Learning status monitoring method, device, equipment and storage medium

Abstract

The embodiment of the present invention discloses a method and apparatus, device, and storage medium for monitoring learning status. The method includes: receiving answer information of a target student submitted by a user terminal, and obtaining answer correction information and an answer data sequence of the target student; obtaining an answer data subsequence and answer correction subinformation, as well as an answer accuracy rate from the answer data sequence and the answer correction information; obtaining the target student's answer plagiarism rate within a target time period based on the answer data subsequences of the target student and each selected student; and determining the target student's learning status within the target time period based on the answer plagiarism rate and the answer accuracy rate. By using the student's answer information as a basis for monitoring the learning status, analyzing the accuracy rate and plagiarism of each student's homework within the target time period, the student's learning status is analyzed, so that the student's current shortcomings and deficiencies can be judged based on the learning status, and then targeted teaching methods can be made to improve teaching quality.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching methods, and in particular to a method and apparatus for monitoring a learning state, equipment, and storage medium. Background Art

[0002] Teaching students in accordance with their aptitude is an important teaching method and principle. In teaching, teachers choose learning methods suitable for each student's characteristics according to their cognitive level, learning ability and personal qualities to provide targeted teaching and make up for the deficiencies in students' learning process.

[0003] Typically, teachers observe students' learning progress to determine their learning status. Currently, according to regulations, the number of students in each class is generally set between 40 and 50. This large number of students makes it difficult for teachers to promptly identify each student's learning status and develop improvement plans, hindering the improvement of teaching quality. Summary of the Invention

[0004] Based on this, it is necessary to propose a learning status monitoring method and device, equipment and storage medium to address the above problems, so as to monitor the students' learning process, understand the students' learning status, and then formulate different teaching plans for different learning statuses to improve the teaching quality.

[0005] To achieve the above objectives, the present application provides a first aspect of a method for monitoring a learning state, the method comprising:

[0006] Receiving answer information of a target student submitted by a user terminal, wherein the answer information includes a target topic and answer data submitted by the target student based on the target topic;

[0007] Searching for a first number, a first standard answer, and a first release time corresponding to the target question in a preset question bank, wherein the question bank contains a plurality of questions, and numbers, standard answers, and release times corresponding to the questions, and the target question belongs to the question bank;

[0008] Correcting the answer data according to the first standard answer to obtain the answer correction information of the target student; sorting the answer data corresponding to the target question according to the first release time of the target question and the first number to obtain the answer data sequence of the target student;

[0009] From the answer data sequence and the answer correction information, select the answer data subsequence and the answer correction subinformation whose first release time is within the target time period, calculate the accuracy rate based on the answer data subsequence and the answer correction subinformation, and obtain the answer accuracy rate of the target student within the target time period;

[0010] Performing similarity analysis on the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period;

[0011] The learning status of the target student in the target time period is determined based on the plagiarism rate and the correct answer rate.

[0012] Furthermore, the question bank also includes question types corresponding to the questions, and the question types include at least objective questions and subjective questions;

[0013] The similarity analysis of the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period specifically includes:

[0014] Obtain a target answer data subsequence with the same release time in the answer data subsequence of the target student;

[0015] Decomposing the answer data in the target answer data subsequence based on the question type to obtain an objective question answer subsequence and a subjective question answer subsequence;

[0016] performing similarity calculations on the objective question answer subsequences and the subjective question answer subsequences of the target student and each selected student, respectively, to obtain a first plagiarism rate for the objective questions and a second plagiarism rate for the subjective questions corresponding to the answer data subsequences;

[0017] A weighted calculation is performed based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period.

[0018] Furthermore, similarity calculation is performed on the objective question answer subsequences and the subjective question answer subsequences of the target student and each selected student, respectively, to obtain a first plagiarism rate of the objective questions and a second plagiarism rate of the subjective questions corresponding to the answer data subsequences, specifically including:

[0019] Using the Smith-Waterman algorithm, a similarity analysis is performed between the target student's objective question answer subsequence and the objective question answer subsequences of each selected student to determine the target student's first answer plagiarism rate;

[0020] A text similarity calculation is performed on the subjective question answer subsequence of the target student and the subjective question answer subsequences of each selected student to determine the plagiarism rate of the second answer of the target student.

[0021] Furthermore, the Smith-Waterman algorithm is used to perform similarity analysis on the target student's objective question answer subsequence and the selected students' objective question answer subsequences to determine the target student's first answer plagiarism rate, specifically including:

[0022] Searching for two sequence segments with the highest similarity between the target student's objective question answer subsequence and the objective question answer subsequences of each selected student based on the Smith-Waterman algorithm;

[0023] If the length of the sequence segment is greater than a preset length threshold, determining a first accuracy rate of the sequence segment of the target student according to the answer correction sub-information;

[0024] When the first accuracy rate is lower than a preset first accuracy rate threshold, calculating a first similarity between the two sequence segments;

[0025] When the first similarity is greater than a preset first similarity threshold, the first plagiarism rate of the target student is calculated according to the first accuracy rate and the first similarity.

[0026] Furthermore, the text similarity calculation is performed on the subjective question answer subsequence of the target student and the subjective question answer subsequences of each selected student to determine the plagiarism rate of the second answer of the target student, specifically including:

[0027] Calculating the text similarity between the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student;

[0028] When the text similarity is greater than a second similarity threshold, determining a second accuracy rate of the target student's subjective question answer subsequence based on the answer correction sub-information;

[0029] A second plagiarism rate of the target student is obtained by calculating according to the second accuracy rate and the text similarity.

[0030] Furthermore, the weighted calculation of the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period specifically includes:

[0031] Performing weighted calculation on the first answer plagiarism rate and the second answer plagiarism rate corresponding to the target answer data subsequence to obtain a third answer plagiarism rate;

[0032] Based on a preset mathematical operation method, the target student's answer plagiarism rate in the target time period is obtained by calculating all the third answer plagiarism rates in the answer data subsequence.

[0033] Furthermore, determining the learning status of the target student in the target time period based on the plagiarism rate and the correct answer rate specifically includes:

[0034] When the target student's correct answer rate in the target time period is greater than a preset third correct rate, it is confirmed that the student's learning status in the target time period is good;

[0035] When the student's correct answer rate within the target time period is not greater than the third correct answer rate, and the plagiarism rate is not greater than a preset plagiarism rate threshold, it is determined that there is a problem with the student's learning status within the target time period;

[0036] When the plagiarism rate of the answer is greater than the plagiarism rate threshold, it is confirmed that the student has plagiarized within the target time period.

[0037] To achieve the above-mentioned purpose, the second aspect of the present application provides a learning status monitoring device, the device comprising a data acquisition module, a data analysis module and a status judgment module;

[0038] The data collection module is configured to receive answer information of a target student submitted by a user terminal, wherein the answer information includes a target topic and answer data submitted by the target student based on the target topic;

[0039] Searching for a first number, a first standard answer, and a first release time corresponding to the target question in a preset question bank, wherein the question bank contains a plurality of questions, and numbers, standard answers, and release times corresponding to the questions, and the target question belongs to the question bank;

[0040] The data analysis module is configured to correct the answer data according to the first standard answer to obtain the answer correction information of the target student, and sort the answer data corresponding to the target question according to the first release time and the first number of the target question to obtain the answer data sequence of the target student;

[0041] From the answer data sequence and the answer correction information, select the answer data subsequence and the answer correction subinformation whose first release time is within the target time period, calculate the accuracy rate based on the answer data subsequence and the answer correction subinformation, and obtain the answer accuracy rate of the target student within the target time period;

[0042] Performing similarity analysis on the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period;

[0043] The status judgment module is used to determine the learning status of the target student in the target time period based on the plagiarism rate and the correct answer rate.

[0044] To achieve the above-mentioned purpose, the third aspect of the present application provides a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0045] To achieve the above-mentioned objectives, the fourth aspect of the present application provides a computer device, comprising a memory and a processor, characterized in that the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in the first aspect.

[0046] The embodiments of the present invention have the following beneficial effects:

[0047] An embodiment of the present invention proposes a method for monitoring a learning status, the method comprising: receiving answer information of a target student submitted by a user terminal, wherein the answer information comprises a target question and answer data submitted by the target student based on the target question; searching a preset question bank for a first number, a first standard answer, and a first release time corresponding to the target question, the question bank comprising a plurality of questions, as well as numbers, standard answers, and release times corresponding to the questions, and the target question belongs to the question bank; correcting the answer data according to the first standard answer to obtain the answer correction information of the target student, and correcting the answer data according to the first release time and first number of the target question. The answer data corresponding to the title is sorted to obtain the target student's answer data sequence; from the answer data sequence and answer correction information, the answer data subsequence and answer correction subinformation whose first release time is within the target time period are selected, and the accuracy rate is calculated based on the answer data subsequence and answer correction subinformation to obtain the target student's answer accuracy rate within the target time period; a similarity analysis is performed on the answer data subsequences of the target student and each selected student to obtain the target student's answer plagiarism rate within the target time period; based on the answer plagiarism rate and answer accuracy rate, the target student's learning status within the target time period is determined. By using the student's answer information as the basis for monitoring the learning status, the accuracy rate and plagiarism of each student's homework within the target time period are analyzed to analyze the student's learning status, so that the student's current shortcomings and deficiencies can be judged based on the learning status, and then targeted teaching methods can be made to improve teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] in:

[0050] Figure 1 Schematic diagram of a flow chart of a method for monitoring a learning state according to an embodiment of the present invention;

[0051] Figure 2 A structural block diagram of a device for monitoring a learning state according to an embodiment of the present invention;

[0052] Figure 3 2 is a diagram showing the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Homework and period exams are important means of testing students' mastery of knowledge points after learning them. Therefore, the embodiments of the present invention determine students' learning status and mastery of knowledge points by analyzing their answers to homework and / or exams.

[0055] The present invention proposes a learning status monitoring method, which monitors students' learning status so that teachers can analyze students' learning status and formulate targeted teaching methods to correct students' learning attitudes and methods, thereby effectively improving students' learning efficiency. Figure 1 , Figure 1 : is a flow chart of a method for monitoring a learning state in an embodiment of the present invention, the method comprising:

[0056] Step 110: receiving the target student's answer information submitted by the user, wherein the answer information includes the target question and the answer data submitted by the target student based on the target question.

[0057] In an embodiment of the present invention, the user terminal can be in the form of a mobile application, web application, desktop software, client software, etc., and students or teachers upload their answers to the server through the user terminal. For example, all students in a class submit their answers through the user terminal, or the teacher collects students' answers and submits them through the user terminal. Based on the answer information submitted by the teacher and / or students through the user terminal, the learning status of each student in a class can be understood.

[0058] In one embodiment of the present invention, the answer information may be online answer information or offline answer information.

[0059] Online answer information refers to the student's responses to the assignment or exam questions after receiving them on the client. This data is then uploaded to the client. If the server needs to collect the student's online answer information, it can send a data collection request to the client. Upon receiving the data collection request, the client sends the student's uploaded online answer information to the server.

[0060] Offline answer data refers to students' responses to questions on paper-based assignments or exams after receiving them. Students or teachers can take photos of the completed assignments or exams and upload them to the user's end. To obtain student answer information, the server sends a data collection request to the user. Upon receiving the data collection request, the user sends the student's offline answer information to the server.

[0061] In one embodiment of the present invention, after receiving offline answer information, the image content can be converted into text content and saved for subsequent data analysis.

[0062] Step 120, searching for the first number, the first standard answer and the first release time corresponding to the target question in the preset question bank, the question bank containing multiple questions, as well as the numbers, standard answers and release times corresponding to the questions, and the target question belongs to the question bank.

[0063] In an embodiment of the present invention, the question bank contains a large number of questions, and the questions in the homework and test papers issued to students are all extracted from the question bank. The question bank not only contains the original questions, but also contains the number, standard answer and release time corresponding to each question.

[0064] Specifically, each question has a unique number for quick identification; the release time of the question is the time when the question is released to students for answering. It is understandable that there can be many questions with the same release time. For example, the release time of homework or exam questions released on the same day is the same.

[0065] After obtaining the student's answer information, you can search the question bank for the release time, standard answer, and number corresponding to each question answered by the student.

[0066] Step 130, correct the answer data according to the first standard answer to obtain the answer correction information of the target student, sort the answer data corresponding to the target question according to the first release time and the first number of the target question, and obtain the answer data sequence of the target student.

[0067] After obtaining the release time, standard answer and number corresponding to each question answered by the student, the student's answer data can be corrected according to the standard answer of each question to determine whether the student's answer data is correct.

[0068] Specifically, by searching the question bank for the first standard answer corresponding to the target question answered by the target student, the answer data of the target question of the target student is corrected according to the first standard answer. Through this method, the answer correction information of all questions of the target student is obtained. The answer correction information may include correct or incorrect, score and other information.

[0069] Since the order of answer information uploaded by students or teachers is different, in order to facilitate subsequent data analysis, the answer data can be preprocessed first and the answer data of all students can be sorted.

[0070] Specifically, the target student's answer data are sorted according to a preset numbering sequence and time sequence to obtain the target student's answer data sequence.

[0071] For example, Table 1 is the answer information of the target students. Table 1 contains the answer data of the questions answered by the target students, the numbers corresponding to the questions and the release time. Based on this, the answer data sequence of the target students can be arranged in chronological order and the order of size of the numbers as A(A1, A2, A3, …), where A1(a1, a2, a3, …), A2(a4, a5, a6, …), A3(a7, a8, a9, …), …; where An is the answer data sequence with the same release time, and an is the answer data.

[0072] Table 1 Target students' answer information

[0073] Answer data First Number First release time Question 1 a1 101 24.01.01 Question 2 a2 102 24.01.01 Question 3 a3 103 24.01.01 Question 4 a4 104 24.01.02 Question 5 a5 105 24.01.02 Question 6 a6 106 24.01.02 Question 7 a7 107 24.01.03 Question 8 a8 108 24.01.03 Question 9 a9 109 24.01.03 …… …… …… ……

[0074] Step 140, from the answer data sequence and answer correction information, filter the answer data subsequence and answer correction subinformation whose first release time is within the target time period, calculate the accuracy rate based on the answer data subsequence and answer correction subinformation, and obtain the answer accuracy rate of the target student within the target time period.

[0075] In this embodiment of the present invention, the student learning status can be monitored in sections according to the teaching content. Since most teaching methods at this stage are chapter-based, the learning status of each student in the class can be observed in each chapter to examine the student's mastery of the knowledge points in a certain chapter and whether the student has plagiarized.

[0076] Specifically, the judgment can be based on the students' answering situation during the teaching period of each chapter. According to the actual teaching situation, a target time period is selected, and the answer data subsequence and answer correction sub-information corresponding to the questions published in the target time period are obtained.

[0077] For example, assuming that the target time period is 24.01.01-24.01.03, and the first release time of the target student's answered questions refers to Table 1, then the target student's answer data subsequence is A'(A1, A2, A3), where A1(a1, a2, a3, ...), A2(a4, a5, a6, ...), A3(a7, a8, a9, ...); among them, An is the answer data sequence with the same release time, and an is the answer data.

[0078] After obtaining the answer data subsequence and the answer correction sub-information, the answer data corresponding to all questions in the answer data subsequence are corrected using the answer correction sub-information, and the target student's answer accuracy rate within the target time period is calculated based on the correction results.

[0079] Step 150 , performing similarity analysis on the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period.

[0080] In an embodiment of the present invention, answer information of many students is obtained, for example, answer information of all students in a class is obtained. It is understandable that each student needs to answer the same questions, and the questions are issued to students in a unified manner. Therefore, for students, the same questions are issued at the same time.

[0081] In this embodiment of the present invention, each student is analyzed for plagiarism during a target time period, as well as the extent of plagiarism. Specifically, a similarity analysis is performed between the target student's answer data subsequences and the answer data subsequences of other students to determine the student's plagiarism rate. The other students are students selected by the teacher. For example, the other students may be in the same class as the target student, and the questions in the answer information submitted by the other students are consistent with the questions in the answer information submitted by the target student.

[0082] In one embodiment of the present invention, Table 2 shows the target student's answer information, and Table 3 shows the answer information of other students. Assuming the target time period is 24.01.01-24.01.03, then the target student's answer data subsequence is A'(A1, A2, A3), where A1 (a1, a2, a3), A2 (a4, a5, a6), and A3 (a7, a8, a9). The answer data subsequence of any other student is B'(B1, B2, B3), where B1 (b1, b2, b3), b2 (b4, b5, b6), and b3 (b7, b8, b9). The similarity between the answer data subsequence A' and the answer data subsequence B' can be calculated to obtain the target student's answer plagiarism rate during the target time period.

[0083] By performing similarity analysis on the target student's answer data subsequence and the answer data subsequence of each other student, the answer data subsequence with the highest similarity to the target student's answer data subsequence among other students is obtained, and then the target student's answer plagiarism rate within the target time period is calculated based on the two answer data subsequences.

[0084] Table 2 Target students' answer information

[0085] Answer data First Number First release time Question 1 a1 101 24.01.01 Question 2 a2 102 24.01.01 Question 3 a3 103 24.01.01 Question 4 a4 104 24.01.02 Question 5 a5 105 24.01.02 Question 6 a6 106 24.01.02 Question 7 a7 107 24.01.03 Question 8 a8 108 24.01.03 Question 9 a9 109 24.01.03

[0086] Table 3 Other students' answer information

[0087] Answer data First Number First release time Question 1 b1 101 24.01.01 Question 2 b2 102 24.01.01 Question 3 b3 103 24.01.01 Question 4 b4 104 24.01.02 Question 5 b5 105 24.01.02 Question 6 b6 106 24.01.02 Question 7 b7 107 24.01.03 Question 8 b8 108 24.01.03 Question 9 b9 109 24.01.03

[0088] Step 160: Determine the learning status of the target student in the target time period based on the plagiarism rate and the correct answer rate.

[0089] If a student has a weak grasp of a chapter's knowledge points, this can be reflected in their accuracy rate. Alternatively, if a student's learning attitude is poor, this can be reflected in their plagiarism rate. Therefore, the plagiarism rate and accuracy rate of a target student can be used to determine their learning status within a target time period. This allows teachers or parents to provide guidance and develop effective teaching plans based on the student's learning status.

[0090] The learning status monitoring method proposed in this invention monitors students' learning status by collecting their answer information. By using comprehensive data and scientific analysis methods to monitor students' learning status over different time periods, teachers or parents can promptly understand students' learning status and provide appropriate guidance and further teaching plans, thereby improving teaching quality.

[0091] Taking into account the diversity of question types, an embodiment of the present invention also proposes different similarity analysis methods based on the characteristics of the answer data corresponding to different question types. The similarity analysis of the answer data between students is performed using the similarity analysis methods corresponding to different question types to improve the accuracy of the analysis results.

[0092] In an embodiment of the present invention, the question bank also includes question types corresponding to the questions, and the question types include at least objective questions and subjective questions. Based on this, a similarity analysis is performed on the target student's answer data subsequences and the selected students' answer data to obtain the target student's answer plagiarism rate within the target time period, specifically including:

[0093] Step 151. Obtain the target student's answer data subsequence with the same release time in the target student's answer data subsequence.

[0094] The same release time can be understood as the questions being released at the same time, and the questions released at the same time can also be considered as homework or exams assigned on the same day.

[0095] Referring to Table 1, the target answer data subsequence can be A1 (a1, a2, a3, ...), A2 (a4, a5, a6, ...), A3 (a7, a8, a9, ...), ...; where an is the answer data.

[0096] Taking into account that the target students may plagiarize different objects every day, the embodiment of the present invention chooses to analyze the subsequences of students' answer data corresponding to the questions released at the same time. The analysis results obtained will be more accurate than the analysis results of the subsequences of answer data corresponding to the questions released at all times within the target time period.

[0097] Step 152: Based on the question type, the answer data in the target answer data subsequence is disassembled to obtain an objective question answer subsequence and a subjective question answer subsequence.

[0098] In an embodiment of the present invention, the answer data is classified according to the question type, and the target answer data subsequence is disassembled into different types of answer data subsequences, and then the characteristics of each type of answer data subsequence are analyzed to improve the accuracy of the analysis results.

[0099] In one embodiment of the present invention, Table 4 is the answer information table of student c. The target answer data subsequence of student c is Ci(c1, c2, c3, ..., c21), where cn is the answer data. Since the question types are divided into two categories: subjective questions and objective questions, the target answer subsequence can be disassembled to obtain the objective question answer subsequence Ci 客 (c1, c2, c3, ..., c12) and the subjective question answer subsequence Ci 主(c13, c14, c15,…, c21).

[0100] Table 4 Student c’s answer information

[0101]

[0102]

[0103] Step 153: Calculate the similarity between the target student and the selected students' objective question answer subsequences and subjective question answer subsequences, and obtain the first answer plagiarism rate of the objective questions and the second answer plagiarism rate of the subjective questions corresponding to the answer data subsequences.

[0104] Since the content of the answer data between objective questions and subjective questions is quite different, in order to reduce the error of the answer plagiarism rate, the embodiment of the present invention selects different analysis methods according to the characteristics of each question type to calculate the answer plagiarism rate of different types of answer data respectively.

[0105] In one embodiment of the present invention, the plagiarism rate is the similarity between the target student's objective question answer subsequence or subjective question answer subsequence and other students' objective question answer subsequences or subjective question answer subsequences.

[0106] Specifically, similarity calculations are performed on the objective question answer subsequences and subjective question answer subsequences of the target student and each selected student, respectively, to obtain the first answer plagiarism rate of the objective question and the second answer plagiarism rate of the subjective question corresponding to the answer data subsequences, specifically including:

[0107] Step 1531. Use the Smith-Waterman algorithm to perform similarity analysis on the target student's objective question answer subsequence and the objective question answer subsequences of each selected student to determine the target student's first answer plagiarism rate.

[0108] The answer data that students fill in when answering objective questions is generally options, such as A, B, C, ... In one embodiment of the present invention, the subsequences of the target student's and other students' objective question answers can be referenced in Table 5. It can be seen that the subsequence of Student 2's answer is most similar to the target student's. Therefore, the plagiarism rate of the target student's first answer can be determined based on the similarity between the target student's and Student 2's objective question answer subsequences.

[0109] Table 5 is an example table of student objective question answering subsequences

[0110] Objective question answering subsequence Target students (A,C,D,A,C,D,B,A,A,A) Student 1 (A,B,D,B,B,D,B,B,B,B) Student 2 (A,C,D,A,C,D,B,A,B,B)

[0111] In order to improve the accuracy of calculating the plagiarism rate and reduce misjudgment, in an embodiment of the present invention, the Smith-Waterman algorithm is used to perform similarity analysis on the target student's objective question answer subsequence and the objective question answer subsequences of each selected student to determine the target student's first answer plagiarism rate, specifically including:

[0112] A. Based on the Smith-Waterman algorithm, find the two sequence segments with the highest similarity between the target student's objective question answer subsequence and the objective question answer subsequences of each selected student.

[0113] The Smith-Waterman algorithm is an algorithm for local sequence alignment (as opposed to global alignment) used to find similar regions between sequences. The purpose of this algorithm is not to align the entire sequence, but to find fragments with high similarity between two sequences.

[0114] For example, based on the example table of student objective question answer subsequences in Table 5, it can be seen that the sequence segment with the highest similarity to the target student is (A, C, D, A, C, D, B, A) of student 2.

[0115] B. If the length of the sequence segment is greater than a preset length threshold, the first accuracy rate of the sequence segment of the target student is determined based on the answer correction sub-information.

[0116] To reduce false positives, before calculating the first answer's plagiarism rate, we first determine the length of the sequence segment with the highest similarity. This segment's length is compared to a preset length threshold. If the length of the sequence segment exceeds the threshold, the plagiarism rate is calculated. If it does not, the student is deemed to have not plagiarized, and the first answer's plagiarism rate is zero.

[0117] In one embodiment of the present invention, the length threshold may be the total length of the target student's objective question answer subsequence*a (0.5<a≤1).

[0118] In this embodiment of the present invention, if the target student's sequence segments are highly similar to those of other students and their accuracy rates are also high, the target student is not considered to have plagiarized. If the target student's sequence segments are highly similar to those of other students but their accuracy rates are low, the target student is considered to have plagiarized. Therefore, before calculating the plagiarism rate, the first accuracy rate of the target student's sequence segments is first calculated based on the answer correction sub-information.

[0119] C. When the first accuracy is lower than a preset first accuracy threshold, a first similarity between the two sequence segments is calculated.

[0120] Specifically, the first accuracy rate is compared with a preset first accuracy rate threshold. When the first accuracy rate is not lower than the preset first accuracy rate threshold, it is considered that the target student has not plagiarized, and the student's first answer plagiarism rate is zero. When the first accuracy rate is lower than the preset first accuracy rate threshold, it is considered that the target student has plagiarized, and the similarity between the two sequence fragments can be calculated.

[0121] D. When the first similarity is greater than a preset first similarity threshold, the target student's first answer plagiarism rate is calculated based on the first correct rate and the first similarity.

[0122] Specifically, if the similarity between the target student's sequence segment and the sequence segments with the highest similarity to other students is too low, the target student is deemed not to have plagiarized. Therefore, the first similarity is compared with a preset first similarity threshold. If the first similarity is not greater than the preset first similarity threshold, the target student is deemed not to have plagiarized, and the first answer plagiarism rate is zero. If the first similarity is greater than the preset first similarity threshold, the target student is deemed to have plagiarized, and the target student's first answer plagiarism rate is calculated based on the first accuracy rate and the first similarity.

[0123] In one embodiment of the present invention, the plagiarism rate of the first answer corresponding to the objective question answer subsequence in the target question answer data subsequence can be calculated by the following formula:

[0124]

[0125] Among them, P1 is the first answer plagiarism rate, z is the first correct rate, and T is the first similarity.

[0126] Step 1532: Calculate text similarity between the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student to determine the target student's second question plagiarism rate.

[0127] There is no fixed answer to the subjective questions. The plagiarism rate of the target student's second answer can be determined by calculating the text similarity of each answer data in the subjective question answer subsequence.

[0128] Specifically, the text similarity calculation is performed on the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student to determine the target student's second answer plagiarism rate, specifically including:

[0129] E. Calculate the text similarity between the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student.

[0130] Specifically, the similarity between the text content in the subjective question answer subsequence of the target student and the text content in the subjective question answer subsequence of each selected student is calculated; if the text content similarity between the subjective question answer subsequence of the target student and a certain student is the highest, the text similarity between the target student and the student is used as the text similarity of the target student.

[0131] Refer to Table 6, which shows the target student's and other students' subjective subsequences. Calculate the text similarity between the target student's subjective subsequence and the subjective subsequences of students 1-4. If the text similarity between the target student and the subjective subsequences of students 1-4 is 40%, 50%, 60%, and 70%, respectively, then use the 70% text similarity between student 4 and the target student as the target student's text similarity.

[0132] Table 6 Example of student subjective question answer subsequence

[0133] Subjective question answering subsequence Target students (a1, a2, a3, a4, a5, a6, a6, a7, a8) Student 1 (b1, b2, b3, b4, b5, b6, b6, b7, b8) Student 2 (c1, c2, c3, c4, c5, c6, c6, c7, c8) Student 3 (d1, d2, d3, d4, d5, d6, d6, d7, d8) Student 4 (e1, e2, e3, e4, e5, e6, e6, e7, e8)

[0134] F. When the text similarity is greater than a second similarity threshold, the second accuracy rate of the target student's subjective question answer subsequence is determined based on the answer correction sub-information.

[0135] Specifically, if the text similarity between the target student and other students is too low, the target student is deemed not to have plagiarized. Therefore, the text similarity is compared with a preset second similarity threshold. If the text similarity is not greater than the preset second similarity threshold, the target student is deemed not to have plagiarized, and the second plagiarism rate is zero. If the text similarity is greater than the preset second similarity threshold, the target student is deemed to have plagiarized, and the second accuracy rate of the target student's subjective question answer subsequence is calculated based on the answer correction sub-information.

[0136] G. Calculate the target student's second answer plagiarism rate based on the second accuracy rate and text similarity.

[0137] In this embodiment of the present invention, the second answer plagiarism rate corresponding to the subjective question answer subsequence in the target answer data subsequence can be calculated by the following formula:

[0138]

[0139] Among them, P2 is the second answer plagiarism rate, z2 is the second correct rate, and T2 is the second similarity.

[0140] Step 154: Perform a weighted calculation based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period.

[0141] In one embodiment of the present invention, the answer plagiarism rate of the target answer data subsequence can be calculated based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to the target answer data subsequence, and then the answer plagiarism rate of the target student in the target time period can be calculated based on the answer plagiarism rates of all target answer data subsequences in the answer data subsequence.

[0142] Specifically, a weighted calculation is performed based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period, specifically including:

[0143] Step 1541. Perform weighted calculation on the first answer plagiarism rate and the second answer plagiarism rate corresponding to the target answer data subsequence to obtain the third answer plagiarism rate.

[0144] Step 1542: Based on a preset mathematical operation method, calculate the plagiarism rate of all third answers of the target student in the answer data subsequence to obtain the plagiarism rate of the target student in the target time period.

[0145] Specifically, the target student's plagiarism rate during the target time period may be an average of all third-question plagiarism rates, or a weighted value of all third-question plagiarism rates.

[0146] In another embodiment of the present invention, a weighted calculation is performed based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period. Alternatively, the plagiarism rate can be:

[0147] Perform average calculation or weighted calculation on the first answer plagiarism rates of all target answer data subsequences within the answer data subsequence to obtain the target first answer plagiarism rate; perform average calculation or weighted calculation on the second answer plagiarism rates of all target answer data subsequences within the answer data subsequence to obtain the target second answer plagiarism rate; perform weighted calculation on the target first answer plagiarism rate and the target second answer plagiarism rate to obtain the target student's answer plagiarism rate within the target time period.

[0148] After obtaining the student's plagiarism rate and correct answer rate, the target student's learning status within the target time period can be determined based on the plagiarism rate and correct answer rate:

[0149] Step 161. When the target student's correct answer rate in the target time period is greater than the preset third correct rate, it is confirmed that the student's learning status in the target time period is good.

[0150] Step 162: When the student's correct answer rate within the target time period is not greater than the third correct rate, and the plagiarism rate is not greater than the preset plagiarism rate threshold, it is determined that there is a problem with the student's learning status within the target time period.

[0151] Specifically, confirming that there are problems with the student's learning status within the target time period means that the student has not mastered the knowledge points within the target period well and has made many mistakes. It is necessary to strengthen the study of knowledge points within the target time period and reasonably adjust the study plan.

[0152] Step 163: When the plagiarism rate is greater than the plagiarism rate threshold, it is confirmed that the student has plagiarized during the target time period.

[0153] By monitoring students' learning status in different time periods through their answer data, parents or teachers can effectively monitor students' learning situation and propose targeted teaching plans based on their learning status, greatly improving the quality of teaching.

[0154] The present invention also provides a monitoring device for learning status, which can be found in Figure 2 , Figure 2 The device for monitoring the learning state in the embodiment of the present invention includes a data acquisition module 201 , a data analysis module 202 and a state judgment module 203 .

[0155] The data collection module 201 is configured to receive the target student's answer information submitted by the user, wherein the answer information includes the target question and the answer data submitted by the target student based on the target question. The module searches for the first number, first standard answer, and first release time corresponding to the target question in a preset question bank, wherein the question bank contains multiple questions, as well as the numbers, standard answers, and release times corresponding to the questions, and the target question belongs to the question bank;

[0156] The data analysis module 202 is configured to correct the answer data according to the first standard answer to obtain the target student's answer correction information, sort the answer data corresponding to the target question according to the first release time and first number of the target question, and obtain the target student's answer data sequence. From the answer data sequence and answer correction information, a subsequence of answer data and answer correction sub-information whose first release time is within the target time period is selected, and the accuracy rate is calculated based on the answer data subsequence and answer correction sub-information to obtain the target student's answer accuracy rate within the target time period; and similarity analysis is performed between the target student's answer data subsequences and each selected student's answer data subsequence to obtain the target student's answer plagiarism rate within the target time period.

[0157] The status judgment module 203 is used to determine the learning status of the target student in the target time period based on the plagiarism rate and the correct answer rate.

[0158] An embodiment of the present invention proposes a method for monitoring learning status. By using students' answer information as the basis for monitoring the learning status, and analyzing the accuracy rate and plagiarism of each student's homework within the target time period, the student's learning status is analyzed so that the student's current shortcomings and deficiencies can be judged based on the learning status, and then targeted teaching methods can be made to improve teaching quality.

[0159] Figure 3 FIG1 shows the internal structure of a computer device in one embodiment of the present invention. The computer device can be a terminal or a system. Figure 3 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. It will be understood by those skilled in the art that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0160] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes each step in the above method embodiment.

[0161] In one embodiment, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, the processor executes the various steps in the above-mentioned method embodiment. It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0162] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0163] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for monitoring a learning state, characterized in that: The method comprises: Receiving answer information of a target student submitted by a user terminal, wherein the answer information includes a target topic and answer data submitted by the target student based on the target topic; Searching for a first number, a first standard answer, and a first release time corresponding to the target question in a preset question bank, wherein the question bank contains a plurality of questions, and numbers, standard answers, and release times corresponding to the questions, and the target question belongs to the question bank; Correcting the answer data according to the first standard answer to obtain the answer correction information of the target student; sorting the answer data corresponding to the target question according to the first release time of the target question and the first number to obtain the answer data sequence of the target student; From the answer data sequence and the answer correction information, select the answer data subsequence and the answer correction subinformation whose first release time is within the target time period, calculate the accuracy rate based on the answer data subsequence and the answer correction subinformation, and obtain the answer accuracy rate of the target student within the target time period; Performing similarity analysis on the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period; Determining the learning status of the target student within the target time period based on the plagiarism rate and the correct answer rate; The question bank also includes question types corresponding to the questions, and the question types include at least objective questions and subjective questions; The similarity analysis of the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period specifically includes: Obtain a target answer data subsequence with the same release time in the answer data subsequence of the target student; Decomposing the answer data in the target answer data subsequence based on the question type to obtain an objective question answer subsequence and a subjective question answer subsequence; performing similarity calculations on the objective question answer subsequences and the subjective question answer subsequences of the target student and each selected student, respectively, to obtain a first plagiarism rate for the objective questions and a second plagiarism rate for the subjective questions corresponding to the answer data subsequences; Performing a weighted calculation based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period; The similarity calculation is performed on the objective question answer subsequences and the subjective question answer subsequences of the target student and each selected student, respectively, to obtain the first plagiarism rate of the objective questions and the second plagiarism rate of the subjective questions corresponding to the answer data subsequences, specifically including: Using the Smith-Waterman algorithm, a similarity analysis is performed between the target student's objective question answer subsequence and the objective question answer subsequences of each selected student to determine the target student's first answer plagiarism rate; Calculating text similarity between the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student to determine the target student's second answer plagiarism rate; The method of using the Smith-Waterman algorithm to perform similarity analysis on the target student's objective question answer subsequence and the selected students' objective question answer subsequences to determine the target student's first answer plagiarism rate specifically includes: Searching for two sequence segments with the highest similarity between the target student's objective question answer subsequence and the objective question answer subsequences of each selected student based on the Smith-Waterman algorithm; If the length of the sequence segment is greater than a preset length threshold, determining a first accuracy rate of the sequence segment of the target student according to the answer correction sub-information; When the first accuracy rate is lower than a preset first accuracy rate threshold, calculating a first similarity between the two sequence segments; When the first similarity is greater than a preset first similarity threshold, the first plagiarism rate of the target student is calculated according to the first accuracy rate and the first similarity.

2. The method according to claim 1, wherein The calculating of text similarity between the target student's subjective question answer subsequence and the selected students' subjective question answer subsequences to determine the target student's second answer plagiarism rate specifically includes: Calculating the text similarity between the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student; When the text similarity is greater than a second similarity threshold, determining a second accuracy rate of the target student's subjective question answer subsequence based on the answer correction sub-information; A second plagiarism rate of the target student is obtained by calculating according to the second accuracy rate and the text similarity.

3. The method according to claim 1, wherein The step of performing weighted calculation based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period specifically includes: Performing weighted calculation on the first answer plagiarism rate and the second answer plagiarism rate corresponding to the target answer data subsequence to obtain a third answer plagiarism rate; Based on a preset mathematical operation method, the target student's answer plagiarism rate in the target time period is obtained by calculating all the third answer plagiarism rates in the answer data subsequence.

4. The method according to claim 1, wherein The determining of the target student's learning status within the target time period based on the plagiarism rate and the correct answer rate specifically includes: When the target student's correct answer rate in the target time period is greater than a preset third correct rate, it is confirmed that the student's learning status in the target time period is good; When the student's correct answer rate within the target time period is not greater than the third correct answer rate, and the plagiarism rate is not greater than a preset plagiarism rate threshold, it is determined that there is a problem with the student's learning status within the target time period; When the plagiarism rate of the answer is greater than the plagiarism rate threshold, it is confirmed that the student has plagiarized within the target time period.

5. A learning status monitoring device, characterized in that: The device includes a data acquisition module, a data analysis module and a status judgment module; The data collection module is configured to receive answer information of a target student submitted by a user terminal, wherein the answer information includes a target topic and answer data submitted by the target student based on the target topic; Searching for a first number, a first standard answer, and a first release time corresponding to the target question in a preset question bank, wherein the question bank contains a plurality of questions, and numbers, standard answers, and release times corresponding to the questions, and the target question belongs to the question bank; The data analysis module is configured to correct the answer data according to the first standard answer to obtain the answer correction information of the target student, and sort the answer data corresponding to the target question according to the first release time and the first number of the target question to obtain the answer data sequence of the target student; From the answer data sequence and the answer correction information, select the answer data subsequence and the answer correction subinformation whose first release time is within the target time period, calculate the accuracy rate based on the answer data subsequence and the answer correction subinformation, and obtain the answer accuracy rate of the target student within the target time period; Performing similarity analysis on the target student's answer data subsequences and the selected students' answer data subsequences to obtain the target student's answer plagiarism rate within the target time period; The status judgment module is used to determine the learning status of the target student in the target time period based on the plagiarism rate and the correct answer rate; The question bank also includes question types corresponding to the questions, and the question types include at least objective questions and subjective questions; The data analysis module is further configured to obtain a target answer data subsequence with the same release time in the target student's answer data subsequence; Decomposing the answer data in the target answer data subsequence based on the question type to obtain an objective question answer subsequence and a subjective question answer subsequence; performing similarity calculations on the objective question answer subsequences and the subjective question answer subsequences of the target student and each selected student, respectively, to obtain a first plagiarism rate for the objective questions and a second plagiarism rate for the subjective questions corresponding to the answer data subsequences; Performing a weighted calculation based on the first answer plagiarism rate and the second answer plagiarism rate corresponding to all target answer data subsequences in the answer data subsequence to obtain the answer plagiarism rate of the target student in the target time period; The data analysis module is further configured to perform similarity analysis on the target student's objective question answer subsequence and the selected students' objective question answer subsequences using the Smith-Waterman algorithm to determine the target student's first answer plagiarism rate; Calculating text similarity between the target student's subjective question answer subsequence and the subjective question answer subsequences of each selected student to determine the target student's second answer plagiarism rate; The data analysis module is further configured to search for two sequence segments with the highest similarity between the target student's objective question answer subsequence and the objective question answer subsequences of each selected student based on the Smith-Waterman algorithm; If the length of the sequence segment is greater than a preset length threshold, determining a first accuracy rate of the sequence segment of the target student according to the answer correction sub-information; When the first accuracy rate is lower than a preset first accuracy rate threshold, calculating a first similarity between the two sequence segments; When the first similarity is greater than a preset first similarity threshold, the first plagiarism rate of the target student is calculated according to the first accuracy rate and the first similarity.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

7. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

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