CN111104455A - Multisource and multidimensional school teaching transverse information difference comparison analysis method - Google Patents
Multisource and multidimensional school teaching transverse information difference comparison analysis method Download PDFInfo
- Publication number
- CN111104455A CN111104455A CN201911321299.XA CN201911321299A CN111104455A CN 111104455 A CN111104455 A CN 111104455A CN 201911321299 A CN201911321299 A CN 201911321299A CN 111104455 A CN111104455 A CN 111104455A
- Authority
- CN
- China
- Prior art keywords
- teaching
- teacher
- students
- student
- information
- 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.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 35
- 238000012360 testing method Methods 0.000 claims abstract description 43
- 238000000034 method Methods 0.000 claims abstract description 11
- 230000006399 behavior Effects 0.000 claims description 22
- 230000007787 long-term memory Effects 0.000 claims description 12
- 230000006403 short-term memory Effects 0.000 claims description 12
- 230000000007 visual effect Effects 0.000 claims description 6
- 238000007596 consolidation process Methods 0.000 claims description 2
- 230000015654 memory Effects 0.000 claims description 2
- 230000006993 memory improvement Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims 1
- 230000009897 systematic effect Effects 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract description 4
- 230000004069 differentiation Effects 0.000 abstract description 3
- 238000011160 research Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Human Resources & Organizations (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Educational Technology (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a multisource multidimensional school teaching transverse information difference comparison analysis method, which is used for carrying out systematic division on test questions so as to realize the corresponding relation between the test questions and knowledge points; collecting information such as the accuracy, answering time, main errors and common problems of different test questions of each student to form a test question database, and constructing a student database for the mastery degree of each knowledge point of different students so as to analyze and classify the test questions and the students; and systematic analysis can be carried out on the teaching effects of the teaching methods of different teachers. Simultaneously can further extend, carry out the test of notional examination question earlier before student's admission to carry out the type differentiation to the student before the teaching, later provide different electronic courses according to different student types, and can arrange different exercise questions or homework according to different student's type is automatic, and then improve holistic teaching quality.
Description
Technical Field
The invention particularly relates to a multisource and multidimensional school teaching transverse information difference comparison analysis method.
Background
With the progress and innovation of technology and concept, various teaching modes are more and more widely applied to modern teaching, so that originally monotonous teaching is richer in diversity and more interesting. For a long time, a great deal of research work is carried out by many research institutions and personnel on how to track the teaching quality of teachers, how to deeply know the learning degree of students to different courses or different knowledge points of the same course and how to master and compare the teaching effect of different teachers to the same course. There is currently no automatic, intelligent and efficient apparatus and means to provide large-scale objective, quantitative statistics. Meanwhile, the best method for feeding back the mastery degree of the knowledge points of the students is to test, but most of the current tests only stay in the category of single scores corresponding to a single student, and a transverse analysis comparison and summarization method for different mastery degrees of the knowledge points of each student is not provided. Therefore, a method for analyzing the teaching behavior of the teacher by using the test and the multi-source multi-dimensional information transverse comparison analysis is provided, so that an optimal teaching method is obtained.
Disclosure of Invention
The invention aims to provide a multisource and multidimensional school teaching transverse information difference comparison and analysis method aiming at the defects of the prior art, and the multisource and multidimensional school teaching transverse information difference comparison and analysis method can well solve the problems.
In order to meet the requirements, the technical scheme adopted by the invention is as follows: the method for comparing and analyzing the difference of the multi-source and multi-dimensional school teaching transverse information comprises the following steps:
s1: establishing a corresponding relation between the test questions and all the knowledge points;
s2: classifying the test questions and the knowledge points establishing the corresponding relationship according to the difficulty level;
s3: the students test questions, and record the accuracy, answering time, error reasons and common problems of the student test to form a single student database;
s4: merging and analyzing a plurality of single student databases to obtain the total student data at a single time point;
s5: repeating S1-S4, collecting a plurality of student overall data at the single time point and forming a visual dynamic curve graph;
s6: inputting teaching information of a single teacher, wherein the teaching information comprises students of the professor, teaching time, teaching knowledge points and teaching modes;
s7: and corresponding the single teacher teaching information with the single student database information to obtain the one-to-one corresponding relation among the teacher, the students, the knowledge points and the teaching time.
S8: analyzing to obtain a single teacher teaching contribution value, wherein the single teacher teaching contribution value corresponds to a single knowledge point;
s9: analyzing the teaching contribution values of the plurality of single teachers, and forming a distribution curve graph of the teaching contribution values of the whole teachers;
s10: summarizing and analyzing the teaching information of a plurality of single teachers;
s11: comparing the summary result with the distribution curve graph of the contribution values of the whole teacher to form a corresponding relation;
s12: obtaining a teacher teaching positive and negative contribution value database according to the corresponding relation, and recording the positive and negative relations of teacher teaching contribution values presented by different teachers and different teaching behaviors;
s13: repeating S7-S12 to obtain a teacher behavior contribution value positive and negative distribution dynamic database and form a visual chart;
s14: and dynamically adjusting the teaching behavior of the teacher to maintain the teaching behavior with the positive contribution value and correcting the teaching behavior with the negative contribution value, thereby improving the overall teaching quality.
The multisource multidimensional school teaching transverse information difference comparison analysis method has the advantages that:
carrying out system division on the test questions so as to realize the corresponding relation between the test questions and the knowledge points; collecting information such as the accuracy, answering time, main errors and common problems of different test questions of each student to form a test question database, and establishing a student database for the mastery degree of each knowledge point of different students so as to analyze and classify the test questions and the students, so that personalized teaching can be carried out according to the characteristics of different test questions and different students in subsequent teaching to improve the teaching efficiency; and systematic analysis can be carried out on the teaching effects of the teaching methods of different teachers.
Simultaneously can further extend, carry out the test of notional examination question earlier before student's admission to carry out the type differentiation to the student before the teaching, later provide different electronic courses according to different student types, and can arrange different exercise questions or homework according to different student's type is automatic, and then improve holistic teaching quality.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 schematically shows a flow chart of a multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments.
In the following description, references to "one embodiment," "an embodiment," "one example," "an example," etc., indicate that the embodiment or example so described may include a particular feature, structure, characteristic, property, element, or limitation, but every embodiment or example does not necessarily include the particular feature, structure, characteristic, property, element, or limitation. Moreover, repeated use of the phrase "in accordance with an embodiment of the present application" although it may possibly refer to the same embodiment, does not necessarily refer to the same embodiment.
Certain features that are well known to those skilled in the art have been omitted from the following description for the sake of simplicity.
According to an embodiment of the present application, a multisource and multidimensional difference comparison and analysis method for school teaching lateral information is provided, as shown in fig. 1, including the following steps:
s1: establishing a corresponding relation between the test questions and all the knowledge points;
s2: classifying the test questions and the knowledge points establishing the corresponding relationship according to the difficulty level;
s3: the students test questions, and record the accuracy, answering time, error reasons and common problems of the student test to form a single student database;
s4: merging and analyzing a plurality of single student databases to obtain the total student data at a single time point;
s5: repeating S1-S4, collecting a plurality of student overall data at the single time point and forming a visual dynamic curve graph;
s6: inputting teaching information of a single teacher, wherein the teaching information comprises students of the professor, teaching time, teaching knowledge points and teaching modes;
s7: and corresponding the single teacher teaching information with the single student database information to obtain the one-to-one corresponding relation among the teacher, the students, the knowledge points and the teaching time.
S8: analyzing to obtain a single teacher teaching contribution value, wherein the single teacher teaching contribution value corresponds to a single knowledge point;
s9: analyzing the teaching contribution values of the plurality of single teachers, and forming a distribution curve graph of the teaching contribution values of the whole teachers;
s10: summarizing and analyzing the teaching information of a plurality of single teachers;
s11: comparing the summary result with the distribution curve graph of the contribution values of the whole teacher to form a corresponding relation;
s12: obtaining a teacher teaching positive and negative contribution value database according to the corresponding relation, and recording the positive and negative relations of teacher teaching contribution values presented by different teachers and different teaching behaviors;
s13: repeating S7-S12 to obtain a teacher behavior contribution value positive and negative distribution dynamic database and form a visual chart;
s14: and dynamically adjusting the teaching behavior of the teacher to maintain the teaching behavior with the positive contribution value and correcting the teaching behavior with the negative contribution value, thereby improving the overall teaching quality.
According to an embodiment of the application, the multisource multidimensional school teaching horizontal information difference comparison analysis method further comprises the following information when the information is input into a teacher: teacher name, working time and historical values of teaching contribution values of a single teacher.
According to an embodiment of the application, the method for calculating the single teacher teaching contribution value in the multisource multidimensional school teaching lateral information difference comparison analysis method comprises the following steps:
S＝m-(M1+M2+M3+…Mn)/n；
wherein S is a single teacher teaching contribution value;
m is the score of the corresponding knowledge point of the student taught by the teacher;
n is the total amount of teachers;
and when the scores of the knowledge points corresponding to the students taught by the teacher are higher than the tie value, the single teacher teaching contribution value of the teacher is positive, and otherwise, the single teacher teaching contribution value of the teacher is negative.
According to an embodiment of the application, the multisource multidimensional school teaching transverse information difference comparison analysis method further comprises the following steps:
s15: and (4) grading and punishing the teacher according to the teaching contribution value of the teacher.
According to an embodiment of the application, the multisource multidimensional school teaching transverse information difference comparison analysis method further comprises the following steps:
s16: analyzing the learning characteristics of the students according to the single student database at different time periods, and classifying the students into a long-term memory type and a short-term memory type;
s17: performing centralized attack and memory enhancement of a single knowledge point on the long-term memory type student;
s18: performing a small number of repeated knowledge point consolidation education on the short-term memory type students;
s19: and classifying the test questions according to different teaching modes of the long-term memory type students and the short-term memory type students to form a long-term memory question bank and a short-term memory question bank.
According to an embodiment of the application, the multisource multidimensional school teaching transverse information difference comparison analysis method further comprises the following steps:
s20: carrying out a staged test on newly-entered students;
s21: classifying the newly-entered students into short-term memory students and long-term memory students according to the stage test result;
s22: and selecting corresponding education modes and test question banks for newly-entered students according to different memory types of the newly-entered students.
According to an embodiment of the application, the multisource multidimensional school teaching transverse information difference comparison analysis method further comprises the following steps:
s23: the long-term memory question bank and the short-term memory question bank are arranged to form an electronic document data packet;
s24: uploading the electronic document data packet to a transaction server;
s25: the transaction server receives an inquiry request sent by a data requester terminal;
s26: the transaction server determines the user characteristic information of the transactor corresponding to the query condition and the user characteristic identification;
s20: and the transaction server receives a purchase request sent by the data requester terminal according to the query result and completes the transaction according to the purchase request.
According to one embodiment of the application, in the multisource multidimensional school teaching transverse information difference comparison analysis method, a query request comprises a query condition and a user characteristic identifier which needs to meet the query condition, the user characteristic identifier is a characteristic category identifier obtained by classifying user characteristic information of a trader, a query result is obtained according to access authority setting parameters of the trader user characteristic information, and the query result is sent to the data requester terminal.
According to one embodiment of the application, the classification of teacher teaching behaviors in the multisource multidimensional school teaching transverse information difference comparison analysis method is performed by adopting the following method:
and performing textualization entry on the teaching behaviors of the teacher into a computer, and classifying the entry information according to the similarity.
According to one embodiment of the application, the formula adopted when the similarity classification is carried out on the input teacher teaching behavior information text in the multisource multidimensional school teaching transverse information difference comparison analysis method is as follows:
Similarity(T1，T2)＝Q(T1∽T2)/(Q(T1)+Q(T2))；
wherein, T1 is a sentence in the information text to be compared;
t2 is a sentence in any one text in the existing text library;
T1-T2 represents the same words contained in sentences T1 and T2;
q () is the number of words
According to one embodiment of the application, the multi-source and multi-dimensional school teaching transverse information difference comparison analysis method
According to one embodiment of the application, the multisource multidimensional school teaching transverse information difference comparison analysis method is used for carrying out systematic division on test questions, so that the corresponding relation between the test questions and knowledge points is realized; collecting information such as the accuracy, answering time, main errors and common problems of different test questions of each student to form a test question database, and establishing a student database for the mastery degree of each knowledge point of different students so as to analyze and classify the test questions and the students, so that personalized teaching can be carried out according to the characteristics of different test questions and different students in subsequent teaching to improve the teaching efficiency; and systematic analysis can be carried out on the teaching effects of the teaching methods of different teachers. Simultaneously can further extend, carry out the test of notional examination question earlier before student's admission to carry out the type differentiation to the student before the teaching, later provide different electronic courses according to different student types, and can arrange different exercise questions or homework according to different student's type is automatic, and then improve holistic teaching quality.
The above-mentioned embodiments only show some embodiments of the present invention, and the description thereof is more specific and detailed, but should not be construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the claims.
Claims (10)
1. A multisource multidimensional school teaching transverse information difference comparison analysis method is characterized by comprising the following steps:
s1: establishing a corresponding relation between the test questions and all the knowledge points;
s2: classifying the test questions and the knowledge points establishing the corresponding relationship according to the difficulty level;
s3: the students test questions, and record the accuracy, answering time, error reasons and common problems of the student test to form a single student database;
s4: merging and analyzing a plurality of single student databases to obtain the total student data at a single time point;
s5: repeating S1-S4, collecting a plurality of student overall data at the single time point and forming a visual dynamic curve graph;
s6: inputting teaching information of a single teacher, wherein the teaching information comprises students of the professor, teaching time, teaching knowledge points and teaching modes;
s7: and corresponding the single teacher teaching information with the single student database information to obtain the one-to-one corresponding relation among the teacher, the students, the knowledge points and the teaching time.
S8: analyzing to obtain a single teacher teaching contribution value, wherein the single teacher teaching contribution value corresponds to a single knowledge point;
s9: analyzing the teaching contribution values of the plurality of single teachers, and forming a distribution curve graph of the teaching contribution values of the whole teachers;
s10: summarizing and analyzing the teaching information of a plurality of single teachers;
s11: comparing the summary result with the distribution curve graph of the contribution values of the whole teacher to form a corresponding relation;
s12: obtaining a teacher teaching positive and negative contribution value database according to the corresponding relation, and recording the positive and negative relations of teacher teaching contribution values presented by different teachers and different teaching behaviors;
s13: repeating S7-S12 to obtain a teacher behavior contribution value positive and negative distribution dynamic database and form a visual chart;
s14: and dynamically adjusting the teaching behavior of the teacher to maintain the teaching behavior with the positive contribution value and correcting the teaching behavior with the negative contribution value, thereby improving the overall teaching quality.
2. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 1, wherein the teacher further includes the following information when entering information: teacher name, working time and historical values of teaching contribution values of a single teacher.
3. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 1, wherein the calculation method of the single teacher teaching contribution value is as follows:
S＝m-(M1+M2+M3+…Mn)/n；
wherein S is a single teacher teaching contribution value;
m is the score of the corresponding knowledge point of the student taught by the teacher;
n is the total amount of teachers;
and when the scores of the knowledge points corresponding to the students taught by the teacher are higher than the tie value, the single teacher teaching contribution value of the teacher is positive, and otherwise, the single teacher teaching contribution value of the teacher is negative.
4. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 1, further comprising the steps of:
s15: and (4) grading and punishing the teacher according to the teaching contribution value of the teacher.
5. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 1, further comprising the steps of:
s16: analyzing the learning characteristics of the students according to the single student database at different time periods, and classifying the students into a long-term memory type and a short-term memory type;
s17: performing centralized attack and memory enhancement of a single knowledge point on the long-term memory type student;
s18: performing a small number of repeated knowledge point consolidation education on the short-term memory type students;
s19: and classifying the test questions according to different teaching modes of the long-term memory type students and the short-term memory type students to form a long-term memory question bank and a short-term memory question bank.
6. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 1, further comprising the steps of:
s20: carrying out a staged test on newly-entered students;
s21: classifying the newly-entered students into short-term memory students and long-term memory students according to the stage test result;
s22: and selecting corresponding education modes and test question banks for newly-entered students according to different memory types of the newly-entered students.
7. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 6, further comprising the steps of:
s23: the long-term memory question bank and the short-term memory question bank are arranged to form an electronic document data packet;
s24: uploading the electronic document data packet to a transaction server;
s25: the transaction server receives an inquiry request sent by a data requester terminal;
s26: the transaction server determines the user characteristic information of the transactor corresponding to the query condition and the user characteristic identification;
s20: and the transaction server receives a purchase request sent by the data requester terminal according to the query result and completes the transaction according to the purchase request.
8. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 7, characterized in that: the inquiry request comprises inquiry conditions and user characteristic identifications which need to meet the inquiry conditions, the user characteristic identifications are characteristic category identifications obtained by classifying the user characteristic information of the trader, the inquiry result is obtained according to the access authority setting parameters of the user characteristic information of the trader, and the inquiry result is sent to the data requester terminal.
9. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 1, wherein the teacher teaching behavior is classified by the following method:
and performing textualization entry on the teaching behaviors of the teacher into a computer, and classifying the entry information according to the similarity.
10. The multi-source multi-dimensional school teaching lateral information difference comparison analysis method according to claim 9, wherein the formula used for similarity classification of the input teacher teaching behavior information text is as follows:
Similarity(T1，T2)＝Q(T1∽T2)/(Q(T1)+Q(T2))；
wherein, T1 is a sentence in the information text to be compared;
t2 is a sentence in any one text in the existing text library;
T1-T2 represents the same words contained in sentences T1 and T2;
q () is the number of words.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911321299.XA CN111104455B (en) | 2019-12-18 | 2019-12-18 | Multi-source multi-dimensional school teaching transverse information difference comparison and analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911321299.XA CN111104455B (en) | 2019-12-18 | 2019-12-18 | Multi-source multi-dimensional school teaching transverse information difference comparison and analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111104455A true CN111104455A (en) | 2020-05-05 |
| CN111104455B CN111104455B (en) | 2023-08-04 |
Family
ID=70423480
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911321299.XA Active CN111104455B (en) | 2019-12-18 | 2019-12-18 | Multi-source multi-dimensional school teaching transverse information difference comparison and analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111104455B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112100976A (en) * | 2020-09-24 | 2020-12-18 | 上海松鼠课堂人工智能科技有限公司 | Knowledge point relation marking method and system |
| CN113222315A (en) * | 2020-12-10 | 2021-08-06 | 成都寻道科技有限公司 | University student in school data management system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130316315A1 (en) * | 2012-05-24 | 2013-11-28 | Jae Song | Method And System For Improving Testing Assessment |
| CN108491994A (en) * | 2018-02-06 | 2018-09-04 | 北京师范大学 | STEM education assessment system and methods based on big data |
| CN108597280A (en) * | 2018-04-27 | 2018-09-28 | 中国人民解放军国防科技大学 | Teaching system and teaching method based on learning behavior analysis |
| CN109272789A (en) * | 2018-10-31 | 2019-01-25 | 安徽网网络科技有限公司 | Learning effect assessment system and appraisal procedure based on data analysis |
| WO2019075826A1 (en) * | 2017-10-20 | 2019-04-25 | 深圳市鹰硕技术有限公司 | Internet teaching platform-based accompanying teaching system |
| WO2019095446A1 (en) * | 2017-11-17 | 2019-05-23 | 深圳市鹰硕音频科技有限公司 | Following teaching system having speech evaluation function |
| CN109948881A (en) * | 2019-01-07 | 2019-06-28 | 北京汉博信息技术有限公司 | A kind of processing method of teaching data |
-
2019
- 2019-12-18 CN CN201911321299.XA patent/CN111104455B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130316315A1 (en) * | 2012-05-24 | 2013-11-28 | Jae Song | Method And System For Improving Testing Assessment |
| WO2019075826A1 (en) * | 2017-10-20 | 2019-04-25 | 深圳市鹰硕技术有限公司 | Internet teaching platform-based accompanying teaching system |
| WO2019095446A1 (en) * | 2017-11-17 | 2019-05-23 | 深圳市鹰硕音频科技有限公司 | Following teaching system having speech evaluation function |
| CN108491994A (en) * | 2018-02-06 | 2018-09-04 | 北京师范大学 | STEM education assessment system and methods based on big data |
| CN108597280A (en) * | 2018-04-27 | 2018-09-28 | 中国人民解放军国防科技大学 | Teaching system and teaching method based on learning behavior analysis |
| CN109272789A (en) * | 2018-10-31 | 2019-01-25 | 安徽网网络科技有限公司 | Learning effect assessment system and appraisal procedure based on data analysis |
| CN109948881A (en) * | 2019-01-07 | 2019-06-28 | 北京汉博信息技术有限公司 | A kind of processing method of teaching data |
Non-Patent Citations (3)
| Title |
|---|
| IQBAL AHMAD 等: "Effects of Corrective Feedback on Academic Achievements of Students: Case of Government Secondary Schools in Pakistan", 《INTERNATIONAL JOURNAL OF SCIENCE AND RESEARCH (IJSR)》, vol. 2, no. 1, pages 36 - 40 * |
| 宗晓岚 等: "大学物理实验智慧课堂教学模式探究", 《合肥师范学院学报》, vol. 37, no. 6, pages 56 - 58 * |
| 王健: "基于WWW的个性化教学系统的研究与实现", 《万方学术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112100976A (en) * | 2020-09-24 | 2020-12-18 | 上海松鼠课堂人工智能科技有限公司 | Knowledge point relation marking method and system |
| CN113222315A (en) * | 2020-12-10 | 2021-08-06 | 成都寻道科技有限公司 | University student in school data management system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111104455B (en) | 2023-08-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109272789A (en) | Learning effect assessment system and appraisal procedure based on data analysis | |
| Patrick et al. | Teaching and research quality indicators and the shaping of higher education | |
| Patterson et al. | Advanced Placement® Exam-Taking and Performance: Relationships with First-Year Subject Area College Grades. Research Report No. 2011-4. | |
| CN114913729B (en) | Question selecting method, device, computer equipment and storage medium | |
| US20140214712A1 (en) | Method for admitting an admissions applicant into an academic institution | |
| Huang et al. | High School Graduation Rates across English Learner Student Subgroups in Arizona. REL 2017-205. | |
| Thomas | An analysis of the research methodology of the Ramirez study | |
| CN112487290A (en) | Internet precision teaching method and system based on big data and artificial intelligence | |
| CN111104455B (en) | Multi-source multi-dimensional school teaching transverse information difference comparison and analysis method | |
| CN114330997A (en) | Intelligent teaching plan generating system based on BOPPPS model | |
| Foy et al. | TIMSS 2003 sampling design | |
| Wu et al. | Student achievement analysis and prediction based on the whole learning process | |
| Kumar | Predicting student success in computer science–a reproducibility study | |
| Harsley et al. | Integrating support for collaboration in a computer science intelligent tutoring system | |
| Takács et al. | Dropping-Out Prevention Of Computer Science Students: Developing Studying, Thinking, And Soft Skills Among Students Using Training Programs | |
| CN110378814A (en) | The intelligent tutoring ecosystem | |
| Hasibuan et al. | Detecting learning style based on level of knowledge | |
| Frischemeier et al. | Preservice teachers' statistical reasoning when comparing groups facilitated by software | |
| Lagrio et al. | Research Competence and Productivity Among School Heads and Teachers: Basis for District Research Capacity Building | |
| Romashkova et al. | Automation of data storage structures intelligent generation about the activities of educational organizations | |
| Idika et al. | Determinants of academic achievement in Economics in public secondary schools in Nsukka Local Government Area Enugu State, Nigeria | |
| Olney et al. | Assessing teacher questions in classrooms | |
| Park | Understanding dropouts among community college students: using cluster analysis and data mining | |
| Hanandeh et al. | Using data mining techniques with open source software to evaluate the various factors affecting academic performance: a case study of students in the faculty of information technology | |
| McCallum et al. | Using data for monitoring and target setting: A practical guide for teachers |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |