CN111104455B - Multi-source multi-dimensional school teaching transverse information difference comparison and analysis method - Google Patents
Multi-source multi-dimensional school teaching transverse information difference comparison and analysis method Download PDFInfo
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- CN111104455B CN111104455B CN201911321299.XA CN201911321299A CN111104455B CN 111104455 B CN111104455 B CN 111104455B CN 201911321299 A CN201911321299 A CN 201911321299A CN 111104455 B CN111104455 B CN 111104455B
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Abstract
The invention provides a multi-source multi-dimensional 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 rate, answering time, main errors, common problems and the like of different test questions of each student to form a test question database, and constructing a student database for the mastering degree of different students on each knowledge point so as to analyze and classify the test questions and the students; and the teaching effects of the teaching methods of different teachers can be systematically analyzed. Meanwhile, the method can further extend that the conceptual test question is tested before the students enter the study, so that the students are differentiated in type before the teaching, then different electronic courses are issued according to different student types, different practice problems or family homework can be automatically arranged according to the different student types, and the overall teaching quality is improved.
Description
Technical Field
The invention particularly relates to a multi-source multi-dimensional school teaching transverse information difference comparison and analysis method.
Background
Along with the progress and innovation of technology and ideas, various teaching modes are increasingly and widely applied to modern teaching, so that the original monotonous teaching is more diversified and interesting. For a long time, how to track the teaching quality of teachers, how to deeply understand the learning degree of students on different courses or different knowledge points of the same course, and how to master and compare the teaching effect of different teachers on the same course, many research institutions and staff perform a great deal of research work, and in general, basic data in the research work mainly depend on the manners of spot check questionnaires, observation along with the hall and subjective statistics performed by the teachers or teaching supervision staff, etc., so that the method has great randomness and subjectivity, low accuracy and complex and time-consuming statistical process. There is currently no automatic, intelligent, and efficient apparatus and means to provide large-scale objective, quantified statistics. Meanwhile, the best method for feeding back the knowledge point mastering degree of the students is to test at present, but most of the current test is only in the category of single student corresponding to single achievement, and no transverse analysis comparison and summary method is adopted for each student for different knowledge point mastering degrees. Therefore, the teaching behavior of the teacher is analyzed by using the method of testing and multi-source multi-dimensional information transverse comparison and analysis, and the optimal teaching method is obtained.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-source multi-dimensional school teaching transverse information difference comparison and analysis method which can well solve the problems.
In order to meet the requirements, the invention adopts the following technical scheme: the multi-source multi-dimensional school teaching transverse information difference comparison and analysis method comprises the following steps:
s1: establishing corresponding relation between the test questions and all knowledge points;
s2: the test questions and the knowledge points which establish the corresponding relation are distinguished and classified according to the difficulty level;
s3: the students test the test questions, and record the correct rate, answering time, error reasons and common questions of the test questions of the students to form a single student database;
s4: combining and analyzing the plurality of single student databases to obtain student overall data at a single time point;
s5: repeating S1 to S4, collecting a plurality of student overall data at the single time point and forming a visual dynamic curve graph;
s6: recording teaching information of a single teacher, wherein the teaching information comprises students of a professor, teaching time, teaching knowledge points and teaching modes;
s7: and (3) corresponding the teaching information of the single teacher to the information of the single student database to obtain the one-to-one correspondence of 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 a plurality of single teacher teaching contribution values and forming an integral teacher teaching contribution value distribution curve graph;
s10: summarizing and analyzing a plurality of single teacher teaching information;
s11: comparing the summarized result with the integral teacher teaching contribution value distribution curve graph to form a corresponding relation;
s12: obtaining a teacher teaching contribution value database according to the corresponding relation, and recording the positive and negative relation of the teacher teaching contribution values presented by different teachers and different teaching behaviors;
s13: s7 to S12 are repeated to obtain a dynamic database of positive and negative distribution of the teacher behavior contribution value, and a visual chart is formed;
s14: and dynamically adjusting the teaching behaviors of the teacher to maintain the teaching behaviors with positive contribution values and correcting the teaching behaviors with negative contribution values, so that the overall teaching quality is improved.
The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method has the following advantages:
carrying out systematic 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 rate, answering time, main errors, common problems and the like of different test questions of each student to form a test question database, and constructing a student database for the mastering degree of each knowledge point of different students, so that analysis is carried out to classify the test questions and the students, and personalized teaching can be carried out with reference to the characteristics of the different test questions and the different students in subsequent teaching, thereby improving teaching efficiency; and the teaching effects of the teaching methods of different teachers can be systematically analyzed.
Meanwhile, the method can further extend that the conceptual test question is tested before the students enter the study, so that the students are differentiated in type before the teaching, then different electronic courses are issued according to different student types, different practice problems or family homework can be automatically arranged according to the different student types, and the overall teaching quality is improved.
Drawings
The accompanying drawings, where like reference numerals refer to identical or similar parts throughout the several views and which are included to provide a further understanding of the present application, are included to illustrate and explain illustrative examples of the present application and do not constitute a limitation on the present application. In the drawings:
fig. 1 schematically illustrates a flow diagram of a multi-source multi-dimensional school teaching lateral information discrepancy comparison analysis method according to one 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 further described in 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. In addition, repeated use of the phrase "according to an embodiment of the present application" does not necessarily refer to the same embodiment, although it may.
Certain features have been left out of the following description for simplicity, which are well known to those skilled in the art.
According to an embodiment of the application, a multi-source multi-dimensional school teaching transverse information difference comparison and analysis method is provided, as shown in fig. 1, and comprises the following steps:
s1: establishing corresponding relation between the test questions and all knowledge points;
s2: the test questions and the knowledge points which establish the corresponding relation are distinguished and classified according to the difficulty level;
s3: the students test the test questions, and record the correct rate, answering time, error reasons and common questions of the test questions of the students to form a single student database;
s4: combining and analyzing the plurality of single student databases to obtain student overall data at a single time point;
s5: repeating S1 to S4, collecting a plurality of student overall data at the single time point and forming a visual dynamic curve graph;
s6: recording teaching information of a single teacher, wherein the teaching information comprises students of a professor, teaching time, teaching knowledge points and teaching modes;
s7: and (3) corresponding the teaching information of the single teacher to the information of the single student database to obtain the one-to-one correspondence of 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 a plurality of single teacher teaching contribution values and forming an integral teacher teaching contribution value distribution curve graph;
s10: summarizing and analyzing a plurality of single teacher teaching information;
s11: comparing the summarized result with the integral teacher teaching contribution value distribution curve graph to form a corresponding relation;
s12: obtaining a teacher teaching contribution value database according to the corresponding relation, and recording the positive and negative relation of the teacher teaching contribution values presented by different teachers and different teaching behaviors;
s13: s7 to S12 are repeated to obtain a dynamic database of positive and negative distribution of the teacher behavior contribution value, and a visual chart is formed;
s14: and dynamically adjusting the teaching behaviors of the teacher to maintain the teaching behaviors with positive contribution values and correcting the teaching behaviors with negative contribution values, so that the overall teaching quality is improved.
According to an embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison analysis method further comprises the following information when information is recorded into a teacher: teacher name, time of day, historical value of single teacher teaching contribution.
According to one embodiment of the application, the calculation method of the single teacher teaching contribution value in the multi-source multi-dimensional school teaching transverse information difference comparison analysis method is as follows:
S＝m-(M1+M2+M3+…Mn)/n；
s is a teaching contribution value of a single teacher;
m is the score of the corresponding knowledge points taught by the teacher;
n is the total amount of teachers;
when the score of the corresponding knowledge points taught by the teacher is higher than the tie value, the teaching contribution value of the single teacher of the teacher is positive, and conversely, negative.
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison and analysis method further comprises the following steps:
s15: and grading, rewarding and punishing the teacher according to the teaching contribution value of the teacher.
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison and analysis method further comprises the following steps:
s16: analyzing learning characteristics of students according to the single student database in different time periods to obtain the learning characteristics, and classifying the students into long-term memory and short-term memory;
s17: carrying out concentrated overcoming and memory strengthening of a single knowledge point on the long-term memory type student;
s18: carrying out a small number of repeated knowledge point consolidation education on the short-term memory students;
s19: and classifying the test questions according to the teaching modes of the long-term memory students and the short-term memory students to form a long-term memory question bank and a short-term memory question bank.
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison and analysis method further comprises the following steps:
s20: performing a stepwise test on newly-entered students;
s21: classifying the newly-admitted students into short-term memory students and long-term memory students according to the staged test results;
s22: the students who newly enter the study select corresponding education modes and test question libraries according to the different memory types of the students.
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison and 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 a query request sent by a data requester terminal;
s26: the transaction server determines user characteristic information of a transactor corresponding to the query condition and the user characteristic identifier;
s20: and the transaction server receives a purchase request sent by the data requester terminal according to the query result, and completes transaction according to the purchase request.
According to one embodiment of the application, the query request in the multi-source multi-dimensional school teaching transverse information difference comparison and analysis method comprises a query condition and user characteristic identifiers required to meet the query condition, wherein the user characteristic identifiers are characteristic category identifiers obtained by classifying all user characteristic information of the transactor, a query result is obtained according to access authority setting parameters of the transactor user characteristic information, and the query result is sent to the data requester terminal.
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison analysis method is used for classifying teacher teaching behaviors by the following method:
the teaching behaviors of teachers are input into a computer in a text mode, and input information is classified according to similarity.
According to one embodiment of the application, the formula adopted when the similarity classification is carried out on the text of the input teacher teaching behavior information in the multi-source multi-dimensional 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 text in the existing text library;
T1-T2 represents the same words contained in sentences T1, T2;
q () is the number of words
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison and analysis method
According to one embodiment of the application, the multi-source multi-dimensional school teaching transverse information difference comparison analysis method carries out systematic division on the test questions, so that the corresponding relation between the test questions and knowledge points is realized; collecting information such as the accuracy rate, answering time, main errors, common problems and the like of different test questions of each student to form a test question database, and constructing a student database for the mastering degree of each knowledge point of different students, so that analysis is carried out to classify the test questions and the students, and personalized teaching can be carried out with reference to the characteristics of the different test questions and the different students in subsequent teaching, thereby improving teaching efficiency; and the teaching effects of the teaching methods of different teachers can be systematically analyzed. Meanwhile, the method can further extend that the conceptual test question is tested before the students enter the study, so that the students are differentiated in type before the teaching, then different electronic courses are issued according to different student types, different practice problems or family homework can be automatically arranged according to the different student types, and the overall teaching quality is improved.
The foregoing examples are merely representative of several embodiments of the present invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit of the invention, which are within the scope of the invention. The scope of the invention should therefore be pointed out with reference to the appended claims.
Claims (9)
1. A multi-source multi-dimensional school teaching transverse information difference comparison and analysis method is characterized by comprising the following steps:
s1: establishing corresponding relation between the test questions and all knowledge points;
s2: the test questions and the knowledge points which establish the corresponding relation are distinguished and classified according to the difficulty level;
s3: the students test the test questions, and record the correct rate, answering time, error reasons and common questions of the test questions of the students to form a single student database;
s4: combining and analyzing the plurality of single student databases to obtain student overall data at a single time point;
s5: repeating S1 to S4, collecting a plurality of student overall data at the single time point and forming a visual dynamic curve graph;
s6: recording teaching information of a single teacher, wherein the teaching information comprises students of a professor, teaching time, teaching knowledge points and teaching modes;
s7: corresponding the teaching information of the single teacher to the information of the single student database to obtain a one-to-one correspondence 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 a plurality of single teacher teaching contribution values and forming an integral teacher teaching contribution value distribution curve graph;
s10: summarizing and analyzing a plurality of single teacher teaching information;
s11: comparing the summarized result with the integral teacher teaching contribution value distribution curve graph to form a corresponding relation;
s12: obtaining a teacher teaching contribution value database according to the corresponding relation, and recording the positive and negative relation of the teacher teaching contribution values presented by different teachers and different teaching behaviors;
s13: s7 to S12 are repeated to obtain a dynamic database of positive and negative distribution of the teacher behavior contribution value, and a visual chart is formed;
s14: dynamically adjusting the teaching behaviors of a teacher to maintain the teaching behaviors with positive contribution values and correcting the teaching behaviors with negative contribution values, so that the overall teaching quality is improved;
the calculation method of the teaching contribution value of the single teacher is as follows:
S=m-(M1+ M 2+ M3+…M n)/n；
s is a teaching contribution value of a single teacher;
m is the score of the corresponding knowledge points taught by the teacher;
n is the total amount of teachers;
when the score of the corresponding knowledge points taught by the teacher is higher than the tie value, the teaching contribution value of the single teacher of the teacher is positive, and conversely, negative.
2. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 1, wherein the method is characterized in that when the teacher is subjected to information recording, the method further comprises the following information: teacher name, time of day, historical value of single teacher teaching contribution.
3. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 1, further comprising the steps of:
s15: and grading, rewarding and punishing the teacher according to the teaching contribution value of the teacher.
4. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 1, further comprising the steps of:
s16: analyzing learning characteristics of students according to the single student database in different time periods to obtain the learning characteristics, and classifying the students into long-term memory and short-term memory;
s17: carrying out concentrated overcoming and memory strengthening of single knowledge points on the long-term memory students;
s18: carrying out a small number of repeated knowledge point consolidation education on the short-term memory students;
s19: and classifying the test questions according to the teaching modes of the long-term memory students and the short-term memory students to form a long-term memory question bank and a short-term memory question bank.
5. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 1, further comprising the steps of:
s20: performing a stepwise test on newly-entered students;
s21: classifying the newly-admitted students into short-term memory students and long-term memory students according to the staged test results;
s22: the students who newly enter the study select corresponding education modes and test question libraries according to the different memory types of the students.
6. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 5, 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 a query request sent by a data requester terminal;
s26: the transaction server determines user characteristic information of a transactor corresponding to the query condition and the user characteristic identifier;
s27: and the transaction server receives a purchase request sent by the data requester terminal according to the query result, and completes transaction according to the purchase request.
7. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 6, wherein the method is characterized by comprising the following steps: the inquiry request comprises inquiry conditions and user characteristic identifiers which are required to meet the inquiry conditions, wherein the user characteristic identifiers are characteristic category identifiers obtained by classifying all user characteristic information of the transactor, inquiry results are obtained according to access authority setting parameters of the user characteristic information of the transactor, and the inquiry results are sent to the data requester terminal.
8. The multi-source multi-dimensional school teaching transverse information difference comparison and analysis method according to claim 1, wherein the classification of teacher teaching behaviors is as follows:
the teaching behaviors of teachers are input into a computer in a text mode, and input information is classified according to the similarity.
9. The multi-source multi-dimensional comparison and analysis method for school teaching transverse information difference according to claim 8, wherein the formula adopted when classifying the similarity of the text of the teaching behavior information of the input teacher 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 text in the existing text library;
T1-T2 represents the same words contained in sentences T1, T2;
q () is the number of words.
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Family Cites Families (7)
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-
2019
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Non-Patent Citations (3)
| Title |
|---|
| Effects of Corrective Feedback on Academic Achievements of Students: Case of Government Secondary Schools in Pakistan;Iqbal Ahmad 等;《International Journal of Science and Research (IJSR)》;第2卷(第1期);第36-40页 * |
| 基于WWW的个性化教学系统的研究与实现;王健;《万方学术》;第三-四章 * |
| 大学物理实验智慧课堂教学模式探究;宗晓岚 等;《合肥师范学院学报》;第37卷(第6期);第56-58页 * |
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