches the question bank matching result, the valid question type is directly confirmed. If the two results conflict, the system prioritizes the more reliable result based on preset weights, and combines the two sets of data to determine the final question type. If text matching fails or there is no valid keyword matching, the device automatically initiates image visual recognition, using visual features such as question layout, operation symbols, and text structure to make a secondary judgment on the question type, compensating for the shortcomings of pure text recognition. Through multi-layered verification and priority rules, it can accurately distinguish various regular, variant, and implicit estimation question types, adapting to the full-scenario math question type recognition needs of primary and secondary schools.

[0040] S130. When the user's incorrect question type is an estimation question, retrieve the corresponding estimation question data.

[0041] In this embodiment of the application, based on the question type judgment result of step S120, all mathematical estimation error data are accurately filtered and summarized. The system fully retrieves the standardized information of each estimation error, including the complete question text, operation elements, operation symbols, operation logic, knowledge point tags, historical error records, standard problem-solving ideas and reference answers, etc., and separately collects and marks them to generate a personalized estimation error dataset for each student.

[0042] Simultaneously, the system combines matching records, credibility data, and visual recognition features retained during the question type identification process to perform a secondary cleaning and verification of the collected estimation error data. This removes invalid data that is misjudged, mismatched, or incomplete, further improving the accuracy and standardization of the dataset. This provides reliable personalized data support for subsequent estimation element analysis, number sequence generation, drag-and-drop interactive practice, and dynamic calculation grading. For non-estimation math errors and errors from other subjects, the system automatically redirects them to the corresponding regular practice modules, excluding them from the estimation-specific interactive practice in this application, thus enabling targeted specialized training.

[0043] Through the aforementioned S110-S130 full-link data processing flow that integrates multi-source information, intelligent processing of incorrect question collection, text structuring, image segmentation, intelligent question type identification, conflict resolution, and precise filtering is completed. This solves the problems of large identification errors in traditional estimation exercises, lack of targeted practice content, and inability to match students' real weaknesses. It ensures the stable implementation of core functions such as subsequent visual step-by-step guidance, dynamic trial calculation interaction, and fun drag-and-drop exercises from the data source, realizing personalized incorrect question review tailored to students' weaknesses and effectively improving the intelligence level and learning effect of estimation-specific exercises.

[0044] S200. Analyze the estimation problem data to obtain the estimation problem analysis data.

[0045] For example, the estimation problem parsing data includes multiple estimation calculation factors, the position of each estimation calculation factor, and the operation relationship.

[0046] S300. Based on the data from the estimation problem analysis, display the result area on the screen, as well as multiple estimation areas corresponding to multiple estimation calculation factor items.

[0047] For example, multiple estimation regions are arranged according to the position and operation relationship of each estimation calculation factor; the estimation regions contain a sequence of numbers.

[0048] In some possible implementations, the value range of each number sequence is related to the value of the corresponding estimation calculation factor. Specifically, in actual operation, the number sequences generated by the device are not randomly generated, but strictly adhere to the characteristics and estimation logic of the current estimation question, using the corresponding original calculated value as the core basis for adaptation. After the system completes the data parsing of the estimation question and breaks down each estimation calculation factor, it matches the value range that fits the estimation learning rules for primary and secondary schools for each independent calculated value, generating alternative number sequences that fit that value. Different calculation factors will generate exclusive, non-universal number sequences.

[0049] For example, taking a real-world application scenario as an example, for a common estimation question type like "312×9", for the calculation factor 312, the system will generate alternative numbers that fit the rounding estimation rules for whole hundreds and whole tens based on the original value; for the calculation factor 9, it will generate similar alternative values ​​that fit the single-digit estimation rules, ensuring that each set of numbers fits the estimation logic of the corresponding value, and that there will be no invalid numbers that are irrelevant to the question's focus or have no practical significance.

[0050] For example, each number sequence takes the value of the corresponding estimated calculation factor as the sequence center value, and takes values ​​within a certain range to obtain the number sequence.

[0051] For example, the range of values ​​is determined according to the estimation rules of the estimation problem.

[0052] For example, such as Figure 3 The image shown is a schematic diagram of the display screen. On the screen, the estimation factors of the original estimation problem are displayed in corresponding positions, along with their calculation relationships. Below these estimation factors, multiple estimation areas are set. A sequence of numbers can be displayed in each estimation area. Users can select from the number sequence based on their estimated values ​​that are close to the estimated factors.

[0053] S400, in response to the user's selection of values ​​in each number sequence, displays the calculation results between the candidate values ​​in multiple number sequences in the result area.

[0054] For example, the above calculation is performed based on the position and operational relationship of all estimated calculation factor items.

[0055] In some possible implementations, the sequence of numbers can be displayed on the screen in different ways. Users can also employ different interaction methods to select different values ​​within the sequence. Various interactive gestures can all be considered as an optional approach to achieving this solution.

[0056] For example, the sequence of numbers can be displayed as columns in the estimation area. Users can select values ​​within the sequence by dragging them on the screen. In response to the user's dragging gesture within the corresponding estimation area, the value dragged to the target position is determined to be the value selected by the user.

[0057] For example, such as Figure 3 As shown, a result area is set to the right of the equal sign of the operation item in the estimation calculation factor item. This result area is used to display the result of the numerical operation of the selected value in the estimation area below.

[0058] In some possible implementations, the calculation results in the results area are dynamically updated as the user updates the selected values ​​in the numerical sequence. In this embodiment, this solution breaks away from the static mode of traditional estimation exercises where answers are given only once and results are displayed with a lag, achieving real-time linkage updates between value selection and calculation results. Every time the user changes or drags to select a new estimated value, the results area immediately refreshes the corresponding calculation results, eliminating the need for manual submission and waiting for grading. Students can intuitively see the differences in results caused by different approximate values. This real-time feedback interaction allows students to independently try various combinations of estimated values, intuitively perceive the relationship between changes in values ​​and changes in calculation results, and actively explore the rules of estimation rounding, moving beyond mechanical and passive answering. Simultaneously, it helps students quickly understand the core logic of estimation, breaking away from the abstract learning mode of purely mental calculation. Through dynamic trial calculations, students gradually cultivate numerical sensitivity and estimation problem-solving thinking, effectively improving their understanding and mastery of estimation knowledge points.

[0059] For example, such as Figure 4 As shown, users can use drag gestures to control the sequence of numbers in one or more estimation areas, allowing them to select values ​​within those areas. The calculation results in the result area are dynamically updated in real time based on the user's selection. Through this human-computer interaction strategy, users can intuitively understand the calculation patterns of estimation problems and more easily grasp their calculation rules. For example, Figure 4 In this simulation, the estimation question tests the user's logical reasoning for estimating integers in units of 100. The number sequence on the right contains 297 as a candidate number. Combining this with the selected number 600 in the number sequence on the left, the result displayed in the result area is 897 based on addition. The user then uses a gesture to update the selected value in the right-hand number sequence to 300. At this point, the result in the result area is updated to 900. If the correct answer is 900, a visual message indicating the correct answer will be displayed after the user submits their answer.

[0060] In some possible implementations, during the execution of steps S200 to S400, this solution can achieve intelligent data generation and verification by calling the AI ​​large-scale model API interface, adapting to the personalized generation needs of various estimation question types. Traditional methods of generating number sequences using fixed templates have poor adaptability, only suitable for a few standard question types, and unable to adapt to the needs of generating variant estimation questions and personalized error questions. After acquiring and parsing the user's estimation question data, this solution encapsulates basic information such as the question text, operational elements, operational relationships, corresponding grade level, and estimation knowledge points into standard request parameters and sends them to the AI ​​large-scale model API interface. The AI ​​large-scale model, relying on massive amounts of primary and secondary school mathematics teaching data and estimation problem-solving rules, combined with the difficulty level and test requirements of the current question, intelligently generates a reasonable range of numerical values ​​and alternative number sequences suitable for the question, no longer limited to fixed numerical templates, and adapting to all conventional question types and innovative variant estimation question types. Meanwhile, the AI ​​model performs a secondary verification on the generated number sequence, checking each candidate value one by one to ensure it conforms to the rounding estimation logic of elementary school mathematics. Invalid data that does not meet the test points, has no practice value, or has excessive numerical deviations are eliminated, ensuring the accuracy and practicality of the generated content. After verification, the device receives standardized data returned by the AI ​​model and renders and displays the number sequence on the corresponding estimation area of ​​the screen according to the original question's calculation layout and element position relationships. This provides accurate, adapted, and personalized practice data for subsequent user drag-and-drop selection and dynamic calculation interaction.

[0061] In some possible implementations, in addition to the cloud-based large model generation method, this solution can also incorporate a lightweight machine learning model to complete data inference and generation locally on the device. This adapts to usage scenarios with no network or low-configuration devices, stably supporting the entire process from steps S200 to S400. Unlike the large model solution that requires network access to call interfaces, this lightweight machine learning model is pre-trained offline and its parameters are fixed. It can be directly deployed on the local electronic device without relying on cloud servers and network transmission, resulting in faster response times and wider adaptability. During the model training phase, a massive amount of solution data for standard estimation questions and variations of estimation questions across primary and secondary school levels is collected. Rounding rules, reasonable numerical ranges, and conventional approximate value samples that align with textbook exam points are compiled to form a standardized training dataset. The dataset is iteratively trained using a simple neural network structure to continuously learn the compliant estimation ranges corresponding to different digit values ​​and different types of calculations, thus solidifying the estimation rules adapted to primary school estimation logic. After the model is deployed, the device analyzes the calculation elements, original values, question type, and grade level difficulty of the estimation question. It then directly inputs these into a local lightweight model for inference calculation, quickly outputting a numerical sequence value range and alternative values ​​adapted to the current question. Simultaneously, the local model fine-tunes the numerical range based on students' past incorrect answering habits, prioritizing the push of approximate values ​​that students frequently misinterpret, further enhancing the targeted nature of the practice. Finally, the device renders the compliant numerical sequence generated by the model into the corresponding estimation area according to the original question's calculation and layout structure, completing the visualization display. The entire process runs locally with extremely low latency, perfectly adapting to offline practice scenarios. It complements the cloud-based AI large model solution, significantly improving the overall adaptability and stability of the solution.

[0062] In some possible implementations, such as Figure 5 As shown, the method also includes the operation of step S500: S500 responds to the user's submission of the calculation result and displays different visual feedback screens to the user on the display screen according to the correctness of the calculation result.

[0063] In some possible implementations, when the calculation result matches the correct answer, the visual feedback screen is marked in green; when the calculation result does not match the correct answer, the visual feedback screen is marked in red along with the correct answer. In this embodiment, this differentiated color-coded visual feedback method is adapted to students' cognitive characteristics, abandoning the monotonous and tedious text-based feedback of traditional exercises, making the answer results more intuitive and clear. The green correct mark can provide students with positive learning motivation, enhance their sense of accomplishment in solving problems, alleviate their fear of difficulty in estimation exercises, and effectively mobilize their enthusiasm for continuous practice. The red incorrect mark combined with the standard answer feedback allows students to know their mistakes immediately after answering a question, and quickly compare their own value deviation and calculation problems with the standard result, without having to manually look up the answer or find the explanation, greatly reducing the difficulty of reviewing incorrect questions. Through simple and intuitive feedback that distinguishes between right and wrong, it helps students quickly correct incorrect estimation thinking, accurately fill in knowledge gaps, improve the efficiency of reviewing incorrect questions, and ensure that every incorrect question practice has an effective consolidation effect, continuously optimizing students' estimation and problem-solving abilities.

[0064] This application provides a method for practicing estimation problems, which has the following beneficial effects: First, it enables personalized and targeted practice, eliminating ineffective rote memorization. This solution departs from the generic practice model of randomly generated or uniformly generated questions. Instead, it uses a multi-source information fusion approach to precisely filter estimation-related errors based on students' individual historical error data, providing targeted practice only for estimation questions where students genuinely made mistakes and where their knowledge gaps are weak. This accurately identifies students' learning weaknesses, abandons homogenous and meaningless rote memorization, and allows estimation practice to focus precisely on individual problems, significantly improving the efficiency of error review and targeted training, truly achieving personalized learning outcomes tailored to individual needs.

[0065] Secondly, this solution addresses the "black box" problem in estimation learning, helping students fully grasp the core logic of estimation. Traditional exercises only assess the final answer, preventing students from experiencing the complete estimation thought process. This solution visualizes and breaks down the estimation problem-solving steps. Through independent estimation areas, selectable number sequences, and dynamic calculation displays, it intuitively presents the complete process of rounding, approximation, and result derivation. Students can clearly see the impact of different numerical values ​​on the calculation result, moving beyond intuitive mental calculation or rote memorization of answers. They can proactively understand the rules of rounding and the principles of calculation, gradually developing standardized estimation problem-solving thinking, and easily handling various regular and variant estimation question types.

[0066] Third, this solution lowers the barrier to estimation practice and enhances students' enthusiasm for independent practice. It abandons the traditional, highly difficult mental arithmetic practice mode, significantly reducing the operational difficulty of estimation practice through lightweight operations such as selectable number sequences, simple drag-and-drop interaction, and real-time dynamic calculation. This makes it suitable for students with weak number sense or basic knowledge. At the same time, the fun and intuitive human-computer interaction changes the traditional, monotonous, and repetitive question-answering experience, effectively alleviating students' fear of estimation questions, enhancing their initiative and persistence in independent practice, and solving the problem of traditional, superficial review of incorrect answers.

[0067] Fourth, it enables real-time and accurate feedback, efficiently identifying and addressing errors. This solution supports real-time updates between numerical selection and calculation results, allowing students to independently attempt calculations and explore, perceiving the correlation between numerical changes and result changes in real time. After submission, differentiated color-coded visual feedback quickly distinguishes between correct and incorrect answers, directly displaying the standard answer in error scenarios. This allows students to immediately identify their value deviations and thinking gaps, accurately pinpointing the errors and solving the problems of traditional practice's singular feedback and inability to trace the root causes of mistakes, greatly improving the effectiveness of reviewing errors and consolidating knowledge points.

[0068] Fifth, the solutions offer enhanced versatility and stability. This application provides two intelligent digital sequence generation solutions, which can be flexibly selected based on the device's operating environment, adapting to different network conditions and device configurations. One solution is a cloud-based AI large-scale model generation method, relying on massive amounts of primary and secondary school teaching data. It can accurately adapt to various common estimation question types, variant question types, and personalized error scenarios, generating personalized alternative values ​​that fit the test points, demonstrating a high degree of intelligent adaptation. The other solution is a local lightweight machine learning model generation method, which does not rely on network or cloud interfaces. It can quickly complete numerical inference and rendering based on the device's locally fixed training rules, offering fast response speed and adapting to usage scenarios without network access and with low-configuration devices. Through intelligent algorithms, the operational stability and versatility of the estimation practice function can be significantly improved, adapting to the estimation question practice needs of all grades in primary and secondary schools.

[0069] Secondly, embodiments of this application also provide an electronic device with learning functionality, such as... Figure 6 As shown, the electronic device 1000 includes a processor 100. The processor 100 is used to execute an estimation problem practice method as described in the first aspect above. Exemplarily, the electronic device 1000 can be a learning machine, mobile phone, tablet computer, or smartwatch, etc.

[0070] Thirdly, embodiments of this application also provide a computer-readable storage medium storing instructions that, when executed on a processor, cause the processor to perform an estimation problem practice method as described in the first aspect above.

[0071] Fourthly, embodiments of this application provide a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the methods described above.

[0072] The processor involved in the embodiments of this application can be a chip. For example, it can be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a graphics processing unit (GPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0073] It should be noted that, in the embodiments of this application, if the above methods are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application.

[0074] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0075] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or modules can be electrical, mechanical, or other forms.

[0077] The modules described above as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network units; some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional modules in the various embodiments of this application may all be integrated into one processing unit, or each module may be a separate unit, or two or more modules may be integrated into one unit; the integrated modules may be implemented in hardware or in a combination of hardware and software functional units. Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; the aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0078] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict. The features disclosed in the several product embodiments provided in this application can be arbitrarily combined to obtain new product embodiments without conflict. The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined to obtain new method embodiments or device embodiments without conflict. The above descriptions are merely embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of estimating a problem practice, characterized by, The method is applied to an electronic device, the electronic device including a display screen; the method includes: Obtain the user's estimation question data; The estimation problem data is parsed to obtain estimation problem parsing data, which includes multiple estimation calculation factor items, the position and operation relationship of each estimation calculation factor item; Based on the estimation problem analysis data, a result area and multiple estimation areas corresponding to the multiple estimation calculation factors are displayed on the display screen; the multiple estimation areas are arranged according to the position corresponding to each of the multiple estimation calculation factors; each estimation area contains a numerical sequence; the value range of each numerical sequence is related to the value of the corresponding estimation calculation factor; In response to the user's selection of a value in each of the number sequences, the result of the operation between the candidate values ​​in the plurality of number sequences is displayed in the result area, the operation being performed based on the position and operation relationship of all the estimated calculation factor items.

2. The estimation problem practice method according to claim 1, characterized in that, The value range of each of the numerical sequences is related to the value of the corresponding estimation calculation factor. Specifically, each numerical sequence takes the value of the corresponding estimation calculation factor as the sequence center value and takes values ​​within a predetermined value range to obtain the numerical sequence.

3. The estimation problem practice method according to claim 2, characterized in that, The range of values ​​is determined according to the estimation rules of the estimation problem.

4. A method for practicing estimation problems according to any one of claims 1-3, characterized in that, Each of the numerical sequences is displayed in a list in the corresponding estimation area; The response to the user's selection of a value in each of the number sequences includes: In response to a user's drag gesture on the number sequence within the corresponding estimation area, the value in the number sequence that is dragged to the target position is determined to be the value selected by the user.

5. The estimation problem practice method according to claim 1, characterized in that, The calculation result in the result area is dynamically updated as the user updates the selected value of the number sequence.

6. The estimation problem practice method according to claim 1, characterized in that, The method further includes: in response to the user's submission of the calculation result, displaying different visual feedback screens to the user on the display screen according to the correctness of the calculation result.

7. The estimation problem practice method according to claim 5, characterized in that, The step of displaying different visual feedback screens to the user on the display screen based on the correctness of the calculation result includes: when the calculation result is consistent with the correct answer, the visual feedback screen is marked in green; when the calculation result is inconsistent with the correct answer, the visual feedback screen is marked in red and shows the correct answer.

8. The estimation problem practice method according to claim 1, characterized in that, The acquisition of the user's estimation data includes: Obtain structured text information from user's incorrect questions, wherein the structured text information includes the text content of the user's incorrect questions; The type of question the user answered incorrectly is determined based on the structured text information. When the user's incorrect answer is an estimation question, the corresponding estimation question data is retrieved.

9. An electronic device with learning function, characterized in that, The electronic device includes a processor; wherein the processor is configured to execute an estimation problem practice method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a processor, cause the processor to perform an estimation problem practice method as described in any one of claims 1-8.

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