CN117875568A - Information technology teaching system based on big data - Google Patents
Information technology teaching system based on big data Download PDFInfo
- Publication number
- CN117875568A CN117875568A CN202410268929.6A CN202410268929A CN117875568A CN 117875568 A CN117875568 A CN 117875568A CN 202410268929 A CN202410268929 A CN 202410268929A CN 117875568 A CN117875568 A CN 117875568A
- Authority
- CN
- China
- Prior art keywords
- teaching
- data
- comparison
- information technology
- mode
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005516 engineering process Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 claims abstract description 54
- 238000004458 analytical method Methods 0.000 claims abstract description 39
- 238000007405 data analysis Methods 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims description 34
- 238000004422 calculation algorithm Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000003032 molecular docking Methods 0.000 claims description 9
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000012417 linear regression Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012098 association analyses Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/27—Regression, e.g. linear or logistic regression
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Educational Technology (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Primary Health Care (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Evolutionary Biology (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the field of information technology teaching systems, in particular to an information technology teaching system based on big data, which comprises an information technology teaching system, wherein the information technology teaching system comprises a big data analysis system, a teaching data synchronization system, a teaching result comparison system, a teaching mode analysis system and a teaching process supervision system, the teaching data synchronization system, the teaching result comparison system, the teaching mode analysis system and the teaching process supervision system are all connected with the big data analysis system, the big data analysis system is connected with the teaching data synchronization system, the teaching data synchronization system is connected with the teaching result comparison system, and the teaching result comparison system is connected with the teaching mode analysis system.
Description
Technical Field
The invention relates to the field of information technology teaching systems, in particular to an information technology teaching system based on big data.
Background
The education big data can gather innumerable data which cannot be seen, collected and not emphasized before, deep mining is carried out on the mixed data, and association analysis is carried out on the big data in other fields, the great advantage of the big data technology is that the big data technology has strong 'prejudgement' capability, along with gradual deep application of the education big data, predictive analysis has huge potential in the aspects of eliminating education uncertainty and providing early intervention, in addition, the big data can enable teachers and machines to truly know the real situation of each student, thereby truly individualized learning resources, learning activities, learning paths, learning tools, services and the like are provided for the students, and at present, higher education has great advantages in the aspects of computer professional traditional education theory and research talents, but most of teaching processes often have no definite teaching effect display mode, so that people often cannot quickly find out proper teaching modes, and thus delaying the learning process.
Disclosure of Invention
The invention aims to solve the defects in the background technology and provides an information technology teaching system based on big data.
In order to achieve the above purpose, the invention adopts the following technical scheme: the information technology teaching system comprises an information technology teaching system, wherein the information technology teaching system comprises a big data analysis system, a teaching data synchronization system, a teaching result comparison system, a teaching mode analysis system and a teaching process supervision system, the teaching data synchronization system, the teaching result comparison system, the teaching mode analysis system and the teaching process supervision system are all connected with the big data analysis system, the big data analysis system is connected with the teaching data synchronization system, the teaching data synchronization system is connected with the teaching result comparison system, the teaching result comparison system is connected with the teaching mode analysis system, and the teaching mode analysis system is connected with the teaching process supervision system.
Preferably, the teaching data synchronization system comprises a teaching data acquisition system, a teaching data recording system and a teaching data docking system, wherein the teaching data acquisition system is connected with the teaching data recording system, and the teaching data recording system is connected with the teaching data docking system.
Preferably, the teaching result comparison system comprises a stage result recording system, a result data comparison system and a result data archiving system, wherein the stage result recording system is connected with the result data comparison system, and the result data comparison system is connected with the result data archiving system.
Preferably, the teaching mode analysis system comprises a teaching process analysis system, a teaching mode calculation system and a teaching mode adjustment system, wherein the teaching process analysis system is connected with the teaching mode calculation system, and the teaching mode calculation system is connected with the teaching mode adjustment system.
Preferably, the teaching process monitoring system comprises a teaching path monitoring system, a teaching result monitoring system and a teaching scheme comparison system, wherein the teaching path monitoring system is connected with the teaching result monitoring system, and the teaching result monitoring system is connected with the teaching scheme comparison system.
Preferably, the formula of the data analysis and calculation algorithm used in the teaching mode analysis system is as follows:
wherein y is a dependent variable, +.>Is an independent variable,/->Is a regression coefficient;
the teaching mode analysis system determines regression coefficients by minimizing the sum of squares of residuals, namely:
wherein->Preferably, when the teaching scheme is compared, the teaching process supervision system adopts a mode of combining mean value comparison, standard deviation comparison and correlation coefficient comparison, and a specific data analysis comparison algorithm formula is as follows:
average value comparison:
the average is the sum of all data in the dataset divided by the number of data, and if the averages of the two datasets differ, they can be considered to be somewhat different;
average value comparison algorithm formula:wherein sum (a) and sum (B) represent the sum of data set a and data set B, respectively, and n represents the number of data;
standard deviation comparison:
standard deviation comparison algorithm formula:
wherein, the method comprises the steps of, wherein, xi and yi represent each data in data set A and data set B, respectively,/->Respectively representing the average value of the data set A and the data set B, and n represents the number of data;
correlation coefficient contrast:
correlation coefficient contrast algorithm formula:
correlation coefficient=corr (a, B)
Wherein a and B represent two data sets, respectively.
Compared with the prior art, the invention has the following beneficial effects:
1. in the teaching process, the stage achievements of students during teaching can be recorded through the stage achievements recording system in the teaching achievements comparison system, later the stage learning achievements of all students can be compared with the previous learning achievements through the achievements data comparison system, the recording and control of the learning states of the students are achieved, meanwhile, the learning achievements of the students can be predicted and deduced through the information technology teaching system, and the actual teaching work is facilitated.
2. The system can be used for filing and recording the learning progress and the learning score of students through the score data filing system, the teaching process analysis system in the teaching mode analysis system can be used for analyzing the stage process of teaching during teaching, the teaching data acquired through the teaching data synchronization system can assist the teaching mode calculation system to calculate and improve the teaching mode and the process, and then the teaching mode can be adjusted and improved through the teaching mode adjustment system, so that the teaching of various students can be realized, the teaching is realized according to the material, and the practical use is facilitated.
3. In the teaching process, the monitoring of each stage in the teaching process can be realized through the teaching path monitoring system in the teaching process monitoring system, the monitoring of each student learning result can be realized through the teaching result monitoring system, meanwhile, the record of the teaching scheme after the improvement of the teaching mode adjusting system can be realized through the teaching scheme comparison system, the record can be compared with the previous teaching scheme, the actual effect of the teaching scheme is mastered, and the teaching work is facilitated.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of an information technology teaching system based on big data;
FIG. 2 is a schematic diagram of the whole architecture of a teaching data synchronization system of the information technology teaching system based on big data;
FIG. 3 is a schematic diagram of the whole architecture of a teaching result comparison system of the information technology teaching system based on big data;
FIG. 4 is a schematic diagram of the whole architecture of a teaching mode analysis system of the information technology teaching system based on big data;
fig. 5 is a schematic diagram of the whole architecture of a teaching process supervision system of the information technology teaching system based on big data.
1. An information technology teaching system; 11. a big data analysis system; 12. a teaching data synchronization system; 121. a teaching data acquisition system; 122. a teaching data recording system; 123. a teaching data docking system; 13. the teaching result comparison system; 131. a stage result recording system; 132. the score data comparison system; 133. a score data archiving system; 14. a teaching mode analysis system; 141. a teaching process analysis system; 142. a teaching mode calculation system; 143. a teaching mode adjustment system; 15. a teaching process supervision system; 151. a teaching path monitoring system; 152. a teaching result monitoring system; 153. teaching scheme contrast system.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
The information technology teaching system based on big data as shown in fig. 1-5 comprises an information technology teaching system 1, wherein the information technology teaching system 1 comprises a big data analysis system 11, a teaching data synchronization system 12, a teaching result comparison system 13, a teaching mode analysis system 14 and a teaching process supervision system 15, the teaching data synchronization system 12, the teaching result comparison system 13, the teaching mode analysis system 14 and the teaching process supervision system 15 are all connected with the big data analysis system 11, the big data analysis system 11 is connected with the teaching data synchronization system 12, the teaching data synchronization system 12 is connected with the teaching result comparison system 13, the teaching result comparison system 13 is connected with the teaching mode analysis system 14, the teaching mode analysis system 14 is connected with the teaching process supervision system 15, people can assist in a teaching process through the information technology teaching system 1, and people can load the information technology teaching system 1 on a multimedia computer in a classroom, and provide teaching assistance for people in daily teaching process through the information technology teaching system 1.
The teaching data synchronization system 12 comprises a teaching data acquisition system 121, a teaching data recording system 122 and a teaching data docking system 123, wherein the teaching data acquisition system 121 is connected with the teaching data recording system 122, the teaching data recording system 122 is connected with the teaching data docking system 123, various items of data during teaching can be acquired through the teaching data acquisition system 121 in the teaching data synchronization system 12, various acquired teaching data can be recorded through the teaching data recording system 122, and the stored teaching data and other systems can be docked and transmitted through the teaching data docking system 123.
The teaching result comparison system 13 comprises a stage result recording system 131, a result data comparison system 132 and a result data archiving system 133, wherein the stage result recording system 131 is connected with the result data comparison system 132, the result data comparison system 132 is connected with the result data archiving system 133, in the teaching process, the stage result of a student during teaching can be recorded through the stage result recording system 131 in the teaching result comparison system 13, and then the learning result of each student at the stage can be compared with the previous learning result through the result data comparison system 132, so that the recording and control of the learning state of the student are realized, the actual teaching work is facilitated, and the archiving and recording of the learning progress and the learning result of the student can be realized through the result data archiving system 133.
The teaching mode analysis system 14 comprises a teaching process analysis system 141, a teaching mode calculation system 142 and a teaching mode adjustment system 143, wherein the teaching mode analysis system 141 is connected with the teaching mode calculation system 142, the teaching mode calculation system 142 is connected with the teaching mode adjustment system 143, the teaching process analysis system 141 in the teaching mode analysis system 14 can analyze the teaching stage process during teaching, the teaching mode calculation system 142 can be assisted to calculate and improve the teaching mode and the teaching process through the teaching data acquired by the teaching data synchronization system 12, and then the teaching mode can be adjusted and improved through the teaching mode adjustment system 143, so that various teaching conditions of students can be realized, teaching is realized according to the material, and the teaching system is beneficial to practical use.
The teaching process monitoring system 15 comprises a teaching path monitoring system 151, a teaching result monitoring system 152 and a teaching scheme comparison system 153, the teaching path monitoring system 151 is connected with the teaching result monitoring system 152, the teaching result monitoring system 152 is connected with the teaching scheme comparison system 153, in the teaching process, monitoring of each stage in the teaching process can be achieved through the teaching path monitoring system 151 in the teaching process monitoring system 15, monitoring of learning results of students can be achieved through the teaching result monitoring system 152, meanwhile, recording of a teaching scheme improved by the teaching mode adjustment system 143 can be achieved through the teaching scheme comparison system 153, comparison with a previous teaching scheme can be achieved, actual effects of the teaching scheme can be mastered, and teaching work is facilitated.
The data analysis and calculation algorithm formula used in the teaching mode analysis system 14 is as follows:
wherein y is a dependent variable, +.>Is an independent variable,/->Is a regression coefficient;
teaching mode analysis system 14 determines regression coefficients by minimizing the sum of squares of residuals, namely:
wherein->The method comprises the steps of carrying out a first treatment on the surface of the By solving the minimization problem, an estimated value of the learning score after predicting and deducting the teaching scheme can be obtained, and the linear regression algorithm is suitable for the situation that a linear relation exists between the independent variable and the dependent variable and can be used for predicting and interpreting the relation between the variables.
When the teaching scheme is compared, the teaching process supervision system 15 adopts a mode of combining mean value comparison, standard deviation comparison and correlation coefficient comparison, and a specific data analysis comparison algorithm formula is as follows:
average value comparison:
the average is the sum of all data in the dataset divided by the number of data, and if the averages of the two datasets differ, they can be considered to be somewhat different;
average value comparison algorithm formula:wherein sum (a) and sum (B) represent the sum of data set a and data set B, respectively, and n represents the number of data;
standard deviation comparison:
the standard deviation is the square root of the average of the squares of the differences between the individual data in the data set and the average, and can be used to measure the degree of discretization of the data, if the standard deviations of the two data sets differ, they can be considered to differ in distribution,
standard deviation comparison algorithm formula:
wherein, the method comprises the steps of, wherein, xi and yi represent each data in data set A and data set B, respectively,/->And->Respectively representing the average value of the data set A and the data set B, and n represents the number of data;
correlation coefficient contrast:
the correlation coefficient is an index for measuring the linear correlation degree between two variables, and if the correlation coefficient of two data sets is close to 1 or-1, a strong linear relationship exists between the two data sets; if the correlation coefficient is close to 0, it can be considered that there is no linear relationship between them,
correlation coefficient contrast algorithm formula:
correlation coefficient=corr (a, B)
Wherein a and B represent two data sets, respectively.
Working principle:
in actual use, people can assist in the teaching process through the information technology teaching system 1, people can load the information technology teaching system 1 on a multimedia computer in a classroom, can provide teaching assistance for people in the daily teaching process through the information technology teaching system 1, can collect various items of data during teaching through the teaching data acquisition system 121 in the teaching data synchronization system 12, can record the collected various items of teaching data through the teaching data recording system 122, can dock and transmit the stored teaching data with the rest systems through the teaching data docking system 123, can record the staged results of students during teaching through the stage result recording system 131 in the teaching result comparison system 13 in the teaching process, can compare the learning results of the stage of each student with the previous learning results through the result data comparison system 132, can facilitate the recording and mastering of the learning state of the students, can archive and record the learning results through the result data acquisition system 133, can realize the archiving and recording of the learning results through the experiment data recording system 133, can realize the adjustment of the teaching data in the teaching system in the teaching process by the improved calculation system 14, can realize the synchronization of the teaching mode of the teaching system in the teaching system, can realize the adjustment of the teaching mode of the teaching system in the teaching process by the improvement of the teaching system, can realize the control of the teaching system in the teaching system by the improvement of the analysis system in the teaching mode of the teaching system, can realize the improvement of the mode of the system in the training mode of the system is realized, the monitoring of each stage in the teaching process can be realized through the teaching path monitoring system 151 in the teaching process monitoring system 15, the monitoring of each student learning result can be realized through the teaching result monitoring system 152, meanwhile, the record of the teaching scheme after the improvement of the teaching mode adjusting system 143 can be realized through the teaching scheme comparison system 153, and the record can be compared with the previous teaching scheme, so that the actual effect of the teaching scheme can be mastered, and the teaching work is facilitated.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The information technology teaching system based on big data comprises an information technology teaching system (1), and is characterized in that: the information technology teaching system (1) comprises a big data analysis system (11), a teaching data synchronization system (12), a teaching result comparison system (13), a teaching mode analysis system (14) and a teaching process supervision system (15), wherein the teaching data synchronization system (12), the teaching result comparison system (13), the teaching mode analysis system (14) and the teaching process supervision system (15) are all connected with the big data analysis system (11), the big data analysis system (11) is connected with the teaching data synchronization system (12), the teaching data synchronization system (12) is connected with the teaching result comparison system (13), the teaching result comparison system (13) is connected with the teaching mode analysis system (14), and the teaching mode analysis system (14) is connected with the teaching process supervision system (15).
2. The big data based information technology teaching system of claim 1, wherein: the teaching data synchronization system (12) comprises a teaching data acquisition system (121), a teaching data recording system (122) and a teaching data docking system (123), wherein the teaching data acquisition system (121) is connected with the teaching data recording system (122), and the teaching data recording system (122) is connected with the teaching data docking system (123).
3. The big data based information technology teaching system of claim 1, wherein: the teaching achievement comparison system (13) comprises a stage achievement recording system (131), a achievement data comparison system (132) and an achievement data archiving system (133), wherein the stage achievement recording system (131) is connected with the achievement data comparison system (132), and the achievement data comparison system (132) is connected with the achievement data archiving system (133).
4. The big data based information technology teaching system of claim 1, wherein: the teaching mode analysis system (14) comprises a teaching process analysis system (141), a teaching mode calculation system (142) and a teaching mode adjustment system (143), wherein the teaching process analysis system (141) is connected with the teaching mode calculation system (142), and the teaching mode calculation system (142) is connected with the teaching mode adjustment system (143).
5. The big data based information technology teaching system of claim 1, wherein: the teaching process supervision system (15) comprises a teaching path monitoring system (151), a teaching result monitoring system (152) and a teaching scheme comparison system (153), wherein the teaching path monitoring system (151) is connected with the teaching result monitoring system (152), and the teaching result monitoring system (152) is connected with the teaching scheme comparison system (153).
6. The big data based information technology teaching system of claim 1, wherein: the formula of the data analysis and calculation algorithm used in the teaching mode analysis system (14) is as follows:
wherein y is a dependent variable, +.>Is an independent variable,/->Is a regression coefficient;
the teaching mode analysis system (14) determines regression coefficients by minimizing the sum of squares of residuals, namely:
wherein, the method comprises the steps of, wherein,
；
by solving the minimization problem, the estimated value of the learning score after predicting and deducting the teaching scheme can be obtained, and the linear regression algorithm is suitable for the situation that a linear relation exists between the independent variable and the dependent variable and can be used for predicting and explaining the relation between the variables.
7. The big data based information technology teaching system of claim 1, wherein: when the teaching scheme is compared, the teaching process supervision system (15) adopts a mode of combining mean value comparison, standard deviation comparison and correlation coefficient comparison, and a specific data analysis comparison algorithm formula is as follows:
average value comparison:
the average is the sum of all data in the dataset divided by the number of data, and if the averages of the two datasets differ, they can be considered to be somewhat different;
average value comparison algorithm formula:wherein->Andrespectively representing the sum of the data set A and the data set B, and n represents the number of data;
standard deviation comparison:
the standard deviation is the square root of the average of the squares of the differences between the individual data in the data set and the average, and can be used to measure the degree of discretization of the data, if the standard deviations of the two data sets differ, they can be considered to differ in distribution,
standard deviation comparison algorithm formula:wherein xi and yi represent each of data set A and data set B, respectivelyData of->Respectively representing the average value of the data set A and the data set B, and n represents the number of data;
correlation coefficient contrast:
the correlation coefficient is an index for measuring the linear correlation degree between two variables, and if the correlation coefficient of two data sets is close to 1 or-1, a strong linear relationship exists between the two data sets; if the correlation coefficient is close to 0, it can be considered that there is no linear relationship between them,
correlation coefficient contrast algorithm formula:
correlation coefficient=corr (a, B)
Wherein a and B represent two data sets, respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410268929.6A CN117875568A (en) | 2024-03-11 | 2024-03-11 | Information technology teaching system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410268929.6A CN117875568A (en) | 2024-03-11 | 2024-03-11 | Information technology teaching system based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117875568A true CN117875568A (en) | 2024-04-12 |
Family
ID=90594928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410268929.6A Pending CN117875568A (en) | 2024-03-11 | 2024-03-11 | Information technology teaching system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117875568A (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108648123A (en) * | 2018-07-13 | 2018-10-12 | 江苏开放大学（江苏城市职业学院） | A method of its management network teaching process of the network teaching platform and utilization based on big data |
| JP2019117552A (en) * | 2017-12-27 | 2019-07-18 | 株式会社管理工学研究所 | Information processing apparatus |
| CN113139885A (en) * | 2020-01-16 | 2021-07-20 | 广州致远科教软件有限公司 | Teaching management system and management method thereof |
| CN113658022A (en) * | 2021-08-20 | 2021-11-16 | 青软创新科技集团股份有限公司 | Big data based teaching mode analysis method |
| CN114038256A (en) * | 2021-11-29 | 2022-02-11 | 西南医科大学 | Teaching interactive system based on artificial intelligence |
| CN116843524A (en) * | 2023-07-06 | 2023-10-03 | 安徽智慧教育科技有限公司 | Post-class auxiliary system for online teaching |
| CN117575687A (en) * | 2023-11-30 | 2024-02-20 | 北京极致车网科技有限公司 | Method and device for monitoring new media operation effect of automobile based on big data |
-
2024
- 2024-03-11 CN CN202410268929.6A patent/CN117875568A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019117552A (en) * | 2017-12-27 | 2019-07-18 | 株式会社管理工学研究所 | Information processing apparatus |
| CN108648123A (en) * | 2018-07-13 | 2018-10-12 | 江苏开放大学（江苏城市职业学院） | A method of its management network teaching process of the network teaching platform and utilization based on big data |
| CN113139885A (en) * | 2020-01-16 | 2021-07-20 | 广州致远科教软件有限公司 | Teaching management system and management method thereof |
| CN113658022A (en) * | 2021-08-20 | 2021-11-16 | 青软创新科技集团股份有限公司 | Big data based teaching mode analysis method |
| CN114038256A (en) * | 2021-11-29 | 2022-02-11 | 西南医科大学 | Teaching interactive system based on artificial intelligence |
| CN116843524A (en) * | 2023-07-06 | 2023-10-03 | 安徽智慧教育科技有限公司 | Post-class auxiliary system for online teaching |
| CN117575687A (en) * | 2023-11-30 | 2024-02-20 | 北京极致车网科技有限公司 | Method and device for monitoring new media operation effect of automobile based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108597280B (en) | Teaching system and teaching method based on learning behavior analysis | |
| Yang et al. | Toward precision education | |
| Pallathadka et al. | Investigating the impact of artificial intelligence in education sector by predicting student performance | |
| CN107918821A (en) | Teachers ' classroom teaching process analysis method and system based on artificial intelligence technology | |
| Zhou et al. | Application analysis of data mining technology in ideological and political education management | |
| Arora et al. | Prediction of educationist's performance using regression model | |
| CN117875568A (en) | Information technology teaching system based on big data | |
| CN111445153B (en) | Method and system for estimating and correcting objective test question attribute mode for education measurement | |
| Walsh et al. | Bootstrapping: a tool for clinical research | |
| Parkavi et al. | Pattern analysis of blooms knowledge level students performance using association rule mining | |
| Sun et al. | Research on the Application of Students' Answered Record Analyze Model and Question Automatic Classify Based on K-Means Clustering Algorithm | |
| Hartman | Incorporating and assessing risk analysis in undergraduate engineering economy courses | |
| Grobe | A regression approach to evaluating instructional programs in science | |
| WO2024056111A2 (en) | Intelligent campus architecture teaching system based on combining virtuality and reality | |
| Fan | An Inquiry‐Based Teaching Model for Nursing Professional Courses Based on Data Mining and Few‐Shot Learning Technology | |
| Jianfeng | Research on the innovation of university ideological and political education methods based on data mining in the big data era | |
| Ding et al. | Application of Multimedia Music Education Technology in Colleges and Universities Based on Regression Model | |
| Khafidli et al. | DATA MINING TO PREDICTE THE TIME OF KHATAMAN TAHFID STUDENTS TAHFID PPTQ AL-ASY'ARIYYAH USING C4. 5 ALGORITHM | |
| Meng et al. | Research on Student Achievement Prediction Method Based on Machine Learning | |
| Xiaogao et al. | Research on High-Risk student prediction based on Big Data | |
| Li et al. | Asymptotic behavior of cox’s partial likelihood and its application to variable selection | |
| Xing et al. | GIS Gas Analysis Training Program Based on K-Means Clustering Method Training Auxiliary Analysis | |
| Wang | Application of clustering algorithm in the evaluation of college students' English reading ability under the framework of big data | |
| Meng et al. | Application of Data Mining in Computer-Aided Bilingual Teaching System | |
| Xue et al. | Correlation Analysis of Physical Education Teaching Quality Assessment Based on Fuzzy Comprehensive Evaluation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |