CN108831229B - Chinese automatic grading method - Google Patents
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- CN108831229B CN108831229B CN201810607348.5A CN201810607348A CN108831229B CN 108831229 B CN108831229 B CN 108831229B CN 201810607348 A CN201810607348 A CN 201810607348A CN 108831229 B CN108831229 B CN 108831229B
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
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Abstract
The invention relates to a Chinese automatic grading method, which comprises the following steps: step 1, the system divides each knowledge point into 9 grades; step 2, the user selects according to the grade and the learning version; step 3, an algorithm engine of the system pushes topics (each topic is divided into 9 grades) to the user; step 4, finally obtaining the grade of each knowledge point of the user through the test of each knowledge point; step 5, generating a report; and 6, pushing different learning paths to each user for learning according to different reports tested by the users. Compared with the prior art, the invention has the following advantages: the current weak knowledge points and the previous weak knowledge points of the user can be accurately known.
Description
Technical Field
The invention relates to the field of Chinese learning, in particular to a Chinese automatic grading method.
Background
In most of products on the market at present, the study of languages is divided by grades, after a student passes the test at 8 grades, the student knows that some knowledge points of the student are not mastered, but the student probably does not learn the knowledge points at 7 grades, but the product on the market at present cannot detect the situation.
Through search, chinese patent publication No. CN1963753A discloses a multidimensional chinese learning application system and method, the learning method includes the following steps: providing a Chinese language learning library; the user starts the electronic device, operates the display screen to select a learning content or inputs a keyword to generate a control signal; and capturing the learning files in the language learning library according to the control signal and the learning files or the control signal and the keywords, and displaying the learning files on a display screen or playing the learning files. The invention has the advantages that: the Chinese character learning system is simple, easy to learn, remember and use, accords with the learning rule of Chinese characters, and has the characteristics of man-machine combination, receiving-giving interaction, connotation, interestingness and the like. However, the invention is a common Chinese learning, and can not be classified according to the actual situation of the user, and can not push the appropriate learning content according to the actual situation of the user.
Disclosure of Invention
The present invention aims at providing one Chinese automatic grading method to overcome the demerits in available technology.
The purpose of the invention can be realized by the following technical scheme:
a Chinese automatic grading method comprises the following steps:
step 1, the system divides each knowledge point into 9 grades;
step 2, the user selects according to the grade and the learning version;
step 3, an algorithm engine of the system pushes topics (each topic is divided into 9 grades) to the user;
step 4, finally obtaining the grade of each knowledge point of the user through the test of each knowledge point;
step 5, generating a report;
and 6, pushing different learning paths to each user for learning according to different reports tested by the users.
Preferably, the knowledge points in step 4 include a real word module, a virtual word module, a sentence pattern module, a common sense module, a translation module and an information module.
Preferably, the initial difficulty of the default question in each knowledge point in the step 4 is 5 grades, the difficulty is increased or decreased according to the answering condition of the user, the difficulty is increased by one grade, the difficulty is decreased by one grade, and the grade corresponding to the knowledge point of the user is judged according to the final result.
Preferably, the report in step 5 includes: (1) the independent level (1-9 levels) of each module, the comprehensive level (1-9 levels) of the user is calculated according to the weight, the user comprehensive level (3), the capability value (visualization), (4) and the error cause analysis (subdivision knowledge points).
Preferably, the weight of each module is undetermined when the comprehensive level is calculated.
Preferably, each topic in the system has a plurality of label attributes dynamically generated after a plurality of users make the questions, and the label attributes comprise answering time and topic difficulty. Based on the label attribute, the algorithm obtains the grasping degree, namely the ability value of the knowledge point of the user from multiple dimensions more accurately according to the answering time and the answering difficulty of the user.
Compared with the prior art, the invention has the following advantages:
1. through an artificial intelligence algorithm, according to the answering time and the answering difficulty of the user, the weak knowledge points of the user can be accurately known.
2. Through a large map, the system can get through three levels of the first, middle and middle, and the user can know which part of knowledge points are not mastered, and can know that the knowledge points are influenced before the user actually has other knowledge points which are not mastered.
Drawings
Figure 1 is a flow chart of the synchronization control software of the present invention,
fig. 2 is a flow of learning the language and literature in summer.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
As shown in fig. 1, an automatic chinese classification method includes the following steps:
step 1, the system divides each knowledge point into 9 grades;
step 2, the user selects according to the grade and the learning version;
step 3, an algorithm engine of the system pushes topics (each topic is divided into 9 grades) to the user;
step 4, finally obtaining the grade of each knowledge point of the user through the test of each knowledge point;
step 5, generating a report;
and 6, pushing different learning paths to each user for learning according to different reports tested by the users.
The knowledge points in the step 4 comprise a real word module, a virtual word module, a sentence pattern module, a common sense module, a translation module and an information module.
And 4, the initial difficulty of the default question in each knowledge point in the step 4 is 5 grades, the difficulty is increased or reduced according to the answering condition of the user, the difficulty is increased by one grade, the difficulty is decreased by one grade, and the grade corresponding to the knowledge point of the user is judged according to the final result.
The report in step 5 comprises: (1) the independent level (1-9 levels) of each module, the comprehensive level (1-9 levels) of the user is calculated according to the weight, the user comprehensive level (3), the capability value (visualization), (4) and the error cause analysis (subdivision knowledge points).
And the weight of each module is undetermined during the comprehensive level calculation.
Each question in the system is provided with a plurality of label attributes which are dynamically generated after a user makes the question, and the label attributes comprise answering time and question difficulty. Based on the label attribute, the algorithm obtains the grasping degree, namely the ability value of the knowledge point of the user from multiple dimensions more accurately according to the answering time and the answering difficulty of the user.
As shown in fig. 2, the data of student answers is sent by the student end, the system returns to the background, the background is sent to the engine of the artificial intelligence algorithm through data processing, and the system grades the pushed questions to the user through the engine.
Firstly, students finish a prior test to prompt the names and the levels of knowledge points which start to learn, then a system push module imports a video, and the students call a video template. After the video is played, the system pushes the study questions, the study question template is called, the students enter a question answering interface to start answering, after the answer is finished, the system judges the right or wrong, the students are prompted to answer the right or wrong, the ability value of the students is updated, and analysis is pushed for the students. And the student enters the next step, calls the learning video template in the system and plays the explanation video. Entering a system to select whether to trigger the wrong cause learning or not, entering a wrong cause learning process if the selection is 'yes', and judging whether the minimum quantity of questions required by promotion is reached or not after the selection is finished; if no, directly judging whether the minimum quantity of the questions required by promotion is reached, if not, continuing to push the exercise questions by the system, if so, judging whether the ability value reaches the standard, if so, directly finishing the learning process by the system, if not, continuing to judge whether the exercise learned by the student reaches the upper limit of the questions, if so, prompting to stop the learning of the current level by the system, carrying out the learning of the next module, and reflecting the information to an allied teacher by the system; if the upper limit of the exercises is not reached, the system continues to push the exercise exercises.
The system calls exercise templates, students enter an answer interface to start answering, the students judge right and wrong through the system, then the students are prompted to answer right or wrong according to the ability value of the students updated right and wrong, the students push and analyze the students, the students enter the next step, the system detects whether explanation videos exist or not, if the explanation videos exist, the templates of learning videos are called, the explanation videos are played, and if the explanation videos do not exist, the system directly judges whether error-cause learning needs to be triggered or not. If the wrong cause learning is needed, entering a wrong cause learning process, and if the wrong cause learning is not needed, directly judging whether the minimum quantity of questions required by promotion is reached. If the student does not reach the minimum quantity of the exercises required by promotion, the system judges whether all the exercise questions of the student round are finished, if the exercise questions of the student round are not finished, the name and the level of the knowledge point for restarting learning are prompted, if all the exercise questions of the student round are finished, the system judges whether the exercise questions are available, if yes, the exercise questions are pushed, and if not, the exercise questions are pushed. If the student reaches the minimum quantity of the questions required by promotion, the system judges whether the ability value of the student reaches the standard, if not, the system judges whether the student reaches the upper limit of the questions, if not, the system judges whether the student completes all exercise questions of the round again, if so, the system stops the learning of the current level of the module, learns the next module, and the information is reflected to the teacher of the allied school; and if the ability value of the student reaches the standard, the learning process is directly finished. After the student completes the learning process, the system prompts the current level learning process of the knowledge point to pass, and a learning report of the current level of the knowledge point is generated.
Judging whether other knowledge points exist in the current grade of the student to be learned according to the learning report and the suggestion of the teacher of the joined school, if so, prompting the name and the grade of the learned knowledge points again, if not, the student enters a comprehensive test link to prompt the student to enter the comprehensive test, pushing the corresponding test questions according to the algorithm, calling a test question template, enabling the student to enter an answer interface to answer the questions, judging whether the student completes all the test questions or not by the system, if not, pushing the corresponding test questions again, and if so, calling the test report template to generate the test report. Judging whether all knowledge points and levels are completely learned or stopped through a test report, if not, prompting the names and levels of the knowledge points which start to be learned again, if all the knowledge points and levels are completely learned, calling learning problems and practice problems which are not done in all the knowledge points and levels in a question bank by a system, calling a question template, entering a question answering interface, answering students, judging whether the learning problems are wrong by the system, prompting the results to the students, simultaneously pushing corresponding analysis, if the analysis videos exist in the system, pushing the videos, if no questions are not answered, directly judging whether the questions are not answered, if yes, continuing to call the learning problems and practice problems which are not done in the question bank, and if no questions are answered, prompting the end of a text course.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A Chinese automatic grading method is characterized by comprising the following steps:
step 1, the system divides each knowledge point into 9 grades;
step 2, the user selects according to the grade and the learning version;
step 3, pushing topics to a user by an artificial intelligence algorithm engine of the system, wherein each topic is divided into 9 grades;
step 4, finally obtaining the grade of each knowledge point of the user through the test of each knowledge point;
step 5, generating a report;
step 6, pushing different learning paths to each user for learning according to different reports tested by the users;
the report in step 5 comprises: (1) the independent level of each module, (2) the comprehensive level of the user is calculated according to the weight, (3) the capacity value, (4) and the error cause analysis; through a large atlas, a user can know that the current knowledge point is influenced only if other knowledge points are not mastered before the user knows which part of knowledge points are not mastered;
each question in the system is provided with a large number of label attributes which are dynamically generated after the user makes the question, the label attributes comprise answering time and question difficulty, and based on the label attributes, an artificial intelligence algorithm obtains the mastery degree, namely the ability value of the user to the knowledge point according to the answering time and the answering difficulty of the user;
in step 6, the learning process of the learning path is as follows: prompting the name and the level of the knowledge point which starts to learn, then leading in a video by a system pushing module, calling a video template by a student, judging whether the video is pushed for the first time or not by the system, if the video is pushed for the first time, continuing to play the video, and not directly closing the video, and having to finish watching the video, if the video is not pushed for the first time, playing the video, but directly closing the video;
after the video is played, the system pushes the study questions, a study question template is called, the students enter a question answering interface to start answering, after the answer is finished, the system judges the right or wrong, prompts the students to answer the right or wrong, updates the ability value of the students and pushes the students to analyze;
the student enters the next step, calls an in-system learning video template and plays an explanation video;
entering a system to select whether to trigger the wrong cause learning or not, entering a wrong cause learning process if the selection is 'yes', and judging whether the minimum quantity of questions required by promotion is reached or not after the selection is finished; if no, directly judging whether the minimum quantity of the questions required by promotion is reached, if not, continuing to push the exercise questions by the system, if so, judging whether the ability value reaches the standard, if so, directly finishing the learning process by the system, if not, continuing to judge whether the exercise learned by the student reaches the upper limit, if so, prompting by the system to stop the learning of the current level, performing the learning of the next module, and reflecting information to an allied teacher by the system; if the upper limit is not reached, the system continues to push the exercise questions;
the system calls exercise templates, students enter an answer interface to start answering, the students judge right and wrong through the system, then the ability values of the students are updated according to the right and wrong, the students are prompted to answer the right or wrong, the students are pushed to analyze, the students enter the next step, the system detects whether explanation videos exist or not, if the explanation videos exist, the templates of learning videos are called, the explanation videos are played, and if the explanation videos do not exist, whether error-cause learning needs to be triggered or not is directly judged;
if the wrong cause learning is needed, entering a wrong cause learning process, and if the wrong cause learning is not needed, directly judging whether the minimum quantity of questions required by promotion is reached;
if the student does not reach the minimum quantity of the exercises required by promotion, the system judges whether all the exercise questions of the student are finished, if the exercise questions of the student do not finish prompting the names and the levels of the knowledge points for restarting learning, if all the exercise questions of the student are finished, the system judges whether the exercise questions of the student are available, if yes, the exercise questions are pushed, and if not, the exercise questions are pushed;
if the student reaches the minimum quantity of the questions required by promotion, the system judges whether the ability value of the student reaches the standard, if not, the system judges whether the student reaches the exercise upper limit, if not, the system judges whether the student completes all exercise exercises of the round again, if so, the system stops the learning of the current level of the module, learns the next module and reflects the information to the teacher of the allied school;
if the ability value of the student reaches the standard, the learning process is directly finished; after the student completes the learning process, the system prompts the current level learning process of the knowledge point to pass, and a learning report of the current level of the knowledge point is generated;
judging whether other knowledge points exist in the current grade of the student to be learned according to the learning report and the suggestion of the teacher of the joined school, if so, prompting the name and the grade of the learned knowledge points again, if not, the student enters a comprehensive test link to prompt the student to enter the comprehensive test, pushing the corresponding test questions according to an algorithm, calling a test question template, enabling the student to enter an answer interface for answering the questions, judging whether the student completes all the test questions or not by a system, if not, pushing the corresponding test questions again, and if so, calling the test report template to generate a test report;
judging whether all knowledge points and levels are completely learned or stopped through a test report, if not, prompting the names and levels of the knowledge points which start to be learned again, if all the knowledge points and levels are completely learned, calling learning problems and practice problems which are not done in all the knowledge points and levels in a problem base by a system, calling a problem template, entering a problem answering interface, answering the students, judging whether the learning problems are wrong by the system, prompting the results to the students, simultaneously pushing corresponding analysis, if the analysis videos exist in the system, pushing the videos, if no problem is not answered, continuously calling the learning problems and practice problems which are not done in the problem base, and if no problem is answered, prompting the course to be ended.
2. The automatic Chinese grading method of claim 1, wherein the knowledge points in step 4 include a real word module, a virtual word module, a sentence pattern module, a common sense module, a translation module and an information module.
3. The automatic Chinese grading method according to claim 1, wherein the default topic initial difficulty in each knowledge point in step 4 is 5 grades, the difficulty is increased or decreased according to the user's answering situation, the difficulty is increased by one grade, the difficulty is decreased by one grade, and the grade corresponding to the knowledge point of the user is determined according to the final result.
4. The automatic Chinese grading method of claim 1, wherein the weight of each module is undetermined when calculating the comprehensive horizon.
5. The method according to claim 1, wherein each question in the system has a plurality of label attributes dynamically generated by the user after making the question, the label attributes include question answering time and question difficulty, and based on the label attributes, the algorithm obtains the ability value of the user's mastery degree to the knowledge point from a multidimensional and more precise manner according to the question answering time and question answering difficulty of the user.
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| CN110111610A (en) * | 2019-05-13 | 2019-08-09 | 上海乂学教育科技有限公司 | Chinese language structure reading method in adaptive learning based on AI algorithm |
| CN110021208A (en) * | 2019-05-16 | 2019-07-16 | 上海乂学教育科技有限公司 | For the efficient training method and system of English Reading topic |
| CN111191910A (en) * | 2019-12-26 | 2020-05-22 | 上海乂学教育科技有限公司 | Learning system based on learning path planning |
| CN113806516A (en) * | 2021-09-22 | 2021-12-17 | 湖北天天数链技术有限公司 | Matching degree determination method and device, electronic equipment and computer readable storage medium |
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