Education and teaching auxiliary method and system based on big data

Abstract

The invention particularly relates to an education and teaching auxiliary method and system based on big data. The big data-based education and teaching assistance method comprises the steps of crawling test questions of each subject and each level, cleaning data and filling missing data; analyzing data features, performing classification evaluation, forming question bank comprehensive data, performing field creation mapping, performing structured and standardized processing, and then performing storage; the test questions of all subjects are subjected to label calibration through a visual platform, a user carries out modification feedback and score liking on the question solving thought and the question answering process of the test questions, the received modification feedback information is compared and analyzed, the optimal question solving thought and the optimal question answering process are selected in combination with score liking, and the optimal question solving thought and the optimal question answering process are stored in a knowledge reserve database. According to the big data-based education and teaching auxiliary method and system, not only can knowledge points be explained and analyzed, but also the user can be helped to obtain the distribution condition and important condition of the knowledge points through big data analysis, the teaching quality and efficiency can be improved, and students can be better served.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an education and teaching auxiliary method and system based on big data. Background Art

[0002] In the era of big data, the advantages of the Internet have promoted the development of education and even gradually changed the traditional education model. With the Internet as a carrier and the support of big data, it provides teaching services to a wider audience in a more convenient and flexible form. The rapid development of "Internet + Education" is gradually promoting the development of education in my country and changing the existing education model and teaching experience in my country.

[0003] Under the current education model, teachers of each subject spend a lot of time studying past exam questions, integrating the knowledge points of the exam questions, and thus teaching students important knowledge points, greatly improving the teaching level. However, this method is time-consuming and laborious, and the target audience is relatively small.

[0004] With the development and maturity of Internet big data, how to promote the development of education through the advantages of big data technology has become a problem that needs to be solved urgently. Based on this, the present invention proposes an education and teaching auxiliary method and system based on big data. Summary of the invention

[0005] In order to remedy the deficiencies of the prior art, the present invention provides a simple and efficient education and teaching auxiliary method and system based on big data.

[0006] The present invention is achieved through the following technical solutions:

[0007] An education and teaching auxiliary method based on big data, characterized in that it includes the following steps:

[0008] Step S1: Data collection and preprocessing

[0009] For custom exams, crawl real exam questions, mock exam questions, and self-edited exam questions for various subjects and levels, clean the collected data, remove duplicate and abnormal data, and fill in missing data;

[0010] Step S2: Information standardization processing

[0011] Analyze data features, classify and evaluate, form comprehensive data of the question bank, create field mapping for the collected data, structure and standardize the data according to the mapping fields, and store the processed data in the Hbase standardized database for program analysis and processing;

[0012] Step S3: Data visualization and knowledge label processing

[0013] Label the test questions of each subject through the visualization platform, divide the knowledge points of the test questions by using the pre-defined labels, and label the solution ideas, answering process, difficulty and importance of each test question;

[0014] Step S4: Feedback and optimization

[0015] Users provide modification feedback and score and like the solution ideas and answer process of the test questions. The system analyzes the received modification feedback information and compares it with the database information. It selects the best solution ideas and answer process based on the score and likes and stores it in the knowledge reserve database.

[0016] In step S1, the crawled content also includes the knowledge points tested, answer details and question difficulty rating corresponding to each test question.

[0017] In step S3, a custom algorithm of a visualization platform is used to process data, by splitting and comparing knowledge points of past exam questions of each subject, marking preset labels, and calculating the number of times, forms and corresponding scores of each knowledge point or question in past exam questions, and ranking each knowledge point from high to low according to the number of times and scores;

[0018] Each time a knowledge point appears, the corresponding value of its importance increases by 1. The higher the value, the greater its importance.

[0019] The value obtained by adding up the scores of each knowledge point is the difficulty level of the corresponding knowledge point. The higher the value, the greater the difficulty level.

[0020] In step S4, a user group forms a unique identification after registration and login, and saves it in the user information table of the database; users include ordinary users and qualified teacher users:

[0021] Ordinary users do not have feedback permissions;

[0022] Qualified teacher users have the right to provide feedback, and can modify and provide feedback on the solution ideas and answering process of the test questions, and can also rate and like the solution ideas and answering process of the test questions;

[0023] After receiving feedback information and ratings and likes, the system analyzes the received information and compares it with the database information. It selects the best problem-solving ideas and answering process based on the ratings and likes and stores them in the knowledge reserve database.

[0024] An education and teaching auxiliary system based on big data, comprising:

[0025] The data collection and preprocessing module is responsible for crawling real test questions, mock test questions and self-compiled test questions of various subjects and levels for custom exams, cleaning the collected data, removing duplicate and abnormal data, and filling in missing data;

[0026] The information standardization processing module is responsible for analyzing data characteristics, classifying and evaluating, forming comprehensive data of the question bank, and mapping the fields of the collected data. The data is structured and standardized according to the mapped fields, and the processed data is stored in the Hbase standardized database for program analysis and processing;

[0027] The data visualization and knowledge label processing module is responsible for labeling the test questions of each subject through the visualization platform, using the pre-defined labels to divide the knowledge points to which the test questions belong, and labeling the solution ideas, answering process, difficulty and importance of each test question;

[0028] The feedback and optimization module is responsible for users' modification feedback and scoring and likes on the solution ideas and answering process of the test questions. The system analyzes the received modification feedback information and compares it with the database information. It selects the best solution ideas and answering process based on the scoring and likes and stores them in the knowledge reserve database.

[0029] The crawled content of the data collection and preprocessing module also includes the test knowledge points, answer details and test question difficulty rating corresponding to each test question.

[0030] The data visualization and knowledge label processing module uses a custom algorithm of the visualization platform to process data, splits and compares the knowledge points of the past exam questions of each subject, marks the preset labels, and calculates the number of times, forms and corresponding scores of each knowledge point or question in the past exam questions, and ranks each knowledge point from high to low according to the number of times and scores;

[0031] Each time a knowledge point appears, the corresponding value of its importance increases by 1. The higher the value, the greater its importance.

[0032] The value obtained by adding up the scores of each knowledge point is the difficulty level of the corresponding knowledge point. The higher the value, the greater the difficulty level.

[0033] In the feedback and optimization module, a user group forms a unique identification after registration and login, and is saved in the database user information table; users include ordinary users and qualified teacher users:

[0034] Ordinary users do not have feedback permissions;

[0035] Qualified teacher users have the right to provide feedback, and can modify and provide feedback on the solution ideas and answering process of the test questions, and can also rate and like the solution ideas and answering process of the test questions;

[0036] After receiving feedback information and ratings and likes, the system analyzes the received information and compares it with the database information. It selects the best problem-solving ideas and answering process based on the ratings and likes and stores them in the knowledge reserve database.

[0037] A computing device, comprising:

[0038] One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0039] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method when executed by a processor.

[0040] The beneficial effect of the present invention is that the education and teaching auxiliary method and system based on big data can not only explain and analyze knowledge points, but also help users obtain the distribution and important situations of knowledge points through big data analysis, which helps to improve the quality and efficiency of teaching and better serve students. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Attached Figure 1 This is a schematic diagram of the education and teaching auxiliary method based on big data of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0044] The educational teaching auxiliary method based on big data includes the following steps:

[0045] Step S1: Data collection and preprocessing

[0046] For custom exams, crawl real exam questions, mock exam questions, and self-edited exam questions for various subjects and levels, clean the collected data, remove duplicate and abnormal data, and fill in missing data;

[0047] Step S2: Information standardization processing

[0048] Analyze data features, classify and evaluate, form comprehensive data of the question bank, create field mapping for the collected data, structure and standardize the data according to the mapping fields, and store the processed data in the Hbase standardized database for program analysis and processing;

[0049] Step S3: Data visualization and knowledge label processing

[0050] Label the test questions of each subject through the visualization platform, divide the knowledge points of the test questions by using the pre-defined labels, and label the solution ideas, answering process, difficulty and importance of each test question;

[0051] Step S4: Feedback and optimization

[0052] Users provide modification feedback and score and like the solution ideas and answer process of the test questions. The system analyzes the received modification feedback information and compares it with the database information. It selects the best solution ideas and answer process based on the score and likes and stores it in the knowledge reserve database.

[0053] In step S1, the crawled content also includes the knowledge points tested, answer details and question difficulty rating corresponding to each test question.

[0054] In step S3, a custom algorithm of a visualization platform is used to process data, by splitting and comparing knowledge points of past exam questions of each subject, marking preset labels, and calculating the number of times, forms and corresponding scores of each knowledge point or question in past exam questions, and ranking each knowledge point from high to low according to the number of times and scores;

[0055] Each time a knowledge point appears, the corresponding value of its importance increases by 1. The higher the value, the greater its importance.

[0056] The value obtained by adding up the scores of each knowledge point is the difficulty level of the corresponding knowledge point. The higher the value, the greater the difficulty level.

[0057] In step S4, a user group forms a unique identification after registration and login, and saves it in the user information table of the database; users include ordinary users and qualified teacher users:

[0058] Ordinary users do not have feedback permissions;

[0059] Qualified teacher users have the right to provide feedback, and can modify and provide feedback on the solution ideas and answering process of the test questions, and can also rate and like the solution ideas and answering process of the test questions;

[0060] After receiving feedback information and ratings and likes, the system analyzes the received information and compares it with the database information. It selects the best problem-solving ideas and answering process based on the ratings and likes and stores them in the knowledge reserve database.

[0061] The knowledge reserve database can be used to analyze the distribution and importance of knowledge points in previous years. Based on this, it can be inferred which knowledge points must be mastered, the frequency of their appearance in previous exams, their importance and difficulty, and help teachers integrate important knowledge points during lectures, focus on analysis and explanation, achieve twice the result with half the effort, and assist teaching in improving teaching quality and efficiency.

[0062] The education and teaching auxiliary system based on big data includes:

[0063] The data collection and preprocessing module is responsible for crawling real test questions, mock test questions and self-compiled test questions of various subjects and levels for custom exams, cleaning the collected data, removing duplicate and abnormal data, and filling in missing data;

[0064] The information standardization processing module is responsible for analyzing data characteristics, classifying and evaluating, forming comprehensive data of the question bank, and mapping the fields of the collected data. The data is structured and standardized according to the mapped fields, and the processed data is stored in the Hbase standardized database for program analysis and processing;

[0065] The data visualization and knowledge label processing module is responsible for labeling the test questions of each subject through the visualization platform, using the pre-defined labels to divide the knowledge points to which the test questions belong, and labeling the solution ideas, answering process, difficulty and importance of each test question;

[0066] The feedback and optimization module is responsible for users' modification feedback and scoring and likes on the solution ideas and answering process of the test questions. The system analyzes the received modification feedback information and compares it with the database information. It selects the best solution ideas and answering process based on the scoring and likes and stores them in the knowledge reserve database.

[0067] The crawled content of the data collection and preprocessing module also includes the test knowledge points, answer details and test question difficulty rating corresponding to each test question.

[0068] The data visualization and knowledge label processing module uses a custom algorithm of the visualization platform to process data, splits and compares the knowledge points of the past exam questions of each subject, marks the preset labels, and calculates the number of times, forms and corresponding scores of each knowledge point or question in the past exam questions, and ranks each knowledge point from high to low according to the number of times and scores;

[0069] Each time a knowledge point appears, the corresponding value of its importance increases by 1. The higher the value, the greater its importance.

[0070] The value obtained by adding up the scores of each knowledge point is the difficulty level of the corresponding knowledge point. The higher the value, the greater the difficulty level.

[0071] In the feedback and optimization module, a user group forms a unique identification after registration and login, and is saved in the database user information table; users include ordinary users and qualified teacher users:

[0072] Ordinary users do not have feedback permissions;

[0073] Qualified teacher users have the right to provide feedback, and can modify and provide feedback on the solution ideas and answering process of the test questions, and can also rate and like the solution ideas and answering process of the test questions;

[0074] After receiving feedback information and ratings and likes, the system analyzes the received information and compares it with the database information. It selects the best problem-solving ideas and answering process based on the ratings and likes and stores them in the knowledge reserve database.

[0075] The computer-readable storage medium stores one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device performs any one of the above methods.

[0076] The computing device comprises:

[0077] One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0078] This big data-based education and teaching assistance method and system uses the advantages of big data in data collection, processing and other aspects to obtain large quantities of knowledge data; uses program algorithms to process data, and through educational experts and front-line workers in various subjects and fields, we split and compare the knowledge points of the real questions we obtained in previous years, and mark them with established labels; calculates the number of times a certain knowledge point or test question appears in the examinations of various provinces in previous years, and conducts a comprehensive analysis in what form, calculates the ranking of the number of times each knowledge point appears in the examination, and continuously adjusts and updates the difficulty and importance of knowledge points, providing strong assistance for teachers to carry out targeted tutoring in the future teaching process.

[0079] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. An education and teaching auxiliary method based on big data, characterized by: The following steps are involved: Step S1: Data collection and preprocessing For custom exams, crawl real exam questions, mock exam questions, and self-edited exam questions for various subjects and levels, clean the collected data, remove duplicate and abnormal data, and fill in missing data; Step S2: Information standardization processing Analyze data features, classify and evaluate, form comprehensive data of the question bank, create field mapping for the collected data, structure and standardize the data according to the mapping fields, and store the processed data in the Hbase standardized database for program analysis and processing; Step S3: Data visualization and knowledge label processing Label the test questions of each subject through the visualization platform, divide the knowledge points of the test questions by using the pre-defined labels, and label the solution ideas, answering process, difficulty and importance of each test question; Step S4: Feedback and optimization Users provide modification feedback and score and like the solution ideas and answer process of the test questions. The system analyzes the received modification feedback information and compares it with the database information. It selects the best solution ideas and answer process based on the score and likes and stores it in the knowledge reserve database.

2. The education and teaching auxiliary method based on big data according to claim 1 is characterized by: In step S1, the crawled content also includes the knowledge points tested, answer details and question difficulty rating corresponding to each test question.

3. The big data-based education and teaching auxiliary method according to claim 1 is characterized by: In step S3, a custom algorithm of a visualization platform is used to process data, by splitting and comparing knowledge points of past exam questions of each subject, marking preset labels, and calculating the number of times, forms and corresponding scores of each knowledge point or question in past exam questions, and ranking each knowledge point from high to low according to the number of times and scores; Each time a knowledge point appears, the corresponding value of its importance increases by 1. The higher the value, the greater its importance. The value obtained by adding up the scores of each knowledge point is the difficulty level of the corresponding knowledge point. The higher the value, the greater the difficulty level.

4. The big data-based education and teaching auxiliary method according to claim 1, characterized in that: In step S4, a user group forms a unique identification after registration and login, and saves it in the user information table of the database; users include ordinary users and qualified teacher users: Ordinary users do not have feedback permissions; Qualified teacher users have the right to provide feedback, and can modify and provide feedback on the solution ideas and answering process of the test questions, and can also rate and like the solution ideas and answering process of the test questions; After receiving feedback information and ratings and likes, the system analyzes the received information and compares it with the database information. It selects the best problem-solving ideas and answering process based on the ratings and likes and stores them in the knowledge reserve database.

5. An education and teaching auxiliary system based on big data, characterized by: include: The data collection and preprocessing module is responsible for crawling real test questions, mock test questions and self-compiled test questions of various subjects and levels for custom exams, cleaning the collected data, removing duplicate and abnormal data, and filling in missing data; The information standardization processing module is responsible for analyzing data characteristics, classifying and evaluating, forming comprehensive data of the question bank, and mapping the fields of the collected data. The data is structured and standardized according to the mapped fields, and the processed data is stored in the Hbase standardized database for program analysis and processing; The data visualization and knowledge label processing module is responsible for labeling the test questions of each subject through the visualization platform, using the pre-defined labels to divide the knowledge points to which the test questions belong, and labeling the solution ideas, answering process, difficulty and importance of each test question; The feedback and optimization module is responsible for users' modification feedback and scoring and likes on the solution ideas and answering process of the test questions. The system analyzes the received modification feedback information and compares it with the database information. It selects the best solution ideas and answering process based on the scoring and likes and stores them in the knowledge reserve database.

6. The big data-based education and teaching auxiliary system according to claim 5 is characterized by: The crawled content of the data collection and preprocessing module also includes the test knowledge points, answer details and test question difficulty rating corresponding to each test question.

7. The education and teaching auxiliary system based on big data according to claim 5 is characterized by: The data visualization and knowledge label processing module uses a custom algorithm of the visualization platform to process data, splits and compares the knowledge points of the past exam questions of each subject, marks the preset labels, and calculates the number of times, forms and corresponding scores of each knowledge point or question in the past exam questions, and ranks each knowledge point from high to low according to the number of times and scores; Each time a knowledge point appears, the corresponding value of its importance increases by 1. The higher the value, the greater its importance. The value obtained by adding up the scores of each knowledge point is the difficulty level of the corresponding knowledge point. The higher the value, the greater the difficulty level.

8. The big data-based education and teaching auxiliary system according to claim 5 is characterized by: In the feedback and optimization module, a user group forms a unique identification after registration and login, and is saved in the database user information table; users include ordinary users and qualified teacher users: Ordinary users do not have feedback permissions; Qualified teacher users have the right to provide feedback, and can modify and provide feedback on the solution ideas and answering process of the test questions, and can also rate and like the solution ideas and answering process of the test questions; After receiving feedback information and ratings and likes, the system analyzes the received information and compares it with the database information. It selects the best problem-solving ideas and answering process based on the ratings and likes and stores them in the knowledge reserve database.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1 to 4.

10. A computing device, characterized in that include: One or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods according to claims 1 to 4.

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