CN113191650B - Student offline teaching management method based on online education software platform data feature matching - Google Patents
Student offline teaching management method based on online education software platform data feature matching Download PDFInfo
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Abstract
The invention discloses a student offline teaching management method based on online education software platform data feature matching, which is characterized in that all students learning on an online education software platform and the number of offline education nodes corresponding to the offline education software platform are counted, learning feature parameters corresponding to all students and node feature parameters corresponding to all offline education nodes are respectively obtained, so that all students are subjected to offline education node matching according to the online learning course name and the geographic position in the learning feature parameters corresponding to all students and the course name and the geographic position in the node feature parameters corresponding to all offline education nodes, and simultaneously, the nodes and seats are intelligently distributed to all students corresponding to all offline education nodes, thereby realizing the online teaching management of students based on the online education software platform data feature matching, improving the management efficiency and the management effect, the student learning experience feeling and the teacher teaching experience feeling can be enhanced.
Description
Technical Field
The invention belongs to the technical field of student teaching management, and particularly relates to a student offline teaching management method based on online education software platform data feature matching.
Background
With the rise and popularization of the mobile internet, the internet changes the life style of people, and meanwhile, the education style is inevitably influenced by the internet, and the online education concept is introduced, so that a large number of online education software platforms are born, and online teaching is just one of the main teaching styles. The online education has the advantages that the time and space limitation of the traditional classroom teaching is broken through, students can learn online at home through mobile phones or computers, and the independent learning capacity of the students is improved. However, the problems that the learning initiative of some students is low, the classroom task completion effect is not ideal, the use condition of learning software is greatly influenced by the external environment and the like still exist, so that the online teaching can not completely replace the offline teaching, and the offline teaching is very necessary. Therefore, some of the existing online education software platforms can not only enable students to learn online, but also be equipped with corresponding offline education network points so that the students can enjoy online learning, and meanwhile, can also carry out learning communication consolidation through the offline education network points.
For the off-line teaching management of students at off-line education sites corresponding to the on-line education software platform, such as student matching and seat allocation, if a traditional blind random management mode is adopted, on one hand, the management efficiency is reduced; on the other hand, students cannot be managed in a targeted manner, so that the management effect is poor, and the learning experience of the students and the teaching experience of teachers are influenced. In view of the above, the invention provides a student offline teaching management method based on online education software platform data feature matching.
Disclosure of Invention
The invention aims to provide a student offline teaching management method based on online education software platform data feature matching, which effectively solves the problems mentioned in the background art by counting all students learning on an online education software platform and the number of offline education sites corresponding to the offline education software platform and respectively acquiring the learning feature parameters corresponding to the students and the site feature parameters corresponding to the offline education sites so as to match the offline education sites for the students and simultaneously intelligently allocate sites for the students corresponding to the offline education sites.
The purpose of the invention can be realized by the following technical scheme:
the off-line teaching management method for students based on-line education software platform data feature matching comprises the following steps;
step 1, on-line learning student statistics and student learning characteristic parameter acquisition: counting all students learning on an online education software platform, numbering the counted students according to a predefined sequence, sequentially marking the students as 1,2, a.
Step 2, offline education website statistics and website characteristic parameter acquisition: counting the number of offline education network points corresponding to the offline education software platform, numbering the counted offline education network points, marking the offline education network points as 1,2, a.
Step 3, education network point statistics under the matched line: according to the serial number sequence of students, sequentially matching the on-line learning course names in the learning characteristic parameters corresponding to the students with the established course names in the characteristic parameters corresponding to the off-line education sites, screening off the off-line education sites successfully matched with the off-line learning course names of the students, recording the off-line education sites successfully matched with the off-line learning course names as off-line education sites, and counting the serial numbers of the off-line education sites corresponding to the students;
step 4, distance sorting of education dots under a matched line: comparing the geographic position in the learning characteristic parameter corresponding to each student with the geographic position in the teaching website corresponding to each matching line corresponding to the student to obtain the distance between the geographic position of each student and the geographic position of the teaching website corresponding to each matching line corresponding to the student, and further performing distance sorting on the teaching website corresponding to each student under each matching line according to the sequence of the distance from near to far to obtain the distance sorting result of each student corresponding to each learning website under each matching line;
step 5, determining the target matched line at the education network: step-by-step pushing of the education network points under the matched lines to the corresponding students according to the sequencing results of the education network points under the matched lines corresponding to the students so as to allow the students to perform on-line learning registration and determine the education network points under the target matched lines corresponding to the students;
step 6, on-line learning ability analysis of students: comparing the education network points under the target matching lines corresponding to the students, and summarizing the students corresponding to the education network points under the same target matching line to obtain the students corresponding to the off-line education network points, wherein the on-line learning capacity of the students corresponding to the off-line education network points is analyzed;
and 7, intelligently distributing seats of the student network: and intelligently distributing seats of the network points to the students corresponding to the offline education network points according to the online learning force analysis results of the students corresponding to the offline education network points.
Preferably, the learning feature parameters include an online learning course name and a geographic location.
Preferably, the website characteristic parameters include an opening course name, a geographic location and a number of students capable of being accommodated.
Preferably, in the step 5, the education website under the target matching line corresponding to each student is determined, and the specific determination process thereof executes the following steps:
s1, respectively extracting the education network points arranged under the first matched line from the sequencing results of the education network points under the matched lines corresponding to the students and pushing the education network points to the corresponding students, wherein the education network points under the first matched line are marked as the education network points under the first matched line so as to allow the students to carry out on-line learning registration, and simultaneously the education network points under the first matched line corresponding to the students count the number of the students who successfully carry out on-line learning registration of the on-line education network points in real time;
s2, extracting the number of students capable of being accommodated by the education network node under the first matching line from the network node characteristic parameters corresponding to the education network node under the first matching line corresponding to each student;
s3, comparing the number of students counted in real time by the first matchline lower education website corresponding to each student with the number of students capable of being accommodated by the offline education website, if the number of the counted students of the first matchline lower education website corresponding to a certain student reaches the number of the students capable of being accommodated by the offline education website, closing an online learning registration channel of the matchline lower education website, judging whether the student successfully registers at the first matchline lower education website, if so, not processing, and if not, executing S4;
s4: extracting the education network points arranged at the second position from the sequencing result of the education network points under each matching line corresponding to the student and pushing the education network points to the student, the second-ranked offline education site is marked as an offline second-ranked education site to allow the student to perform online learning registration and is processed according to the method of S1-S3, meanwhile, whether the student successfully registers under the second match line is judged, if so, and if the matched line lower education network node is not successfully registered, sequentially extracting the next matched line lower education network node from the sequencing result of the matched line lower education network node corresponding to the student and pushing the next matched line lower education network node to the student until the student is successfully registered, counting the number of the matched line lower education network node successfully registered by the student at the moment, and recording the matched line lower education network node successfully registered as the target matched line lower education network node.
Preferably, in step 6, the online learning ability of each student corresponding to each offline education website is analyzed, and the specific analysis method is as follows:
r1, counting the numbers of the off-line education websites corresponding to the students, wherein the numbers can be recorded as 1, 2.., k., l;
r2, extracting on-line learning records corresponding to the students from the on-line education software platform according to the numbers of the students corresponding to the off-line education network points, acquiring learning time points corresponding to the on-line learning records, and numbering the extracted on-line learning records corresponding to the students according to the sequence of the learning time points, wherein the numbers are respectively marked as 1,2, a, u;
r3 obtaining the time length of the course, the time length of the study and the frequency of the interaction with the teacher corresponding to the study record of each student on each line, and forming the parameter set P of the on-line learning ability of the studentjk(pjk w1,pjk w2,…,pjk wa,...,pjk wu)，pjk wa is a numerical value corresponding to an on-line learning force parameter of a learning record on the a-th line of the kth student corresponding to the jth off-line education website, w is an on-line learning force parameter, and w is r1, r2 and r3 which are respectively represented as course duration, learning duration and frequency of interaction with a teacher;
r4, obtaining the post-class test score of the learning record of each line corresponding to each student, and calculating the post-class learning effect coefficient of the learning record of each line corresponding to each student;
and R5, counting the on-line learning force coefficient of each student corresponding to each off-line education website according to the on-line learning force parameter set of the students and the after-class learning effect coefficient recorded on each line of each student corresponding to each off-line education website.
Preferably, the calculation formula of the after-class learning effect coefficient corresponding to the learning record of each line for each student is
In the formula of lambdajka is expressed as the learning effect coefficient after class of the learning record on the a-th line of the kth student corresponding to the jth off-line education network point,gjka、g′jkand a is respectively expressed as a post-class test score and a full-scale test score corresponding to the learning record on the a-th line of the kth student of the jth offline education website.
Preferably, the calculation formula of the on-line learning force coefficient of each student corresponding to each off-line education website is
In the formula
Expressed as the on-line learning force coefficient, p, of the jth off-line education website corresponding to the kth studentjk r2a、pjk r1a、pjk r3and a is respectively expressed as the learning duration, the course duration and the interaction frequency with the teacher of the jth offline education website corresponding to the learning record on the ith line of the kth student.
Preferably, in step 7, the online learning force analysis result of each student corresponding to each offline education site is used to intelligently allocate seats to each student corresponding to each offline education site, and the specific intelligent allocation method includes the following steps:
f1, classifying the students according to the on-line learning force coefficients of the off-line education network points corresponding to the students to obtain the learning force grades corresponding to the students, and classifying the students corresponding to the same learning force grades to obtain a plurality of students corresponding to the same learning force grades;
f2, dividing the network seats corresponding to each offline education network into seat areas according to the arrangement positions of the seats, and numbering the seat areas, wherein each seat area corresponds to each learning ability grade one by one;
f3, distributing a plurality of students corresponding to the same learning force levels corresponding to each offline education network point on corresponding seats according to the corresponding relation between the seat areas and the learning force levels.
Preferably, the students are classified into learning force grades according to the on-line learning force coefficients of the students corresponding to the off-line education nodes, the specific classification process includes comparing the on-line learning force coefficients of the students corresponding to the off-line education nodes with the on-line learning force coefficient ranges corresponding to the learning force grades in the learning force database, and if the on-line learning force coefficients of a certain student are in the on-line learning force coefficient ranges corresponding to the learning force grades, the learning force grade corresponding to the student is the learning force grade.
Preferably, in the process of assigning seats in F3, if the number of students at an offline education site corresponding to a same learning force level is greater than the number of seats in the seat area corresponding to the learning force level, the number of the seat areas with no seats at the offline education site corresponding to the seats at the same learning force level is obtained, the seat areas with no seats at the offline are recorded as free seat areas, the number of the free seat areas and the distance between each free seat area and the seat area corresponding to the same learning force level are counted, and the nearest free seat area to the seat area corresponding to the same learning force level is selected, so that the remaining students corresponding to the same learning force level are assigned to the nearest free seat area.
The invention has the following beneficial effects:
(1) the invention counts all students on the online education software platform and the number of offline education network points corresponding to the offline education software platform, and respectively obtain the learning characteristic parameters corresponding to students and the website characteristic parameters corresponding to offline education websites, thereby performing offline education site matching on each student according to the online learning course name and the geographic position in the characteristic parameter corresponding to each student and the open course name and the geographic position in the characteristic parameter corresponding to each offline education site, meanwhile, the intelligent distribution of the seats of the network points is carried out on the students corresponding to the off-line education network points, the off-line teaching management of the students based on the on-line education software platform data feature matching is realized, the management efficiency is improved, the management effect is also improved, and the learning experience of the students and the teaching experience of teachers are enhanced.
(2) According to the online learning course matching method and device, during offline education site matching of students according to the online learning course names and the geographic positions in the learning characteristic parameters corresponding to the students and the established course names and the geographic positions in the site characteristic parameters corresponding to the offline education sites, offline education sites are screened out from the offline education sites according to the online learning course names corresponding to the students, online offline learning and name reporting matching of the students is performed on the students through the online learning course names corresponding to the students in a nearby step-by-step matching mode, and the obtained target offline education sites can meet the requirements of the learning course names corresponding to the students, can also achieve nearby offline learning of the students, greatly facilitate the traveling of the students and avoid the phenomenon that the offline learning interest of the students is reduced due to the fact that the offline learning distance is long.
(3) According to the online learning force distribution method, the online learning force coefficients of the students are combined in the intelligent distribution process of the seats of the network points for the students corresponding to the offline education network points, so that the students with the same learning force level are intensively distributed, the students with the same learning force level can communicate with each other conveniently, a learning atmosphere is created, the learning effect can be improved quickly, the teacher can perform targeted management on the students with the same learning force level conveniently, the personalized and targeted levels of management are improved, and the management level of offline teaching of the students is improved.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the off-line teaching management method for students based on-line education software platform data feature matching comprises the following steps;
step 1, on-line learning student statistics and student learning characteristic parameter acquisition: counting all students of online education software platform learning, numbering the counted students according to a predefined sequence, sequentially marking the students as 1,2, a.
Step 2, offline education website statistics and website characteristic parameter acquisition: counting the number of offline education network points corresponding to the offline education software platform, numbering the counted offline education network points, marking the offline education network points as 1,2, a.
Step 3, statistics of education network points under the matchline: according to the serial number sequence of students, sequentially matching the on-line learning course names in the learning characteristic parameters corresponding to the students with the established course names in the characteristic parameters corresponding to the off-line education sites, screening off the off-line education sites successfully matched with the off-line learning course names of the students, recording the off-line education sites successfully matched with the off-line learning course names as off-line education sites, and counting the serial numbers of the off-line education sites corresponding to the students;
step 4, distance sorting of education dots under a matched line: comparing the geographic position in the learning characteristic parameter corresponding to each student with the geographic position in the feature parameter corresponding to the education network node under each matching line corresponding to the student to obtain the distance between the geographic position of each student and the geographic position of the education network node under each matching line corresponding to the student, and further performing distance sorting on the education network nodes under each matching line corresponding to each student according to the sequence of the distances from near to far to obtain the distance sorting result of the education network nodes under each matching line corresponding to each student;
step 5, determining the target matched line at the education network: and pushing the education network points under the matched lines to the corresponding students step by step according to the sequencing result of the education network points under the matched lines corresponding to the students so as to allow the students to perform online learning registration and determine the education network points under the target matched lines corresponding to the students, wherein the specific determination process executes the following steps:
s1, respectively extracting the education network points arranged under the first matched line from the sequencing results of the education network points under the matched lines corresponding to the students and pushing the education network points to the corresponding students, wherein the education network points under the first matched line are marked as the education network points under the first matched line so as to allow the students to carry out on-line learning registration, and simultaneously the education network points under the first matched line corresponding to the students count the number of the students who successfully carry out on-line learning registration of the on-line education network points in real time;
s2, extracting the number of students capable of being accommodated by the education network node under the first matching line from the network node characteristic parameters corresponding to the education network node under the first matching line corresponding to each student;
s3, comparing the number of students counted in real time by the first matchline lower education website corresponding to each student with the number of students capable of being accommodated by the offline education website, if the number of the counted students of the first matchline lower education website corresponding to a certain student reaches the number of the students capable of being accommodated by the offline education website, closing an online learning registration channel of the matchline lower education website, judging whether the student successfully registers at the first matchline lower education website, if so, not processing, and if not, executing S4;
s4: extracting the education network points arranged at the second position from the sequencing result of the education network points under each matched line corresponding to the student and pushing the education network points to the student, the second-ranked offline education site is marked as an offline second-ranked education site to allow the student to perform online learning registration and is processed according to the method of S1-S3, meanwhile, whether the student successfully registers under the second match line is judged, if so, if the matched line lower education network node is not successfully registered, sequentially extracting the next matched line lower education network node from the sequencing result of each matched line lower education network node corresponding to the student and pushing the next matched line lower education network node to the student until the student is successfully registered, counting the number of the matched line lower education network node successfully registered by each student at the moment, and recording the matched line lower education network node successfully registered as the target matched line lower education network node;
in the offline education website matching process of each student, offline education websites are screened out from offline education websites according to online learning course names corresponding to the students, then the offline education websites corresponding to the students are matched in an online learning registration mode in a nearby step-by-step matching mode, and the obtained target offline education websites can meet the requirements of the learning course names corresponding to the students and can also realize the online learning of the students, thereby greatly facilitating the trip of the students and avoiding the phenomenon that the offline learning interest of the students is reduced due to the fact that the offline learning distance is long;
step 6, on-line learning ability analysis of students: the on-line learning method comprises the following steps of comparing education network points under target matching lines corresponding to students, summarizing the students corresponding to the education network points under the same target matching line to obtain the students corresponding to the off-line education network points, and analyzing the on-line learning capacity of the students corresponding to the off-line education network points at the moment, wherein the specific analysis method comprises the following steps:
r1, counting the numbers of the off-line education websites corresponding to the students, wherein the numbers can be recorded as 1, 2.., k., l;
r2, extracting on-line learning records corresponding to the students from the on-line education software platform according to the numbers of the students corresponding to the off-line education network points, acquiring learning time points corresponding to the on-line learning records, and numbering the extracted on-line learning records corresponding to the students according to the sequence of the learning time points, wherein the numbers are respectively marked as 1,2, a, u;
r3 obtaining the time length of the course, the time length of the study and the frequency of the interaction with the teacher corresponding to the study record of each student on each line, and forming the parameter set P of the on-line learning ability of the studentjk(pjk w1,pjk w2,…,pjk wa,...,pjk wu)，pjk wa is a numerical value corresponding to an on-line learning force parameter of a learning record on the a-th line of the kth student corresponding to the jth off-line education website, w is an on-line learning force parameter, and w is r1, r2 and r3 which are respectively represented as course duration, learning duration and frequency of interaction with a teacher;
r4, obtaining the test score after class of the learning record corresponding to each line of each student, and calculating the learning effect coefficient after class of the learning record corresponding to each line of each student
In the formula ofjka is expressed as the after-class learning effect coefficient of the learning record on the a-th line of the kth student corresponding to the jth off-line education website, gjka、g′jka is respectively expressed as a post-class test score and a full-scale test score which correspond to the learning record of the ith line of the kth student on the jth offline education website;
r5, according to the on-line learning force parameter set of students and the after-class learning effect coefficient recorded on each line of each student corresponding to each off-line education network point, the on-line learning force coefficient of each student corresponding to each off-line education network point is counted
In the formula
Expressed as the on-line learning power coefficient, p, of the jth off-line education site corresponding to the kth studentjk r2a、pjk r1a、pjk r3a is respectively expressed as the learning duration, the course duration and the interaction frequency with the teacher of the jth offline education website corresponding to the learning record of the kth student on the ath line;
and 7, intelligently distributing seats of the student network: according to the online learning force analysis result of each student corresponding to each offline education network point, intelligently allocating the network point seats of each student corresponding to each offline education network point, wherein the specific intelligent allocation mode comprises the following steps:
f1, dividing the learning force grade of each student according to the on-line learning force coefficient of each off-line education network point corresponding to each student, wherein the specific dividing process is to compare the on-line learning force coefficient of each off-line education network point corresponding to each student with the on-line learning force coefficient range corresponding to each learning force grade in the learning force database, if the on-line learning force coefficient of a certain student is in the on-line learning force coefficient range corresponding to a certain learning force grade, the learning force grade corresponding to the student is the learning force grade, thus obtaining the learning force grade corresponding to each student, and classifying the students corresponding to the same learning force grade to obtain a plurality of students corresponding to the same learning force grade;
f2, dividing the website seats corresponding to each offline education website into seat areas according to the arrangement positions of the seats, and numbering the seat areas, wherein the seat areas correspond to the learning force grades one by one;
f3, correspondingly distributing a plurality of students corresponding to the same learning force levels of each offline education network on corresponding seats according to the corresponding relationship between the seat areas and the learning force levels, and in the process of distributing the seats, if the number of the students corresponding to the same learning force levels of a certain offline education network is larger than that of the seats corresponding to the learning force levels, acquiring the number of the vacant seat areas corresponding to the offline education network at the moment, marking the vacant seat areas on the seats as vacant seat areas, counting the number of the vacant seat areas and the distance of each vacant seat area from the corresponding seat area of the same learning force level, and screening out the vacant seat area nearest to the corresponding seat area of the same learning force level, thereby distributing the rest students corresponding to the same learning force level nearby in the nearest vacant seat area, deeply optimizes the distribution mode of the seats of the network points and facilitates the management of teachers.
This embodiment is carrying out the online learning power coefficient through combining each student to each student that off-line education website corresponds in the on-line seat intelligent allocation in-process, concentrate the distribution with the student of same learning power grade, on the one hand be convenient for communicate each other between the student of same learning power grade, build the study atmosphere, and then can promote the learning effect sooner, on the other hand also be convenient for mr carries out the pertinence management to the student of same learning power grade, the individuality of management has been improved, the pertinence level, and then the management level of teaching under the student's line has been promoted.
According to the online education software platform and the online education management method, the number of all students studying on the online education software platform and the number of offline education sites corresponding to the offline education software platform are counted, the learning characteristic parameters corresponding to all students and the site characteristic parameters corresponding to all offline education sites are respectively obtained, offline education site matching is performed on all students, and site seat intelligent distribution is performed on all students corresponding to all offline education sites.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (9)
1. The student offline teaching management method based on online education software platform data feature matching is characterized by comprising the following steps;
step 1, on-line learning student statistics and student learning characteristic parameter acquisition: counting all students learning on an online education software platform, numbering the counted students according to a predefined sequence, sequentially marking the students as 1,2,..,. i,.., n, and simultaneously acquiring learning characteristic parameters corresponding to the students according to login accounts of the students;
step 2, offline education website statistics and website characteristic parameter acquisition: counting the number of offline education network points corresponding to the offline education software platform, numbering the counted offline education network points, marking the offline education network points as 1,2, a.
Step 3, statistics of education network points under the matchline: according to the serial number sequence of students, sequentially matching the on-line learning course names in the learning characteristic parameters corresponding to the students with the established course names in the characteristic parameters corresponding to the off-line education sites, screening off the off-line education sites successfully matched with the off-line learning course names of the students, recording the off-line education sites successfully matched with the off-line learning course names as off-line education sites, and counting the serial numbers of the off-line education sites corresponding to the students;
step 4, distance sorting of education dots under the matched line: comparing the geographic position in the learning characteristic parameter corresponding to each student with the geographic position in the feature parameter corresponding to the education network node under each matching line corresponding to the student to obtain the distance between the geographic position of each student and the geographic position of the education network node under each matching line corresponding to the student, and further performing distance sorting on the education network nodes under each matching line corresponding to each student according to the sequence of the distances from near to far to obtain the distance sorting result of the education network nodes under each matching line corresponding to each student;
step 5, determining the education network points under the target matching line: step-by-step pushing of the education network points under the matched lines to the corresponding students according to the sequencing results of the education network points under the matched lines corresponding to the students so as to allow the students to perform on-line learning registration and determine the education network points under the target matched lines corresponding to the students;
step 6, on-line learning ability analysis of students: comparing the education sites under the target matching lines corresponding to the students, and then summarizing the students corresponding to the education sites under the same target matching line to obtain the students corresponding to the education sites under each line, and analyzing the on-line learning force of the students corresponding to the education sites under each line;
and 7, intelligently distributing seats of the student network: according to the online learning force analysis result of each offline education network corresponding to each student, intelligently distributing network seats for each student corresponding to each offline education network;
in the step 6, the online learning ability of each student corresponding to each offline education website is analyzed, and the specific analysis method is as follows:
r1, counting the numbers of the off-line education websites corresponding to the students, wherein the numbers can be recorded as 1, 2.., k., l;
r2, extracting on-line learning records corresponding to the students from the on-line education software platform according to the numbers of the students corresponding to the off-line education network points, acquiring learning time points corresponding to the on-line learning records, and numbering the extracted on-line learning records corresponding to the students according to the sequence of the learning time points, wherein the numbers are respectively marked as 1,2, a, u;
r3 obtaining the time length of the course, the time length of the study and the frequency of the interaction with the teacher corresponding to the study record of each student on each line, and forming the parameter set P of the on-line learning ability of the studentjk(pjk w1,pjk w2,…,pjk wa,...,pjk wu)，pjk wa is a numerical value corresponding to an on-line learning force parameter of a learning record on the a-th line of the kth student corresponding to the jth off-line education website, w is an on-line learning force parameter, and w is r1, r2 and r3 which are respectively represented as course duration, learning duration and frequency of interaction with a teacher;
r4, obtaining the post-class test score of the learning record of each line corresponding to each student, and calculating the post-class learning effect coefficient of the learning record of each line corresponding to each student;
and R5, counting the on-line learning force coefficient of each student corresponding to each off-line education website according to the on-line learning force parameter set of the students and the after-class learning effect coefficient recorded on each line of each student corresponding to each off-line education website.
2. The method for student offline teaching management based on online education software platform data feature matching according to claim 1, wherein: the learning characteristic parameters comprise the names and the geographic positions of the online learning courses.
3. The method for student offline teaching management based on online education software platform data feature matching according to claim 1, wherein: the website characteristic parameters comprise the names of the opened courses, the geographic positions and the number of students capable of being accommodated.
4. The method for student offline teaching management based on online education software platform data feature matching according to claim 1, wherein: in the step 5, the education website under the target matching line corresponding to each student is determined, and the specific determination process includes the following steps:
s1, respectively extracting the education network points arranged under the first matched line from the sequencing results of the education network points under the matched lines corresponding to the students and pushing the education network points to the corresponding students, wherein the education network points under the first matched line are marked as the education network points under the first matched line so as to allow the students to carry out on-line learning registration, and simultaneously the education network points under the first matched line corresponding to the students count the number of the students who successfully carry out on-line learning registration of the on-line education network points in real time;
s2, extracting the number of students capable of being accommodated by the education network node under the first matching line from the network node characteristic parameters corresponding to the education network node under the first matching line corresponding to each student;
s3, comparing the number of students counted in real time by the first matchline lower education website corresponding to each student with the number of students capable of being accommodated by the offline education website, if the number of the counted students of the first matchline lower education website corresponding to a certain student reaches the number of the students capable of being accommodated by the offline education website, closing an online learning registration channel of the matchline lower education website, judging whether the student successfully registers at the first matchline lower education website, if so, not processing, and if not, executing S4;
s4: extracting the education network points arranged at the second position from the sequencing result of the education network points under each matched line corresponding to the student and pushing the education network points to the student, the second-ranked offline education site is marked as an offline second-ranked education site to allow the student to perform online learning registration and is processed according to the method of S1-S3, meanwhile, whether the student successfully registers at the education website under the second matching line is judged, if so, and if the matched line lower education network node is not successfully registered, sequentially extracting the next matched line lower education network node from the sequencing result of the matched line lower education network node corresponding to the student and pushing the next matched line lower education network node to the student until the student is successfully registered, counting the number of the matched line lower education network node successfully registered by the student at the moment, and recording the matched line lower education network node successfully registered as the target matched line lower education network node.
5. The method for student offline teaching management based on online education software platform data feature matching according to claim 1, wherein: the calculation formula of the after-class learning effect coefficient of the learning record corresponding to each line of each student is
In the formula ofjka is expressed as the after-class learning effect coefficient of the learning record on the a-th line of the kth student corresponding to the jth off-line education website, gjka、g′jkand a is respectively expressed as a post-class test score and a full-scale test score corresponding to the learning record on the a-th line of the kth student of the jth offline education website.
6. The method for student offline teaching management based on online education software platform data feature matching according to claim 5, wherein: the calculation formula of the on-line learning force coefficient of each off-line education network corresponding to each student is
In the formula
Expressed as the on-line learning force coefficient, p, of the jth off-line education website corresponding to the kth studentjk r2a、pjk r1a、pjk r3a is respectively expressed as the learning on the a-th line of the jth off-line education website corresponding to the k-th studentThe recorded learning duration, the recorded course duration and the frequency of interaction with the teacher.
7. The method for student offline teaching management based on online education software platform data feature matching according to claim 1, wherein: in the step 7, the online learning ability analysis result of each student corresponding to each offline education site is used for intelligently allocating the site seats to each student corresponding to each offline education site, and the specific intelligent allocation mode comprises the following steps:
f1, classifying the students according to the on-line learning force coefficients of the off-line education network points corresponding to the students to obtain the learning force grades corresponding to the students, and classifying the students corresponding to the same learning force grades to obtain a plurality of students corresponding to the same learning force grades;
f2, dividing the network seats corresponding to each offline education network into seat areas according to the arrangement positions of the seats, and numbering the seat areas, wherein each seat area corresponds to each learning ability grade one by one;
f3, distributing a plurality of students corresponding to the same learning power grades corresponding to each offline education network point on corresponding seats according to the corresponding relation between the seat areas and the learning power grades.
8. The method for student offline teaching management based on online education software platform data feature matching according to claim 7, wherein: the online learning force coefficient of each offline education network point corresponding to each student is compared with the online learning force coefficient range corresponding to each learning force level in the learning force database, and if the online learning force coefficient of a certain student is in the online learning force coefficient range corresponding to a certain learning force level, the learning force level corresponding to the student is the learning force level.
9. The method for student offline teaching management based on online education software platform data feature matching according to claim 7, wherein: in the process of allocating seats in F3, if the number of students with the same learning force level corresponding to an offline education site is greater than the number of seats in the seat area corresponding to the learning force level, a number of the seat area with vacant seats corresponding to the offline education site is obtained, the seat area with vacant seats is marked as a vacant seat area, the number of vacant seat areas and a distance from each vacant seat area to the seat area corresponding to the same learning force level are counted, and a vacant seat area closest to the seat area corresponding to the same learning force level is selected, so that the remaining students with the same learning force level are allocated to the nearest vacant seat area.
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