CN115936940A - Mathematical simulation teaching system and method based on big data - Google Patents
Mathematical simulation teaching system and method based on big data Download PDFInfo
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Abstract
The invention relates to a mathematical simulation teaching system and a mathematical simulation teaching method based on big data, wherein the system comprises a big data platform, a mathematical teaching knowledge base platform, a simulation teaching platform and a local student feedback end, the mathematical teaching knowledge base platform is used for acquiring data related to mathematical teaching knowledge from the big data platform, the mathematical teaching knowledge base platform is used for sending the mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform, the simulation teaching data of the simulation teaching platform are transmitted to the local student feedback end, the local student feedback end is also used for supporting students to upload difficulty level feedback data for different mathematical teaching knowledge, and the mathematical teaching knowledge base platform is also used for marking different difficulty levels for different mathematical teaching knowledge according to the data of the big data platform and the feedback data through comprehensive weighting.
Description
Technical Field
The invention particularly relates to a mathematical simulation teaching system and method based on big data.
Background
The mathematical simulation teaching technology based on big data belongs to a new technology, the simulation mathematical teaching is generally realized only through virtual reality or a traditional database technology in the related prior art, and in the process of realizing the mathematical teaching, the core lies in the acquisition of mathematical knowledge and the classification of difficulty, for example, in the prior art patent document CN110908518B, a mathematical knowledge resource module is arranged, correspondingly, the mathematical teaching knowledge resource module is used for storing the mathematical teaching knowledge, and a mathematical knowledge difficulty classification module is also arranged, and is used for performing level classification processing on the difficulty of the mathematical teaching knowledge. However, in the prior art, the classification of the difficulty of the mathematical teaching knowledge is usually performed manually, which is inefficient, and the abundant big data resources cannot be effectively utilized in the updating process.
Disclosure of Invention
The invention aims to provide a mathematical simulation teaching system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the mathematical simulation teaching system based on big data comprises a big data platform, a mathematical teaching knowledge base platform, a simulation teaching platform and a local student feedback end, wherein the big data platform is connected with the mathematical teaching knowledge base platform, the mathematical teaching knowledge base platform is connected with the simulation teaching platform, and the simulation teaching platform is connected with the local student feedback end; the mathematical teaching knowledge base platform is used for acquiring data related to mathematical teaching knowledge from the big data platform, extracting corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marking different difficulty levels for different mathematical teaching knowledge; the mathematical teaching knowledge base platform is used for sending mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform and completing simulation teaching by the simulation teaching platform; the simulation teaching data of the simulation teaching platform are transmitted to a local student feedback end, and are interacted with students by the local student feedback end, the local student feedback end is also used for supporting the students to upload the difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end to the simulation teaching platform and then to the mathematical teaching knowledge base platform; the mathematical teaching knowledge base platform is also used for marking different difficulty levels for different mathematical teaching knowledge according to the large data platform data and the feedback data; the big data platform and the math teaching knowledge base platform are configured on a big data server; the simulation teaching platform is configured on a local server or a host; the local student feedback end is configured at the local host client or the mobile terminal.
Further, the data related to the mathematical education knowledge includes advanced mathematical education knowledge, specifically includes series advanced mathematical education knowledge, extreme advanced mathematical education knowledge, calculus advanced mathematical education knowledge, spatial analysis geometric advanced mathematical education knowledge, linear algebra advanced mathematical education knowledge, series advanced mathematical education knowledge, ordinary differential equation advanced mathematical education knowledge.
Further, extracting the difficulty level of the corresponding mathematical education knowledge from the data on the mathematical education knowledge includes first obtaining the data on the mathematical education knowledge, retrieving all the knowledge points of the corresponding mathematical education knowledge from the data on the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, calculating all the knowledge points 1/Pi in a certain mathematical education knowledge with 1/Pi as the difficulty level of the knowledge point, calculating the sum of all the knowledge points 1/Pi, and taking the sum of all the knowledge points 1/Pi as the difficulty level of the corresponding mathematical education knowledge.
Further, extracting the difficulty level of the corresponding mathematical education knowledge from the data related to the mathematical education knowledge includes obtaining the data related to the mathematical education knowledge, retrieving all the corresponding knowledge points of the mathematical education knowledge from the data related to the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, calculating all the knowledge points ai/Pi in a certain mathematical education knowledge with ai/Pi as the difficulty level of the knowledge point, calculating the sum of all the knowledge points ai/Pi, and setting the sum of all the knowledge points ai/Pi as the difficulty level of the corresponding mathematical education knowledge with ai as the dynamic weight of the corresponding knowledge point, wherein the dynamic weight ai is set by statistical analysis in advance.
Further, the setting of the dynamic weight by the statistical analysis comprises: and counting corresponding mathematical test error rates of different knowledge points, analyzing whether the error rates of the different knowledge points are related to the difficulty of the knowledge points, if so, taking the error rates as the dynamic weight of the knowledge points, and otherwise, taking the average error rate of all the knowledge points as the dynamic weight of the knowledge points.
Further, the math simulation teaching system based on big data also comprises a processor, and the processor is used for executing commands of the big data platform and/or the math teaching knowledge base platform and/or the simulation teaching platform and/or the local student feedback end.
The mathematical simulation teaching method based on big data comprises the steps that a mathematical teaching knowledge base platform obtains data related to mathematical teaching knowledge from a big data platform, extracts corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marks different difficulty levels on different mathematical teaching knowledge; the mathematical teaching knowledge base platform sends mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform, and the simulation teaching platform completes simulation teaching; the simulation teaching data of the simulation teaching platform are transmitted to a local student feedback end, and are interacted with students by the local student feedback end; through a local student feedback end, students upload difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end to a simulation teaching platform and then to a mathematical teaching knowledge base platform; and the mathematical teaching knowledge base platform comprehensively weights different difficulty levels of different mathematical teaching knowledge marks according to the data of the big data platform and the feedback data.
Compared with the prior art, the invention has the beneficial effects that:
the method and the device can classify the difficulty of the mathematical teaching knowledge through full-automatic intellectualization, the efficiency is higher, different difficulty grades of different mathematical teaching knowledge marks are comprehensively weighted according to the data of the big data platform and the feedback data, the precision is higher, and rich big data resources can be effectively utilized. The specific mathematical teaching knowledge base platform acquires data related to mathematical teaching knowledge from the big data platform, extracts corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marks different difficulty levels on different mathematical teaching knowledge; the mathematical teaching knowledge base platform sends the mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform, and the simulation teaching platform completes the simulation teaching; the simulation teaching data of the simulation teaching platform are transmitted to a local student feedback end, and are interacted with students by the local student feedback end; through a local student feedback end, students upload difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end to a simulation teaching platform and then to a mathematical teaching knowledge base platform; and the mathematical teaching knowledge base platform comprehensively weights different difficulty levels of different mathematical teaching knowledge marks according to the data of the big data platform and the feedback data.
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FIG. 1 is a block diagram of the system components of the present application;
fig. 2 is a flow chart of a method of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The application discloses a big data-based mathematical simulation teaching system, which comprises a big data platform A, a mathematical teaching knowledge base platform B, a simulation teaching platform C and a local student feedback end D, wherein the big data platform A is connected with the mathematical teaching knowledge base platform B, the mathematical teaching knowledge base platform B is connected with the simulation teaching platform C, and the simulation teaching platform C is connected with the local student feedback end D, as shown in FIG. 1; the mathematical teaching knowledge base platform B is used for acquiring data related to mathematical teaching knowledge from the big data platform A, extracting corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marking different difficulty levels for different mathematical teaching knowledge; the mathematical teaching knowledge base platform B is used for sending mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform C, and the simulation teaching platform C completes simulation teaching; the simulation teaching data of the simulation teaching platform C are transmitted to a local student feedback end D, and are interacted with students by the local student feedback end D, the local student feedback end D is also used for supporting the students to upload difficulty level feedback data of different mathematical teaching knowledge, and the feedback data are transmitted from the local student feedback end D to the simulation teaching platform C and then to a mathematical teaching knowledge base platform B; the mathematical teaching knowledge base platform B is also used for marking different difficulty levels for different mathematical teaching knowledge according to the large data platform data and the feedback data; the big data platform A and the math teaching knowledge base platform B are configured on a big data server; the simulation teaching platform C is configured on a local server or a host; and the local student feedback end D is configured at the local host client or the mobile terminal.
In the specific implementation, referring to fig. 2, the math teaching knowledge base platform B acquires data related to math teaching knowledge from the big data platform a, extracts corresponding difficulty levels of the math teaching knowledge from the data related to the math teaching knowledge, and marks different difficulty levels for different math teaching knowledge; the mathematical teaching knowledge base platform B sends mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform C, and the simulation teaching platform C completes simulation teaching; the simulation teaching data of the simulation teaching platform C are transmitted to a local student feedback end D, and are interacted to students by the local student feedback end D; through a local student feedback end, a student uploads difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end D to a simulation teaching platform C and then to a mathematical teaching knowledge base platform B; and the mathematical teaching knowledge base platform B marks different difficulty levels on different mathematical teaching knowledge according to the large data platform data and the feedback data by comprehensive weighting.
The method and the device can classify the difficulty of the mathematical teaching knowledge through full-automatic intellectualization, the efficiency is higher, different difficulty grades of different mathematical teaching knowledge marks are comprehensively weighted according to the data of the big data platform and the feedback data, the precision is higher, and rich big data resources can be effectively utilized.
Optionally, the data related to the mathematical education knowledge includes advanced mathematical education knowledge, specifically includes series advanced mathematical education knowledge, extreme advanced mathematical education knowledge, calculus advanced mathematical education knowledge, spatial analytic geometry advanced mathematical education knowledge, linear algebra advanced mathematical education knowledge, series advanced mathematical education knowledge, ordinary differential equation advanced mathematical education knowledge.
Optionally, the extracting the difficulty level of the corresponding mathematical education knowledge from the data on the mathematical education knowledge includes first obtaining the data on the mathematical education knowledge, retrieving all the knowledge points of the corresponding mathematical education knowledge from the data on the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, calculating all the knowledge points 1/Pi in a certain mathematical education knowledge with 1/Pi as the difficulty level of the knowledge point, calculating the sum of all the knowledge points 1/Pi, and taking the sum of all the knowledge points 1/Pi as the difficulty level of the corresponding mathematical education knowledge.
Optionally, the extracting the difficulty level of the mathematical education knowledge from the data related to the mathematical education knowledge includes first obtaining the data related to the mathematical education knowledge, retrieving all the corresponding knowledge points of the mathematical education knowledge from the data related to the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, taking ai/Pi as the difficulty level of the knowledge point, calculating all the knowledge points ai/Pi in a certain mathematical education knowledge, calculating the sum of all the knowledge points ai/Pi, taking the sum of all the knowledge points ai/Pi as the difficulty level of the corresponding mathematical education knowledge, wherein ai is the dynamic weight of the corresponding knowledge point, and the dynamic weight ai is set by statistical analysis in advance.
For example, all the knowledge points of the mathematical teaching knowledge are retrieved from the data of the mathematical teaching knowledge, wherein the mathematical teaching knowledge is the definite integral in the mathematical teaching knowledge such as calculus and the like, all the knowledge points of the mathematical teaching knowledge are retrieved from the data of the definite integral teaching knowledge, and all the knowledge points comprise the concept and property of the definite integral, an integral basic formula, an element transformation method and a partial integral method of the definite integral, abnormal integral and an arrest method function of the abnormal integral, so that the occurrence probability Pi of each knowledge point, namely the probability that the concept and property of the definite integral, the integral basic formula, the element transformation method and the partial integral method of the definite integral, the abnormal integral and the arrest method function of the abnormal integral appear in the data is counted.
Optionally, the setting of the dynamic weight by the statistical analysis includes: and counting corresponding mathematical test error rates of different knowledge points, analyzing whether the error rates of the different knowledge points are related to the difficulty of the knowledge points, if so, taking the error rates as the dynamic weight of the knowledge points, otherwise, taking the average error rate of all the knowledge points as the dynamic weight of the knowledge points.
For example, the different knowledge points include concepts and properties of definite integrals, integral basic formulas, element transformation methods and partial integral methods of definite integrals, abnormal integrals, and convergence methods of abnormal integrals, and then the mathematical test error rates of the mathematical test error rates for the different knowledge points, i.e., the concepts and properties of statistical definite integrals, the integral basic formulas, the element transformation methods and partial integral methods of definite integrals, the abnormal integrals, and the convergence methods of abnormal integrals, are counted.
Optionally, the system further comprises a processor for executing commands of the big data platform and/or the mathematics teaching knowledge base platform and/or the simulation teaching platform and/or the local student feedback terminal.
In a specific implementation, the application discloses a mathematical simulation teaching system based on big data, as shown in fig. 1, the system comprises a big data platform a, a mathematical teaching knowledge base platform B, a simulation teaching platform C and a local student feedback end D, wherein the big data platform a is connected with the mathematical teaching knowledge base platform B, the mathematical teaching knowledge base platform B is connected with the simulation teaching platform C, and the simulation teaching platform C is connected with the local student feedback end D; the mathematical teaching knowledge base platform B is used for acquiring data related to mathematical teaching knowledge from the big data platform A, extracting corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marking different difficulty levels for different mathematical teaching knowledge; the mathematical teaching knowledge base platform B is used for sending mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform C, and the simulation teaching platform C completes simulation teaching; the simulation teaching data of the simulation teaching platform C are transmitted to a local student feedback end D, and are interacted with students by the local student feedback end D, the local student feedback end D is also used for supporting the students to upload difficulty level feedback data of different mathematical teaching knowledge, and the feedback data are transmitted from the local student feedback end D to the simulation teaching platform C and then to a mathematical teaching knowledge base platform B; the mathematical teaching knowledge base platform B is also used for marking different difficulty levels for different mathematical teaching knowledge according to the large data platform data and the feedback data by comprehensive weighting; the big data platform A and the math teaching knowledge base platform B are configured on a big data server; the simulation teaching platform C is configured on a local server or a host; the local student feedback end D is configured at a local host client or a mobile terminal; the data related to the mathematical teaching knowledge comprises advanced mathematical teaching knowledge, specifically comprises series advanced mathematical teaching knowledge, extreme advanced mathematical teaching knowledge, calculus advanced mathematical teaching knowledge, space analytic geometry advanced mathematical teaching knowledge, linear algebra advanced mathematical teaching knowledge, series advanced mathematical teaching knowledge and ordinary differential equation advanced mathematical teaching knowledge, wherein the difficulty level of extracting the corresponding mathematical teaching knowledge from the data related to the mathematical teaching knowledge comprises the steps of firstly obtaining the data related to the mathematical teaching knowledge, retrieving all corresponding mathematical teaching knowledge points from the data related to the mathematical teaching knowledge, counting the occurrence probability Pi of each knowledge point, taking 1/Pi as the difficulty level of the knowledge point, calculating all knowledge points 1/Pi in a certain mathematical teaching knowledge, calculating the sum of all knowledge points 1/Pi, and taking the sum of all knowledge points 1/Pi as the difficulty level of the corresponding mathematical teaching knowledge. Or, the difficulty level of the mathematical education knowledge extracted from the data related to the mathematical education knowledge comprises the steps of firstly obtaining the data related to the mathematical education knowledge, searching all corresponding knowledge points of the mathematical education knowledge from the data related to the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, calculating all knowledge points ai/Pi in a certain mathematical education knowledge by taking ai/Pi as the difficulty level of the knowledge point, calculating the sum of all knowledge points ai/Pi, and taking the sum of all knowledge points ai/Pi as the difficulty level of the corresponding mathematical education knowledge, wherein ai is the dynamic weight of the corresponding knowledge point, and the dynamic weight ai is set by statistical analysis in advance.
Wherein the statistical analysis setting dynamic weight comprises: and counting corresponding mathematical test error rates of different knowledge points, analyzing whether the error rates of the different knowledge points are related to the difficulty of the knowledge points, if so, taking the error rates as the dynamic weight of the knowledge points, and otherwise, taking the average error rate of all the knowledge points as the dynamic weight of the knowledge points.
The application also discloses a mathematical simulation teaching method based on big data, which comprises the steps that a mathematical teaching knowledge base platform B acquires data related to mathematical teaching knowledge from a big data platform A, extracts corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marks different difficulty levels for different mathematical teaching knowledge, as shown in FIG. 2; the mathematical teaching knowledge base platform B sends mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform C, and the simulation teaching platform C completes simulation teaching; the simulation teaching data of the simulation teaching platform C are transmitted to a local student feedback end D, and are interacted with students by the local student feedback end D; through a local student feedback end, a student uploads difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end D to a simulation teaching platform C and then to a mathematical teaching knowledge base platform B; and the mathematical teaching knowledge base platform B marks different difficulty levels on different mathematical teaching knowledge according to the large data platform data and the feedback data by comprehensive weighting.
Claims (7)
1. The mathematics simulation teaching system based on big data is characterized by comprising a big data platform, an mathematics teaching knowledge base platform, a simulation teaching platform and a local student feedback end, wherein the big data platform is connected with the mathematics teaching knowledge base platform; the mathematical teaching knowledge base platform is used for acquiring data related to mathematical teaching knowledge from the big data platform, extracting corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marking different difficulty levels for different mathematical teaching knowledge; the mathematical teaching knowledge base platform is used for sending mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform, and the simulation teaching platform completes simulation teaching; the simulation teaching data of the simulation teaching platform are transmitted to a local student feedback end, and are interacted with students by the local student feedback end, the local student feedback end is also used for supporting the students to upload the difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end to the simulation teaching platform and then to the mathematical teaching knowledge base platform; the mathematical teaching knowledge base platform is also used for marking different difficulty levels for different mathematical teaching knowledge according to the large data platform data and the feedback data; the big data platform and the math teaching knowledge base platform are configured on a big data server; the simulation teaching platform is configured on a local server or a host; the local student feedback end is configured at the local host client or the mobile terminal.
2. The big-data based math simulation teaching system of claim 1 wherein the data related to math teaching knowledge comprises higher math teaching knowledge, specifically including series higher math teaching knowledge, extreme higher math teaching knowledge, calculus higher math teaching knowledge, space analytic geometry higher math teaching knowledge, linear algebra higher math teaching knowledge, series higher math teaching knowledge, ordinary differential equation higher math teaching knowledge.
3. The big-data based simulation teaching system of mathematics as claimed in claim 1, wherein extracting the difficulty level of the corresponding mathematical education knowledge from the data on the mathematical education knowledge comprises first obtaining the data on the mathematical education knowledge, retrieving all the knowledge points of the corresponding mathematical education knowledge from the data on the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, calculating all the knowledge points 1/Pi in a certain mathematical education knowledge with 1/Pi as the difficulty level of the knowledge point, calculating the sum of all the knowledge points 1/Pi, and taking the sum of all the knowledge points 1/Pi as the difficulty level of the corresponding mathematical education knowledge.
4. The big data based math simulation teaching system of claim 1 wherein extracting the difficulty level of the mathematical education knowledge from the data on the mathematical education knowledge comprises first obtaining the data on the mathematical education knowledge, retrieving all the knowledge points of the mathematical education knowledge from the data on the mathematical education knowledge, counting the occurrence probability Pi of each knowledge point, calculating all the knowledge points ai/Pi in a certain mathematical education knowledge with ai/Pi as the difficulty level of the knowledge point, calculating the sum of all the knowledge points ai/Pi with the sum of all the knowledge points ai/Pi as the difficulty level of the corresponding mathematical education knowledge with ai being the dynamic weight of the corresponding knowledge point, the dynamic weight ai being set by statistical analysis in advance.
5. The big data based math simulation teaching system of claim 1 wherein the statistical analysis setting dynamic weights comprises: and counting corresponding mathematical test error rates of different knowledge points, analyzing whether the error rates of the different knowledge points are related to the difficulty of the knowledge points, if so, taking the error rates as the dynamic weight of the knowledge points, and otherwise, taking the average error rate of all the knowledge points as the dynamic weight of the knowledge points.
6. The big data based math simulation teaching system of claim 1 further comprising a processor for executing commands of the big data platform and/or the math teaching knowledge base platform and/or the simulation teaching platform and/or the local student feedback terminal.
7. The mathematical simulation teaching method based on the big data is characterized by comprising the steps that a mathematical teaching knowledge base platform acquires data related to mathematical teaching knowledge from a big data platform, extracts corresponding difficulty levels of the mathematical teaching knowledge from the data related to the mathematical teaching knowledge, and marks different difficulty levels on different mathematical teaching knowledge; the mathematical teaching knowledge base platform sends the mathematical teaching knowledge marked with different difficulty levels to the simulation teaching platform, and the simulation teaching platform completes the simulation teaching; the simulation teaching data of the simulation teaching platform are transmitted to a local student feedback end, and are interacted with students by the local student feedback end; through a local student feedback end, students upload difficulty level feedback data of different mathematical teaching knowledge, and the feedback data is transmitted from the local student feedback end to a simulation teaching platform and then to a mathematical teaching knowledge base platform; and the mathematical teaching knowledge base platform marks different difficulty levels for different mathematical teaching knowledge according to the large data platform data and the feedback data by comprehensive weighting.
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